We analyzed the effects of age, semester and their interaction on the self-reflection of competencies in technical skills courses using moderated regression and simple slope analyses, as previously published. A univariate analysis of variance (ANOVA) with post hoc Tukey HSD testing was used to analyze group means in estimated competencies using IBM SPSS Statistics V.28. Results: An analysis of variance revealed a significant increase in self-assessed competencies when comparing pre- vs. post-course evaluation data in all 35 depicted items . A total of 65.5% of the items were adjusted significantly differently, revealing modified self-reflected pre-course levels compared to those stated before. A moderated regression analysis revealed that age 2 = 0.001, F = 1.88, p < 0.665) = 0.012, p < 0.912) and their interaction 2 = 0.001 = 10.72, p < 0.227) did not explain a significant amount of the variance in self-reflection variance. A simple slope analysis of earlier  and later semesters of study  did not differentiate from zero. Conclusions: The presented evaluation paradigm proved to be a useful tool to encourage students to initiate self-regulatory and self-reflective behavior. The cohesive evaluation of the large cohort of attendees in extracurricular, facultative skills labs courses was helpful in terms of quality assessments and future adaptations. Further evaluation paradigms should be implemented to assess other influencing factors, such as gender, on self-reflection, since age and semester did not explain significant differences in the model.Objectives: Standardized extracurricular skills labs courses have been developed in recent decades and are important approaches in peer-assisted medical learning (PAL). To provide high quality training and achieve effective learning strategies, continuous evaluations and quality assessments are essential. This research aims to evaluate quality data from medical students participating in extracurricular skills labs courses at Ruhr-University Bochum to prospectively optimize concepts and didactical training and standardize processes. Additionally, we set out to assess and quantify drivers that are influencing factors of the self-reflection of competencies. Methods: The analysis was based on a routine assessment of  Peer-assisted learning (PAL) is defined as a teaching method whereby students (“peers”) replace the professional educator, helping teach other students specific skills and to assist them by achieving competencies ,2. EspecThe guidelines of the National Competence-based Catalogue of Learning Objectives for Medicine (NKLM 2.0) of the German Medical Faculty Association (MFT) highlight competence-based practical medical education (CBME) as the main and most important approach ,7,8. To In Germany, most medical faculties (>90%) have established skills labs in recent decades. However, a need for improving methods and studies in terms of standardization, quality assessments and developing teaching concepts has arisen regarding PAL concepts in the skills labs landscape . These nAlthough many concepts have been developed in the past in terms of evaluated self-regulated and competency-based learning among participating students ,18,19,20self-reflected) view prior to course participation [A focus has been placed on the assessment of evaluation concepts and data coming from participants in practical, peer-assisted, extracurricular training in skills courses ,24. To ecipation ,25. Herecipation ,27.We set out to evaluate quality assessment data from medical students participating in extracurricular skills labs courses at the Ruhr-University Bochum. With this, we aimed to identify certain components which might prospectively optimize concepts and didactical training as well as standardize processes. This might be transferable to other medical faculties. This research quantifies influencing factors of self-assessed properties with regard to self-regulatory behavior of practical competencies. We evaluated technical concepts to identify course-specific aspects, since earlier interventions analyzed the self-regulatory behavior of participants in the first aid and physical examination course ,28.n = 503 attendees of PAL courses at the Ruhr-University Bochum. We analyzed data from three semesters between 2018 and 2020 from students voluntarily participating in the evaluation of our skills labs. Participants were from all different semesters of the entire medical program of the faculty. As a first approach, we analyzed the demographic data of participants.We investigated data from Standardized peer-assisted courses were exclusively delivered by peers after they had been trained by didactical and medical experts in the field of the specific course content. Therefore, structured course manuals were implemented.pre), after (2: post), and after the course with a retrospective assessment of competencies before participating in the course (3: post-pre) as previously published [pre- vs. post-, pre- vs. post-pre and post- vs. post-pre-evaluation data, we analyzed the influence of age, semester of study and its interaction on the self-reflection of competencies, depicted as the concordant self-reflection as earlier described [post-pre- minus pre- assessment. Negative scores indicate that participants reveled a more negative  assessment of their own competencies before the course and afterwards retrospectively adjusted these scores to better self-assessed competencies [n = 335 participants), implementing concepts for teaching blood collection, surgery, peripheral indwelling catheters, changing bandages and central indwelling catheters.The evaluation concept consisted of a self-assessment of competencies before  with post hoc Tukey HSD testing for pairwise analysis of different estimates on the self-assessment of competencies in IBM SPSS Statistics V.28. Adjustment for multiple testing was applied using Bonferroni corrections. Dependent variables were tested for normal distribution using the Kolmogorov–Smirnov test. The homogeneity of variances was asserted using Levene’s Test. For unequal variances, values were reported with Welch’s test. Chi-square tests were used for analyses of categorical and descriptive variables. A moderated regression analysis was conducted to analyze the interaction effect of age and semester of study, as well as their interaction, on the concordant self-reflection of competencies in technical course concepts. Simple slope analyses were calculated to illustrate the effects .The following table provides participant ages, semesters of study and whether participants were professionally trained previously or had participated in the courses before .n = 503 students participated in courses and the evaluation . In total, aluation .An analysis of participants with regard to the study semester revealed that most participants attended during their fifth semester  and the majority of all participants were 20  or 21  years old .As a first approach, we assessed data from the general and non-course-specific part of the presented evaluation paradigm, documenting attendees’ satisfaction and overall impression with regard to their course participation. Data were surveyed in ten items, as well as two concluding questions, with regard to the overall course evaluation . The same differences were depicted for the post-pre- vs. post-data . Regarding the pre- vs. post-pre-evaluation, a significant change in the self-assessment was observed in 23 out of 35 domains  = 1.88, p < 0.665), semester of study  = 0.012, p < 0.912) and the interaction of both  = 10.72, p < 0.227) did not explain a significant amount of variance in the self-reflected variance . A simple slope analysis was conducted to illustrate the effects in a figure. Slopes for a tendentially low semester of study  and high semester  did not significantly differentiate from zero. Illustrations revealed that participants tended to have a more critical assessment of their competencies before the course and this was afterwards retrospectively adjusted to better self-assessed pre-competencies. This was, however, independent from the age and semester of participants , whereby more than one-third (38%) was 20 or 21 years old. With regard to earlier investigations, which described that age is associated with a better developed personality and trained emotional regulative processes , this coThe main focus was set on the evaluation of self-assessed competencies, whereby the evaluation concept proved that participants subjectively benefitted from course participation. This was documented in all assessed 35 evaluation items.However, as a limitation of the presented data here, no objectifiable measures of competencies are given; thus, future approaches should expand the focus to practical competency increases, e.g., by assessing the skills of participants and discrepancies between the self-perceived competencies. An appropriate approach could be to evaluate competencies in summative tests like “Objective Structured Clinical Examinations (OSCE)” which is currently being considered for implementation in curricular medical trainings with regard to the implementation of new licensing regulations for medical professionals. This limitation mainly concerns the assessment of trained competencies, whereby an assessment of self-regulatory behavior itself seems difficult to evaluate. pre- vs. post-assessments given, but also self-reflected pre-competencies. Hereby, about 65.5% of the assessments significantly differed when compared to the pre-data. This is nearly congruent with previous assessments of first aid and examination courses, whereby 72.7% differed significantly. [pre-competencies suggests that the evaluation paradigm forced students to make use of self-reflective or self-regulated behavior. Since the ability for self-reflection was previously postulated as an important value for older health professionals, the evaluation concept should additionally be expanded in the courses as a useful tool to train medical students in self-regulatory competencies [As a main advantage of the presented data, not only were icantly.  The factetencies .pre-levels retrospectively to better pre-course levels . This might be explainable at least partly by gender. An analysis of demographic data revealed that more than two-thirds (67.9%) of the attendees were female. Earlier research postulated that gender has an influence on self-regulatory behavior. [Remarkably, in nearly all assessments (22 out of 23 items), students corrected their initial ehavior. ,36,37 Heehavior. . It is rAs a further limitation, other—and to date unknown—factors may be involved which have not been further assessed, since self-reflection was evaluated as part of the established routine evaluation design and no further specific questionnaires were implemented. Specific scales have previously been described for the assessment of self-reflection competencies, such as the “Self-Regulation of Learning Self-Report Scale (SRL-SRS)”, and should therefore be implemented in future assessments. The depicted illustrated graphs in the simple slope analysis proved to be a useful psychometric tool to document that participants tended to have a more critical assessment of their own competencies before course participation, which afterwards adjusted to better pre-course competencies, regardless of age and semester of study, which is therewith partly contrary to earlier findings .The presented evaluation paradigm is a useful tool to encourage students to initiate self-regulatory and self-reflective behavior. The first cohesive evaluation of the presented large cohort of attendees in our extracurricular, facultative skills labs was helpful in terms of a quality assessment and future adaptations. Further longitudinal evaluation paradigms will be implemented with standardized questionnaires for an assessment of self-regulatory skills and should objectify trained practical skills. Other influencing factors of self-reflection of skills in medical education, such as gender, should be analyzed, since age and semester did not explain significant differences in the analyzed model.


Emerging evidence points towards the involvement of the cerebellum in the processing of emotions and pathophysiology of mood disorders. However, cerebellar and related cognitive alterations in youth with pediatric bipolar disorder (PBD) and those at high risk to develop the disorder, such as bipolar offspring (BD-OFF) are not clearly defined.To investigate cerebellar gray and white matter volumes, cognition, and their relationship in youth with PBD and BD-OFF.Thirty youth  with PBD, 30 BD-OFF and 40 healthy controls (HC) were recruited. Study participants underwent a computer-based cognitive battery assessing affective processing, executive function, attention, psychomotor speed, and learning. Three-tesla MRI scan was performed to assess cerebellar white and gray matter volumes. Cerebellar segmentation was performed with FreeSurfer. Statistical analyses include between-group differences in cognitive domains, cerebellar gray, and white matter volumes. Relationships between cerebellar volumes and cognitive domains were examined.Youth with PBD showed greater cerebellar gray matter volumes than both BD-OFF and HC, whereas no differences were present between BD-OFF and HC. Both youth with PBD and BD-OFF showed altered processing of negative emotions and a bias towards positive emotions. In youth with PBD and BD-OFF, greater impairment in the processing of emotions correlated with greater cerebellar gray matter volumes.The present findings corroborate hypotheses on cerebellar involvement in the processing of emotions and the pathophysiology of PBD. The presence of cerebellar dysfunction in BD-OFF is unclear. Also,Notably, the study of anatomical underpinnings of these cognitive alterations might be a viable tool to identify reliable neuromarkers of the progression and predisposition to mood disorders early on. In the last decades, structural magnetic resonance imaging (sMRI) studies revealed several volumetric alterations in youth with PBD and BD-OFF in areas related to the previously mentioned cognitive domains , 15-17. This study assessed cerebellar gray and white matter volumes, and cognitive functions in youth with PBD, BD-OFF, and healthy controls (HC). Since there is robust evidence documenting the presence of alterations in executive function and emotion processing in PBD, we expected PBD to show alterations in these two domains compared to HC , 12, 39.2This study was approved by the Institutional Review Boards (IRB) at the Baylor College of Medicine in Houston, TX and the University of Texas, Houston, TX (IRB number: H-50361). The parent or legal guardian of each study participant was informed of the procedures and potential risks involved in this study before they provided written consent for their child/adolescent to participate. Each study participant verbally assented to their participation in this study.2.11 for demographics). Study participants included females and males, 7-17 years old. Suitability for MRI scanning was also required for all the youths to be included. Youth with PBD also required (i) A diagnosis of PBD; (ii) the absence of an eating disorder, ADHD and anxiety disorders without comorbid PBD. BD-OFF also required: (i) at least a first-degree relative with a diagnosis of bipolar disorder (BD); (ii) absence of a diagnosis of a mood disorder , cyclothymic disorder, dysthymic disorder). HC also required: the absence of psychiatric illness, and the absence of any psychiatric illness in first-degree relatives. Exclusion criteria for the whole sample include (i) comorbid substance use disorder; (ii) intellectual disability; (iii) comorbid autism spectrum disorder; (iv) severe neurological conditions.The study participants were 100 in total; 30 in the PBD group, 30 in the BD-OFF group, and 40 in the HC group  (MINI)  were con2.3https://www.cambridgecognition.com/cantab/), notably the Affective Go/no-go task (AGN), the Cambridge Gambling Task (CGT), the Stockings of Cambridge task (SOC), Match to Sample Visual Search task (MTS), the Big/Little Circle task (BLC). Cognitive domains assessed were affective processing, executive functions, attention and psychomotor speed, and learning.Study participants completed a series of tasks within the Cambridge Neuropsychological Test Automated Battery  and negative words . Before each round, the participant is instructed to press a button on the target which is either the positive or negative words. If asked to press on a positive word and the participant presses on a negative word, this is considered a commission error (CE) for negative words. On the other hand, if the participant is asked to press on a positive word and the participant does not do so, then this is considered an omission error for positive words. Outcome measures are reaction times (AGN-RT) for positive (AGN-RT-positive) and negative (AGN-RT-negative) words, CE for positive (AGN-CE-positive) and negative (AGN-CE-negative) words and omissions for positive (AGN-omissions-positive) and negative (AGN-omissions-negative) words.Affective processing was assessed with the AGN. In this task, participants are presented with positive words (2.3.2Executive functions were assessed using the Cambridge Gambling task (CGT) and the Stockings of Cambridge task (SOC).The CGT is specifically tailored to assess decision-making and risk-taking behavior. Study participants are presented with a row of ten boxes across the top of the screen: some are red, and some are blue. They must use the 'Red' and 'Blue' buttons at the bottom of the screen to choose the box color in which they think the token is hidden. The study participants start with 100 points and select a proportion of these points to bet on their decision. A circle in the center of the screen displays the current bet value, which will either incrementally increase or decrease (depending on the task variant selected). Participants press this button when it shows the proportion of their score they would like to bet. These points will either be added to or taken from their total score, depending on their decision and where the token is hidden. Outcomes are delay aversion (CGT-delay aversion), deliberation time (CGT-deliberation time), proportion bet (CGT-proportion bet), quality of decision making , risk adjustment (CGT-risk adjustment), risk taking (CGT-risk taking).The SOC is an executive function task designed to measure spatial planning and strategic reasoning. It presents the study participant with balls in stockings in a specific pattern on the top and bottom of the screen, and the study participant is instructed to manipulate the balls on the bottom of the screen to match the top. Outcome measures include the number of problems solved in minimum moves (SOC-moves).2.3.3Attention and psychomotor speed were evaluated with the Match to Sample Visual Search task (MTS). In this task, the study participant is shown a complex visual pattern in the middle of the screen. After a brief delay, a varying number of similar patterns are shown in a circle of boxes around the edge of the screen. Only one of these patterns matches the pattern in the center of the screen, and the participant must indicate which it is by selecting it. The outcome measure for this task is accuracy (MTS-% of correct choices).2.3.4Learning has been assessed with the Big/Little Circle task (BLC). In this task, two circles are displayed. The study participants must first select the smaller of the two circles, then, after 20 trials, select the larger circle for a further 20 trials. The outcome is the study participant’s ability to select the correct circle (BLC-% of correct selection).2.4Study participants underwent a structural MRI scan to determine cerebellar gray and white matter volumes. Neuroimaging scans were acquired using a research dedicated Philips Ingenia 3T MRI system. T1-weighted neuroimaging scans were acquired for anatomical analysis with the following scanner parameters - Repetition time (TR) = 8.1 ms, echo time (TE) = 3.68 ms, field of view (FOV)= 256 mm × 180 mm voxel size = 1 mm × 1 mm × 1 mm. Cerebellar volumes were segmented and quantified using FreeSurfer suite version 6.0 (38). FreeSurfer is a fully automated software that performs motion correction, intensity normalization, automated topology correction, and atlas-based cortical/subcortical segmentation and labeling of MRI images. Cerebellar gray and white matter were corrected for intra-cranial volume following the proportion method.2.5Statistical analyses for this study were performed with IBM SPSS Statistics 22.0 for MAC (IBM Corporation). FreeSurfer was used to analyze the data.2.5.1i.e., age, IQ, CDRS and YMRS scores), and chi-square tests for nominal variables  were performed to assess for group differences in demographic and clinical characteristics (Table 1).One-way factorial analyses of variance (ANOVAs) for continuous variables  were used as independent variables, whereas CANTAB tests scores, and left and right cerebellar gray and white matter volumes were used as dependent variables. Age, sex, and IQ were used as variables of no interest. Correction for multiple testing  was performed. ANCOVAs were followed by Bonferroni post hoc tests to examine between-group differences with p < .05 as the level of significance. Cohen’s d was used as a measure of effect size. To further minimize the likelihood of type-I error, univariate ANCOVAs were preceded by overall multivariate analyses of covariance (MANCOVA), in which CANTAB tests scores, left and right gray and white matter volumes were used as dependent variables, whereas groups were used as an independent variable. Age, gender, and IQ were used as covariates. Since only AGN and the CGT task embedded multiple outcomes, MANCOVAs were limited to these two tasks.One-way factorial analyses of covariance (ANCOVAs) were performed to investigate between-group differences in cognition, cerebellar gray, and white matter volumes. In each ANCOVA, the three study groups  were used to compare relationships between gray and white matter cerebellar volumes and cognitive performances among groups. Specifically, each CANTAB test score was used as a response variable, left and right gray and white matter volumes were used as predictors and, the study group was used as a covariate. Age, gender, and IQ were entered as variables of no interest. A significant interaction effect was further investigated with multiple univariate hierarchical regressions for each group  separately, controlling for the effect of age, gender, and IQ. Pearson’s chi square analyses were then used to assess the direction of the associations found.33.11, demographic differences were non-significant among groups. Regarding the severity of manic symptoms, youth with PBD were mildly hypomanic, so they were not severely clinically impaired on mania scores. Similarly, the youth with PBD were sub-clinically depressed, as their depression scores were lower than the clinical cut-off of 40 on the CDRS scale. As regards psychopharmacological treatments, subjects with PBD assumed more antidepressants (ADs), antipsychotics (APs) and mood stabilizers (MSs) than BD-OFF.The study sample included 30 study participants with PBD, 30 BD-OFF, and 40 HC. As shown in Table 3.2p = .009) and CGT  tasks.MANCOVAs revealed a global main effect for AGN  and the left  cerebellar volumes. One-way ANCOVAs revealed significant differences in both left and right gray and white matter cerebellar volumes amongst groups. Post-hoc analyses revealed that youth with PBD showed significantly greater gray matter cerebellar volumes bilaterally than both BD-OFF and HC. The latter two groups did not show significant differences. No differences emerged amongst groups in left and right white matter volumes. Results are shown in Table 3.MANCOVAs revealed a main global effect for the right . Multiple hierarchical regression analyses identified a significant relationship between the left and right cerebellar gray matter and AGN-CE-positive in both youths with PBD and BD-OFF. In HC, regression only approached significance. Partial correlation analyses showed that bilateral cerebellar gray matter volumes were positively correlated with the number of CE-positive in both youths with PBD and BD-OFF. Results are shown in Table 4 and Fig. 1.As differences amongst groups were limited to the left and right cerebellar gray matter volumes, AGN-CE-positive and AGN-omission-positive, CGT-deliberation times, GLMs were limited to these variables. Significant groups by right- and left- cerebellar gray matter volume interactions were found for AGN-CE positive . As regards the possible effect of medications multiple two-way ANOVAs were performed in youth with PBD and BD-OFF only. In each ANOVA, gray and white matter cerebellar volumes were dependent variables, whereas groups (PBD and BD-OFF) and the presence or absence of AD, AP, MS, and stimulants were independent variables. The small number of subjects receiving benzodiazepines (BDZ) impeded performing a reliable statistic. Therefore, between-group differences regarding cerebellar volumes were recalculated after subtracting the subjects under BDZ . No interaction effects were found . After subtracting subjects with BDZ, results remained unchanged . Therefore, the effect of the duration of illness and medications was not further investigated.Since the duration of illness and medication have been proven to affect brain volumes , 45, the4Results can be summarized as follows: 1) youth with PBD had worse performances in affective processing executive function than HC; 2) youth with PBD had larger cerebellar gray matter volumes than both BD-OFF and HC; 3) BD-OFF had worse performances in affective processing than HC; 4) cerebellar gray matter volumes correlated with impairment in affective processing in both youth with PBD and BD-OFF.et al. 2007 [et al. 2019 [et al. 2019 [et al. 2011 [et al. included youth with PBD and psychotic features. Secondly, sample sizes in the studies of Demigroen et al. and James et al. are small. Thirdly, all the studies used different MRI segmentation techniques. On the other hand, neurodevelopmental trajectories should be considered while interpreting our findings. Gray matter volume follows a U-shaped trajectory over time and reaches its peak across puberty [i.e., the microglia-mediated, targeted elimination of functional synapses, takes place with the aim of increasing the efficacy of neural networks. Given the present study’s mean age, i.e. 13 years-old, the present findings suggest poor synaptic pruning in youth with PBD. Synaptic pruning has not been confirmed empirically in PBD [et al. [Results from this study confirm previous findings reporting alterations in affective processing , 47 and al. 2007 , whereasal. 2019 , Mobergeal. 2019  and Jameal. 2011 . Differe puberty . Gray ma puberty . Then, sy in PBD , howevery in PBD , who sho [et al.  who foun [et al. , 56 and  [et al. . This pr [et al. -60.The positive relationship between cerebellar gray matter volumes and AGN-CE-positive corroborates findings on the cerebellar involvement in affective regulation . The ceri.e., positive bias, than HC, thus corroborating previous evidence of altered negative emotion processing in BD-OFF [et al. [et al. [i.e., poor emotion recognition and processing. After the illness onset, cerebellar alterations would be generalized enough to be detected by segmentation techniques.BD-OFF showed greater AGN-CE-positive, n BD-OFF . Positivn BD-OFF  and has n BD-OFF . The corn BD-OFF -33 and w [et al. , who fou [et al. , results [et al. . Alterna [et al.  demonstr5The small sample size in this study does not allow generalizability of the results found, and larger sample sizes are needed to confirm the present findings. Furthermore, a longitudinal design is needed to clarify hypotheses on developmental structural-cognitive trajectories in youth with PBD and BD-OFF. Additionally, results are hampered by non-specific methods assessing white matter alterations. More sophisticated techniques, such as tractography, together with more specific indices of white matter fibers alterations, such as fractional anisotropy and main diffusivity, are needed to better define white matter alterations. As previously stated, more sophisticated segmentation methods are also necessary to localize cerebellar gray matter alterations in these two populations. Analyses performed on the possible effect of medications were limited only to drug classes. Additional studies are needed to investigate the effects of specific medications, which have been proven to affect behavior , 74, andThe present findings corroborate hypotheses on cerebellar involvement in mechanisms related to emotional control in PBD. Alterations in processes partially subserved by cerebellum and present in youth with PBD are also present in BD-OFF, even though they are not corroborated by structural alterations. Additional studies, with larger sample sizes and more sophisticated MRI techniques are needed to clarify the relationship between cerebellar alteration and cognition in youth with PBD and BD-OFF.


In 2020, we reported the first patient with concomitant COVID-19 and paracoccidioidomycosis (PCM). Since then, no other cases have been recorded in the literature. We aim to update information on the occurrence of COVID-19 in patients with PCM followed at a reference center for infectious diseases at Rio de Janeiro, Brazil.We reviewed the medical records from patients diagnosed with PCM who presented with clinical symptoms, radiological findings, and/or laboratory diagnosis of COVID-19 at any time during their acute or follow-up care. The clinical profiles of these patients were described.Between March 2020 and September 2022, we identified six individuals with COVID-19 among the 117 patients with PCM evaluated. The median age was 38 years and the male to female ratio 2:1. Most patients (n = 5) presented for evaluation due to acute PCM. The severity of COVID-19 ranged from mild to severe in acute PCM and only the single patient with chronic PCM died.Paracoccidioides.There is a range of disease severity in COVID-19 and PCM co-infection and concomitant disease may represent a severe association, especially in the chronic type of the mycosis with pulmonary involvement. As COVID-19 and chronic PCM share similar clinical aspects and PCM is neglected, it is probable that COVID-19 has been hampering simultaneous PCM diagnosis, which can explain the absence of new co-infection reports. With the continued persistence of COVID-19 globally, these findings further suggest that more attention by providers is necessary to identify co-infections with  Paracoccidioides is the causative agent of PCM and it was recently included in the World Health Organization (WHO) fungal priority pathogens list to guide research, development and public health action. Since the beginning of COVID-19 pandemic, only one case of concomitant PCM and COVID-19 has been reported. Our work aims to investigate the occurrence of COVID-19 in patients with PCM followed at a reference center for infectious diseases in a Brazilian endemic region for PCM. Between March 2020 and September 2022, we identified six individuals with COVID-19 among the 117 patients with PCM evaluated. Although we found a high range of disease severity in the cases analyzed, concomitant disease also represented a severe association. As COVID-19 and PCM may share similar clinical aspects and PCM is neglected, it is probable that COVID-19 has been hampering simultaneous PCM diagnosis, which can explain the absence of new co-infection reports. Our findings call for more attention by health professionals to identify co-infections with Paracoccidioides.Paracoccidioidomycosis (PCM) is a severe invasive mycosis, affecting the lungs and other important organs of patients in Latin America, notably Brazil.  Paracoccidioidomycosis (PCM) is a severe systemic mycosis, endemic in Latin America, mainly in Brazil. The infection typically occurs after inhalation of infective fungal propagules in the course of activities involving soil management and may progress acutely or chronically to cause clinical disease, chronic manifestations being most common, especially in rural workers. The lungs are the most affected organs in chronic PCM, while the mononuclear phagocyte system is involved in the acute “juvenile” cases of this mycosis . Rio de When COVID-19 emerged in Brazil in late February 2020 , we expeIn 2020, we reported the first case of COVID-19 and PCM co-infection in a young male patient, and discussed the clinical profile and severity of this association . Since tThe Research Ethics Committee of INI/Fiocruz approved this study (appreciation numbers: 18524919.1.0000.5262 and 26066619.0.0000.5262). A written consent form was obtained from the patients included in the study. The patients’ data were anonymized/de-identified to protect patients’ privacy/confidentiality.We performed a review of medical records from patients with PCM followed at the Evandro Chagas National Institute of Infectious Diseases (INI/Fiocruz) between March 2020 and September 2022, who presented clinical, radiological, and/or laboratory findings of COVID-19 during their care, from initial diagnosis to the post treatment phase.Paracoccidioides serum antibody titers detected by quantitative double immunodiffusion test; (c) confirmed case: presence of fungal elements suggestive of Paracoccidioides spp. in direct examination, histopathology or culture [We defined the following criteria for COVID-19 diagnosis: (a) suspected case: presence of clinical symptoms, notably fever and/or respiratory symptoms along with a potential exposure to SARS-CoV-2 through a close contact with a confirmed or suspected case; (b) probable case: suspected case with typical radiological findings of COVID-19; (c) confirmed case: suspected case with a positive antigen or molecular test against SARS-CoV-2 performed in the healthcare facilities, by trained professionals. The PCM diagnosis was based on the recommendations of the Brazilian Guidelines for the clinical management of PCM [Baixada Fluminense, an urban region with low socioeconomic conditions, where recently an increase in acute PCM case numbers was documented [In the period of study, we identified six patients with COVID-19 among 117 patients evaluated PCM, including the patient we previously reported [cumented . Four paThree patients presented with mild symptoms of COVID-19, including low-grade fever, headache, and other common cold symptoms . The other three patients required hospitalization due to hypoxia. One patient was considered to have moderately severe disease and was treated with high-flow nasal oxygen, corticosteroids, and favipravir. He had 13 days of intensive care unit (ICU) stay, and did not develop additional complications of COVID-19. Both patients with severe COVID-19 required invasive mechanical ventilation. One patient had acute PCM under treatment with pulmonary involvement and we previously reported this case . At the Invasive fungal infections have been an important complication of COVID-19 infection especially in severe disease, with a significant incidence of mucormycosis, aspergillosis and candidiasis . Due to Paracoccidioides spp. or to exacerbate PCM.In our patient populations, we did not observe an increase in PCM cases following COVID-19 as reported for other endemic mycoses . TherefoFive of the six patients herein studied had acute PCM, without prior comorbidities. At present, it is unclear whether this finding is due to the new epidemiological profile of PCM in the state of Rio de Janeiro characterized by a higher incidence of acute cases  or whethin vitro activity against SARS-CoV-2 [in vitro antifungal activity against P. brasiliensis [Paracoccidioides spp., which may perhaps explain, in part, the absence of post-COVID-19 PCM cases. The patients from this study did not receive any drugs with known clinical activity against SARS-CoV-2, except favipravir, which does not have antifungal activity against Paracoccidioides spp. [It is noteworthy that itraconazole, which is currently the first line oral antifungal drug to treat PCM, has RS-CoV-2 –17. AlthRS-CoV-2 , PCM patiliensis . Therefodes spp. Paracoccidioides manifests in mild to lethal outcomes with increased odds of poor outcomes when the lungs are compromised due to PCM. As COVID-19 and chronic PCM share similar clinical aspects and this mycosis is neglected, it is likely that COVID-19 has been hampering PCM diagnosis, which can explain the absence of reports of new co-infected cases in regions endemic for Paracoccidioides sp. In addition, according to the authors’ personal experience, acute PCM cases continue to be misdiagnosed as other diseases such as tuberculosis and lymphoma. Therefore, clinicians must be aware of this possible association of increased COVID-19 severity in PCM endemic areas. The research advances of new therapeutic options for COVID-19 and studies repurposing drugs to both PCM and COVID-19 may help to understand the epidemiology of this co-infection, as well as to promote advances in the therapeutic management of both diseases. Notably, mild COVID-19 cases, especially after vaccination, can account for the lack of co-infection reports. Regardless, more studies are needed for a better comprehension of this important issue, and mandatory reporting of PCM should be a public health priority.Our findings suggest that co-infection with SARS-CoV-2 and


The primary chronic symptom of patients with heart failure with preserved ejection fraction (HFpEF) is severe exercise intolerance. The inability to adequately increase heart rate during exercise (chronotropic incompetence) is commonly present in HFpEF patients and contributes importantly to exercise intolerance in these patients. Since HFpEF patients often have cardiac comorbidities such as hypertension, coronary artery disease, and atrial fibrillation, beta-blockers are frequently prescribed for the treatment of these comorbidities. However, there is a concern that beta-blockers may worsen chronotropic incompetence by slowing heart rate in HFpEF patients and may further exacerbate their symptoms. There are several studies on the effects of beta-blocker withdrawal in HFpEF patients. We aim to perform the systematic review and meta-analysis of studies on the effects of beta-blocker withdrawal in HFpEF patients.2). The secondary outcome will be 6-minute walk distance. Other outcomes of interest will be health-related quality of life, plasma BNP levels, and cardiac structure and function.This meta-analysis will include randomized controlled trials and prospective cohort studies on the effect of beta-blocker withdrawal in HFpEF patients. Information of studies will be collected from PubMed, Web of Science, and Scopus. The primary outcome will be peak oxygen uptake (peak VOThis systematic review and meta-analysis will evaluate whether beta-blocker withdrawal is beneficial for HFpEF patients, providing evidence regarding beta-blocker withdrawal in these patients.Systematic review registration:INPLASY202370066. Nearly half of patients with heart failure in the community have preserved ejection fraction (EF) ,2. The pSince HFpEF patients are likely to have cardiac comorbidities such as hypertension, coronary artery disease (CAD), and atrial fibrillation (AF), beta-blockers are commonly used for the treatment of these comorbidities . Howeverhttps://www.doi.org; DOI: 10.37766/inplasy2023.7.0066). This protocol for meta-analysis will be performed according to the Preferred Reporting Items for Systematic Review and Meta-analysis Protocols (PRISMA-P) statement [This study has been registered on International Platform of Registered Systematic Review and Meta-analysis Protocols with registration number of INPLASY202370066 #5 #1 AND #2 AND #4 (primary and secondary outcomes)#6 #1 AND #3 AND #4 (other outcomes)Literature search will also be conducted by manual screening of reference lists of relevant reviews and retrieved articles. Two researchers (HF and TK) will independently perform the literature search and will extract study-level data from included studies. Disagreements will be resolved by consensus. The literature search will be repeated before completing the data extraction, and potential new studies published during the work process will be added to the result. Study selection will be conducted in a PRISMA-compliant flow chart . Only arRCTs and prospective cohort studies will be included. Retrospective cohort and case–control studies will be excluded.Inclusion criteria for this meta-analysis will be: (1) include HFpEF patients treated with beta-blockers; (2) compare between beta-blocker withdrawal and beta-blocker continuation; and (3) assess exercise capacity, health-related quality of life, plasma BNP levels, or cardiac structure or function.2). The secondary outcome will be 6-minute walk distance. Other outcomes of interest will be health-related quality of life, plasma BNP levels, and cardiac structure and function. In the measures of health-related quality of life, the scores of the Minnesota Living With Heart Failure Questionnaire, the Kansas City Cardiomyopathy Questionnaire, and the 36-item Short-Form Survey will be extracted. In the measures of cardiac structure, left ventricular (LV) mass and left atrial volume will be extracted. In the measures of LV systolic function, LVEF and early systolic mitral annular velocity (s’) will be extracted. In the measures of LV diastolic function, early diastolic mitral annular velocity (e’) and the ratio of early diastolic mitral inflow to annular velocities (E/e’) will be extracted given the linear relationship with LV diastolic dysfunction grade.The primary outcome will be peak oxygen uptake (peak VOTwo reviewers (HF and TK) will independently extract relevant data from retrieved studies, including author, study design, study time, country, number of participants, baseline characteristics , clinical outcomes , and information on the methodological quality . Disagreements will be resolved by consensus. We will contact the corresponding author of eligible studies when insufficient information is available to perform our meta-analysis.http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp). The quality of evidence for the outcomes will be evaluated by use of the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system [The Cochrane Risk of Bias tool will be used to assess quality of RCTs included. The qua) system . The quaI2 statistic; for the Cochran’s Q and I2 statistic, a p value of <0.1 and I2>50%, will be considered significant, respectively. When there is significant heterogeneity, the data will be pooled using a random-effects model, otherwise a fixed-effects model will be used. When there are more than 10 studies included, publication bias will be assessed graphically using a funnel plot and mathematically using Egger test. For these analyses, Comprehensive Meta Analysis Software version 2  and STATA 16 software  will be used.For continuous outcomes, the effect size for the intervention will be calculated by the difference between the means of the intervention and control groups at the end of the intervention. If the outcome is measured on the same scale, the weighted mean difference and 95% confidence interval (CI) will be calculated. Otherwise, the standardized mean difference and 95% CI will be calculated. For each outcome, heterogeneity will be assessed using the Cochran’s Q and When the number of included studies reporting the primary outcome in the present meta-analysis is less than 3, the above-mentioned pooled analysis will not be conducted. Instead, a systematic narrative synthesis will be provided with information presented in the text and tables to summarize and explain the characteristics and findings of the included studies. The narrative synthesis will explore the relationship and findings both within and between the included studies, in compliance with the guidance from the Centre for Reviews and Dissemination.Subgroup analysis stratified by study design (RCT or prospective cohort study) will be performed. Meta-regression will be used to determine whether the effect of beta-blocker withdrawal on exercise capacity will be confounded by baseline clinical characteristics such as age, sex, New York Heart Association functional class, AF, and CAD. Based on an earlier report, when the number of included studies reporting the primary outcome in the present meta-analysis is less than 5, sensitivity analysis will not be performed .This meta-analysis is a literature study. Ethical approval is not required because this meta-analysis will not involve any subject directly.It is well established that beta-blockers decrease LV oxygen consumption requirements by reducing heart rate, systolic blood pressure, and myocardial contractility and thereby improve myocardial ischemia . AdditioOne RCT reported the beneficial effect of beta-blocker withdrawal on exercise capacity in HFpEF patients . HoweverTo the best of our knowledge, this is the first systematic review and meta-analysis protocol on the effect of beta-blocker withdrawal in HFpEF patients. This systematic review and meta-analysis will evaluate whether beta-blocker withdrawal is beneficial for HFpEF patients, providing evidence regarding beta-blocker withdrawal in these patients.S1 Checklist(DOC)Click here for additional data file.


Intelligent devices, which significantly improve the quality of life and work efficiency, are now widely integrated into people’s daily lives and work. A precise understanding and analysis of human motion is essential for achieving harmonious coexistence and efficient interaction between intelligent devices and humans. However, existing human motion prediction methods often fail to fully exploit the dynamic spatial correlations and temporal dependencies inherent in motion sequence data, which leads to unsatisfactory prediction results. To address this issue, we proposed a novel human motion prediction method that utilizes dual-attention and multi-granularity temporal convolutional networks (DA-MgTCNs). Firstly, we designed a unique dual-attention (DA) model that combines joint attention and channel attention to extract spatial features from both joint and 3D coordinate dimensions. Next, we designed a multi-granularity temporal convolutional networks (MgTCNs) model with varying receptive fields to flexibly capture complex temporal dependencies. Finally, the experimental results from two benchmark datasets, Human3.6M and CMU-Mocap, demonstrated that our proposed method significantly outperformed other methods in both short-term and long-term prediction, thereby verifying the effectiveness of our algorithm. With the rapid development of artificial intelligence technology, an increasing number of intelligent devices are being applied in industrial production and daily human life. Human motion prediction, a key technology for enhancing device intelligence, aims to capture the intrinsic temporal evolution within historical human motion sequences to generate predictions for future motion. Human motion prediction has been widely applied in fields such as autonomous driving , human–cHuman motion prediction is a typical task in the computer vision field. Traditional human motion prediction algorithms, such as hidden Markov models (HMMs) , GaussiaAs more and more large-scale motion capture datasets become available, an increasing number of deep learning models have been designed and have demonstrated excellent performance, such as convolutional neural networks (CNNs) , graph n(a) Spatial relationship modeling: In most previous studies, spatial joint graphs were designed based on the human physical structure, typically utilizing graph neural networks (GNNs)  to captu(b) Simultaneously capturing complex short-term and long-term temporal dependencies: Most research has employed temporal learning components to capture temporal correlations. RNNs are a classic approach, but they face gradient vanishing or exploding issues when learning long time sequences. More advanced models such as LSTM and GRU mitigate the issue of vanishing gradients to a certain degree, but pose challenges in training and lack a parallel computation capability. Self-attention mechanisms ,27 attemTo tackle these challenges in human motion prediction, a novel method based on dual attention and multi-granularity temporal convolutional networks (DA-MgTCNs) was proposed. This approach effectively captures spatial correlations and multi-scale temporal dependencies. Specifically, joint attention and channel attention were combined to design a dual-attention structure for extracting spatial features and capturing information on spatial correlations between and within human joints. TCNs were employed to model long-term temporal dependencies, and the concept of multi-granularity was introduced into the TCN to further enhance performance. The multi-granularity TCN (MgTCN) employed convolution kernels of varying scales in its convolution operations across multiple branches, enabling it to effectively capture multi-scale temporal dependencies in a flexible manner.The MgTCN module was comprised of a combination of multi-granularity causal convolutions, dilated convolutions, and residual connections. Each branch of the module was composed of multiple causal convolution layers with varying dilation factors. This design enabled the adaptive selection of different receptive fields based on varying motion styles and joint trajectory features for short-term and long-term human motion prediction.The main contributions of this paper are as follows:(1) We designed a dual-attention model for extracting inter-joint and intra-joint spatial features, more effectively mining spatial relationships between joints and different motion styles, providing richer information sources for motion prediction.(2) We introduced a multi-granularity temporal convolutional network (MgTCN) that employed multi-channel TCNs with different receptive fields for learning, thus achieving discriminative fusion at different time granularities, flexibly capturing complex short-term and long-term temporal dependencies, and thereby further improving the model’s performance.(3) We conducted extensive experiments on the Human3.6M and CMU-MoCap datasets, demonstrating that our method outperformed most state-of-the-art approaches in short-term and long-term prediction, verifying the effectiveness of the proposed algorithm.The remainder of this paper is organized as follows: In this section, we review the literature relevant to our dual-attention multi-granularity temporal convolutional networks (DA-MgTCNs) model, focusing on existing methods for human motion prediction, temporal convolutional networks (TCNs), multi-granularity (Mg) convolutions, and attention mechanisms.The development of human motion prediction has evolved through several phases. Traditional methods primarily rely on statistical approaches, such as hidden Markov models (HMMs) , GaussiaWith the rapid development of deep learning, researchers have started to apply it to human motion prediction tasks. Recurrent neural networks (RNNs) have been widely adopted for the temporal information modeling of human motion. Some representative works include Fragkiadaki et al. ’s RNN moTo address this issue, researchers have improved RNNs. Chiu et al.  used LSTThe temporal convolutional network (TCN) was developed to address these issues. The fundamental TCN architecture includes causal convolution, dilated convolution, and residual blocks . CompareA single-scale TCN might not be sufficient to capture the multi-scale temporal correlations in motion sequences in human motion prediction tasks. To capture complicated short-term and long-term temporal connections, researchers have developed a method known as multi-granularity convolution, which can combines multi-scale information fusion . By adjuAdditionally, understanding spatial relationships is essential when attempting to predict human motion. In order to overcome this constraint, attention mechanisms were incorporated into the model to extract the spatial correlations of joints. There is still untapped potential in the area of human motion prediction, despite the widespread use of attention mechanisms in natural language processing ,41 and iT time steps, where T specifies the number of input time steps; J the number of human pose joints; and N body joints.Our goal was to forecast future human posture sequences based on previous 3D human pose sequences. Three-dimensional joint positions were employed as the pose representation to prevent the ambiguity produced by the joint angle representation. A graphical representation of the human pose was created by analysing the properties of human joint positions over time. Let terature ,45. As aWe employed a residual depth network consisting of DA-MgTCN modules to capture the global spatial correlation and multi-scale temporal dependence of human motion. Each DA-MgTCN module consisted of a two-branch attention structure module (DA) and a multi-granularity TCN module (MgTCN) connected in series to capture the inter-temporal dependence of historical motion sequences. The DA module was used to extract spatially significant information from joint-level and channel-level dimensions. A combination of multi-granularity convolution and TCN was used in the MgTCN module to increase the prediction quality and adapt to varied forms of human motion and multi-scale time. The complete model architecture was trained end-to-end, with global and local residual connections improving the deep neural network’s performance. Each DA-MgTCN component is described in detail below.The self-attentive mechanism is regarded as an efficient method for modeling remote dependencies. Tang et al.  and Cai As a result, we proposed a dual-attention module that took into account both joint-level attention and channel-level attention in order to extract joint-related and channel-related information for spatial correlation. The DA module is depicted in the lower left corner of X, a linear transformation was first performed using the weight matrices Q, the key K, and the value V. These two branches shared the same embeddings: Q, K, and V. The embeddings are partially reorganized into Q, K, and V in the joint branch) and Q, K, and V in the channel branch) dimensions. The joint and channel attention were used to simultaneously mine the dependencies between joints in the space and channel dimensions. This was computed as follows:V matrices, respectively. Given a human motion feature Equation :(5)X^=FJAfter obtaining the spatially noticed feature representation To learn human motion temporal features efficiently, we extended the concept of temporal multi-granularity convolutional kernels to TCN networks and proposed MgTCNs for extracting temporal features at multiple scales for different motion styles. The MgTCN module is shown in the lower right corner of i, and k is the kernel size.Causal convolution: The output at the tth timestamp for standard 1D convolution is calculated from the k elements around the previous layer with time step t, which is not reasonable for the human motion prediction task . The goaDilated convolution: Causal convolution captures historical data inadequately. Increasing the network’s depth or number of layers can help it capture historical data linearly. However, increasing the network depth exponentially increases the number of parameters, making network training more difficult. Oord et al.  suggesteDilated convolution is implemented by adding expansion parameters to the moving convolutional kernel. Compared to traditional deep convolutional networks, dilation convolution can obtain a larger receptive field without significantly increasing the number of parameters, thus capturing information over a longer time range. This approach can focus on both local details and motion trends over a longer time span when dealing with human motion prediction tasks.F on element s of the sequence is computed as:d is the dilation factor, k is the size of the filter, and the convolution kernel is restricted to slide only at the current position and to its left .Dilated causal convolution can be expressed by the following equation. For a filter R for a three-layer convolution is calculated as:The receptive field Our TCN was calculated as:Multi-granularity convolution: In order to handle complex, multi-action, multi-joint predictions of the human body, MgTCN required the use of convolutional kernel filters with different granularities to extract time-series features at different scales. This was necessary to meet the needs of short- and long-term predictions that require the capture of time-series features of different lengths. Three MgTCN time series were processed separately, which made it possible to combine multiple time granularities in the feature extraction process, which could better represent a large range of spatio-temporal features. Therefore, integrating time-series data with different time granularities to obtain better results is a challenge.In order to handle the complex multi-action and multi-joint prediction of the human body, one must integrate time-series data with different time granularities to obtain better results. MgTCN used convolutional kernel filters with different granularities to extract time-series features at different scales. This satisfied the need for capturing short- and long-term forecasts of time-series features of different lengths. Three time series were treated separately in MgTCN, which allowed us to combine multiple temporal granularities in the feature extraction process to better represent certain large ranges of spatio-temporal features.The MgTCN network output could be used to extract multi-temporal granularity features (short-term and long-term) using the aforementioned spatial and temporal feature extraction steps. We combined the data from these three TCN channels and used the equation below to make predictions in order to achieve the integration of the multi-granularity information.With this multi-granularity temporal convolution (MgTCN) method, we could both observe the general trend of human motion in the long-term and capture the outliers of short-term changes. This temporal correlation facilitated predictive power.Residual connection skips a layer of the network and adds its output to the next layer’s output. This eliminates gradient fading by propagating the gradient straight from the back layer to the front layer. This architecture simplifies neural network representation learning in deeper structures.N is the number of human joints, T is the number of time steps in the future series, ith joint at the jth time step, and To train our DA-MgTCN model, we employed an end-to-end training technique. The mean position per joint error (MPJPE) loss function between the anticipated motion sequence and the ground truth motion sequence was used to analyze the difference between the predicted outcomes and the true pose, which was defined as follows:We optimized the loss function using the improved Adam method (AdamW ), which In this section, we evaluate the performance of the proposed method using two large-scale human motion capture benchmark datasets: Human3.6M and CMU-Mocap.Human3.6M  is the lhttp://mocap.cs.cmu.edu/, accessed on 13 June 2023, is a 3D human motion dataset released by Carnegie Mellon University that used 12 Vicon infrared MX-40 cameras to record the positions of 41 sensors attached to the human body, describing human motion. The dataset can be divided into six motion themes, including human interaction, interaction with environment, locomotion, physical activities and sports, situations and scenarios, and test motions.CMU-MoCap, available at These motion themes can be further subdivided into 23 sub-motion themes. The same data preprocessing method as in the literature  was adopAll experiments in this paper were implemented using the PyTorch deep learning framework. The experimental environment was Ubuntu 20.04 with an NVIDIA A100 GPU. During the training process, the batch normalization size was set to 16, and the AdamW optimizer was used to optimize the model. The initial learning rate was set to 0.003, with decay by 5% every 5 epochs. The model was trained for 60 epochs, and each experiment was conducted three times. The average result was taken to ensure a more robust evaluation of the model’s performance. The input motion prediction length was 25 frames (1000 ms), and the prediction generated 25 frames (1000 ms). The choice and configuration of the relevant hyperparameters are shown in The same evaluation metrics as those used in existing algorithms ,45 were Human3.6M: Based on the existing work, we divided the prediction results into short-term (80–400 ms) and long-term predictions (500–1000 ms). The experimental results are shown in Our experimental results revealed that the proposed DA-MgTCN method outperformed most baseline methods in both short-term and long-term motion prediction. In particular, it can be observed from the experimental results that the proposed DA-MgTCN method outperformed most baseline methods in short-term motion prediction and improved more significantly in long-term prediction, with each MPJPE index reaching the optimum and obtaining excellent prediction results for both the 560 mm and 1000 mm MPJPE metrics. This success can be attributed to the ability of DA-MgTCN to fully capture spatial correlation and multi-granularity temporal features, which was a key factor in enhancing the model’s prediction accuracy.Qualitative comparison: We visualized the results of the aforementioned motion prediction to further assess the model’s performance. CMU-MoCap: To further validate the generalization of the DA-MgTCN method, we compared its performance with existing algorithms on the CMU-MoCap dataset, including Res. sup. [es. sup. , convSeqes. sup. , DMGNN [es. sup. , LTD [25es. sup. , MSR [49es. sup. . The expes. sup. ’s methodTo deeply evaluate the contribution of each component in our model, we conducted a series of ablation experiments on the Human3.6M dataset. These experiments focused on the impact of the channel-attention (channel-att) and multi-grained (Mg) convolution modules on the model’s performance. The results of the experiments are shown in In terms of channel attention, the prediction accuracy significantly decreased when only joint attention was used without dual attention. The multi-granularity convolutional TCN module showed excellent performance in capturing long-term temporal dependence, thus improving the long-term prediction accuracy. Furthermore, when the channel-att or Mg module was removed, the error at 1000 ms increased by 1.9% and 4.0%, respectively, on the Human3.6M dataset, and by 2.9% and 4.0%, respectively, on the CMU-MoCap dataset. The best performance could be achieved by combining these two components. The multi-granularity model demonstrated better performance compared to the single-granularity model, especially for long prediction cycles. Additionally, the use of learnable weight parameters led to better prediction performance compared to fixed weights. This suggested that by designing a multi-granularity temporal structure, we could extract the temporal correlation between different time periods more effectively, thus improving the prediction performance.Effects of the Number of DA-MgTCNs: Further, to validate the effect of multiple DA-MgTCNs in the model, we increased the number of DA-MgTCNs from 6 to 10 in step 2 and determined the prediction error and running time cost for both dataset predictions, as shown in In summary, the experimental results in this paper revealed the importance of the DA-MgTCN method using the dual-attention and multi-granularity convolutional design in terms of performance improvement. Modeling joint motion trajectories using dual-attention, dual-branch spatial attention, and multi-granularity temporal convolution could provide richer and smoother temporal contextual information related to joint motion, which was conducive to adequately modeling spatial global dependencies and enabling the model to encode joint information with hidden dependencies at a distance, thus improving the overall performance of the model for both short-term and long-term motion prediction.In addition to the qualitative results presented in In this paper, we proposed a novel human motion prediction method leveraging dual-channel attention and multi-granularity temporal convolutional networks (DA-MgTCNs) to accurately understand and analyze human motion. Our method combined a dual-attention mechanism and a multi-granularity temporal convolutional networks model to address the challenging problem of extracting inter-joint and intra-joint spatial features. Moreover, the multi-granularity temporal convolutional networks model facilitated the design of a TCN with different convolutional kernel granularities, enabling the learning of richer multi-scale temporal information and further enhancing the performance of the model. Extensive experiments were conducted on two large-scale datasets, Human3.6M and CMU-MoCap. The experimental results demonstrated that the proposed method significantly outperformed other approaches in both short-term and long-term prediction tasks, thus validating the effectiveness of the proposed algorithm. In future work, we aim to further optimize the network structure and parameter settings and extend the application of our model to spatio-temporal prediction tasks in real-world scenarios, such as robot perception and interaction.


Salient object detection (SOD), which is used to identify the most distinctive object in a given scene, plays an important role in computer vision tasks. Most existing RGB-D SOD methods employ a CNN-based network as the backbone to extract features from RGB and depth images; however, the inherent locality of a CNN-based network limits the performance of CNN-based methods. To tackle this issue, we propose a novel Swin Transformer-based edge guidance network (SwinEGNet) for RGB-D SOD in which the Swin Transformer is employed as a powerful feature extractor to capture the global context. An edge-guided cross-modal interaction module is proposed to effectively enhance and fuse features. In particular, we employed the Swin Transformer as the backbone to extract features from RGB images and depth maps. Then, we introduced the edge extraction module (EEM) to extract edge features and the depth enhancement module (DEM) to enhance depth features. Additionally, a cross-modal interaction module (CIM) was used to integrate cross-modal features from global and local contexts. Finally, we employed a cascaded decoder to refine the prediction map in a coarse-to-fine manner. Extensive experiments demonstrated that our SwinEGNet achieved the best performance on the LFSD, NLPR, DES, and NJU2K datasets and achieved comparable performance on the STEREO dataset compared to 14 state-of-the-art methods. Our model achieved better performance compared to SwinNet, with 88.4% parameters and 77.2% FLOPs. Our code will be publicly available.  Salient object detection (SOD) is an important preprocessing method in computer vision tasks, with applications in video detection and segmentation , semantiCNN-based models for RGB SOD have yielded great performance in localizing salient objects ,5,6,7,8.The first issue is that CNN-based models cannot effectively capture long-range dependencies. Long-range semantic information plays an important role in identifying and locating salient objects . Due to The second issue is that depth maps are often noisy. The performance of RGB-D SOD models relies on reliable RGB images and depth maps. Misleading information in depth maps degrades the performance of RGB-D SOD models.Global context information helps reduce errors created via poor depth maps. Transformers can extract features and model long-range dependencies, and Transformer-based methods have achieved outstanding performance in various computer vision tasks ,27,28,29Swin Transformer-based models are relatively weak in their ability to model local context information. Therefore, Swin Transformer-based models should pay more attention to local feature information.Based on the investigation above, we propose a novel Swin Transformer-based edge guidance network (SwinEGNet) that enhances feature locality to boost the performance of RGB-D SOD. We employed the Swin Transformer as the backbone to extract features from RGB images and depth maps for capturing long-range dependencies. We introduced a depth enhancement module (DEM) and a cross-modal interaction module to enhance local features. Unlike other methods, we employed edge clues to enhance depth features rather than edge clues as decoder guidance to directly refine the final prediction map. We designed the edge extraction module (EEM) to extract edge information and the depth enhancement module (DEM) to enhance depth features. Furthermore, we used a cross-modal interaction module to effectively integrate information from global and local contexts. To effectively explore the features of each layer, we employed a cascaded decoder to progressively refine our saliency maps.A novel edge extraction module (EEM) is proposed, which generates edge features from the depth features.A newly designed edge-guided cross-modal interaction was employed to effectively integrate cross-modal features, where the depth enhancement module was employed to enhance the depth feature and the cross-modal interaction module was employed to encourage cross-modal interaction from global and local aspects.A novel Swin Transformer-based edge guidance network (SwinEGNet) for RGB-D SOD is proposed. The proposed SwinEGNet was evaluated with four evaluation metrics and compared to 14 state-of-the-art (SOTA) RGB-D SOD methods on six public datasets. Our model achieved better performance with less parameters and FLOPs than SwinNet, as shown in Our main contributions are summarized as follows:The remainder of this paper is structured as follows: The current status of RGB-D salient object detection is presented in CNN-based RGB-D salient object detection: Benefitting from the development of deep learning and depth sensors, many CNN-based RGB-D SOD methods have recently been proposed. Compared to RGB SOD methods, RGB-D SOD models employ depth clues as complementary information and have shown outstanding performance in salient object detection. Most RGB-D SOD models adopt CNN-based networks to extract features and focus on cross-modal fusion strategies to improve salient object detection performance. Various frameworks and fusion strategies have been proposed to effectively merge cross-modal cross-scale features ,23,30,31To address the insufficiency of obtaining global semantic information of CNN-based networks, Liu et al.  proposedTransformer-based RGB-D salient object detection: The Transformer was first employed for machine translation and gradually introduced in computer vision tasks. Dosovitskiy et al.  proposedIn this section, we present the proposed Swin Transformer-based edge guidance network (SwinEGNet). We provide an overview of our method and describe its main components in detail, including the feature encoder, edge extraction module, edge-guided cross-modal interaction module, cascaded decoder, and loss function.As illustrated in In contrast to other Transformers, the Swin Transformer computes multihead self-attention within a local window instead of the whole input to model locality relationships. Furthermore, it employs a shifted window operation to model long-range dependence across windows. Therefore, the Swin Transformer is suitable for feature extraction because it incorporates the merits of the Transformer and CNN. Considering the performance and computational complexity, we adopted the Swin-B Transformer as the backbone to extract features from RGB images and depth maps, which accept an input size of 384 × 384.RGB images and depth maps are fed into two independent Swin Transformers for feature extraction. Considering the first layer contains redundant noisy information, the extracted features of the last four layers are employed for feature fusion. The features can be expressed as follows:To extract edge features, we propose an edge extraction module (EEM). The extracted edge features are fed into EGCIM to enhance the depth feature. The details of the proposed EEM are illustrated in Shallow layers contain low-level information such as structure clues, while deep layers contain global semantic information. They are all helpful in extracting edge information. In contrast to other methods that employ parts of the depth features for edge prediction, we employed all depth features for edge extraction, and the edge features were progressively refined in a coarse-to-fine manner.In particular, the depth features Next, the edge feature performs a concatenation operation and a 3 × 3 convolutional layer with a BatchNorm and a ReLU function to generate the edge feature To enhance depth features and encourage cross-modal feature interaction, we designed an edge-guided cross-modal interaction module (EGCIM) to integrate features from both modalities, including a depth enhancement module (DEM) and a cross-modal interaction module (CIM).Depth enhancement module: Though Transformer-based methods sufficiently capture global context information, they are relatively weak at capturing local context information compared to CNN-based methods. Therefore, it is necessary to utilize local clues like edge information to enhance the depth features. We designed a depth enhancement module (DEM) to enhance the depth features, which introduces edge information extracted from the depth features to these features for depth enhancement. The detailed structure of DEM is shown in The depth features Cross-modal interaction module: We used a cross-modal interaction module (CIM) to effectively combine RGB and depth modalities. The CIM contains a global attention branch and a local attention branch to enhance globality and locality. In addition, a residual connection is adopted to combine the fused features with RGB features for the preservation of the RGB images’ original information. The local information of the depth features enhances the RGB features to sharpen the details of salient objects, and the global context information of the depth features enhances the RGB features to locate the salient object.As shown in The cascaded encoder can effectively leverage the multilevel features and eliminate the noise in low-level features, which improves the accuracy of salient maps. Moreover, deep-layer supervision performs better than single supervision . TherefoDetection loss is composed of the weighted binary cross-entropy (BCE) loss ss LIoUω , which hDatasets: We evaluated the proposed method on six widely used benchmark datasets: STEREO (1000 image pairs) , NJU2K  , maximum.5) [Fm) , maximumFm) [Em) , and mea[Em) [M) . S-measu follows:MAE=1W×H∑−4, which was then divided by 10 for every 60 epochs. All images were resized to 384 × 384 for training and testing. The single-channel depth image was replicated to a three-channel image, which was the same as the RGB image. Data augment strategies, including random flipping, rotating, and border clipping, were employed to augment the training data. The model was trained for 120 epochs.We implemented our model on PyTorch with one NVIDIA A4000 GPU. The Swin Transformer that has been pretrained on ImageNet was employed as our backbone network. The parameters of the Swin-B model were initialized with the pretrained parameters, and the remaining parameters were initialized with PyTorch default settings. The Adam optimizer was employed to train the proposed model with a batch size of 5, a momentum of 0.9, and a weight decay of 0.1. The initial learning rate was 1 × 102DFNet [Quantitative comparison: We compared the proposed network with 14 SOTA CNN-based methods and Transformer-based methods, which were CMW , JLDCF , HINet , DSA2F , CFIDNet2DFNet , SPSNet 2DFNet , AFNet [2DFNet , HiDANet2DFNet , MTForme2DFNet , VST [432DFNet , and Swi2DFNet . The comQualitative comparison: We qualitatively compared seven representative methods on challenging scenes. The first scene had a similar foreground and background (first row), the second scene had poor depth map (second row and third row), the third scene had a complex background (fourth row and fifth row), the fourth scene had a small object (sixth row), the fifth scene had multiple objects (seventh row and eighth row), and the sixth scene had a fine structure (ninth row). As shown in We conducted comprehensive ablation studies on LFSD and STEREO datasets to evaluate the effectiveness of the proposed modules in our proposed model.Effectiveness of Swin Transformer backbone: We replaced the feature encoder with ResNet50 to verify the effectiveness of the Swin Transformer backbone. As shown in Effectiveness of EGCIM: To explore the effectiveness of EGCIM, we replaced EGCIM with a multiplication operation. In Effectiveness of DEM in EGCIM: To verify the effectiveness of DEM in EGCIM, we removed DEM from our full model. In Effectiveness of CIM in EGCIM: We replaced CIM with a multiplication operation to verify the effectiveness of CIM in EGCIM. In We conducted a complexity comparison with the other five models on the number of parameters and FLOPs, as shown in We show some failure cases on the challenging scenes in In this paper, we propose a novel Swin Transformer-based edge guidance network for RGB-D SOD. We employed the Swin Transformer as the backbone to extract features from RGB images and depth maps for capturing the long-range dependencies. Additionally, we proposed using the edge extraction module (EEM), the depth enhancement module, and the cross-modal interaction module (CIM) to enhance the local features. The EEM extracts edge features from the depth features, and the DEM employs edge information to enhance the depth features. The CIM effectively fuses RGB features and depth features from global and local contexts. With all these modules working together, our SwinEGNet model can accurately localize salient objects in various complex scenarios with sharp boundaries. Countless comparison studies and ablation experiments demonstrated that the proposed SwinEGNet showed outstanding performance on six widely used RGB-D SOD benchmark datasets. As an independent module, EEM can be applied to related tasks. In the future, we will extend our model to RGB-T salient object detection.


Aortic stenosis (AS) is the most prevalent primary valve lesion demanding intervention. Two main treatment options are surgical aortic valve replacement or transcatheter aortic valve implantation. There is an unmet need for biomarkers that could predict treatment outcomes and become a helpful tool in guiding Heart Team in the decision-making process. Micro-ribonucleic acids (microRNAs/miRs) have emerged as potential biomarkers thoroughly studied in recent years. In this review, we aimed to summarize the current knowledge about the role of miRNAs in AS based on human subject research. Much research investigating miRNAs’ role in AS has been conducted so far. We included 32 original human subject research relevant to the discussed field. Most of the presented miRNAs were studied only by a single research group. Nevertheless, several miRNAs appeared more than once, sometimes with high consistency between different studies but sometimes with apparent discrepancies. The molecular aspects of diseases are doubtlessly exciting and provide invaluable insights into the pathophysiology. Nevertheless, translating these findings, regarding biomarkers such as miRNAs, into clinical practice requires much effort, time, and further research with a focus on validating existing evidence. Aortic stenosis (AS) is the most prevalent primary valve lesion demanding intervention–either surgical or transcatheter . The maiTwo main treatment options are surgical aortic valve replacement (AVR) or transcatheter aortic valve implantation (TAVI). The decision-making process is complex and should be carried out after the Heart Team evaluation . The maiMiRNAs are a subtype of ribonucleic acids (RNAs) consisting of about 22 nucleotides . They arThe pathophysiology of AS development includes inflammation, endothelial dysfunction, and osteogenic differentiation of interstitial cells. All these lead to progressive calcification and subsequent outflow obstruction . Since mIn this review, we aimed to summarize the current knowledge about the role of miRNAs in AS based on human subject research.In our review, we included only original human subject research. Reviews, letters to the editors, and commentaries were not included. We searched the PubMed Database, and after a thorough evaluation of its records, we included 32 original human subject studies relevant to the discussed field.We divided these studies into the following parts: (i) altered microRNA expression in patients with aortic stenosis, (ii) altered microRNA expression in patients with calcific aortic valve disease, (iii) differences in microRNA expression between aortic stenosis patients with bicuspid and tricuspid aortic valves, (iv) comparison of microRNA expression between calcified and adjacent non-calcified aortic valve tissues in the same patients, (v) predictive values of microRNAs after aortic valve replacement and transcatheter aortic valve implantation .All studies discussed further in this subsection with additional data are summarized in Beaumont et al. analyzed the role of miRs in the progression of myocardial fibrosis in patients with AS. The authors included patients suffering from AS who underwent AVR and divided them according to their myocardial collagen volume fraction (CVF)—a marker of fibrosis. It was found that patients with severe fibrosis (SF) presented lower myocardial expression levels of miR-18b and miR-122 compared to patients without SF. Moreover, the authors found an inverse correlation of miR-122 with CVF and transforming growth factor-β type 1 (TGF-β1) .Røsjø et al. examined the levels of miR-210 in patients with moderate to severe AS and compared them to healthy controls. The levels of miR-210 were increased in patients with AS compared to controls. Moreover, the authors showed that higher levels of miR-210 were significantly associated with increased mortality in patients with AS during a median follow-up of about 3.5 years . Coffey Interesting research was conducted by Gallego et al. They recruited patients with AS undergoing AVR and divided them based on the cardiomyocyte apoptotic index (CMAI) into two groups: (i) with low CMAI and (ii) with high CMAI. Moreover, cardiovascular-disease-free control subjects constituted the additional control group. It was found that miR-10b, miR-125b-2*, and miR-338-3p were downregulated in both groups of patients with AS compared to controls. Moreover, all three miRs were downregulated in patients with high CMAI compared to patients with low CMAI. Interestingly, all the levels of these miRs correlated inversely with CMAI .Takahashi et al. aimed to study the expression of several miRs in circulating osteogenic progenitor cells (COPCs) from patients with calcific aortic valve disease (CAVD) undergoing AVR or TAVI. The authors showed upregulation of miR-30c and downregulation of miR-31, miR-106a, miR-148a, miR-204, miR-211, and miR-424 in patients with AS compared to control individuals. Moreover, it was found that miR-30c correlated weekly with the degree of aortic valve calcification. Interestingly, the levels of all investigated miRs changed after the procedure (AVR or TAVI): the level of miR-30c decreased, while the levels of remaining miRs increased. AVR caused more significant changes in miR levels than TAVI .Beaumont et al. published research conducted on the same population as the previously mentioned research by Gallego et al., but this time they focused on different miRs. Moreover, in this investigation, they measured miR expression in both the myocardium and the blood. They found that the miR-19b and miR-133a level was lower in patients with AS than in control subjects in the myocardium and in the blood. Moreover, they found that miR-19b was downregulated in patients suffering from AS with coexisting heart failure (HF) compared to patients with AS but without HF .Differently from previously described research, Song et al. studied the expression of miRs in aortic valve interstitial cells (AVICs) in patients with AS undergoing AVR. Patients undergoing heart transplantation due to cardiomyopathy served as controls. The authors found that miR-204 was downregulated and miR-486 upregulated in patients with AS compared to controls . Xu et aSimilarly, Fiedler et al. assessed several microRNAs expression in the aortic valves of patients suffering from AS undergoing AVR. They compared it to the expression of these microRNAs in the aortic valves of healthy controls from a biobank. They showed increased miR-21, miR-24, and miR-143 levels in patients with AS. Further experiments proved that miR-143 regulated the expression of matrix gla protein (MGP) via direct binding to its 3′UTR region. Noteworthily, MGP plays a crucial role in maintaining homeostasis in the aortic valves .Interestingly, Petrkova et al. investigated the relationship between atherosclerosis in patients with AS and miRNA expression. They included patients undergoing AVR due to AS. They showed higher levels of miR-146a in valvular tissues obtained from patients with atherosclerosis compared to non-atherosclerotic patients .To summarize, many studies have revealed that patients suffering from AS presented with changed expression of various miRs. This applied to measurements conducted in plasma, aortic valve tissues, myocardium, and even COPCs. Although the results are undoubtedly promising, the multitude of studied miRs hampers drawing firm cause-and-effect conclusions.As in the previous paragraph, all studies discussed in this subsection with additional data are summarized in Jiang et al. further studied the importance of miRNAs in CAVD. They compared miRNA levels between calcified and non-calcified aortic valves. They showed downregulation of miR-135-5p, miR-204-5p, miR-335-3p, and miR-664a-3p in valves from CAVD patients compared to valves from patients undergoing heart transplantation. They further investigated miR-664a-3p in vitro and discovered its involvement in the regulation of osteogenic differentiation of human aortic valve interstitial cells (hAVICs) .Wang et al. focused on the role of miR-629-3p in AS. They compared its expression levels in aortic valves between patients with CAVD and without CAVD, both undergoing AVR. Most importantly, they observed the downregulation of miR-629-3p in valves from CAVD patients compared to the control group. Further in vitro investigation revealed that miR-629-3p and its target, TAGLN, played an anti-calcification role in the osteogenic differentiation of hAVICs . LikewisFinally, Yang et al. investigated the involvement of miR-22 in CAVD pathogenesis. They compared miR-22 expression in aortic valve samples between CAVD patients undergoing AVR and non-CAVD patients undergoing heart transplantation. It was found that miR-22 was upregulated in CAVD patients compared to non-CAVD patients. The authors conducted further in vitro assessments, concluding that miR-22 accelerated the osteogenic differentiation of hAVICs .All the discussed studies show that miRNAs may play a role in the pathogenesis of CAVD. Although most studies focused only on altered miRNA expression, which can be only a result, not a cause, insights from in vitro analyses suggest miRNA involvement in the calcification process. The regulation of osteogenic differentiation of hAVICs is one of the most critical processes in which miRNAs are involved in this context.Consistently, we summarized all studies discussed in this subsection with additional data in Like in the previous research, Zheng et al. conducted a study comparing the levels of various miRNAs in aortic valve samples between patients with a bicuspid aortic valve (BAV) and a tricuspid aortic valve (TAV), both suffering from AS and undergoing AVR. They showed increased levels of miR-330-3p, miR-659, and miR-663 and decreased levels of miR-146 in patients with BAVs compared to control patients with TAVs. They further studied the role of miR-330-3p in vitro and demonstrated that the upregulation of miR-330-3p promoted the calcification progress [Not only does miRNA expression differ between patients with AS and healthy/control individuals, but there is also a difference between AS patients with BAVs and TAVs. Although only a few studies have investigated this problem, it seems intriguing and worth studying in future research.All studies discussed in this subsection with additional data are summarized in Lu et al. conducted similar research to Jiao et al., including patients with degenerative CAVD undergoing AVR. They analyzed miR-138 expression in calcified and non-calcified areas of aortic valve samples obtained from included patients. They demonstrated the significant downregulation of miR-138 in calcific compared to non-calcific aortic valves. In further in vitro studies, they obtained results suggesting that miR-138 plays an inhibitory role in the osteogenic differentiation of hAVICs, which seems counterintuitive, considering the results presented by Jiao et al. .Finally, Yu et al. investigated miR-25-3p in CAVD patients undergoing AVR. Like other research teams, they compared expression levels between diseased aortic valve tissue and adjacent normal tissue. MiR-25-3p appeared to be upregulated in calcific valves. The authors concluded that miR-25-3p and the whole TGFBR2/miR-25-3p/TWIST1 axis played a role in osteoblast differentiation in hAVICs .All the studies above show that calcific and non-calcific parts differ in miRNA expression. This suggests that miRNA may be directly involved in calcification and further aortic valve degeneration. Almost every study observed the involvement of hAVICs and their osteogenic differentiation in the studied process.Chen et al. aimed to investigate miR-1, miR-133, and miR-378 in predicting LVH in patients with AS. They found that miR-378 was an independent predictor of this condition. The authors also found that the miR-1, miR-133, and miR-378 levels were significantly lower in patients with AS than in healthy controls . KleeberFabiani et al. included patients with severe AS and made comparisons within this group based mainly on TTE parameters. They found that (i) patients with reduced ejection fraction had increased levels of miR-1 and miR-133, (ii) patients characterized by low flow condition had increased levels of miR-1 and miR-21, (iii) miR-21 was increased in patients with reduced global longitudinal strain, (iv) during one-year follow-up miR-21 was found to be an independent predictor of reverse remodeling while miR-29 independently predicted systolic function increase . EyileteAs discussed above, miRNA utility goes beyond diagnosis. It may have potential prognostic and predictive value. There is a long way to go in implementing them in routine clinical practice, but it is undoubtedly exciting and connects science and personalized medicine.Much research investigating miRNAs’ role in AS has been conducted so far. Scientists have often assessed miRNAs using microarrays and validated the results with quantitative reverse transcription-polymerase chain reaction (qRT-PCR). In our review, we focused only on the results confirmed via qRT-PCR. After presenting all the existing knowledge in the field, some important and exciting issues were discussed.Most of the presented miRNAs were studied only by a single research group. Nevertheless, several miRNAs have appeared more than once, sometimes with high consistency between different studies but sometimes with apparent discrepancies.Within those characterized by high consistency, miR-21 and miR-204 are undoubtedly worth mentioning. MiR-21 was the research subject in five cited studies ,17,23,42On the other hand, miR-22 and miR-133 are worth discussing due to some inconsistencies. Yang et al., in their very recent research, discovered that miR-22 is downregulated in the valves of patients with CAVD. Coffey et al. demonstrated partially different but reasonably explainable results a few years earlier: patients with AS without coexisting CAD were found to have decreased levels of miR-22, as in the study by Yang et al. However, patients with AS and coexisting CAD presented increased levels of miR-22 compared to controls. The results from the research of Fabiani et al. regarding miR-133a is more surprising . Two resThe subjectivity of investigated miRNA choice is an essential limitation of many discussed studies. In most cases, the first step was a microarray, which found differently expressed miRNAs. The validation phase using qRT-PCR included only a few miRNAs, often chosen based von the extent of change in expression level or by the ‘literature review’. Therefore, instead of validating results obtained in previous research with some potential new findings, almost every study focused on a different miRNA, which led to a multitude of data that should now be validated.The molecular aspects of diseases are doubtlessly exciting and provide invaluable insights into their pathophysiology. Nevertheless, translating these findings, regarding biomarkers such as miRNAs, into clinical practice requires much effort, time, and further research with a focus on validating existing evidence.Much research has already been conducted. Nevertheless, there is still much to be carried out. First, it is essential to validate obtained results in a larger cohort. Once the validation has been completed, the research should focus on creating a miRNA-based diagnostic panel for AS utilizing miRNAs with the most altered expression. In an ideal but not unrealistic scenario, such a panel could also help identify the severity of AS.Secondly, miRNAs might help predict interventional treatment outcomes, either AVR or TAVI. In this context, they would be instrumental, especially for patients at the borderline of qualification for AVR or TAVI.Last but not least, miRNAs may be used as therapeutic targets or therapeutics. Nevertheless, before such a breakthrough is made, there are many steps to be taken. Crucially, the cause-and-effect relationship between miRNA expression and AS deterioration must be determined. Only then can preclinical studies, potentially followed by clinical ones, lead to novel, miRNA-based therapeutics used in AS treatment.