Reduction Techniques Research Articles

Trans-femoral transcatheter intramyocardial septal radiofrequency ablation: a novel therapeutic approach for obstructive hypertrophic cardiomyopathy

Abstract Background Septal reduction techniques play a pivotal role in mitigating morbidity and mortality among patients with obstructive hypertrophic cardiomyopathy (oHCM). While trans-apical intramyocardial septal radiofrequency ablation (PIMSRA or LIWEN procedure) has shown promise in reducing left ventricular outflow tract (LVOT) gradients and improving symptoms in oHCM patients, it is associated with complications such as cardiac tamponade and atrioventricular block. Objective This study introduces a novel approach for transcatheter radiofrequency ablation of hypertrophic septum via femoral access, utilizing the DragonFire Transcatheter Myocardial Puncture and Radiofrequency Ablation System. We aim to evaluate its safety and medium-term outcomes in patients with oHCM. Methods A prospective, single-center, single-arm study of tf-PIMSRA was conducted in patients with drug-refractory oHCM. Ten patients were enrolled and underwent the procedure under the guidance of transesophageal echocardiography. The primary outcome was 30-day major adverse clinical events, with secondary outcomes including 90-day improvements in outflow obstruction and quality of life. Results All patients, with an average age of 57±10 years, including 4 males, underwent the procedure successfully without any complications, including major adverse clinical events or conduction abnormalities. The average duration of the procedure was 125.2±34.6 minutes, with an average ablation time of 38.69±18.29 minutes and ablation energy of 3.60±1.49KJ. At 90-day follow-up, significant reductions were observed in peak LVOT gradients (resting gradient: from 82.00 [56.00] mmHg to 14.00 [6.25] mmHg; p < 0.001; provoked gradient: from 102.00 [76.25] mmHg to 33.00 [16.50] mmHg; p = 0.002), and interventricular septum thickness (from 22.00 [19.00] mm to 17.00 [15.00] mm; p = 0.002). These improvements were associated with enhancements in NYHA functional classification (from 3.00 [2.75] to 1.00 [1.00]; p < 0.0001), Kansas City Cardiomyopathy Questionnaire scores (from 59.00 [44.50] to 80.00 [77.25]; p < 0.02), 6-minute walk distance (from 441 [400] m to 480 [465] m; p < 0.01), and pro B-type natriuretic peptide levels (from 1738.00 [1113.25] pg/ml to 252.00 [200.00] pg/ml; p = 0.002). All patients achieved New York Heart Association functional class I or II at follow-up. Conclusions Transfemoral PIMSRA appears to be a safe and effective procedure for relieving LVOT obstruction and symptoms in patients with drug-refractory oHCM. These findings support further investigation through randomized clinical trials comparing it with established septal reduction therapies.Patient-Level Outcomes for the trans-fem

Structural and magnetic properties of high magnetization FexCo100-x nanoparticles investigated at the nanoscale: Unveiling the origin of the observed anisotropy

In this work the authors have performed the synthesis of FexCo100-x (0<x<100) alloy nanoparticles (NPs) with different compositions, as well as pure Fe and Co NPs for comparison, by a chemical reduction technique. The subsequent characterization demonstrated excellent quality NPs with the expected bcc cubic (for Fe and FeCo alloys) and hcp hexagonal (for Co NPs) structures showing a room temperature magnetization as high as 235 emu/g for the Fe66Co34 composition alloy. Nevertheless, this soft magnetic character is accompanied by determined values of effective anisotropy as high as 2 MJ/m3. Aiming to deep into the properties of these FeCo alloys as well as to unveil the origin of that observed high anisotropy value, we now present an extensive study at the nanoscale of the synthesized Fe, Co and FexCo100-x alloy nanoparticles by using nuclear techniques as neutron powder diffraction, EXAFS and XANES spectroscopies. Mössbauer spectroscopy revealed that the FeCo alloys are in an A2 disordered solid solution. The obtained results, combined with AFM/MFM images, have demonstrated that despite the cubic bcc structure observed for all FeCo alloys (in excellent concordance with the pure Fe one) the NPs show a "flaky" shape of 50–60 nm size (diameter) but only 3–4 nm thickness, giving rise to a strong shape anisotropy contribution to the observed total effective anisotropy.

Distributed Photovoltaic Communication Anomaly Detection Based on Spatiotemporal Feature Collaborative Modeling

As distributed photovoltaic (PV) technology rapidly develops and is widely applied, the methods of cyberattacks are continuously evolving, posing increasingly severe threats to the communication networks of distributed PV systems. Recent studies have shown that the Transformer model, which effectively integrates global information and handles long-distance dependencies, has garnered significant attention. Based on this, our research proposes a model named STformer, which is applied to the task of attack detection in distributed PV communication. Specifically, we propose a temporal attention mechanism and a variable attention mechanism. The temporal attention mechanism focuses on capturing subtle changes and trends in data sequences over time, ensuring a highly sensitive recognition of patterns inherent in time-series data. In contrast, the variable attention mechanism analyzes the intrinsic relationships and interactions between different variables, uncovering critical correlations that may indicate abnormal behavior or potential attacks. Additionally, we incorporate the Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction technique. This technique not only helps reduce computational complexity but, in certain cases, can enhance anomaly detection performance. Finally, compared to classical and advanced methods, STformer demonstrates satisfactory performance in simulation experiments.

Home cryolipolysis – A burning issue

Cryolipolysis is a non-surgical technique for fat reduction whereby adipose tissue is exposed to low temperatures to induce apoptosis. This leads to a slow reabsorption of the excess tissue. The aim of this report is to increase awareness of the burns-related risks associated with home cryolipolysis treatments, with the goal of improving regulations around such treatments and preventing similar injuries from occurring in the future. This case report highlights the potential complications associated with cryolipolysis. We present the case of a 25-year-old female with a background history of bulimia nervosa who sustained mixed-depth burns to the abdomen and upper arms following a cryolipolysis treatment. She self-delivered using an online-bought machine. Although cryolipolysis is considered a safe method for the reduction of local adiposity, it is not without risk. Burns secondary to cryolipolysis treatments have occurred, though typically by unregulated providers, not self-use as in this case. There is a need for improved regulation of the administration of cryolipolysis treatments. Health-care professionals and patients should be aware of the possible complications that can arise. Improved regulation of cryolipolysis machine purchase and use is vital to prevent machine misuse by unregulated providers and members of the public.

E-Brachy: New dosimetry package for electronic brachytherapy sources.

Large reported variability in the material composition and geometrical components of the Xoft electronic high-dose-rate brachytherapy Causes inter-source discrepancy in the source output. This variability is due to the manual manufacturing and assembly of thesources. This study aimed to develop a dosimetry software tool called E-Brachy to characterize the Xoft source and quantify the discrepancies in its photon spectrum and dosimetricproperties. E-Brachy is based on the Geant4 Monte Carlo toolkit and consists of two parts. In part one, the geometry and material composition for the source received in the computer-aided design format from the vendor were converted to the geometry description markup language format using the GUIMesh Python tool and integrated into the E-Brachy software. There was a large variation in material composition and thickness for some of the tube components. The simulation started from electrons and resulted in x-ray generations in the anode region. Multithreading, a track length estimation, and the uniform bremsstrahlung splitting variance reduction techniques were used to decrease the simulation time and increase the x-ray production. The photon energy, position, and momentum were saved into a phase space file as the photon exited the source, but before interacting with the external environment. The obtained x-ray energy spectrum was compared with measurements from the National Institute of Standards and Technology (NIST). In part two, by sampling from the generated photons, the dose rates and dosimetric parameters according to the TG-43 protocol were calculated for model S7500 and compared to the ones previously calculated for model S700 source, which were deemed identical by themanufacturer. The material composition that resulted in the most similar spectrum as the measured NIST spectrum with Pearson's correlation coefficient of 0.99 and a calculated Euclidean difference of keV was chosen for further dosimetric analysis of the model S7500 source. Characteristic peaks showed the presence of tungsten, yttrium, and silver in the source components. Differences in dose rates between the two source models surpassed 20% for polar angles , reaching a peak at cm and . The differences in the radial dose function values were within 5%. The relative difference in percentage between the anisotropy function values of the two models was closer to 0 for smaller values, but at higher polar angles, they increased to 300%. A software package called E-Brachy was successfully developed for the characterization and dosimetry of Xoft electronic brachytherapy sources. E-Brachy can be combined with spectral measurements to investigate the inter- and intra-source variability. The software package was tested by comparing the simulated spectra from the S7500 Xoft source model with NIST measurements and its TG-43 parameters with the S700 model. The TG-43 parameters between the two sources significantly exceed the recommendations ofTG-56.

Revolutionizing endometriosis treatment: automated surgical operation through artificial intelligence and robotic vision.

Clinical limitations due to poverty significantly impact the lives and health of many individuals globally. Nevertheless, this challenge can be addressed with modern technologies, particularly through robotics and artificial intelligence. This study aims to address these challenges using advanced technologies in robotic surgery and artificial intelligence, proposing a method to fully automate endometriosis robotic surgery with a focus on interpretability, accuracy, and reliability. A methodology for fully automatic endometriosis surgery is introduced. Given the complexity of endometriosis lesions detection, they are categorized by their anatomical location to improve system interpretability. Then, three ensemble U-Net frameworks are designed to detect and localize common types of endometriosis lesions intraoperatively. A cross-training approach is employed, exploring U-Net models with diverse neural architectures-such as ResNet50, ResNet101, VGG19, InceptionV3, MobileNet, and EfficientNetB7-to develop U-Net ensemble models for precise endometriosis lesions segmentation. A novel image augmentation technique is also introduced, enhancing the segmentation models' accuracy and reliability. Furthermore, two U-Net models are developed to localize the ovaries and uterus, mitigating unexpected noise and bolstering the method's accuracy and reliability. The image segmentation models, assessed using the Intersection over Union (IoU) metric, achieved outstanding results: 97.57% for ovarian, 96.35% for uterine, and 92.58% for peritoneal endometriosis. This study proposes a fully automatic method for some common types of endometriosis surgery, including ovarian endometriomas and superficial endometriosis. This method is centered around three ensemble U-Net frameworks and a noise reduction technique using two additional U-Nets for localizing the ovaries and uterus. This approach has the potential to significantly improve the accuracy and reliability of robotic surgeries, potentially reducing healthcare costs and improving outcomes for patients worldwide.

Methods for Human Reliability Analysis in Dentistry.

Human error (HE) is one of the main causes of accidents in different organizations and industries. Dentistry is a medical branch with a high risk of error since it involves complex manual tasks that must be performed with a high degree of accuracy. To understand the various aspects of HE in dentistry, which is crucial for developing strategies to mitigate its impact on patients' safety, it is necessary to perform a human reliability analysis (HRA). However, there is scarce data on the use of HRA in dentistry. In this paper, we give a brief description of the main phases of HRA with an emphasis on HRA methods that could be used in dentistry. Since HRA methods have been designed for diverse industrial applications, we discuss their possible application in dentistry. Among the discussed methods, the Systematic Human Error Reduction and Prediction Approach (SHERPA) and the Human Error Assessment and Reduction Technique were identified as the best candidates for performing HRA in dentistry. This is of great importance since understanding and addressing HEs is crucial for improving patient safety and the overall quality of dental care.

Optimizing Machine Learning-Based Intrusion Detection Systems for Enhanced Security in Cloud Computing Environments.

In today’s cloud computing environments, robust network security is crucial due to the inherently distributed and dynamic nature of cloud systems. Traditional Network Intrusion Detection Systems (NIDS) often face challenges in handling large-scale, rapidly evolving data and sophisticated attack vectors unique to cloud settings. This paper presents an optimized Machine Learning (ML)-based NIDS framework designed specifically for cloud infrastructures, leveraging feature selection, dimensionality reduction, and algorithmic tuning techniques to enhance detection accuracy and reduce false positives. Integrating supervised and unsupervised learning methods, this NIDS system can detect both known and novel attack types efficiently. By utilizing cloud-specific datasets and real-time traffic monitoring, it adapts well to the scalability and elasticity requirements of cloud environments. Additionally, the system incorporates automated optimization techniques, such as hyperparameter tuning and ensemble learning, to further enhance performance. Experimental results show improvements in detection rates, reduced computational overhead, and better scalability, making it a promising solution for modern cloud-based infrastructures.

A universal hippocampal memory code across animals and environments.

How learning is affected by context is a fundamental question of neuroscience, as the ability to generalize learning to different contexts is necessary for navigating the world. An example of swift contextual generalization is observed in conditioning tasks, where performance is quickly generalized from one context to another. A key question in identifying the neural substrate underlying this ability is how the hippocampus (HPC) represents task-related stimuli across different environments, given that HPC cells exhibit place-specific activity that changes across contexts (remapping). In this study, we used calcium imaging to monitor hippocampal neuron activity as animals performed a conditioning task across multiple spatial contexts. We investigated whether hippocampal cells, which encode both spatial locations (place cells) and task-related information, could maintain their task representation even when their spatial encoding remapped in a new spatial context. To assess the consistency of task representations, we used advanced dimensionality reduction techniques combined with machine learning to develop manifold representations of population level HPC activity. The results showed that task-related neural representations remained stable even as place cell representations of spatial context changed, thus demonstrating similar embedding geometries of neural representations of the task across different spatial contexts. Notably, these patterns were not only consistent within the same animal across different contexts but also significantly similar across different animals, suggesting a standardized neural encoding or 'neural syntax' in the hippocampus. These findings bridge a critical gap between memory and navigation research, revealing how the hippocampus maintains cognitive consistency across different spatial environments. These findings also suggest that hippocampal function is governed by a neural framework shared between animals, an observation that may have broad implications for understanding memory, learning, and related cognitive processes. Looking ahead, this work opens new avenues for exploring the fundamental principles underlying hippocampal encoding strategies.

Throughput improvement in ACO-OFDM-based VLC systems using noise cancellation and precoding techniques

One of the primary challenges faced by visible light communication (VLC) systems employing optical orthogonal frequency division multiplexing is the peak-to-average power ratio (PAPR). This study is dedicated to designing, simulating, and evaluating bit error rate (BER) and PAPR reduction methods tailored for the VLC broadcasting system. The asymmetric clipped optical orthogonal frequency division multiplexing (ACO-OFDM) scheme is highlighted in this work for its impressive performance. Therefore, the proposed PAPR mitigation methodologies applied to ACO-OFDM. The proposed PAPR reduction strategy involves 5 distinct precoding methodologies. The PAPR was mitigated by 3.485 dB after applying the DST precoding methodology. Still, the WHT precoding methodology can achieve PAPR reduction by 1.131 dB, without BER performance degradation, with respect to the conventional ACO-OFDM system. Furthermore, the work addresses another challenge in VLC systems: the bit error rate (BER). This is accomplished by introducing approaches to Time Domain Noise Cancelation and Frequency Domain Noise Cancelation (FDNC). The BER performance of these 2 receiver models is nearly the same. The simulation results indicate the system performance enhancement after applying noise cancellation approaches by 1.65 dB at the 4-QAM modulation scheme and 2.97 dB at the 1024-QAM modulation scheme. The 16-QAM modulation scheme, after applying DST and WHT methodologies alongside noise cancellation approaches, can enhance both PAPR by 20.83% and 6.76%, but the Eb/N0 performance enhancement by 10.10% and 14.64%, respectively. Additionally, the effectiveness and validity of the proposed schemes are verified by comparing them with relevant literature reviews on PAPR reduction techniques and selecting an optimal choice among them.

The Role of the Tarteel Application in Maintaining the Memorization of Al-Qur'an Memorizers

The Quran is considered the word of Allah and was revealed to the last messenger by the angel Gabriel. It is a religious duty for Muslims to read the Quran, as they believe it to be the only reliable source of life guidance. The Prophet Muhammad SAW was instructed to have specialists write down the verses whenever they were revealed, ensuring that the Quran was constantly maintained. Memorizing the Quran is considered one of the greatest virtues and the highest form of worship before Allah SWT. There are various techniques for memorizing the Quran, including the Sorongan method, which has drawbacks such as lower time efficiency and effectiveness, as well as difficulties with memorization, including boredom and forgetfulness. To make memorizing the Quran easier and more effective, an application has been developed, providing personalized and interactive features tailored to individual needs. Additionally, the application addresses the monotony of the memorization process and can improve concentration and memory quality. The innovative Tarteel AI technology combines artificial intelligence to make learning the Quran more organized and effective. The application offers benefits such as reciting, memorizing, and searching the Quran. This study employs a descriptive qualitative literature research methodology and uses data reduction, data presentation, and data analysis techniques to acquire reliable data. Keywords: Application, Tarteel, Memorizing, Al-Qur'an

A Review of Artificial Intelligence-Based Dyslexia Detection Techniques.

Dyslexia is a learning disorder affecting an individual's ability to recognize words and understand concepts. It remains underdiagnosed due to its complexity and heterogeneity. The use of traditional assessment techniques, including subjective evaluation and standardized tests, increases the likelihood of delayed or incorrect diagnosis. Timely identification is essential to provide personalized treatment and improve the individual's quality of life. The development of artificial intelligence techniques offers a platform to identify dyslexia using behavior and neuroimaging data. However, the limited datasets and black-box nature of ML models reduce the generalizability and interpretability of dyslexia detection (DD) models. The dimensionality reduction technique (DRT) plays a significant role in providing dyslexia features to enhance the performance of machine learning (ML)- and deep learning (DL)-based DD techniques. In this review, the authors intend to investigate the role of DRTs in enhancing the performance of ML- and DL-based DD models. The authors conducted a comprehensive search across multiple digital libraries, including Scopus, Web of Science, PubMed, and IEEEXplore, to identify articles associated with DRTs in identifying dyslexia. They extracted 479 articles using these digital libraries. After an extensive screening procedure, a total of 39 articles were included in this review. The review findings revealed various DRTs for identifying critical dyslexia patterns from multiple modalities. A significant number of studies employed principal component analysis (PCA) for feature extraction and selection. The authors presented the essential features associated with DD. In addition, they outlined the challenges and limitations of existing DRTs. The authors emphasized the need for the development of novel DRTs and their seamless integration with advanced DL techniques for robust and interpretable DD models.

Functional quantile principal component analysis.

This paper introduces functional quantile principal component analysis (FQPCA), a dimensionality reduction technique that extends the concept of functional principal components analysis (FPCA) to the examination of participant-specific quantiles curves. Our approach borrows strength across participants to estimate patterns in quantiles, and uses participant-level data to estimate loadings on those patterns. As a result, FQPCA is able to capture shifts in the scale and distribution of data that affect participant-level quantile curves, and is also a robust methodology suitable for dealing with outliers, heteroscedastic data or skewed data. The need for such methodology is exemplified by physical activity data collected using wearable devices. Participants often differ in the timing and intensity of physical activity behaviors, and capturing information beyond the participant-level expected value curves produced by FPCA is necessary for a robust quantification of diurnal patterns of activity. We illustrate our methods using accelerometer data from the National Health and Nutrition Examination Survey, and produce participant-level 10%, 50%, and 90% quantile curves over 24 h of activity. The proposed methodology is supported by simulation results, and is available as an R package.

Stress Maneuver for Basilar Invagination Reduction: A Technical Report

BackgroundBasilar invagination (BI) is a prolapse of the odontoid process cranially and posteriorly towards the foramen magnum. Several surgical treatment options are available for this condition. Herein, we describe a stress maneuver technique for BI reduction using a single-stage posterior approach. MethodsWe described the case of a 56-year-old male diagnosed with type A BI and brainstem compression, who was treated with posterior fossa decompression and occipitocervical fusion. The BI was reduced intraoperatively using a stress maneuver, and cranial traction or atlantoaxial joint distraction was not necessary. ResultsPostoperative imaging revealed a reduction of the odontoid process and adequate brainstem decompression. The patient showed progressive improvement of his symptoms. ConclusionIn selected cases, stress maneuvers for BI reduction can be a less morbid, easier, and replicable option within the already existing technical arsenal.

Reduction of high-grade spondylolisthesis using minimally invasive spine surgery-transforaminal lumbar interbody fusion "trial-in-situ" technique: a technical note with case series.

This retrospective case series evaluated the effectiveness of minimally invasive spine surgery-transforaminal lumbar interbody fusion (MIS-TLIF) using the "trial-in-situ " technique for reducing high-grade spondylolisthesis. The surgical management of grade ≥III spondylolisthesis has been controversial, with various methods documented in the literature, including in-situ fusion, in-situ trans-sacral delta fixation, distraction techniques, and external reduction techniques. Recently, MIS techniques have gained popularity. This study analyzed 18 cases of high-grade spondylolisthesis treated with MIS-TLIF using the "trial-in-situ " technique. The clinical outcomes were assessed using the Visual Analog Scale (VAS) and the modified Oswestry Disability Index (mODI) scores. The spinopelvic parameters and sagittal balance were also analyzed. Preoperatively, the spinopelvic parameters were deranged, with a mean pelvic tilt of 28.31°, which improved to 13.91° postoperatively. Similarly, the sacral slope improved from 45.65° to 38.01°. VAS and mODI scores improved postoperatively, indicating the effectiveness of the "trial-in-situ " technique in reducing high-grade spondylolisthesis and achieving a better sagittal profile and spinopelvic parameters. The findings indicate that MIS-TLIF using the "trial-in-situ " technique is a viable and effective method for treating high-grade spondylolisthesis.

Optimizing Heart Disease Diagnosis: Feature Selection Techniques for Enhanced Machine Learning Model Performance

Heart disease is a growing global concern, affecting people across various age groups and genders. Detecting heart failure early is crucial, and ongoing research leverages advancements in healthcare technology, machine learning, imaging techniques, and data science to analyze vast datasets for this purpose. However, not all data attributes contribute equally to diagnosing heart disease, and the inclusion of irrelevant features can increase resource demands and potentially lead to inaccurate predictions with fatal consequences. This study focuses on feature extraction and reduction techniques to identify the most critical attributes for heart disease diagnosis, balancing resource efficiency with diagnostic accuracy. Using a dataset from the UCI repository, which includes both continuous and categorical features, we standardize the data and split it into training and testing sets in an 80:20 ratio. We then apply feature selection techniques to machine learning models such as K-nearest neighbor, decision tree classifier, SVM, logistic regression, and random forest. The models' predictive performance is evaluated using confusion matrices and ROC curves, demonstrating the impact of feature selection on diagnostic accuracy.

Maximizing the impact of megaprojects: Urgent implications of financial inclusion drive for effective anti-poverty measures

Megaprojects are becoming more and more essential for poverty reduction and inclusive development. Regrettably, the CPEC mega project has yet to yield successful outcomes in Pakistan. The incorporation of financial inclusion (FI) has the potential to catalyze its outcomes. Despite abundant studies on advanced CPEC applications and poverty reduction techniques, the relationship between FI and CPEC has been the subject of relatively little research. Therefore, we analyzed CPECD and FI, specifically emphasizing economic opportunity, growth, and poverty reduction (PR). The study accounted for the viewpoints of small and medium-sized enterprises (SMEs). Using Amos 24 and the Process Macro, we analyze the mediational and moderated mediational models. The findings show that CPECD reduces poverty. Opportunity and growth both significantly influence the outcome. FI practices enhance the opportunity and growth link between CPECD and PR. Therefore, it is imperative to address the issue of FI shrinkage, as it could impede the efforts of SMEs to promote inclusive growth and alleviate poverty.

In Vitro Antitumor, Antioxidant, and Hemolytic Activities of Chlorella sorokiniana Methanol Extracts and Collective Fractions

Chlorella species are fast-growing microalgae with significant industrial applications. The aim of the present study was to investigate the antitumor, antioxidant, and hemolytic activities of Chlorella sorokiniana UTEX 1230 crude methanol extracts and fractions. Ch. sorokiniana crude methanol extracts and collective fractions (CFs) were obtained from lyophilized biomass by maceration and column chromatography. Antitumor assays against murine lymphoma L5178Y-R and human breast cancer MCF-7 cells were performed by the colorimetric 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) reduction technique, using human peripheral blood mononuclear cells (PBMC) as the control group. Antioxidant and hemolytic activities were evaluated using the 2,2-diphenyl-1-picrylhydrazyl radical scavenging assay (DPPH) and erythrocyte hemolysis, respectively. We showed that crude methanol extracts (IC50) increased L5178Y-R and MCF-7 cell growth inhibition, without affecting PBMC. In addition, all evaluated CFs showed significantly higher antioxidant activity than the positive control (ascorbic acid). CF3 and CF4 showed the highest cytotoxicity against L5178Y-R, whereas CF3, CF4, and CF5 caused the highest antitumor activity against MCF-7 cells. CF3, CF4, and CF5 induced significantly higher hemolytic activity compared with all other fractions. CF characterization revealed loliolide, cinnamic acid, methyl dihydrojasmonate, salsalvamide A, 1-monolinolenin, cryptophycin 29, costunolide, riboflavin lumicrome, and germicidin B, which have been related to antitumor and antioxidant activities. In conclusion, we demonstrated that Ch. sorokiniana extracts and fractions possess antitumor and antioxidant potential, without affecting human erythrocytes and PBMC.

Retraction Note: A fuzzy entropy technique for dimensionality reduction in recommender systems using deep learning

Retraction Note: A fuzzy entropy technique for dimensionality reduction in recommender systems using deep learning

Comparative Outcomes of Superomedial and Inferior Pedicles in Breast Reduction and Mastopexy: A Meta-Analysis of 5123 Breasts.

Superomedial and inferior pedicles are two commonly used techniques in breast reduction and mastopexy. This systematic review and meta-analysis aims to compare the clinical outcomes associated with these two techniques. PubMed, Scopus, and Web of Science were searched for relevant studies. We included all studies with data comparing superomedial and inferior pedicles. Statistical analyses were performed using RevMan version 5.4. The search yielded 1075 studies, of which 15 were included in the meta-analysis, encompassing 2633 patients (5123 breasts), with 3491 breasts receiving superomedial pedicles and 1632 breasts receiving inferior pedicles. Superomedial pedicles were associated with significantly shorter operative length (MD = - 24.71, 95% CI = - 37.63 to - 11.79, p = 0.0002), higher BREAST-Q breast satisfaction scores (MD = 10.34, 95% CI = 7.72 to 12.96, p < 0.00001), lower infection rates (RR = 0.46, 95% CI = 0.24 to 0.86, p = 0.02), higher incidence of seroma (RR = 3.00, 95% CI = 1.15 to 7.79, p = 0.02), and higher incidence of decreased nipple-areola complex (NAC) sensation (RR = 1.50, 95% CI = 1.12 to 2.01, p = 0.006). No significant differences were observed in asymmetry, fat necrosis, NAC loss, and hematoma. Superomedial pedicles demonstrated higher incidences of decreased NAC sensation and seroma formation, lower incidence of infection, shorter operative length, and higher BREAST-Q breast satisfaction scores compared to inferior pedicles. Further research is needed to confirm these findings and explore the long-term aesthetic outcomes associated with both techniques. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .