Integrating multiple medical imaging techniques,including Magnetic Resonance Imaging(MRI),Computed Tomography,Positron Emission Tomography(PET),and ultrasound,provides a comprehensive view of the patient health status...Integrating multiple medical imaging techniques,including Magnetic Resonance Imaging(MRI),Computed Tomography,Positron Emission Tomography(PET),and ultrasound,provides a comprehensive view of the patient health status.Each of these methods contributes unique diagnostic insights,enhancing the overall assessment of patient condition.Nevertheless,the amalgamation of data from multiple modalities presents difficulties due to disparities in resolution,data collection methods,and noise levels.While traditional models like Convolutional Neural Networks(CNNs)excel in single-modality tasks,they struggle to handle multi-modal complexities,lacking the capacity to model global relationships.This research presents a novel approach for examining multi-modal medical imagery using a transformer-based system.The framework employs self-attention and cross-attention mechanisms to synchronize and integrate features across various modalities.Additionally,it shows resilience to variations in noise and image quality,making it adaptable for real-time clinical use.To address the computational hurdles linked to transformer models,particularly in real-time clinical applications in resource-constrained environments,several optimization techniques have been integrated to boost scalability and efficiency.Initially,a streamlined transformer architecture was adopted to minimize the computational load while maintaining model effectiveness.Methods such as model pruning,quantization,and knowledge distillation have been applied to reduce the parameter count and enhance the inference speed.Furthermore,efficient attention mechanisms such as linear or sparse attention were employed to alleviate the substantial memory and processing requirements of traditional self-attention operations.For further deployment optimization,researchers have implemented hardware-aware acceleration strategies,including the use of TensorRT and ONNX-based model compression,to ensure efficient execution on edge devices.These optimizations allow the approach to function effectively in real-time clinical settings,ensuring viability even in environments with limited resources.Future research directions include integrating non-imaging data to facilitate personalized treatment and enhancing computational efficiency for implementation in resource-limited environments.This study highlights the transformative potential of transformer models in multi-modal medical imaging,offering improvements in diagnostic accuracy and patient care outcomes.展开更多
The development of an efficient dual-function catalytic-sorption system,which seamlessly integrates reaction and separation into a single step for extractant-free systems,represents a transformative advancement in oxi...The development of an efficient dual-function catalytic-sorption system,which seamlessly integrates reaction and separation into a single step for extractant-free systems,represents a transformative advancement in oxidative desulfurization(ODS)process.In this work,we introduce a novel dualfunction amphiphilic biochar(Mo/CBC)catalyst,functionalized with MoO_(3-x)featuring abundant oxygen vacancies,for highly effective extractant-free ODS.The polarity of the biochar was precisely tailored by varying the amount of KOH,leading to the creation of amphiphilic carriers.Subsequent ball milling facilitated the successful loading of MoO_(3-x)onto the biochar surface via an impregnation-calcination route leveraging carbon reduction,resulting in the synthesis of amphiphilic Mo/CBC catalysts.The amphiphilic nature of these catalysts ensures their stable dispersion within the oil phase,while also facilitating their interaction with the oxidant H2O2 and the adsorption of sulfur-containing oxidation products.Characterization techniques,including EPR,XPS,and in situ XRD,verified the existence of abundant oxygen vacancies obtained by carbon reduction on the amphiphilic Mo/CBC catalysts,which significantly boosted their activity in an extractant-free ODs system.Remarkably,the amphiphilic Mo/CBC catalyst displayed exceptional catalytic performance,achieving a desulfurization efficiency of 99.6%in just 10 min without extraction solvent.DFT theoretical calculations further revealed that H_(2)O_(2)readily dissociates into two OH radicals on the O_(vac)-MoO_(3),overcoming a low energy barrier.This process was identified as a key contributor to the catalyst's outstanding ODS performance.Furthermore,other biochar sources,such as rice straw,bamboo,rapeseed oil cake,and walnut oil cake,were investigated to produce Mo-based amphiphilic biochar catalysts,which all showed excellent desulfurization performance.This work establishes a versatile and highly efficient dual-function catalytic-sorption system by designing amphiphilic biochar catalysts enriched with oxygen vacancies,paving the way for the development of universally applicable ODS catalysts for industrial applications.展开更多
The extensive heterogeneity and the limited availability of effective targeted therapies contribute to the challenging prognosis and restricted survival observed in triple-negative breast cancer(TNBC).Recent research ...The extensive heterogeneity and the limited availability of effective targeted therapies contribute to the challenging prognosis and restricted survival observed in triple-negative breast cancer(TNBC).Recent research indicates the aberrant expression of diverse tyrosine kinases(TKs)within this cancer,contributing significantly to tumor cell proliferation,survival,invasion,and migration.The contemporary paradigm shift towards precision medicine has highlighted TKs and their receptors as promising targets for pharmacotherapy against a range of malignancies,given their pivotal roles in tumor initiation,progression,and advancement.Intensive investigations have focused on various monoclonal antibodies(mAbs)and small molecule inhibitors that specifically target proteins such as epidermal growth factor receptor(EGFR),vascular endothelial growth factor(VEGF),vascular endothelial growth factor receptor(VEGFR),cellular mesenchymal-epithelial transition factor(c-MET),human epidermal growth factor receptor 2(HER2),among others,for combating TNBC.These agents have been studied both in monotherapy and in combination with other chemotherapeutic agents.Despite these advances,a substantial terrain of unexplored potential lies within the realm of TK-targeted therapeutics,which hold promise in reshaping the therapeutic landscape.This review summarizes the various TK-targeted therapeutics that have undergone scrutiny as potential therapeutic interventions for TNBC,dissecting the outcomes and revelations stemming from diverse clinical investigations.A key conclusion from the umbrella clinical trials evidences the necessity for in-depth molecular characterization of TNBC for the maximum efficiency of TK-targeted therapeutics,either as standalone treatments or a combination.Moreover,our observation highlights that the outcomes of TK-targeted therapeutics in TNBC are substantially influenced by the diversity of the patient cohort,emphasizing the prioritization of individual patient genetic/molecular profiles for precise TNBC patient stratification for clinical studies.展开更多
Nuclear receptors(NRs)function as crucial transcription factors in orchestrating essential functions within the realms of development,host defense,and homeostasis of body.NRs have garnered increased attention due to t...Nuclear receptors(NRs)function as crucial transcription factors in orchestrating essential functions within the realms of development,host defense,and homeostasis of body.NRs have garnered increased attention due to their potential as therapeutic targets,with drugs directed at NRs demonstrating significant efficacy in impeding chronic disease progression.Consequently,these pharmacological agents hold promise for the treatment and management of various diseases.Accumulating evidence emphasizes the regulatory role of exosome-derived microRNAs(miRNAs)in chronic inflammation,disease progression,and therapy resistance,primarily by modulating transcription factors,particularly NRs.By exploiting inflammatory pathways such as protein kinase B(Akt)/mammalian target of rapamycin(mTOR),nuclear factor kappa-B(NF-κB),signal transducer and activator of transcription 3(STAT3),and Wnt/β-catenin signaling,exosomes and NRs play a pivotal role in the panorama of development,physiology,and pathology.The internalization of exosomes modulates NRs and initiates diverse autocrine or paracrine signaling cascades,influencing various processes in recipient cells such as survival,proliferation,differentiation,metabolism,and cellular defense mechanisms.This comprehensive review meticulously examines the involvement of exosome-mediated NRs regulation in the pathogenesis of chronic ailments,including atherosclerosis,cancer,diabetes,liver diseases,and respiratory conditions.Additionally,it elucidates the molecular intricacies of exosome-mediated communication between host and recipient cells via NRs,leading to immunomodulation.Furthermore,it outlines the implications of exosome-modulated NR pathways in the prophylaxis of chronic inflammation,delineates current limitations,and provides insights into future perspectives.This review also presents existing evidence on the role of exosomes and their components in the emergence of therapeutic resistance.展开更多
Background: Surgical site infections (SSIs) are considered as result of the healthcare quality in hospitals. Objective: to study SSI at Saint Jean de Dieu Hospital Tanguieta (SJDHT), prior to the implementation of a p...Background: Surgical site infections (SSIs) are considered as result of the healthcare quality in hospitals. Objective: to study SSI at Saint Jean de Dieu Hospital Tanguieta (SJDHT), prior to the implementation of a permanent monitoring system. Method: transversal, and descriptive study with prospective data collection was performed from 1 July to 31 janvier 2017 in the department of general surgery of SJDHT. The hospital lacks in a microbiology unit. All patients who underwent surgery during this period were included and the monitoring lasted one month. SSIs diagnostic was carried out according to WHO criteria as described in the Practical Guide for the Prevention of Nosocomial Infections published in 2002. Statistical tests (χ-square and Student’s t-test) were applied and p 0.05 were statistically significant. Results: Of 343 patients recorded, 105 (30.6%) had SSI. Their age averaged 40.3 years and the sex-ratio (men/women) was 2.8. The emergency surgery resulted in a 50.0% rate of SSI (p = 0.00). The SSI rate for clean and clean-contaminated surgery was 6.3% against 94.6% for infected surgery (p = 0.00). The SSI rates were 100% and 66.7% for NNISS = 2 and NNISS = 1 (p = 0.00), respectively. Superficial SSI rate was 13.3%, while deep SSI and organ/space SSI were 46.7% and 40%, respectively. The hospital stay of patients with SSI was three times longer than the length of patients without SSI (p = 0.00). Conclusion: SSIs are real burden at SJDHT. Appropriate measures must be adopted to reduce its prevalence.展开更多
The production of poly-α-olefins(PAOs)has attracted attention due to their excellent viscosity-temperature dependence,wear characteristics,oxidative properties,and high thermal stability.In this study,indene extracte...The production of poly-α-olefins(PAOs)has attracted attention due to their excellent viscosity-temperature dependence,wear characteristics,oxidative properties,and high thermal stability.In this study,indene extracted during coal tar refining was used as a raw material to synthesize a bis(indenyl)zirconium dichloride metallocene catalyst.A PAO with low viscosity and a high viscosity index was produced via the oligomerization of 1-decene in the presence of both the prepared metallocene and a methylaluminoxane(MAO)co-catalyst.Notably,the effects of different synthesis reaction parameters,such as Al:Zr ratio,amount of catalyst,and reaction temperature,on the conversion ratio and product selectivity were investigated in detail.The produced PAO was thoroughly characterized using Fourier-transform infrared,^(13)C,and^(1)H nuclear magnetic resonance spectroscopies;gas chromatography;and viscosity measurements.At 70℃,the metallocene catalyst created more stable active sites.In addition,the alkylation effect of MAO was noticeable.Interestingly,the obtained catalysis results demonstrated that a high conversion ratio of~93%was achieved at a low reaction temperature of 70℃,with a catalyst dosage of 0.0848 mmol and Al:Zr ratio of 8.48mmol:0.0848mmol.Moreover,under these optimal conditions,the kinematic viscosity of PAO was 4.25 mm2/s at 100℃,and the viscosity index was 139,indicating good viscosity-temperature properties.展开更多
Many plant species have a startling degree of morphological similarity,making it difficult to split and categorize them reliably.Unknown plant species can be challenging to classify and segment using deep learning.Whi...Many plant species have a startling degree of morphological similarity,making it difficult to split and categorize them reliably.Unknown plant species can be challenging to classify and segment using deep learning.While using deep learning architectures has helped improve classification accuracy,the resulting models often need to be more flexible and require a large dataset to train.For the sake of taxonomy,this research proposes a hybrid method for categorizing guava,potato,and java plumleaves.Two new approaches are used to formthe hybridmodel suggested here.The guava,potato,and java plum plant species have been successfully segmented using the first model built on the MobileNetV2-UNET architecture.As a second model,we use a Plant Species Detection Stacking Ensemble Deep Learning Model(PSD-SE-DLM)to identify potatoes,java plums,and guava.The proposed models were trained using data collected in Punjab,Pakistan,consisting of images of healthy and sick leaves from guava,java plum,and potatoes.These datasets are known as PLSD and PLSSD.Accuracy levels of 99.84%and 96.38%were achieved for the suggested PSD-SE-DLM and MobileNetV2-UNET models,respectively.展开更多
Every new generation of antennas is characterized by increased accuracy and faster transmission speeds.However,patch antennas have been known to damage human health.This type of antenna sends out electromagnetic waves...Every new generation of antennas is characterized by increased accuracy and faster transmission speeds.However,patch antennas have been known to damage human health.This type of antenna sends out electromagnetic waves that increase the temperature of the human head and prevent nerve strands from functioning properly.This paper examines the effect of the communication between the patch antenna and the brain on the head’s temperature by developing a hypothetical multi-input model that achieves more accurate results.These inputs are an individual’s blood and tissue,and the emission power of the antenna.These forces depend on the permeability and conductivity characteristics of the metal from which the antenna is fabricated.The proposed model is the first one that links the material the antenna is manufactured from and the head’s temperature.The results show that there are only a small number of materials that should be used as antenna covers.These materials are in the form of thin films.By using these thin films at different temperatures,the risk to the head can be reduced.This paper finds that the best results were obtained when the patch antenna was made of one of the following materials operating at a specific temperature:traditional materials at 305°K;casting cast steel at around 295°K;bismuth telluride(Bi2Te3)at 290°K;or barium sodium niobate at 310°K.展开更多
It is critical to have precise data about Lithium-ion batteries,such as the State-of-Charge(SoC),to maintain a safe and consistent functioning of battery packs in energy storage systems of electric vehicles.Numerous s...It is critical to have precise data about Lithium-ion batteries,such as the State-of-Charge(SoC),to maintain a safe and consistent functioning of battery packs in energy storage systems of electric vehicles.Numerous strategies for estimating battery SoC,such as by including the coulomb counting and Kalman filter,have been established.As a result of the differences in parameter values between each cell,when these methods are applied to highcapacity battery packs,it has difficulties sustaining the prediction accuracy of overall cells.As a result of aging,the variation in the parameters of each cell is higher as more time is spent in operation.It is suggested in this study to establish an SoC estimate model for a Lithium-ion battery by employing an enhanced Deep Neural Network(DNN)approach.This is because the proposed DNN has a substantial hidden layer,which can accurately predict the SoC of an unknown driving cycle during training,making it ideal for SoC estimation.To evaluate the nonlinearities between voltage and current at various SoCs and temperatures,the proposed DNN is applied.Using current and voltage data measured at various temperatures throughout discharge/charge cycles is necessary for training and testing purposes.When the method has been thoroughly trained with the data collected,it is used for additional cells cycle tests to predict their SoC.The simulation has been conducted for two different Li-ion battery datasets.According to the experimental data,the suggested DNN-based SoC estimate approach produces a low mean absolute error and root-mean-square-error values,say less than 5%errors.展开更多
The oil industries are an important part of a country’s economy.The crude oil’s price is influenced by a wide range of variables.Therefore,how accurately can countries predict its behavior and what predictors to emp...The oil industries are an important part of a country’s economy.The crude oil’s price is influenced by a wide range of variables.Therefore,how accurately can countries predict its behavior and what predictors to employ are two main questions.In this view,we propose utilizing deep learning and ensemble learning techniques to boost crude oil’s price forecasting performance.The suggested method is based on a deep learning snapshot ensemble method of the Transformer model.To examine the superiority of the proposed model,this paper compares the proposed deep learning ensemble model against different machine learning and statistical models for daily Organization of the Petroleum Exporting Countries(OPEC)oil price forecasting.Experimental results demonstrated the outperformance of the proposed method over statistical and machine learning methods.More precisely,the proposed snapshot ensemble of Transformer method achieved relative improvement in the forecasting performance compared to autoregressive integrated moving average ARIMA(1,1,1),ARIMA(0,1,1),autoregressive moving average(ARMA)(0,1),vector autoregression(VAR),random walk(RW),support vector machine(SVM),and random forests(RF)models by 99.94%,99.62%,99.87%,99.65%,7.55%,98.38%,and 99.35%,respectively,according to mean square error metric.展开更多
A tumor is referred to as“intracranial hard neoplasm”if it grows near the brain or central spinal vessel(neoplasm).In certain cases,it is possible that the responsible cells are neurons situated deep inside the brai...A tumor is referred to as“intracranial hard neoplasm”if it grows near the brain or central spinal vessel(neoplasm).In certain cases,it is possible that the responsible cells are neurons situated deep inside the brain’s structure.This article discusses a strategy for halting the progression of brain tumor.A precise and accurate analytical model of brain tumors is the foundation of this strategy.It is based on an algorithm known as kill chain interior point(KCIP),which is the result of a merger of kill chain and interior point algorithms,as well as a precise and accurate analytical model of brain tumors.The inability to obtain a clear picture of tumor cell activity is the biggest challenge in this endeavor.Based on the motion of swarm robots,which are considered a subset of artificial intelligence,this article proposes a new notion of this kind of behavior,which may be used in various situations.The KCIP algorithm that follows is used in the analytical model to limit the development of certain cell types.According to the findings,it seems that different KCIP speed ratios are beneficial in preventing the development of brain tumors.It is hoped that this study will help researchers better understand the behavior of brain tumors,so as to develop a new drug that is effective in eliminating the tumor cells.展开更多
Background There are 103 million displaced people worldwide,41%of whom are children.Data on the provision of surgery in humanitarian settings are limited.Even scarcer is literature on pediatric surgery performed in hu...Background There are 103 million displaced people worldwide,41%of whom are children.Data on the provision of surgery in humanitarian settings are limited.Even scarcer is literature on pediatric surgery performed in humanitarian settings,particularly protracted humanitarian settings.Methods We reviewed patterns,procedures,and indications for pediatric surgery among children in Nyarugusu Refugee Camp using a 20-year retrospective dataset.Results A total of 1221 pediatric surgical procedures were performed over the study period.Teenagers between the ages of 12 and 17 years were the most common age group undergoing surgery(n=991;81%).A quarter of the procedures were performed on local Tanzanian children seeking care in the camp(n=301;25%).The most common procedures performed were cesarean sections(n=858;70%),herniorrhaphies(n=197;16%),and exploratory laparotomies(n=55;5%).Refugees were more likely to undergo exploratory laparotomy(n=47;5%)than Tanzanian children(n=7;2%;p=0.032).The most common indications for exploratory laparotomy were acute abdomen(n=24;44%),intestinal obstruction(n=10;18%),and peritonitis(n=9;16%).Conclusions There is a significant volume of basic pediatric general surgery performed in the Nyarugusu Camp.Services are used by both refugees and local Tanzanians.We hope this research will inspire further advocacy and research on pediatric surgical services in humanitarian settings worldwide and illuminate the need for including pediatric refugee surgery within the growing global surgery movement.展开更多
基金supported by the Deanship of Research and Graduate Studies at King Khalid University under Small Research Project grant number RGP1/139/45.
文摘Integrating multiple medical imaging techniques,including Magnetic Resonance Imaging(MRI),Computed Tomography,Positron Emission Tomography(PET),and ultrasound,provides a comprehensive view of the patient health status.Each of these methods contributes unique diagnostic insights,enhancing the overall assessment of patient condition.Nevertheless,the amalgamation of data from multiple modalities presents difficulties due to disparities in resolution,data collection methods,and noise levels.While traditional models like Convolutional Neural Networks(CNNs)excel in single-modality tasks,they struggle to handle multi-modal complexities,lacking the capacity to model global relationships.This research presents a novel approach for examining multi-modal medical imagery using a transformer-based system.The framework employs self-attention and cross-attention mechanisms to synchronize and integrate features across various modalities.Additionally,it shows resilience to variations in noise and image quality,making it adaptable for real-time clinical use.To address the computational hurdles linked to transformer models,particularly in real-time clinical applications in resource-constrained environments,several optimization techniques have been integrated to boost scalability and efficiency.Initially,a streamlined transformer architecture was adopted to minimize the computational load while maintaining model effectiveness.Methods such as model pruning,quantization,and knowledge distillation have been applied to reduce the parameter count and enhance the inference speed.Furthermore,efficient attention mechanisms such as linear or sparse attention were employed to alleviate the substantial memory and processing requirements of traditional self-attention operations.For further deployment optimization,researchers have implemented hardware-aware acceleration strategies,including the use of TensorRT and ONNX-based model compression,to ensure efficient execution on edge devices.These optimizations allow the approach to function effectively in real-time clinical settings,ensuring viability even in environments with limited resources.Future research directions include integrating non-imaging data to facilitate personalized treatment and enhancing computational efficiency for implementation in resource-limited environments.This study highlights the transformative potential of transformer models in multi-modal medical imaging,offering improvements in diagnostic accuracy and patient care outcomes.
基金supported by the National Natural Science Foundation of China(22162008)the Science and Technology Supporting Project of Guizhou Province([2022]208)+1 种基金the Guizhou Province Local Government Overseas Study Programthe open project of Guizhou Provincial Double Carbon and Renewable Energy Technology Innovation Research Institute.
文摘The development of an efficient dual-function catalytic-sorption system,which seamlessly integrates reaction and separation into a single step for extractant-free systems,represents a transformative advancement in oxidative desulfurization(ODS)process.In this work,we introduce a novel dualfunction amphiphilic biochar(Mo/CBC)catalyst,functionalized with MoO_(3-x)featuring abundant oxygen vacancies,for highly effective extractant-free ODS.The polarity of the biochar was precisely tailored by varying the amount of KOH,leading to the creation of amphiphilic carriers.Subsequent ball milling facilitated the successful loading of MoO_(3-x)onto the biochar surface via an impregnation-calcination route leveraging carbon reduction,resulting in the synthesis of amphiphilic Mo/CBC catalysts.The amphiphilic nature of these catalysts ensures their stable dispersion within the oil phase,while also facilitating their interaction with the oxidant H2O2 and the adsorption of sulfur-containing oxidation products.Characterization techniques,including EPR,XPS,and in situ XRD,verified the existence of abundant oxygen vacancies obtained by carbon reduction on the amphiphilic Mo/CBC catalysts,which significantly boosted their activity in an extractant-free ODs system.Remarkably,the amphiphilic Mo/CBC catalyst displayed exceptional catalytic performance,achieving a desulfurization efficiency of 99.6%in just 10 min without extraction solvent.DFT theoretical calculations further revealed that H_(2)O_(2)readily dissociates into two OH radicals on the O_(vac)-MoO_(3),overcoming a low energy barrier.This process was identified as a key contributor to the catalyst's outstanding ODS performance.Furthermore,other biochar sources,such as rice straw,bamboo,rapeseed oil cake,and walnut oil cake,were investigated to produce Mo-based amphiphilic biochar catalysts,which all showed excellent desulfurization performance.This work establishes a versatile and highly efficient dual-function catalytic-sorption system by designing amphiphilic biochar catalysts enriched with oxygen vacancies,paving the way for the development of universally applicable ODS catalysts for industrial applications.
基金supported by the Department of Biotechnology(DBT),Government of India(BT/556/NE/U-Excel/).
文摘The extensive heterogeneity and the limited availability of effective targeted therapies contribute to the challenging prognosis and restricted survival observed in triple-negative breast cancer(TNBC).Recent research indicates the aberrant expression of diverse tyrosine kinases(TKs)within this cancer,contributing significantly to tumor cell proliferation,survival,invasion,and migration.The contemporary paradigm shift towards precision medicine has highlighted TKs and their receptors as promising targets for pharmacotherapy against a range of malignancies,given their pivotal roles in tumor initiation,progression,and advancement.Intensive investigations have focused on various monoclonal antibodies(mAbs)and small molecule inhibitors that specifically target proteins such as epidermal growth factor receptor(EGFR),vascular endothelial growth factor(VEGF),vascular endothelial growth factor receptor(VEGFR),cellular mesenchymal-epithelial transition factor(c-MET),human epidermal growth factor receptor 2(HER2),among others,for combating TNBC.These agents have been studied both in monotherapy and in combination with other chemotherapeutic agents.Despite these advances,a substantial terrain of unexplored potential lies within the realm of TK-targeted therapeutics,which hold promise in reshaping the therapeutic landscape.This review summarizes the various TK-targeted therapeutics that have undergone scrutiny as potential therapeutic interventions for TNBC,dissecting the outcomes and revelations stemming from diverse clinical investigations.A key conclusion from the umbrella clinical trials evidences the necessity for in-depth molecular characterization of TNBC for the maximum efficiency of TK-targeted therapeutics,either as standalone treatments or a combination.Moreover,our observation highlights that the outcomes of TK-targeted therapeutics in TNBC are substantially influenced by the diversity of the patient cohort,emphasizing the prioritization of individual patient genetic/molecular profiles for precise TNBC patient stratification for clinical studies.
基金supported by BT/556/NE/U-Excel/2016 grant awarded to ABKThe authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Project under grant number RGP2/485/45.
文摘Nuclear receptors(NRs)function as crucial transcription factors in orchestrating essential functions within the realms of development,host defense,and homeostasis of body.NRs have garnered increased attention due to their potential as therapeutic targets,with drugs directed at NRs demonstrating significant efficacy in impeding chronic disease progression.Consequently,these pharmacological agents hold promise for the treatment and management of various diseases.Accumulating evidence emphasizes the regulatory role of exosome-derived microRNAs(miRNAs)in chronic inflammation,disease progression,and therapy resistance,primarily by modulating transcription factors,particularly NRs.By exploiting inflammatory pathways such as protein kinase B(Akt)/mammalian target of rapamycin(mTOR),nuclear factor kappa-B(NF-κB),signal transducer and activator of transcription 3(STAT3),and Wnt/β-catenin signaling,exosomes and NRs play a pivotal role in the panorama of development,physiology,and pathology.The internalization of exosomes modulates NRs and initiates diverse autocrine or paracrine signaling cascades,influencing various processes in recipient cells such as survival,proliferation,differentiation,metabolism,and cellular defense mechanisms.This comprehensive review meticulously examines the involvement of exosome-mediated NRs regulation in the pathogenesis of chronic ailments,including atherosclerosis,cancer,diabetes,liver diseases,and respiratory conditions.Additionally,it elucidates the molecular intricacies of exosome-mediated communication between host and recipient cells via NRs,leading to immunomodulation.Furthermore,it outlines the implications of exosome-modulated NR pathways in the prophylaxis of chronic inflammation,delineates current limitations,and provides insights into future perspectives.This review also presents existing evidence on the role of exosomes and their components in the emergence of therapeutic resistance.
文摘Background: Surgical site infections (SSIs) are considered as result of the healthcare quality in hospitals. Objective: to study SSI at Saint Jean de Dieu Hospital Tanguieta (SJDHT), prior to the implementation of a permanent monitoring system. Method: transversal, and descriptive study with prospective data collection was performed from 1 July to 31 janvier 2017 in the department of general surgery of SJDHT. The hospital lacks in a microbiology unit. All patients who underwent surgery during this period were included and the monitoring lasted one month. SSIs diagnostic was carried out according to WHO criteria as described in the Practical Guide for the Prevention of Nosocomial Infections published in 2002. Statistical tests (χ-square and Student’s t-test) were applied and p 0.05 were statistically significant. Results: Of 343 patients recorded, 105 (30.6%) had SSI. Their age averaged 40.3 years and the sex-ratio (men/women) was 2.8. The emergency surgery resulted in a 50.0% rate of SSI (p = 0.00). The SSI rate for clean and clean-contaminated surgery was 6.3% against 94.6% for infected surgery (p = 0.00). The SSI rates were 100% and 66.7% for NNISS = 2 and NNISS = 1 (p = 0.00), respectively. Superficial SSI rate was 13.3%, while deep SSI and organ/space SSI were 46.7% and 40%, respectively. The hospital stay of patients with SSI was three times longer than the length of patients without SSI (p = 0.00). Conclusion: SSIs are real burden at SJDHT. Appropriate measures must be adopted to reduce its prevalence.
基金supported by the Chinese Academy of Sciences Strategic Pilot Science and Technology Special (Class A)(XDA21020000)the National Natural Science Foundation of China (22072175,21673272)support from the Ulam program,awarded by the Polish National Agency for Academic Exchange (NAWA),Poland,under project No.PPN/ULM/2020/1/00006/DEC/1
文摘The production of poly-α-olefins(PAOs)has attracted attention due to their excellent viscosity-temperature dependence,wear characteristics,oxidative properties,and high thermal stability.In this study,indene extracted during coal tar refining was used as a raw material to synthesize a bis(indenyl)zirconium dichloride metallocene catalyst.A PAO with low viscosity and a high viscosity index was produced via the oligomerization of 1-decene in the presence of both the prepared metallocene and a methylaluminoxane(MAO)co-catalyst.Notably,the effects of different synthesis reaction parameters,such as Al:Zr ratio,amount of catalyst,and reaction temperature,on the conversion ratio and product selectivity were investigated in detail.The produced PAO was thoroughly characterized using Fourier-transform infrared,^(13)C,and^(1)H nuclear magnetic resonance spectroscopies;gas chromatography;and viscosity measurements.At 70℃,the metallocene catalyst created more stable active sites.In addition,the alkylation effect of MAO was noticeable.Interestingly,the obtained catalysis results demonstrated that a high conversion ratio of~93%was achieved at a low reaction temperature of 70℃,with a catalyst dosage of 0.0848 mmol and Al:Zr ratio of 8.48mmol:0.0848mmol.Moreover,under these optimal conditions,the kinematic viscosity of PAO was 4.25 mm2/s at 100℃,and the viscosity index was 139,indicating good viscosity-temperature properties.
基金funding this work through the Research Group Program under the Grant Number:(R.G.P.2/382/44).
文摘Many plant species have a startling degree of morphological similarity,making it difficult to split and categorize them reliably.Unknown plant species can be challenging to classify and segment using deep learning.While using deep learning architectures has helped improve classification accuracy,the resulting models often need to be more flexible and require a large dataset to train.For the sake of taxonomy,this research proposes a hybrid method for categorizing guava,potato,and java plumleaves.Two new approaches are used to formthe hybridmodel suggested here.The guava,potato,and java plum plant species have been successfully segmented using the first model built on the MobileNetV2-UNET architecture.As a second model,we use a Plant Species Detection Stacking Ensemble Deep Learning Model(PSD-SE-DLM)to identify potatoes,java plums,and guava.The proposed models were trained using data collected in Punjab,Pakistan,consisting of images of healthy and sick leaves from guava,java plum,and potatoes.These datasets are known as PLSD and PLSSD.Accuracy levels of 99.84%and 96.38%were achieved for the suggested PSD-SE-DLM and MobileNetV2-UNET models,respectively.
基金This research was funded by King Khalid University—KSA,grant number(R.G.P.2/35/40)and the APC was funded by King Khalid University—KSA.
文摘Every new generation of antennas is characterized by increased accuracy and faster transmission speeds.However,patch antennas have been known to damage human health.This type of antenna sends out electromagnetic waves that increase the temperature of the human head and prevent nerve strands from functioning properly.This paper examines the effect of the communication between the patch antenna and the brain on the head’s temperature by developing a hypothetical multi-input model that achieves more accurate results.These inputs are an individual’s blood and tissue,and the emission power of the antenna.These forces depend on the permeability and conductivity characteristics of the metal from which the antenna is fabricated.The proposed model is the first one that links the material the antenna is manufactured from and the head’s temperature.The results show that there are only a small number of materials that should be used as antenna covers.These materials are in the form of thin films.By using these thin films at different temperatures,the risk to the head can be reduced.This paper finds that the best results were obtained when the patch antenna was made of one of the following materials operating at a specific temperature:traditional materials at 305°K;casting cast steel at around 295°K;bismuth telluride(Bi2Te3)at 290°K;or barium sodium niobate at 310°K.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University(KKU)for funding this research project Number(R.G.P.2/133/43).
文摘It is critical to have precise data about Lithium-ion batteries,such as the State-of-Charge(SoC),to maintain a safe and consistent functioning of battery packs in energy storage systems of electric vehicles.Numerous strategies for estimating battery SoC,such as by including the coulomb counting and Kalman filter,have been established.As a result of the differences in parameter values between each cell,when these methods are applied to highcapacity battery packs,it has difficulties sustaining the prediction accuracy of overall cells.As a result of aging,the variation in the parameters of each cell is higher as more time is spent in operation.It is suggested in this study to establish an SoC estimate model for a Lithium-ion battery by employing an enhanced Deep Neural Network(DNN)approach.This is because the proposed DNN has a substantial hidden layer,which can accurately predict the SoC of an unknown driving cycle during training,making it ideal for SoC estimation.To evaluate the nonlinearities between voltage and current at various SoCs and temperatures,the proposed DNN is applied.Using current and voltage data measured at various temperatures throughout discharge/charge cycles is necessary for training and testing purposes.When the method has been thoroughly trained with the data collected,it is used for additional cells cycle tests to predict their SoC.The simulation has been conducted for two different Li-ion battery datasets.According to the experimental data,the suggested DNN-based SoC estimate approach produces a low mean absolute error and root-mean-square-error values,say less than 5%errors.
文摘The oil industries are an important part of a country’s economy.The crude oil’s price is influenced by a wide range of variables.Therefore,how accurately can countries predict its behavior and what predictors to employ are two main questions.In this view,we propose utilizing deep learning and ensemble learning techniques to boost crude oil’s price forecasting performance.The suggested method is based on a deep learning snapshot ensemble method of the Transformer model.To examine the superiority of the proposed model,this paper compares the proposed deep learning ensemble model against different machine learning and statistical models for daily Organization of the Petroleum Exporting Countries(OPEC)oil price forecasting.Experimental results demonstrated the outperformance of the proposed method over statistical and machine learning methods.More precisely,the proposed snapshot ensemble of Transformer method achieved relative improvement in the forecasting performance compared to autoregressive integrated moving average ARIMA(1,1,1),ARIMA(0,1,1),autoregressive moving average(ARMA)(0,1),vector autoregression(VAR),random walk(RW),support vector machine(SVM),and random forests(RF)models by 99.94%,99.62%,99.87%,99.65%,7.55%,98.38%,and 99.35%,respectively,according to mean square error metric.
文摘A tumor is referred to as“intracranial hard neoplasm”if it grows near the brain or central spinal vessel(neoplasm).In certain cases,it is possible that the responsible cells are neurons situated deep inside the brain’s structure.This article discusses a strategy for halting the progression of brain tumor.A precise and accurate analytical model of brain tumors is the foundation of this strategy.It is based on an algorithm known as kill chain interior point(KCIP),which is the result of a merger of kill chain and interior point algorithms,as well as a precise and accurate analytical model of brain tumors.The inability to obtain a clear picture of tumor cell activity is the biggest challenge in this endeavor.Based on the motion of swarm robots,which are considered a subset of artificial intelligence,this article proposes a new notion of this kind of behavior,which may be used in various situations.The KCIP algorithm that follows is used in the analytical model to limit the development of certain cell types.According to the findings,it seems that different KCIP speed ratios are beneficial in preventing the development of brain tumors.It is hoped that this study will help researchers better understand the behavior of brain tumors,so as to develop a new drug that is effective in eliminating the tumor cells.
基金American Society of Tropical Medicine and Hygiene(award:Benjamin Keane Fellowship)and ZOE received support from Ruth L.Kirschstein National Research Service Award(NRSA)T32 Appointment(Award:2T32AR67708-6,PI:Clemens,T.)and Association for Academic Surgery(award:Global Surgery Research Fellowship).SR received support from the Johns Hopkins School of Medicine Deans Summer Research Fund.
文摘Background There are 103 million displaced people worldwide,41%of whom are children.Data on the provision of surgery in humanitarian settings are limited.Even scarcer is literature on pediatric surgery performed in humanitarian settings,particularly protracted humanitarian settings.Methods We reviewed patterns,procedures,and indications for pediatric surgery among children in Nyarugusu Refugee Camp using a 20-year retrospective dataset.Results A total of 1221 pediatric surgical procedures were performed over the study period.Teenagers between the ages of 12 and 17 years were the most common age group undergoing surgery(n=991;81%).A quarter of the procedures were performed on local Tanzanian children seeking care in the camp(n=301;25%).The most common procedures performed were cesarean sections(n=858;70%),herniorrhaphies(n=197;16%),and exploratory laparotomies(n=55;5%).Refugees were more likely to undergo exploratory laparotomy(n=47;5%)than Tanzanian children(n=7;2%;p=0.032).The most common indications for exploratory laparotomy were acute abdomen(n=24;44%),intestinal obstruction(n=10;18%),and peritonitis(n=9;16%).Conclusions There is a significant volume of basic pediatric general surgery performed in the Nyarugusu Camp.Services are used by both refugees and local Tanzanians.We hope this research will inspire further advocacy and research on pediatric surgical services in humanitarian settings worldwide and illuminate the need for including pediatric refugee surgery within the growing global surgery movement.
