Exploring Estimation Procedures for Reducing Dimensionality in Psychological Network Modeling.

To understand psychological data, it is crucial to examine the structure and dimensions of variables. In this study, we examined alternative estimation algorithms to the conventional GLASSO-based exploratory graph analysis (EGA) in network psychometric models to assess the dimensionality structure of the data. The study applied Bayesian conjugate or Jeffreys' priors to estimate the graphical structure and then used the Louvain community detection algorithm to partition and identify groups of nodes, which allowed the detection of the multi- and unidimensional factor structures. Monte Carlo simulations suggested that the two alternative Bayesian estimation algorithms had comparable or better performance when compared with the GLASSO-based EGA and conventional parallel analysis (PA). When estimating the multidimensional factor structure, the analytically based method (i.e., EGA.analytical) showed the best balance between accuracy and mean biased/absolute errors, with the highest accuracy tied with EGA but with the smallest errors. The sampling-based approach (EGA.sampling) yielded higher accuracy and smaller errors than PA; lower accuracy but also lower errors than EGA. Techniques from the two algorithms had more stable performance than EGA and PA across different data conditions. When estimating the unidimensional structure, the PA technique performed the best, followed closely by EGA, and then EGA.analytical and EGA.sampling. Furthermore, the study explored four full Bayesian techniques to assess dimensionality in network psychometrics. The results demonstrated superior performance when using Bayesian hypothesis testing or deriving posterior samples of graph structures under small sample sizes. The study recommends using the EGA.analytical technique as an alternative tool for assessing dimensionality and advocates for the usefulness of the EGA.sampling method as a valuable alternate technique. The findings also indicated encouraging results for extending the regularization-based network modeling EGA method to the Bayesian framework and discussed future directions in this line of work. The study illustrated the practical application of the techniques to two empirical examples in R.

Comparing community detection algorithms in psychometric networks: A Monte Carlo simulation.

Identifying the correct number of factors in multivariate data is fundamental to psychological measurement. Factor analysis has a long tradition in the field, but it has been challenged recently by exploratory graph analysis (EGA), an approach based on network psychometrics. EGA first estimates a network and then applies the Walktrap community detection algorithm. Simulation studies have demonstrated that EGA has comparable or better accuracy for recovering the same number of communities as there are factors in the simulated data than factor analytic methods. Despite EGA's effectiveness, there has yet to be an investigation into whether other sparsity induction methods or community detection algorithms could achieve equivalent or better performance. Furthermore, unidimensional structures are fundamental to psychological measurement yet they have been sparsely studied in simulations using community detection algorithms. In the present study, we performed a Monte Carlo simulation using the zero-order correlation matrix, GLASSO, and two variants of a non-regularized partial correlation sparsity induction methods with several community detection algorithms. We examined the performance of these method-algorithm combinations in both continuous and polytomous data across a variety of conditions. The results indicate that the Fast-greedy, Louvain, and Walktrap algorithms paired with the GLASSO method were consistently among the most accurate and least-biased overall.

Dimensionality assessment in the presence of wording effects: A network psychometric and factorial approach.

This study proposes a procedure for substantive dimensionality estimation in the presence of wording effects, the inconsistent response to regular and reversed self-report items. The procedure developed consists of subtracting an approximate estimate of the wording effects variance from the sample correlation matrix and then estimating the substantive dimensionality on the residual correlation matrix. This is achieved by estimating a random intercept factor with unit loadings for all the regular and unrecoded reversed items. The accuracy of the procedure was evaluated through an extensive simulation study that manipulated nine relevant variables and employed the exploratory graph analysis (EGA) and parallel analysis (PA) retention methods. The results indicated that combining the proposed procedure with EGA or PA achieved high accuracy in estimating the substantive latent dimensionality, but that EGA was superior. Additionally, the present findings shed light on the complex ways that wording effects impact the dimensionality estimates when the response bias in the data is ignored. A tutorial on substantive dimensionality estimation with the R package EGAnet is offered, as well as practical guidelines for applied researchers.

Detecting non-content-based response styles in survey data: An application of mixture factor analysis.

It is common for some participants in self-report surveys to be careless, inattentive, or lacking in effort. Data quality can be severely compromised by responses that are not based on item content (non-content-based [nCB] responses), leading to strong biases in the results of data analysis and misinterpretation of individual scores. In this study, we propose a specification of factor mixture analysis (FMA) to detect nCB responses. We investigated the usefulness and effectiveness of the FMA model in detecting nCB responses using both simulated data (Study 1) and real data (Study 2). In the first study, FMA showed reasonably robust sensitivity (.60 to .86) and excellent specificity (.96 to .99) on mixed-worded scales, suggesting that FMA had superior properties as a screening tool under different sample conditions. However, FMA performance was poor on scales composed of only positive items because of the difficulty in distinguishing acquiescent patterns from valid responses representing high levels of the trait. In Study 2 (real data), FMA detected a minority of cases (6.5%) with highly anomalous response patterns. Removing these cases resulted in a large increase in the fit of the unidimensional model and a substantial reduction in spurious multidimensionality.

Unique Variable Analysis: A Network Psychometrics Method to Detect Local Dependence.

The local independence assumption states that variables are unrelated after conditioning on a latent variable. Common problems that arise from violations of this assumption include model misspecification, biased model parameters, and inaccurate estimates of internal structure. These problems are not limited to latent variable models but also apply to network psychometrics. This paper proposes a novel network psychometric approach to detect locally dependent pairs of variables using network modeling and a graph theory measure called weighted topological overlap (wTO). Using simulation, this approach is compared to contemporary local dependence detection methods such as exploratory structural equation modeling with standardized expected parameter change and a recently developed approach using partial correlations and a resampling procedure. Different approaches to determine local dependence using statistical significance and cutoff values are also compared. Continuous, polytomous (5-point Likert scale), and dichotomous (binary) data were generated with skew across a variety of conditions. Our results indicate that cutoff values work better than significance approaches. Overall, the network psychometrics approaches using wTO with graphical least absolute shrinkage and selector operator with extended Bayesian information criterion and wTO with Bayesian Gaussian graphical model were the best performing local dependence detection methods overall.

Exploratory Bi-factor Analysis with Multiple General Factors.

Exploratory bi-factor analysis (EBFA) is a very popular approach to estimate models where specific factors are concomitant to a single, general dimension. However, the models typically encountered in fields like personality, intelligence, and psychopathology involve more than one general factor. To address this circumstance, we developed an algorithm (GSLiD) based on partially specified targets to perform exploratory bi-factor analysis with multiple general factors (EBFA-MGF). In EBFA-MGF, researchers do not need to conduct independent bi-factor analyses anymore because several bi-factor models are estimated simultaneously in an exploratory manner, guarding against biased estimates and model misspecification errors due to unexpected cross-loadings and factor correlations. The results from an exhaustive Monte Carlo simulation manipulating nine variables of interest suggested that GSLiD outperforms the Schmid-Leiman approximation and is robust to challenging conditions involving cross-loadings and pure items of the general factors. Thereby, we supply an R package (bifactor) to make EBFA-MGF readily available for substantive research. Finally, we use GSLiD to assess the hierarchical structure of a reduced version of the Personality Inventory for DSM-5 Short Form (PID-5-SF).

Outcomes of a modified, low-cost, veno-arterial extracorporeal membrane oxygenation (V-A ECMO) for elective, periprocedural support of high-risk percutaneous cardiac interventions: An experience from a latinamerican center.

INTRODUCTION: High-risk procedures in interventional cardiology include a wide spectrum of clinical and anatomical scenarios related to a higher periprocedural morbidity and mortality. The prophylactic use of short-term mechanical circulatory support (ST-MCS) may improve both the safety and efficacy of the intervention by leading to more stable procedural hemodynamics. However, the significant costs may limit its use in resource constrained settings. To overcome this limitation, we ideated a modified, low-cost, veno-arterial extracorporeal membrane oxygenator (V-A ECMO) setup. METHODS: We conducted an observational prospective study including all patients undergoing a high-risk interventional cardiology procedure at our institution under prophylactic ST-MCS using a modified, low-cost version of V-A ECMO, where some components of the standard V-A ECMO circuit were replaced by supplies used for cardiac surgical cardiopulmonary bypass, achieving a cost reduction of 72%. We assessed in-hospital and mid-term outcomes, including procedural success, post-procedure complications and mortality. RESULTS: Between March 2016 and December 2021, ten patients underwent high-risk IC procedures with prophylactic use of V-A ECMO. Isolated percutaneous intervention (PCI) was performed in six patients, isolated transcatheter aortic valve replacement (TAVR) in two, and a combined procedure (PCI + TAVR) in two. Mean ejection fraction was 34% (range 20-64%). Mean STS PROM was 16.2% (range 9.5-35.8%) and mean EuroScore was 23.7% (range 1.5-60%). The planned intervention was successfully performed in all cases. There were no reports of V-A ECMO malfunction. In nine patients the VA-ECMO was withdrawn immediately after the procedure but one patient required extended - 24 h - support with no significant issues. One patient experienced a periprocedural myocardial infarction and another developed a femoral pseudoaneurysm. In-hospital and 30-day survival were 100%, and 1-year survival was 80%. CONCLUSIONS: High-risk procedures in interventional cardiology can be successfully performed under prophylactic ST-MCS using a modified, low-cost V-A ECMO, suitable for limited-resource settings.

Ontogeny and anatomy of Bouteloua (Poaceae: Chloridoideae) species display a basipetal branch formation and a novel modified leaf structure in grasses.

BACKGROUND AND AIMS: Shoot ontogenesis in grasses follows a transition from a vegetative phase into a reproductive phase. Current studies provide insight into how branch and spikelet formation occur during the reproductive phase. However, these studies do not explain all the complex diversity of grass inflorescence forms and are mostly focused on model grasses. Moreover, truncated inflorescences of the non-model grass genus Urochloa (Panicoideae) with formation of primary branches have basipetal initiation of branches. Bouteloua species (Chloridoideae) are non-model grasses that form truncated inflorescences of primary branches with apical vestiges of uncertain homology at the tips of branching events and sterile florets above the lowermost fertile floret. Sterile florets are reduced to rudimentary lemmas composed of three large awns diverging from an awn column. Conflict about the awn column identity of this rudimentary lemma is often addressed in species descriptions of this genus. We test if Bouteloua species can display basipetal initiation of branches and explore the identity of vestiges and the awn column of rudimentary lemmas. METHODS: We surveyed the inflorescence ontogeny and branch/awn anatomy of Bouteloua species and compared results with recent ontogenetic studies of chloridoids. KEY RESULTS: Bouteloua arizonica has florets with basipetal maturation. Branches display basipetal branch initiation and maturation. Branch vestiges are formed laterally by meristems during early branching events. The spikelet meristem forms the awn column of rudimentary lemmas. Vestiges and sterile floret awns have anatomical similarities to C4 leaves. CONCLUSIONS: Basipetal initiation of branches is a novel feature for Chloridoideae grasses. Branch vestiges are novel vegetative grass structures. Sterile floret awn columns are likely to be extensions of the rachilla.

Focusing on cybersickness: pervasiveness, latent trajectories, susceptibility, and effects on the virtual reality experience.

Although virtual reality (VR) usage has become widespread in the last decade, its adoption has been hampered by experiences of user discomfort known as cybersickness. The present study, in line with the "2020 cybersickness R&D agenda", sought to provide a broad examination of the cybersickness phenomenon, assessing its pervasiveness, latent trajectories, impacts on the VR experience, and predictor variables. The study was composed of 92 participants living in the Dominican Republic with ages ranging from 18 to 52 years (M = 26.22), who experienced a 10-min VR immersion in two environments designed for psychotherapy. The results indicated that cybersickness was pervasive, with 65.2% of the participants experiencing it, and 23.9% severely. Additionally, the latent trajectories of cybersickness were positive and curvilinear, with large heterogeneity across individuals. Cybersickness also had a substantive negative impact on the user experience and the intentions to adopt the VR technology. Finally, motion sickness susceptibility, cognitive stress, and recent headaches uniquely predicted greater severity of cybersickness, while age was negatively related. These combined results highlight the critical role that cybersickness plays on the VR experience and underscore the importance of finding solutions to the problems, such as technological advancements or special usage protocols for the more susceptible individuals. Supplementary Information: The online version contains supplementary material available at 10.1007/s10055-022-00636-4.

On Omega Hierarchical Estimation: A Comparison of Exploratory Bi-Factor Analysis Algorithms.

As general factor modeling continues to grow in popularity, researchers have become interested in assessing how reliable general factor scores are. Even though omega hierarchical estimation has been suggested as a useful tool in this context, little is known about how to approximate it using modern bi-factor exploratory factor analysis methods. This study is the first to compare how omega hierarchical estimates were recovered by six alternative algorithms: Bi-quartimin, bi-geomin, Schmid-Leiman (SL), empirical iterative empirical target rotation based on an initial SL solution (SLiD), direct SL (DSL), and direct bi-factor (DBF). The algorithms were tested in three Monte-Carlo simulations including bi-factor and second-order structures and presenting complexities such as cross-loadings or pure indicators of the general factor and structures without a general factor. Results showed that SLiD provided the best approximation to omega hierarchical under most conditions. Overall, neither SL, bi-quartimin, nor bi-geomin produced an overall satisfactory recovery of omega hierarchical. Lastly, the performance of DSL and DBF depended upon the average discrepancy between the loadings of the general and the group factors. The re-analysis of eight classical datasets further illustrated how algorithm selection could influence judgments regarding omega hierarchical.

Entropy Fit Indices: New Fit Measures for Assessing the Structure and Dimensionality of Multiple Latent Variables.

The accurate identification of the content and number of latent factors underlying multivariate data is an important endeavor in many areas of Psychology and related fields. Recently, a new dimensionality assessment technique based on network psychometrics was proposed (Exploratory Graph Analysis, EGA), but a measure to check the fit of the dimensionality structure to the data estimated via EGA is still lacking. Although traditional factor-analytic fit measures are widespread, recent research has identified limitations for their effectiveness in categorical variables. Here, we propose three new fit measures (termed entropy fit indices) that combines information theory, quantum information theory and structural analysis: Entropy Fit Index (EFI), EFI with Von Neumman Entropy (EFI.vn) and Total EFI.vn (TEFI.vn). The first can be estimated in complete datasets using Shannon entropy, while EFI.vn and TEFI.vn can be estimated in correlation matrices using quantum information metrics. We show, through several simulations, that TEFI.vn, EFI.vn and EFI are as accurate or more accurate than traditional fit measures when identifying the number of simulated latent factors. However, in conditions where more factors are extracted than the number of factors simulated, only TEFI.vn presents a very high accuracy. In addition, we provide an applied example that demonstrates how the new fit measures can be used with a real-world dataset, using exploratory graph analysis.

A modern network approach to revisiting the Positive and Negative Affective Schedule (PANAS) construct validity.

INTRODUCTION: The factor structure of the Positive and Negative Affective Schedule (PANAS) is still a topic of debate. There are several reasons why using Exploratory Graph Analysis (EGA) for scale validation is advantageous and can help understand and resolve conflicting results in the factor analytic literature. OBJECTIVE: The main objective of the present study was to advance the knowledge regarding the factor structure underlying the PANAS scores by utilizing the different functionalities of the EGA method. EGA was used to (1) estimate the dimensionality of the PANAS scores, (2) establish the stability of the dimensionality estimate and of the item assignments into the dimensions, and (3) assess the impact of potential redundancies across item pairs on the dimensionality and structure of the PANAS scores. METHOD: This assessment was carried out across two studies that included two large samples of participants. RESULTS AND CONCLUSION: In sum, the results are consistent with a two-factor oblique structure.

[Open surgical treatment of thoracoabdominal aortic aneurysm. Experience in 45 patients]. / Cirugía convencional del aneurisma tóraco-abdominal.

BACKGROUND: Thoracoabdominal aortic aneurysm (TAAA) is an infrequent disease and demands a highly specialized and experienced management. Open repair (OR) is the gold standard but it is associated with significant morbidity and mortality. Paraplegia and renal failure are the most important complications. AIM: To report our results with OR treatment of TAAA. MATERIAL AND METHODS: Descriptive study including all patients with TAAA operated electively and consecutively by OR between 1983 and 2019. Main outcomes are operative mortality, renal and neurological morbidity, and long-term survival. RESULTS: We report 45 operated patients aged 33 to 84 years, 74% males. Aneurysm extension according to Crawford classification was I in 18%, II in 18 %, III in 36% and IV in 29%. Operative mortality was 4%. The frequency of paraplegia or paraparesis at discharge was 9%. No patient was discharged on hemodialysis. Survival at 5 and 10 years were 60% and 40% respectively. CONCLUSIONS: OR of TAAA is a complex procedure. Our results show perioperative mortality rates comparable to highly experienced centers. Although being a major procedure, OR remains an alternative to treat this serious condition.

Rubber Dam Isolation Survey (RDIS) for adhesive restorative treatments.

INTRODUCTION: Rubber dam isolation is considered as an essential component of modern adhesive dentistry. However, dental students do not always use it due to several barriers they face on their clinical practice. The aim of this study was to design and validate a questionnaire based on the COM-B model to measure students' implementation of rubber dam isolation in restorative treatments with adhesive materials. MATERIALS AND METHODS: A 7-item questionnaire was developed based on the COM-B model, with questions measuring the Capability, Opportunity (Relevance and Resources), and Motivation to perform rubber dam isolation (Behaviour). Content validation of the questionnaire was conducted by experts in aesthetic/restorative dentistry that assessed the clarity, coherence and relevance of the questions. The final survey was administered to a dental student population from three large private universities in the Dominican Republic. Descriptive analysis, t tests, polychoric correlations and a path analysis were carried out to establish the validity of the instrument. RESULTS: A total of 382 students from three universities completed the questionnaire. According to the COM-B path model, the significant predictors of the implementation of rubber dam isolation were Capability and Motivation for University A, Motivation and Opportunity-Resources for University B, and Opportunity-Relevance and Capability for University C. CONCLUSIONS: The RDIS is a very short, easy to administer and valid questionnaire that can be applied by the universities to determine where they need to focus their interventions to achieve better rubber dam isolation implementation by their students.

Iteration of Partially Specified Target Matrices: Application to the Bi-Factor Case.

The current study proposes a new bi-factor rotation method, Schmid-Leiman with iterative target rotation (SLi), based on the iteration of partially specified target matrices and an initial target constructed from a Schmid-Leiman (SL) orthogonalization. SLi was expected to ameliorate some of the limitations of the previously presented SL bi-factor rotations, SL and SL with target rotation (SLt), when the factor structure either includes cross-loadings, near-zero loadings, or both. A Monte Carlo simulation was carried out to test the performance of SLi, SL, SLt, and the two analytic bi-factor rotations, bi-quartimin and bi-geomin. The results revealed that SLi accurately recovered the bi-factor structures across the majority of the conditions, and generally outperformed the other rotation methods. SLi provided the biggest improvements over SL and SLt when the bi-factor structures contained cross-loadings and pure indicators of the general factor. Additionally, SLi was superior to bi-quartimin and bi-geomin, which performed inconsistently across the types of factor structures evaluated. No method produced a good recovery of the bi-factor structures when small samples (N = 200) were combined with low factor loadings (0.30-0.50) in the specific factors. Thus, it is recommended that larger samples of at least 500 observations be obtained.

Histological Benefits of Sealants in Tracheal Lesions in Wistar Rats.

INTRODUCTION: The purpose of our study was to compare the effect, on the inflammatory response and fibrosis formation, of four commercially available sealant products applied on an injured trachea in a Wistar rat population. MATERIALS AND METHODS: We compared four different sealants: cyanoacrylate, fibrin/thrombin, albumin/glutaraldehyde, and polyethylene glycol-based hydrogel (PEG). Rats were organized into six groups of similar size. Four of them were experimental, one was a baseline control group (BCG), and the sixth one was a reference control group (RCG). The RCG and experimental groups underwent the same surgical intervention with tracheal puncture, but no sealant was applied in the RCG. The BCG underwent the same cervical and peritracheal dissection without tracheal puncture. Rats were euthanized after eight weeks. RESULTS: The operation was performed on 54 rats, of which 12 died, leaving a final sample of 42 rats. Macroscopic analysis revealed no superficial tracheal or vascular fistulas, nor signs of local abscess. Although the groups treated with cyanoacrylate, fibrin/thrombin, and albumin/glutaraldehyde showed some degree of fibrosis, the treated area of the PEG group showed neither inflammatory nor scar signs. Microscopic assessment of the BCG and RCG showed no remarkable findings. With the exception of the PEG group, which had a light fibrosis and poor inflammatory response as did the BCG and RCG groups, the other groups showed varying degrees of fibrosis and cicatrization. CONCLUSION: Our study showed that the group treated with PEG had a mild inflammatory and fibrotic response, which is useful in tracheal or tracheobronchial surgical procedures. However, groups treated with cyanoacrylate, fibrin/thrombin, and albumin/glutaraldehyde showed fibrosis and cicatrization.

Brain tumor response to nimotuzumab treatment evaluated on magnetic resonance imaging.

BACKGROUND: Nimotuzumab, a humanized monoclonal antibody anti-epidermal growth factor receptor, has been shown to improve survival and quality of life in patients with pediatric malignant brain tumor. It is necessary, however, to increase the objective response criteria to define the optimal therapeutic schedule. The aim of this study was to obtain magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) quantitative information related to dimensions and morphology, molecular mobility and metabolic activity of the lesion and surroundings in order to evaluate any changes through time. METHODS: Fourteen pediatric patients treated with nimotuzumab were evaluated on MRI and MRS for >2 years. Each patient was their own control. The MRI/MRS pulse sequence parameters were standardized to ensure experimental reproducibility. RESULTS: A total of 71.4% of patients had stable disease; 21.4% had objective response and 7.1% had progression of disease during the >2 year evaluation period. CONCLUSIONS: MRI/MRS data with clinical information provide a clearer picture of treatment response and confirm once again that nimotuzumab is effective in the treatment of pediatric brain tumor. These imaging procedures can be a useful tool for the clinical evaluation of study protocol in clinical practice.

Anatomical Bases of the Temporal Muscle Trigger Points.

Method: Temporal muscles of 14 adult cadavers were studied. The muscle bellies were divided into six areas, three superior (1.2 and 3) and three inferior areas (4, 5, and 6) lower, according to a Cartesian plane to analyze and describe the entry points of the branches of the deep temporal nerves into the muscle. The branching distribution was analyzed using Poisson log-linear tests with Bonferroni post hoc tests for comparison between groups (sextants) (p < 0.05). Results: Deep temporal nerve entry points were found in the temporal muscle in all areas. Most of the branches were observed in areas 2 and 5, which coincide with the muscle fibers responsible for mandible elevation and related to the previously described MTPs. Fewer branches were found in areas 1 and 6, where contraction produces mandible retraction. Conclusion: There is an anatomical correlation between the branching pattern of the deep temporal nerve and temporal muscle trigger points. Adequate knowledge of the innervation of the temporal muscle may help elucidate the pathophysiology of myofascial syndromes and provide a rational basis for interventional or conservative approaches and help surgeons avoid iatrogenic lesions to the deep temporal nerve lesion.

Ultra-diluted complex additive in the diet reveals benefits in the intestinal tract of nursery-phase piglets.

This study aimed to assess an ultra-diluted (UD) complex, as a replacement for an antimicrobial growth promoter in diets, on growth performance, intestinal health, and inflammatory response of nursery piglets. The experiment lasted 37 days and involved 126 animals weaned at 21 ± 1.3 d, with an initial body weight of 5.62 ± 1.16 kg. Piglets were assigned to six dietary treatments in a randomized block design with seven replicates and three piglets per pen as experimental unit. The treatments were: positive control (PC)- basal diet + 120 mg/kg of chlorohydroxyquinoline; negative control (NC)- basal diet without additives; and NC containing 4.5; 6.0; 7.5 or 9.0 kg of UD additive/ton diet. Performance data were calculated, and daily diarrhea was observed. Blood samples were collected for hematological analysis. At the end of the experiment, one animal per pen was slaughtered for organ weighing, pH, and the collection of intestinal samples for histopathology. Feces and cecal contents were collected for microbiological and antibiogram analyses. There was no difference in the performance between the treatments. Throughout the study, UD levels were equal to those of PC for diarrhea occurrence. Higher levels of UD complex led to higher total leukocyte counts. The 4.5 treatment showed a reduction in total and thermotolerant Enterobacteriaceae populations in piglet feces and an increase in lactic acid bacteria compared to PC. All treatments resulted in fewer duodenal histopathological alterations than those in the NC group. The use of UD additives, especially at 4.5 kg/ton, is a good alternative to chlorohydroxyquinoline in piglet diets.

Study to evaluate the aesthetic clinical impact of an autologous antiaging serum.

Since ancient times, humans have fought a still-unwinnable battle against aging and time. The possibility of processing our own blood in order to obtain certain precious substances for a particular purpose has opened the gates for the development of new treatments, indications, and techniques. In this study, we obtained an autologous serum with very high concentrations of some growth factors and anti-inflammatory cytokines using a special syringelike device that exposed the blood to medical-grade glass spheres in a closed system. The application of this autologous conditioned antiaging serum achieved local beauty enhancement results by improving skin hydration, smoothness, and elasticity.