The potential mechanism and clinical application value of remote ischemic conditioning in stroke

Some studies have confirmed the neuroprotective effect of remote ischemic conditioning against stroke. Although numerous animal researches have shown that the neuroprotective effect of remote ischemic conditioning may be related to neuroinflammation, cellular immunity, apoptosis, and autophagy, the exact underlying molecular mechanisms are unclear. This review summarizes the current status of different types of remote ischemic conditioning methods in animal and clinical studies and analyzes their commonalities and differences in neuroprotective mechanisms and signaling pathways. Remote ischemic conditioning has emerged as a potential therapeutic approach for improving stroke-induced brain injury owing to its simplicity, non-invasiveness, safety, and patient tolerability. Different forms of remote ischemic conditioning exhibit distinct intervention patterns, timing, and application range. Mechanistically, remote ischemic conditioning can exert neuroprotective effects by activating the Notch1/phosphatidylinositol 3-kinase/Akt signaling pathway, improving cerebral perfusion, suppressing neuroinflammation, inhibiting cell apoptosis, activating autophagy, and promoting neural regeneration. While remote ischemic conditioning has shown potential in improving stroke outcomes, its full clinical translation has not yet been achieved.

Capability matchmaking of semantic web services with preconditions and effects

In order to solve the problem that the current matchmaking methods for semantic web service mainly focus on the matchmaking of IO （inputs, outputs） descriptions which may result in one-sidedness, a description-logic-based IOPE （inputs, outputs, preconditions, effects） description and matchmaking method is proposed for semantic web service. The description logic concept is used to annotate service IO and the description logic assertion is employed to describe service PE（preconditions, effects）. TBox subsumption checking is used to measure the subsumption relationship between IO descriptions of service request and advertising; ABox consistency checking is used for checking the logical implication between PE descriptions of service request and advertising. Based upon the logical implication, four kinds of PE matching degrees are proposed to measure and compare the pros and cons of the results of matchmaking. They are the exact, perfect, side-effect and common match. Experiments show that the method has a higher precision rate under the same recall rate compared with the existing method.

Effects of Service Condition on Rolling Contact Fatigue Failure Mechanism and Lifetime of Thermal Spray Coatings——A Review

The service condition determines the Roiling Contact Fatigue（RCF） failure mechanism and lifetime under ascertain material structure integrity parameter of thermal spray coating. The available literature on the RCF testing of thermal spray coatings under various condition services is considerable; it is generally difficult to synthesize all of the result to obtain a comprehensive understanding of the parameters which has a great effect on a thermal spray coating＇s resistance of RCF. The effects of service conditions（lubrication states, contact stresses, revolve speed, and slip ratio） on the changing of thermal spray coatings＇ contact fatigue lifetime is introduced systematically. The effects of different service condition on RCF failure mechanism of thermal spray coating from the change of material structure integrity are also summarized. Moreover, In order to enhance the RCF performance, the parameter optimal design formula of service condition and material structure integrity is proposed based on the effect of service condition on thermal spray coatings＇ contact fatigue lifetime and RCF failure mechanism. The shortage of available literature and the forecast focus in future researches are discussed based on available research. The explicit result of RCF lifetime law and parameter optimal design formula in term of lubrication states, contact stresses, revolve speed, and slip ratio, is significant to improve the RCF performance on the engineering application.

Identifying Existing Bus Service Condition and Analyzing Customer Satisfaction of Bus Service in Dhaka City

Bus systems provide a versatile form of public transportation with the flexibility to serve a variety of access needs throughout an urban area. Efficient operation of the public transport is a key factor for the improvement of living condition in Dhaka city. Transport service performance should be assessed from the passengers’ perspectives to evaluate these perceptions in transportation planning. The exploration of service quality of bus is essential in order to provide a better service in future. The objective of this research is to access overall customer satisfaction in public bus transport of Dhaka city. A questionnaire survey was conducted in five locations in Dhaka city. Bus users’ satisfaction was analyzed by discrete choice logit model towards quality, reliability, safety and security and service provided by buses functioning in Dhaka city. The result indicated that more than half of the respondents believed that the present condition of bus service is not satisfactory. In case of the quality of buses, people think that the body and sitting arrangement of the buses are uncomfortable. Regarding the safety and security condition, main three reasons of dissatisfaction of passengers’ are unsafe driving practices, poor boarding and alighting facilities and lack of law enforcing agencies surveillance. Reasons which make the service unsatisfactory include irregular service provided by buses, regular overcrowding, lack of good standard buses and lack of cleanliness. In spite of these negative views, users’ possessed a positive attitude for buses which is the low travel cost.

Urban road segment level of service based on bicycle users' perception under mixed traffic conditions

During the past two decades, several methodologies are endorsed to assess the compatibility of roadways for bicycle use under homogeneous traffic conditions. However, these methodologies cannot be adopted under heterogeneous traffic where on-street bicyclists encounter a complex interaction with various types of vehicles and show divergent operational characteristics. Thus, the present study proposes an initial model suitable for urban road segments in mid-sized cities under such complex situations. For analysis purpose, various operational and physical factors along with user perception data sets （13,624 effective ratings in total） were collected from 74 road segments. Eight important road attributes affecting the bicycle service quality were identified using the most recent and most promising machine learning technique namely, random forest. The identified variables are namely, effective width of outside through lane, pavement condition index, traffic volume, traffic speed, roadside commercial activities, interruptions by unauthorized stoppages of intermittent public transits, vehicular ingress-egress to on-street parking area, and frequency of driveways carrying a high volume of traffic. Service prediction models were developed using ordered probit and ordered logit modeling structures which meet a confidence level of 95%. Prediction performances of developed models were assessed in terms of several statistical parameters and the ordered probit model outperformed the ordered logit model. Incorporating outputs of the probit model, a pre- dictive equation is presented that can identify under what level a segment is offering services for bicycle use. The service levels offered by roadways were classified into six categories varying from ＇excellent＇ to ＇worst＇ （A-F）.

Effects of Operation Conditions on Service Life of Ladle Lining

Effects of operation conditions such as ladle temperature, remining time of molten steel, slag basicity, slag oxidation, slag viscosity , vacuum treatment, ultra-high temperature, gas blowing and stirring, intermittent operation, and different refining equipment on service life of ladle lining refractories were researched. The following conclusions are drawn ： （1） molten steel temperature rising, remaiaing time prolonging, slag oxidation ability enhancing, slag viscosity and basicity decreasing can aecelerate the corrosion of ladle lining ; （2） ultra-high temperature and vacuum treatment of ladle not only acceler- ate the fusion corrasion of ladle lining, but also make the carbon containing refractories react forming gases leading to more corrosion, so carbon containing refractories are not stdtable.for the smelting conditions of long term vacuum treatment and ultra-high temperature, especially carbon containing refractories added with conventional additives such as A1 and Si powders ; （ 3 ） argon blowing does not accelerate the corrosion of ladle lining obvioasly, but oxygen blowing does; （4） the corrosion degree of refractories varies with the refining equipment, and the service life of ladle lining decreases according to a certain proportion with the refining ratio increasing.

Necessary Conditions and Situational Variables of Payments for Ecosystem Services

Clarifying the necessary conditions for the emergence of payments for ecosystem services (PES) and the situational variables that affect PES is the basis for their interpretation, prediction, and selection. This article proposes an analytical framework for the emergence of PES and argues that the key to determining whether PES can occur and whether a selected PES program is appropriate is to evaluate the net gain. When payers anticipate that a PES program will provide a satisfactory number of ES and a net gain over the opportunity cost and will cover all costs, it is assumed that the program will be implemented. When it is difficult to accurately evaluate the net gain of PES, the situational variables that affect the costs and benefits need to be examined. The group characteristics, ES characteristics, spatial and temporal contacts between the suppliers and demanders, correlation with private goods and additionality are important situational variables that affect the emergence and choice of PES.

Training of Food Providers for Improved Environmental Conditions of Food Service Outlets in Urban Area Nigeria

Environmental conditions and hygiene practices under which foods are prepared and served for public consumption are a matter of concern. The study was conducted to investigate the impact of training of food providers on environmental conditions of food service outlets (FSO) in and around a university community in Southwestern Nigeria. Structured questionnaire and “Spot Check Observation” (SCO) were used to assess the hygiene practices and environmental condition of the FSO respectively. Sixteen (16) High Density Food Service Outlets (HDFSO) and twelve (12) Low Density Food Service Outlets (LDFSO) were investigated. From the findings, mean score of SCO for HDFSO was low (2.93 ± 0.09) compared to LDFSO (4.32 ± 0.56) (p = 0.014). There were significant differences in source of cooking water (p = 0.003), solid waste disposal method (p = 0.031), liquid waste disposal method (p = 0.023) and toilet facilities (p = 0.001) between HDFSO and LDFSO. Significant differences existed in the environmental hygiene between HDFSO and LDFSO (p r = 0.789, p = 0.038), age (r = 0.631, p = 0.045), income (r = 0.623, p = 0.004) of food service providers and environmental condition. Service providers that were >40 years of age were more likely to score high in SCO in HDFSO than the other age groups (OR = 1.80, 95%CI = 1.21, 2.68). Those with tertiary education were twice more likely to score high in SCO compared to those with lower educational qualifications in LDFSO (OR = 2.03, 95%CI = 1.48 2.78). Those who earned above ?40,000.00 were more likely to have higher SCO among both categories. A 3-day food safety workshop was organized among both groups at different times, three months later, SCO was conducted and scores improved greatly. Constant training of food service providers is important to improving the environmental condition of food service outlets in high-density urban areas in order to ensure food safety.

Conditioned medium from human dental pulp stem cells treats spinal cord injury by inhibiting microglial pyroptosis

Human dental pulp stem cell transplantation has been shown to be an effective therapeutic strategy for spinal cord injury.However,whether the human dental pulp stem cell secretome can contribute to functional recovery after spinal cord injury remains unclear.In the present study,we established a rat model of spinal cord injury based on impact injury from a dropped weight and then intraperitoneally injected the rats with conditioned medium from human dental pulp stem cells.We found that the conditioned medium effectively promoted the recovery of sensory and motor functions in rats with spinal cord injury,decreased expression of the microglial pyroptosis markers NLRP3,GSDMD,caspase-1,and interleukin-1β,promoted axonal and myelin regeneration,and inhibited the formation of glial scars.In addition,in a lipopolysaccharide-induced BV2 microglia model,conditioned medium from human dental pulp stem cells protected cells from pyroptosis by inhibiting the NLRP3/caspase-1/interleukin-1βpathway.These results indicate that conditioned medium from human dental pulp stem cells can reduce microglial pyroptosis by inhibiting the NLRP3/caspase-1/interleukin-1βpathway,thereby promoting the recovery of neurological function after spinal cord injury.Therefore,conditioned medium from human dental pulp stem cells may become an alternative therapy for spinal cord injury.

Uncertainties of landslide susceptibility prediction: Influences of random errors in landslide conditioning factors and errors reduction by low pass filter method

In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.

Energy Consumption Control of an Air Conditioner Using Web Services

Air-conditioning (AC) systems have the highest power consumption among the appliances and consumer devices used at residential homes and buildings. Reducing their energy use will lower peak time usage and lower CO2 emissions. Recently, employment of the Information and Communications Technologies (ICT) to the power grid has smartened the grid. In the smart grid new opportunities emerge for AC energy consumption control. The aim of this paper is to reduce the air conditioning energy consumption of residential customers. It proposes an architecture that provides easy management and control using sensor network web services. A simulation thermal model of a house considers house data and outside temperature is presented. Simulation results showed a proposed temperature control method can have significant energy saving while maintaining customer comfort.

Genesis of LCT Pegmatites during Early Paleozoic Orogeny of the North Qinling Orogenic Belt,China:Emplacement Conditions and Structural Control

The Guanpo pegmatite field in the North Qinling orogenic belt(NQB),China,hosts the most abundant LCT pegmatites.However,their emplacement conditions and structural control remain unexplored.In this contribution,we investigated it combining pegmatite orientation measurement with oxygen isotope geothermometry and fluid inclusion study.The orientations of type A1 pegmatites(P_(f)<σ_(2))are predominantly influenced by P-and T-fractures due to simple shearing in Shiziping dextral thrust shear zone during D_(2)deformation,whereas type A2 pegmatites(contemporaneous with D_(4))are governed by hydraulic fractures aligned with S_(0)and S_(0+1)stemming from fluid pressure(P_(f)<σ_(2)).Additionally,type B pegmatites(P_(f)≤σ_(2))exhibit orientations shaped by en echelon extensional fractures in local ductile shear zones(contemporaneous with D_(3)).The albite-quartz oxygen isotope geothermometry and microthermometric analysis of fluid inclusions in elbaites from the latest pegmatites(including types B and A2)suggest that the crystallization P-T for late magmatic and hydrothermal stages are 527.5-559.2℃,320℃,3.1-3.6 kbar and 2.0 kbar,respectively.Our observations along with previous studies suggest that the genesis of the LCT pegmatites was a long-term,multi-stage event during early Paleozoic orogeny(including the collision stage)of the NQB,and was facilitated by various local fractures.

Ambient-Condition Strategy for Production of Hollow Ga_(2)O_(3)@rGO Crystalline Nanostructures Toward Efficient Lithium Storage

Crystallineγ-Ga_(2)O_(3)@rGO core-shell nanostructures are synthesized in gram scale,which are accomplished by a facile sonochemical strategy under ambient condition.They are composed of uniformγ-Ga_(2)O_(3)nanospheres encapsulated by reduced graphene oxide(rGO)nanolayers,and their formation is mainly attributed to the existed opposite zeta potential between the Ga_(2)O_(3)and rGO.The as-constructed lithium-ion batteries(LIBs)based on as-fabricatedγ-Ga_(2)O_(3)@rGO nanostructures deliver an initial discharge capacity of 1000 mAh g^(-1)at 100 mA g^(-1)and reversible capacity of 600 mAh g^(-1)under 500 mA g^(-1)after 1000 cycles,respectively,which are remarkably higher than those of pristineγ-Ga_(2)O_(3)with a much reduced lifetime of 100 cycles and much lower capacity.Ex situ XRD and XPS analyses demonstrate that the reversible LIBs storage is dominant by a conversion reaction and alloying mechanism,where the discharged product of liquid metal Ga exhibits self-healing ability,thus preventing the destroy of electrodes.Additionally,the rGO shell could act robustly as conductive network of the electrode for significantly improved conductivity,endowing the efficient Li storage behaviors.This work might provide some insight on mass production of advanced electrode materials under mild condition for energy storage and conversion applications.

The coordinated evolution of ecological environment,public service,and tourism economy along the Silk Road Economic Belt,using the Dual-Carbon Targets

Carbon peak and carbon neutrality(dual-carbon)are important targets for the international response to climate change.The Silk Road Economic Belt is a strategic resource region and is important for future ecological environment and tourism development.Based on the“dual-carbon”targets,the Single index quantification,Multiple index synthesis,and Poly-criteria integration evaluation model were used in this study to measure the coordinated development index of the ecological environment,public service,and tourism economy along the Silk Road Economic Belt and to analyze its spatial and temporal evolution.Further,it explores the dynamic evolution and development trend of the three systems using the Kernel Density and Grey Markov Prediction Model.The results show that the coordinated development index along this region needs to be improved during the study period.Furthermore,the coordinated development index of the Southwest region is relatively higher than that of the Northwest region.From the development trend of the three systems,all of them develop in a stable manner;however,the tourism economy system is easily affected by external disturbances.The coordinated development index of the three systems changes dynamically and tends to be in a good state of coordination.There is a certain spatial and temporal heterogeneity.The gravity center of the coordinated development index has been in the Southwest region.During the forecast period,the coordinated development index along this region will improve significantly,while insufficient and unbalanced development will continue.

Autologous hematopoietic stem cell transplantation conditioning regimens and chimeric antigen receptor T cell therapy in various diseases

Conditioning regimens employed in autologous stem cell transplantation have been proven useful in various hematological disorders and underlying malignancies;however,despite being efficacious in various instances,negative consequences have also been recorded.Multiple conditioning regimens were extracted from various literature searches from databases like PubMed,Google scholar,EMBASE,and Cochrane.Conditioning regimens for each disease were compared by using various end points such as overall survival(OS),progression free survival(PFS),and leukemia free survival(LFS).Variables were presented on graphs and analyzed to conclude a more efficacious conditioning regimen.In multiple myeloma,the most effective regimen was high dose melphalan(MEL)given at a dose of 200/mg/m2.The comparative results of acute myeloid leukemia were presented and the regimens that proved to be at an admirable position were busulfan(BU)+MEL regarding OS and BU+VP16 regarding LFS.In case of acute lymphoblastic leukemia(ALL),BU,fludarabine,and etoposide(BuFluVP)conferred good disease control not only with a paramount improvement in survival rate but also low risk of recurrence.However,for ALL,chimeric antigen receptor(CAR)T cell therapy was preferred in the context of better OS and LFS.With respect to Hodgkin’s lymphoma,mitoxantrone(MITO)/MEL overtook carmustine,VP16,cytarabine,and MEL in view of PFS and vice versa regarding OS.Non-Hodgkin’s lymphoma patients were administered MITO(60 mg/m2)and MEL(180 mg/m2)which showed promising results.Lastly,amyloidosis was considered,and the regimen that proved to be competent was MEL 200(200 mg/m2).This review article demonstrates a comparison between various conditioning regimens employed in different diseases.

Attribute Reduction of Hybrid Decision Information Systems Based on Fuzzy Conditional Information Entropy

The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable task.Currently available attribute reduction algorithms,including those based on Pawlak attribute importance,Skowron discernibility matrix,and information entropy,struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values,and attributes with fuzzy boundaries and abnormal values.In order to address the aforementioned issues,this paper delves into the study of attribute reduction withinHDISs.First of all,a novel metric based on the decision attribute is introduced to solve the problem of accurately measuring the differences between nominal attribute values.The newly introduced distance metric has been christened the supervised distance that can effectively quantify the differences between the nominal attribute values.Then,based on the newly developed metric,a novel fuzzy relationship is defined from the perspective of“feedback on parity of attribute values to attribute sets”.This new fuzzy relationship serves as a valuable tool in addressing the challenges posed by abnormal attribute values.Furthermore,leveraging the newly introduced fuzzy relationship,the fuzzy conditional information entropy is defined as a solution to the challenges posed by fuzzy attributes.It effectively quantifies the uncertainty associated with fuzzy attribute values,thereby providing a robust framework for handling fuzzy information in hybrid information systems.Finally,an algorithm for attribute reduction utilizing the fuzzy conditional information entropy is presented.The experimental results on 12 datasets show that the average reduction rate of our algorithm reaches 84.04%,and the classification accuracy is improved by 3.91%compared to the original dataset,and by an average of 11.25%compared to the other 9 state-of-the-art reduction algorithms.The comprehensive analysis of these research results clearly indicates that our algorithm is highly effective in managing the intricate uncertainties inherent in hybrid data.

Neural Network-Based State Estimation for Nonlinear Systems With Denial-of-Service Attack Under Try-Once-Discard Protocol

Dear Editor,This letter deals with state estimation issues of discrete-time nonlinear systems subject to denial-of-service(DoS)attacks under the try-once-discard(TOD)protocol.More specifically,to reduce the communication burden,a TOD protocol with novel update rules on protocol weights is designed for scheduling measurement outputs.In addition,unknown nonlinear functions vulnerable to DoS attacks are considered due to the openness and vulnerability of the network.

Deep Reinforcement Learning-Based Task Offloading and Service Migrating Policies in Service Caching-Assisted Mobile Edge Computing

Emerging mobile edge computing(MEC)is considered a feasible solution for offloading the computation-intensive request tasks generated from mobile wireless equipment(MWE)with limited computational resources and energy.Due to the homogeneity of request tasks from one MWE during a longterm time period,it is vital to predeploy the particular service cachings required by the request tasks at the MEC server.In this paper,we model a service caching-assisted MEC framework that takes into account the constraint on the number of service cachings hosted by each edge server and the migration of request tasks from the current edge server to another edge server with service caching required by tasks.Furthermore,we propose a multiagent deep reinforcement learning-based computation offloading and task migrating decision-making scheme(MBOMS)to minimize the long-term average weighted cost.The proposed MBOMS can learn the near-optimal offloading and migrating decision-making policy by centralized training and decentralized execution.Systematic and comprehensive simulation results reveal that our proposed MBOMS can converge well after training and outperforms the other five baseline algorithms.

Detection of Real-Time Distributed Denial-of-Service (DDoS) Attacks on Internet of Things (IoT) Networks Using Machine Learning Algorithms

The primary concern of modern technology is cyber attacks targeting the Internet of Things.As it is one of the most widely used networks today and vulnerable to attacks.Real-time threats pose with modern cyber attacks that pose a great danger to the Internet of Things(IoT)networks,as devices can be monitored or service isolated from them and affect users in one way or another.Securing Internet of Things networks is an important matter,as it requires the use of modern technologies and methods,and real and up-to-date data to design and train systems to keep pace with the modernity that attackers use to confront these attacks.One of the most common types of attacks against IoT devices is Distributed Denial-of-Service(DDoS)attacks.Our paper makes a unique contribution that differs from existing studies,in that we use recent data that contains real traffic and real attacks on IoT networks.And a hybrid method for selecting relevant features,And also how to choose highly efficient algorithms.What gives the model a high ability to detect distributed denial-of-service attacks.the model proposed is based on a two-stage process:selecting essential features and constructing a detection model using the K-neighbors algorithm with two classifier algorithms logistic regression and Stochastic Gradient Descent classifier(SGD),combining these classifiers through ensemble machine learning(stacking),and optimizing parameters through Grid Search-CV to enhance system accuracy.Experiments were conducted to evaluate the effectiveness of the proposed model using the CIC-IoT2023 and CIC-DDoS2019 datasets.Performance evaluation demonstrated the potential of our model in robust intrusion detection in IoT networks,achieving an accuracy of 99.965%and a detection time of 0.20 s for the CIC-IoT2023 dataset,and 99.968%accuracy with a detection time of 0.23 s for the CIC-DDoS 2019 dataset.Furthermore,a comparative analysis with recent related works highlighted the superiority of our methodology in intrusion detection,showing improvements in accuracy,recall,and detection time.

Autophagy and mitophagy as potential therapeutic targets in diabetic heart condition:Harnessing the power of nanotheranostics

Autophagy and mitophagy pose unresolved challenges in understanding the pathology of diabetic heart condition(DHC),which encompasses a complex range of cardiovascular issues linked to diabetes and associated cardiomyopathies.Despite significant progress in reducing mortality rates from cardiovascular diseases(CVDs),heart failure remains a major cause of increased morbidity among diabetic patients.These cellular processes are essential for maintaining cellular balance and removing damaged or dysfunctional components,and their involvement in the development of diabetic heart disease makes them attractive targets for diagnosis and treatment.While a variety of conventional diagnostic and therapeutic strategies are available,DHC continues to present a significant challenge.Point-of-care diagnostics,supported by nanobiosensing techniques,offer a promising alternative for these complex scenarios.Although conventional medications have been widely used in DHC patients,they raise several concerns regarding various physiological aspects.Modern medicine places great emphasis on the application of nanotechnology to target autophagy and mitophagy in DHC,offering a promising approach to deliver drugs beyond the limitations of traditional therapies.This article aims to explore the potential connections between autophagy,mitophagy and DHC,while also discussing the promise of nanotechnology-based theranostic interventions that specifically target these molecular pathways.