Privacy-preserved learning from non-i.i.d data in fog-assisted IoT:A federated learning approach

With the prevalence of the Internet of Things(IoT)systems,smart cities comprise complex networks,including sensors,actuators,appliances,and cyber services.The complexity and heterogeneity of smart cities have become vulnerable to sophisticated cyber-attacks,especially privacy-related attacks such as inference and data poisoning ones.Federated Learning(FL)has been regarded as a hopeful method to enable distributed learning with privacypreserved intelligence in IoT applications.Even though the significance of developing privacy-preserving FL has drawn as a great research interest,the current research only concentrates on FL with independent identically distributed(i.i.d)data and few studies have addressed the non-i.i.d setting.FL is known to be vulnerable to Generative Adversarial Network(GAN)attacks,where an adversary can presume to act as a contributor participating in the training process to acquire the private data of other contributors.This paper proposes an innovative Privacy Protection-based Federated Deep Learning(PP-FDL)framework,which accomplishes data protection against privacy-related GAN attacks,along with high classification rates from non-i.i.d data.PP-FDL is designed to enable fog nodes to cooperate to train the FDL model in a way that ensures contributors have no access to the data of each other,where class probabilities are protected utilizing a private identifier generated for each class.The PP-FDL framework is evaluated for image classification using simple convolutional networks which are trained using MNIST and CIFAR-10 datasets.The empirical results have revealed that PF-DFL can achieve data protection and the framework outperforms the other three state-of-the-art models with 3%–8%as accuracy improvements.

Heart failure with preserved ejection fraction and the first law of thermodynamics

In heart failure with preserved ejection fraction,significant left ventricular diastolic abnormalities are present,despite a normal systolic ejection fraction.This article will consider whether this is consistent with the law of conservation of energy,also know as the first law of thermodynamics.

Epicardial adipose tissue in obesity with heart failure with preserved ejection fraction: Cardiovascular magnetic resonance biomarker study

BACKGROUND Obesity has become a serious public health issue,significantly elevating the risk of various complications.It is a well-established contributor to Heart failure with preserved ejection fraction(HFpEF).Evaluating HFpEF in obesity is crucial.Epicardial adipose tissue(EAT)has emerged as a valuable tool for validating prognostic biomarkers and guiding treatment targets.Hence,assessing EAT is of paramount importance.Cardiovascular magnetic resonance(CMR)imaging is acknowledged as the gold standard for analyzing cardiac function and mor-phology.We hope to use CMR to assess EAT as a bioimaging marker to evaluate HFpEF in obese patients.AIM To assess the diagnostic utility of CMR for evaluating heart failure with preserved ejection fraction[HFpEF;left ventricular(LV)ejection fraction≥50%]by measuring the epicardial adipose tissue(EAT)volumes and EAT mass in obese patients.METHODS Sixty-two obese patients were divided into two groups for a case-control study based on whether or not they had heart failure with HFpEF.The two groups were defined as HFpEF+and HFpEF-.LV geometry,global systolic function,EAT volumes and EAT mass of all subjects were obtained using cine magnetic resonance sequences.RESULTS Forty-five patients of HFpEF-group and seventeen patients of HFpEF+group were included.LV mass index(g/m2)of HFpEF+group was higher than HFpEF-group(P<0.05).In HFpEF+group,EAT volumes,EAT volume index,EAT mass,EAT mass index and the ratio of EAT/[left atrial(LA)left-right(LR)diameter]were higher compared to HFpEF-group(P<0.05).In multivariate analysis,Higher EAT/LA LR diameter ratio was associated with higher odds ratio of HFpEF.CONCLUSION EAT/LA LR diameter ratio is highly associated with HFpEF in obese patients.It is plausible that there may be utility in CMR for assessing obese patients for HFpEF using EAT/LA LR diameter ratio as a diagnostic biomarker.Further prospective studies,are needed to validate these proof-of-concept findings.

Preserved egg white alleviates DSS-induced colitis in mice through the reduction of oxidative stress,modulation of inflammatory cytokines,NF-κB,MAPK and gut microbiota composition

Traditional Chinese preserved egg products have exhibited some anti-inflammatory effects,but their mechanisms of action remain unknown.This study aimed to investigate the anti-inflammatory effects of preserved egg white(PEW)treatment on dextran sulfate sodium(DSS)-induced colitis in mice and the underlying mechanisms.The results showed that treatment with PEW in mice with DSS-induced colitis for 14 days effectively improved the clinical signs,inhibited the secretion and gene expression of pro-inflammatory cytokines,and reduced myeloperoxidase(MPO)activity and oxidative stress levels.In addition,western blotting results showed that PEW significantly suppressed DSS-induced phosphorylation levels of nuclear factor-kappa B(NF-κB)p65 and p38 mitogen-activated protein kinase(MAPK)in colon tissues of mice with colitis.PEW also enhanced the production of short-chain fatty acids(SCFAs)and modulated gut microbiota composition in mice with DSS-induced colitis,including increasing the relative abundance of beneficial bacteria Lachnospiraceae,Ruminococcaceae and Muribaculaceae,and reducing the relative abundance of harmful bacteria Proteobacteria.Taken together,our study demonstrated that preserved egg white could alleviate DSS-induced colitis in mice through the reduction of oxidative stress,modulation of inflammatory cytokines,NF-κB,MAPK and gut microbiota composition.

Targeting epicardial adipose tissue:A potential therapeutic strategy for heart failure with preserved ejection fraction with type 2 diabetes mellitus

Heart failure with preserved ejection fraction(HFpEF)is a heterogeneous syndrome with various comorbidities,multiple cardiac and extracardiac pathophysiologic abnormalities,and diverse phenotypic presentations.Since HFpEF is a heterogeneous disease with different phenotypes,individualized treatment is required.HFpEF with type 2 diabetes mellitus(T2DM)represents a specific phenotype of HFpEF,with about 45%-50% of HFpEF patients suffering from T2DM.Systemic inflammation associated with dysregulated glucose metabolism is a critical pathological mechanism of HFpEF with T2DM,which is intimately related to the expansion and dysfunction(inflammation and hypermetabolic activity)of epicardial adipose tissue(EAT).EAT is well established as a very active endocrine organ that can regulate the pathophysiological processes of HFpEF with T2DM through the paracrine and endocrine mechanisms.Therefore,suppressing abnormal EAT expansion may be a promising therapeutic strategy for HFpEF with T2DM.Although there is no treatment specifically for EAT,lifestyle management,bariatric surgery,and some pharmaceutical interventions(anti-cytokine drugs,statins,proprotein convertase subtilisin/kexin type 9 inhibitors,metformin,glucagon-like peptide-1 receptor agonists,and especially sodium-glucose cotransporter-2 inhibitors)have been shown to attenuate the inflammatory response or expansion of EAT.Importantly,these treatments may be beneficial in improving the clinical symptoms or prognosis of patients with HFpEF.Accordingly,well-designed randomized controlled trials are needed to validate the efficacy of current therapies.In addition,more novel and effective therapies targeting EAT are needed in the future.

Computer Forensics Framework for Efficient and Lawful Privacy-Preserved Investigation

Privacy preservation(PP)in Digital forensics(DF)is a conflicted and non-trivial issue.Existing solutions use the searchable encryption concept and,as a result,are not efficient and support only a keyword search.Moreover,the collected forensic data cannot be analyzed using existing well-known digital tools.This research paper first investigates the lawful requirements for PP in DF based on the organization for economic co-operation and development OECB)privacy guidelines.To have an efficient investigation process and meet the increased volume of data,the presented framework is designed based on the selective imaging concept and advanced encryption standard(AES).The proposed framework has two main modules,namely Selective Imaging Module(SIM)and Selective Analysis Module(SAM).The SIM and SAM modules are implemented based on advanced forensic format 4(AFF4)and SleuthKit open source forensics frameworks,respectively,and,accordingly,the proposed framework is evaluated in a forensically sound manner.The evaluation result is compared with other relevant works and,as a result,the proposed solution provides a privacy-preserving,efficient forensic imaging and analysis process while having also sufficient methods.Moreover,the AFF4 forensic image,produced by the SIM module,can be analyzed not only by SAM,but also by other well-known analysis tools available on the market.

Privacy Preserved Brain Disorder Diagnosis Using Federated Learning

Federated learning has recently attracted significant attention as a cutting-edge technology that enables Artificial Intelligence(AI)algorithms to utilize global learning across the data of numerous individuals while safeguarding user data privacy.Recent advanced healthcare technologies have enabled the early diagnosis of various cognitive ailments like Parkinson’s.Adequate user data is frequently used to train machine learning models for healthcare systems to track the health status of patients.The healthcare industry faces two significant challenges:security and privacy issues and the personalization of cloud-trained AI models.This paper proposes a Deep Neural Network(DNN)based approach embedded in a federated learning framework to detect and diagnose brain disorders.We extracted the data from the database of Kay Elemetrics voice disordered and divided the data into two windows to create training models for two clients,each with different data.To lessen the over-fitting aspect,every client reviewed the outcomes in three rounds.The proposed model identifies brain disorders without jeopardizing privacy and security.The results reveal that the global model achieves an accuracy of 82.82%for detecting brain disorders while preserving privacy.

SDGNN: Symmetry-Preserving Dual-Stream Graph Neural Networks

Dear Editor,This letter proposes a symmetry-preserving dual-stream graph neural network(SDGNN) for precise representation learning to an undirected weighted graph(UWG). Although existing graph neural networks(GNNs) are influential instruments for representation learning to a UWG, they invariably adopt a unique node feature matrix for illustrating the sole node set of a UWG.

Privacy-Preserving Consensus-Based Distributed Economic Dispatch of Smart Grids via State Decomposition

This paper studies the privacy-preserving distributed economic dispatch(DED)problem of smart grids.An autonomous consensus-based algorithm is developed via local data exchange with neighboring nodes,which covers both the islanded mode and the grid-connected mode of smart grids.To prevent power-sensitive information from being disclosed,a privacy-preserving mechanism is integrated into the proposed DED algorithm by randomly decomposing the state into two parts,where only partial data is transmitted.Our objective is to develop a privacy-preserving DED algorithm to achieve optimal power dispatch with the lowest generation cost under physical constraints while preventing sensitive information from being eavesdropped.To this end,a comprehensive analysis framework is established to ensure that the proposed algorithm can converge to the optimal solution of the concerned optimization problem by means of the consensus theory and the eigenvalue perturbation approach.In particular,the proposed autonomous algorithm can achieve a smooth transition between the islanded mode and the grid-connected mode.Furthermore,rigorous analysis is given to show privacy-preserving performance against internal and external eavesdroppers.Finally,case studies illustrate the feasibility and validity of the developed algorithm.

Implementation of a New Solution for the Preservation of Anatomical Specimens Made of Non-Toxic Substances

The preservation of anatomical pieces in Veterinary Anatomy is essential since it is not possible to dissect all domestic species. Most techniques use reagents with high levels of toxicity such as formaldehyde. The objective of this work was to develop a new preservation technique that uses reagents with zero toxicity and that allows obtaining preserved pieces suitable for anatomical studies. The alcohol propylene glycol technique was developed, the method of which uses a fixation step with alcohol, sodium chloride, commercial vinegar and subsequently the impregnation of the preservation solution made from propylene glycol and commercial vinegar, which are non-toxic. As a result of this work, adequately preserved sheep hearts were obtained that preserved their morphology with slight changes in size and weight, which did not affect their external and internal anatomical structure. Its coloration was not substantially affected, remaining a little lighter. The pieces obtained showed flexibility which allowed dissections to be carried out. The time to develop the technique was 20 days. A comparative study was carried out with the phenolated glycerin technique that uses toxic reagents (formaldehyde and phenol) and the pieces obtained with the alcohol propylene glycol technique were of better quality, observing that the pieces with phenolated glycerin tend to darken and are more rigid. And the time to develop the technique is 24 days. In conclusion, a preservation technique for anatomical pieces was developed that allowed the preservation of the organs under study, which allow their use for anatomical studies, and which have been preserved without changes until the time of this publication (8 months) and there are pieces preserved with this technique for 2 years.

Extracellular Vesicles from Mesenchymal Stromal Cells (imEVs) Improve Cold Preservation of Isolated Mitochondria

Mitochondrial organelle transplantation (MOT) is an innovative strategy for the treatment of mitochondrial dysfunction such as cardiac ischemic reperfusion injuries, Parkinson’s diseases, brain and spinal cord injuries, and amyotrophic lateral sclerosis (ALS). However, one of the major challenges for widespread usage is a methodology for preservation of isolated mitochondria. Extracellular vesicles (EVs) are phospholipid bilayer-enclosed vesicles released from cells. EVs carry a cargo of proteins, nucleic acids, lipids, metabolites, and even organelles such as mitochondria. Purpose: To test if EVs enhance the stability of isolated mitochondria. Methods: We mixed isolated mitochondria of fibroblasts with EVs of mesenchymal stromal cells (imEVs) (9:1 in volume) and stored the mixture at 2°C - 6°C for different time periods. We measured morphology, mitochondrial membrane potential (MMP) and mitochondrial ATP content at 0, 2, 5 days. Key findings: After 2 days of storage, the mito-chondria without imEVs lost approximate 70% MMP (RFU: 1822 ± 68), compared to the fresh mitochondria (RFU: 5458 ± 52) (p 0.05). In agreement with MMP, mitochondria without imEVs lost significant mitochondrial ATP content (p 0.05), after 2 days of cold storage, compared to fresh mitochondria. Microscopy showed that imEVs promoted aggregation of isolated mitochondria. Summary: The preliminary data showed that imEVs enhanced the stability of isolated mitochondria in cold storage.

2P3FL:A Novel Approach for Privacy Preserving in Financial Sectors Using Flower Federated Learning

The increasing data pool in finance sectors forces machine learning(ML)to step into new complications.Banking data has significant financial implications and is confidential.Combining users data from several organizations for various banking services may result in various intrusions and privacy leakages.As a result,this study employs federated learning(FL)using a flower paradigm to preserve each organization’s privacy while collaborating to build a robust shared global model.However,diverse data distributions in the collaborative training process might result in inadequate model learning and a lack of privacy.To address this issue,the present paper proposes the imple-mentation of Federated Averaging(FedAvg)and Federated Proximal(FedProx)methods in the flower framework,which take advantage of the data locality while training and guaranteeing global convergence.Resultantly improves the privacy of the local models.This analysis used the credit card and Canadian Institute for Cybersecurity Intrusion Detection Evaluation(CICIDS)datasets.Precision,recall,and accuracy as performance indicators to show the efficacy of the proposed strategy using FedAvg and FedProx.The experimental findings suggest that the proposed approach helps to safely use banking data from diverse sources to enhance customer banking services by obtaining accuracy of 99.55%and 83.72%for FedAvg and 99.57%,and 84.63%for FedProx.

A Cloud-Fog Enabled and Privacy-Preserving IoT Data Market Platform Based on Blockchain

The dynamic landscape of the Internet of Things(IoT)is set to revolutionize the pace of interaction among entities,ushering in a proliferation of applications characterized by heightened quality and diversity.Among the pivotal applications within the realm of IoT,as a significant example,the Smart Grid(SG)evolves into intricate networks of energy deployment marked by data integration.This evolution concurrently entails data interchange with other IoT entities.However,there are also several challenges including data-sharing overheads and the intricate establishment of trusted centers in the IoT ecosystem.In this paper,we introduce a hierarchical secure data-sharing platform empowered by cloud-fog integration.Furthermore,we propose a novel non-interactive zero-knowledge proof-based group authentication and key agreement protocol that supports one-to-many sharing sets of IoT data,especially SG data.The security formal verification tool shows that the proposed scheme can achieve mutual authentication and secure data sharing while protecting the privacy of data providers.Compared with previous IoT data sharing schemes,the proposed scheme has advantages in both computational and transmission efficiency,and has more superiority with the increasing volume of shared data or increasing number of participants.

Apatite Fission Track Thermochronology of Granite from the Xiazhuang Uranium Ore Field,South China:Implications for Exhumation History and Ore Preservation

Xiazhuang uranium ore field,located in the southern part of the Nanling Metallogenic Belt,is considered one of the largest granite-related U regions in South China.In this paper,we contribute new apatite fission track data and thermal history modeling to constrain the exhumation history and evaluate preservation potential of the Xiazhuang Uranium ore field.Nine Triassic outcrop granite samples collected from different locations of Xiazhuang Uranium ore field yield AFT ages ranging from 43 to 24 Ma with similar mean confined fission track lengths ranging from 11.8±2.0 to 12.9±1.9μm and Dpar values between 1.01 and 1.51μm.The robustness time-temperature reconstructions of samples from the hanging wall of Huangpi fault show that the Xiazhuang Uranium ore field experienced a time of monotonous and slow cooling starting from middle Paleocene to middle Miocene(~60-10 Ma),followed by relatively rapid exhumation in the late Miocene(~10-5 Ma)and nearly thermal stability in the Pliocene-Quaternary(~5-0 Ma).The amount of exhumation after U mineralization since the Middle Paleogene was estimated as~4.3±1.8 km according to the integrated thermal history model.Previous studies indicate that the ore-forming ages of U deposits in the Xiazhuang ore field are mainly before Middle Paleocene and the mineralization depths are more than 4.4±1.2 km.Therefore,the exhumation history since middle Paleocene plays important roles in the preservation of the Xiazhuang Uranium ore field.

Exploring the combination of biochar‐amended soil and automated irrigation technology for water regulation and preservation in green infrastructure

Biochar is a carbon sink material with the potential to improve water retention in various soils.However,for the long‐term maintenance of green infrastructure,there is an additional need to regulate the water contents in the covers to maintain vegetation growth in semiarid conditions.In this study,biochar‐amended soil was combined with subsurface drip irrigation,and the water preservation characteristics of this treatment were investigated through a series of one‐dimensional soil column tests.To ascertain the best treatment method specific to semiarid climatic conditions,the test soil was amended with 0%,1%,3%,and 5%biochar.Automatic irrigation devices equipped with soil moisture sensors were used to control the subsurface water content with the aim of enhancing vegetation growth.Each soil column test lasted 150 h,during which the volumetric water contents and soil suction data were recorded.The experimental results reveal that the soil specimen amended with 3%biochar is the most water‐saving regardless of the time cost.Soil with a higher biochar content(e.g.,5%)consumes a more significant amount of water due to the enhancement of the water‐holding capacity.Based on the experimental results,it can be concluded that the appropriate ratio can be determined within 1%–3%,which can reduce not only the amount of irrigated/used water but also the time cost.Such technology can be explored for water content regulation in green infrastructure and the development of barriers for protecting the environment around deep underground waste containment.

Trusted Certified Auditor Using Cryptography for Secure Data Outsourcing and Privacy Preservation in Fog-Enabled VANETs

With the recent technological developments,massive vehicular ad hoc networks(VANETs)have been established,enabling numerous vehicles and their respective Road Side Unit(RSU)components to communicate with oneanother.The best way to enhance traffic flow for vehicles and traffic management departments is to share thedata they receive.There needs to be more protection for the VANET systems.An effective and safe methodof outsourcing is suggested,which reduces computation costs by achieving data security using a homomorphicmapping based on the conjugate operation of matrices.This research proposes a VANET-based data outsourcingsystem to fix the issues.To keep data outsourcing secure,the suggested model takes cryptography models intoaccount.Fog will keep the generated keys for the purpose of vehicle authentication.For controlling and overseeingthe outsourced data while preserving privacy,the suggested approach considers the Trusted Certified Auditor(TCA).Using the secret key,TCA can identify the genuine identity of VANETs when harmful messages aredetected.The proposed model develops a TCA-based unique static vehicle labeling system using cryptography(TCA-USVLC)for secure data outsourcing and privacy preservation in VANETs.The proposed model calculatesthe trust of vehicles in 16 ms for an average of 180 vehicles and achieves 98.6%accuracy for data encryption toprovide security.The proposedmodel achieved 98.5%accuracy in data outsourcing and 98.6%accuracy in privacypreservation in fog-enabled VANETs.Elliptical curve cryptography models can be applied in the future for betterencryption and decryption rates with lightweight cryptography operations.

Privacy-Preserving Healthcare and Medical Data Collaboration Service System Based on Blockchain and Federated Learning

As the volume of healthcare and medical data increases from diverse sources,real-world scenarios involving data sharing and collaboration have certain challenges,including the risk of privacy leakage,difficulty in data fusion,low reliability of data storage,low effectiveness of data sharing,etc.To guarantee the service quality of data collaboration,this paper presents a privacy-preserving Healthcare and Medical Data Collaboration Service System combining Blockchain with Federated Learning,termed FL-HMChain.This system is composed of three layers:Data extraction and storage,data management,and data application.Focusing on healthcare and medical data,a healthcare and medical blockchain is constructed to realize data storage,transfer,processing,and access with security,real-time,reliability,and integrity.An improved master node selection consensus mechanism is presented to detect and prevent dishonest behavior,ensuring the overall reliability and trustworthiness of the collaborative model training process.Furthermore,healthcare and medical data collaboration services in real-world scenarios have been discussed and developed.To further validate the performance of FL-HMChain,a Convolutional Neural Network-based Federated Learning(FL-CNN-HMChain)model is investigated for medical image identification.This model achieves better performance compared to the baseline Convolutional Neural Network(CNN),having an average improvement of 4.7%on Area Under Curve(AUC)and 7%on Accuracy(ACC),respectively.Furthermore,the probability of privacy leakage can be effectively reduced by the blockchain-based parameter transfer mechanism in federated learning between local and global models.

Research on the Method of Heat Preservation and Heating for the Drilling System of Polar Offshore Drilling Platform

This study investigates the heat dissipation mechanism of the insulation layer and other plane insulation layers in the polar drilling rig system.Combining the basic theory of heat transfer with the environmental requirements of polar drilling operations and the characteristics of polar drilling processes,we analyze the factors that affect the insulation effect of the drilling rig system.These factors include the thermal conductivity of the insulation material,the thickness of the insulation layer,ambient temperature,and wind speed.We optimize the thermal insulation material of the polar drilling rig system using a steady-state method to measure solid thermal conductivity.By analyzing the distribution of temperature in space after heating,we optimize the distribution and air outlet angle of the heater using Fluent hydrodynamics software.The results demonstrate that under polar conditions,polyisocyanurate with stable thermodynamic properties is selected as the thermal insulation material.The selection of thermal insulation material and thickness significantly affects the thermal insulation effect of the system but has little effect on its heating effect.Moreover,when the air outlet angle of the heater is set to 32.5°,the heating efficiency of the system can be effectively improved.According to heat transfer equations and heat balance theory,we determine that the heating power required for the system to reach 5°C is close to numerical simulation.

Bipolar hip arthroplasty using conjoined tendon preserving posterior lateral approach in treatment of displaced femoral neck fractures

BACKGROUND Hip fractures account for 23.8%of all fractures in patients over the age of 75 years.More than half of these patients are older than 80 years.Bipolar hemiarthroplasty(BHA)was established as an effective management option for these patients.Various approaches can be used for the BHA procedure.However,there is a high risk of postoperative dislocation.The conjoined tendon-preserving posterior(CPP)lateral approach was introduced to reduce postoperative dislocation rates.AIM To evaluate the effectiveness and safety of the CPP lateral approach for BHA in elderly patients.METHODS We retrospectively analyzed medical data from 80 patients with displaced femoral neck fractures who underwent BHA.The patients were followed up for at least 1 year.Among the 80 patients,57(71.3%)were female.The time to operation averaged 2.3 d(range:1-5 d).The mean age was 80.5 years(range:67-90 years),and the mean body mass index was 24.9 kg/m^(2)(range:17-36 kg/m^(2)).According to the Garden classification,42.5%of patients were typeⅢand 57.5%of patients were typeⅣ.Uncemented bipolar hip prostheses were used for all patients.Torn conjoined tendons,dislocations,and adverse complications during and after surgery were recorded.RESULTS The mean postoperative follow-up time was 15.3 months(range:12-18 months).The average surgery time was 52 min(range:40-70 min)with an average blood loss of 120 mL(range:80-320 mL).The transfusion rate was 10%(8 of 80 patients).The gemellus inferior was torn in 4 patients(5%),while it was difficult to identify in 2 patients(2.5%)during surgery.The posterior capsule was punctured by the fractured femoral neck in 3 patients,but the conjoined tendon and the piriformis tendon remained intact.No patients had stem varus greater than 3 degrees or femoral fracture.There were no patients with stem subsidence more than 5 mm at the last follow-up.No postoperative dislocations were observed throughout the follow-up period.No significance was found between preoperative and postoperative mean Health Service System scores(87.30±2.98 vs 86.10±6.10,t=1.89,P=0.063).CONCLUSION The CPP lateral approach can effectively reduce the incidence of postoperative dislocation without increasing perioperative complications.For surgeons familiar with the posterior lateral approach,there is no need for additional surgical instruments,and it does not increase surgical difficulty.

Insights into upper blepharoplasty: Conservative volume-preserving techniques

This editorial commentary critically examines the systematic review by Miotti et al,which discusses the evolving trends in upper lid blepharoplasty towards a conservative,volume-preserving approach.The review emphasizes the shift from traditional tissue resection to techniques that maintain anatomical integrity,paralleling broader trends in panfacial rejuvenation.Miotti et al delve into the nuances of fat pad management,advocating for conservation over reduction to sustain natural contours and improve long-term aesthetic outcomes.This perspective is supported by comparative studies and empirical data,such as those from Massry and Alghoul et al,highlighting the benefits of conservative approaches in terms of patient satisfaction and aesthetic longevity.The review also stresses the importance of surgeon discretion in adapting procedures to diverse patient demographics,particularly in addressing distinct features such as the Asian upper eyelid.However,it identifies a significant gap in long-term comparative research,underscoring the need for future studies to substantiate the safety and efficacy of these minimalist techniques.Overall,Miotti et al.'s work contributes profoundly to the discourse on personalized,conservative cosmetic surgery,urging ongoing research to refine and validate surgical best practices in upper eyelid blepharoplasty.