Artificial intelligence-assisted repair of peripheral nerve injury: a new research hotspot and associated challenges

Artificial intelligence can be indirectly applied to the repair of peripheral nerve injury.Specifically,it can be used to analyze and process data regarding peripheral nerve injury and repair,while study findings on peripheral nerve injury and repair can provide valuable data to enrich artificial intelligence algorithms.To investigate advances in the use of artificial intelligence in the diagnosis,rehabilitation,and scientific examination of peripheral nerve injury,we used CiteSpace and VOSviewer software to analyze the relevant literature included in the Web of Science from 1994–2023.We identified the following research hotspots in peripheral nerve injury and repair:(1)diagnosis,classification,and prognostic assessment of peripheral nerve injury using neuroimaging and artificial intelligence techniques,such as corneal confocal microscopy and coherent anti-Stokes Raman spectroscopy;(2)motion control and rehabilitation following peripheral nerve injury using artificial neural networks and machine learning algorithms,such as wearable devices and assisted wheelchair systems;(3)improving the accuracy and effectiveness of peripheral nerve electrical stimulation therapy using artificial intelligence techniques combined with deep learning,such as implantable peripheral nerve interfaces;(4)the application of artificial intelligence technology to brain-machine interfaces for disabled patients and those with reduced mobility,enabling them to control devices such as networked hand prostheses;(5)artificial intelligence robots that can replace doctors in certain procedures during surgery or rehabilitation,thereby reducing surgical risk and complications,and facilitating postoperative recovery.Although artificial intelligence has shown many benefits and potential applications in peripheral nerve injury and repair,there are some limitations to this technology,such as the consequences of missing or imbalanced data,low data accuracy and reproducibility,and ethical issues(e.g.,privacy,data security,research transparency).Future research should address the issue of data collection,as large-scale,high-quality clinical datasets are required to establish effective artificial intelligence models.Multimodal data processing is also necessary,along with interdisciplinary collaboration,medical-industrial integration,and multicenter,large-sample clinical studies.

Toward a Learnable Climate Model in the Artificial Intelligence Era

Artificial intelligence(AI)models have significantly impacted various areas of the atmospheric sciences,reshaping our approach to climate-related challenges.Amid this AI-driven transformation,the foundational role of physics in climate science has occasionally been overlooked.Our perspective suggests that the future of climate modeling involves a synergistic partnership between AI and physics,rather than an“either/or”scenario.Scrutinizing controversies around current physical inconsistencies in large AI models,we stress the critical need for detailed dynamic diagnostics and physical constraints.Furthermore,we provide illustrative examples to guide future assessments and constraints for AI models.Regarding AI integration with numerical models,we argue that offline AI parameterization schemes may fall short of achieving global optimality,emphasizing the importance of constructing online schemes.Additionally,we highlight the significance of fostering a community culture and propose the OCR(Open,Comparable,Reproducible)principles.Through a better community culture and a deep integration of physics and AI,we contend that developing a learnable climate model,balancing AI and physics,is an achievable goal.

Application of artificial hibernation technology in acute brain injury

Controlling intracranial pressure,nerve cell regeneration,and microenvironment regulation are the key issues in reducing mortality and disability in acute brain injury.There is currently a lack of effective treatment methods.Hibernation has the characteristics of low temperature,low metabolism,and hibernation rhythm,as well as protective effects on the nervous,cardiovascular,and motor systems.Artificial hibernation technology is a new technology that can effectively treat acute brain injury by altering the body’s metabolism,lowering the body’s core temperature,and allowing the body to enter a state similar to hibernation.This review introduces artificial hibernation technology,including mild hypothermia treatment technology,central nervous system regulation technology,and artificial hibernation-inducer technology.Upon summarizing the relevant research on artificial hibernation technology in acute brain injury,the research results show that artificial hibernation technology has neuroprotective,anti-inflammatory,and oxidative stress-resistance effects,indicating that it has therapeutic significance in acute brain injury.Furthermore,artificial hibernation technology can alleviate the damage of ischemic stroke,traumatic brain injury,cerebral hemorrhage,cerebral infarction,and other diseases,providing new strategies for treating acute brain injury.However,artificial hibernation technology is currently in its infancy and has some complications,such as electrolyte imbalance and coagulation disorders,which limit its use.Further research is needed for its clinical application.

A data-driven model of drop size prediction based on artificial neural networks using small-scale data sets

An artificial neural network(ANN)method is introduced to predict drop size in two kinds of pulsed columns with small-scale data sets.After training,the deviation between calculate and experimental results are 3.8%and 9.3%,respectively.Through ANN model,the influence of interfacial tension and pulsation intensity on the droplet diameter has been developed.Droplet size gradually increases with the increase of interfacial tension,and decreases with the increase of pulse intensity.It can be seen that the accuracy of ANN model in predicting droplet size outside the training set range is reach the same level as the accuracy of correlation obtained based on experiments within this range.For two kinds of columns,the drop size prediction deviations of ANN model are 9.6%and 18.5%and the deviations in correlations are 11%and 15%.

Artificial intelligence-driven radiomics study in cancer:the role of feature engineering and modeling

Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients’anatomy.However,the interpretation of medical images can be highly subjective and dependent on the expertise of clinicians.Moreover,some potentially useful quantitative information in medical images,especially that which is not visible to the naked eye,is often ignored during clinical practice.In contrast,radiomics performs high-throughput feature extraction from medical images,which enables quantitative analysis of medical images and prediction of various clinical endpoints.Studies have reported that radiomics exhibits promising performance in diagnosis and predicting treatment responses and prognosis,demonstrating its potential to be a non-invasive auxiliary tool for personalized medicine.However,radiomics remains in a developmental phase as numerous technical challenges have yet to be solved,especially in feature engineering and statistical modeling.In this review,we introduce the current utility of radiomics by summarizing research on its application in the diagnosis,prognosis,and prediction of treatment responses in patients with cancer.We focus on machine learning approaches,for feature extraction and selection during feature engineering and for imbalanced datasets and multi-modality fusion during statistical modeling.Furthermore,we introduce the stability,reproducibility,and interpretability of features,and the generalizability and interpretability of models.Finally,we offer possible solutions to current challenges in radiomics research.

The future of artificial hibernation medicine:protection of nerves and organs after spinal cord injury

Spinal cord injury is a serious disease of the central nervous system involving irreversible nerve injury and various organ system injuries.At present,no effective clinical treatment exists.As one of the artificial hibernation techniques,mild hypothermia has preliminarily confirmed its clinical effect on spinal cord injury.However,its technical defects and barriers,along with serious clinical side effects,restrict its clinical application for spinal cord injury.Artificial hibernation is a futureoriented disruptive technology for human life support.It involves endogenous hibernation inducers and hibernation-related central neuromodulation that activate particular neurons,reduce the central constant temperature setting point,disrupt the normal constant body temperature,make the body adapt"to the external cold environment,and reduce the physiological resistance to cold stimulation.Thus,studying the artificial hibernation mechanism may help develop new treatment strategies more suitable for clinical use than the cooling method of mild hypothermia technology.This review introduces artificial hibernation technologies,including mild hypothermia technology,hibernation inducers,and hibernation-related central neuromodulation technology.It summarizes the relevant research on hypothermia and hibernation for organ and nerve protection.These studies show that artificial hibernation technologies have therapeutic significance on nerve injury after spinal co rd injury through inflammatory inhibition,immunosuppression,oxidative defense,and possible central protection.It also promotes the repair and protection of res pirato ry and digestive,cardiovascular,locomoto r,urinary,and endocrine systems.This review provides new insights for the clinical treatment of nerve and multiple organ protection after spinal cord injury thanks to artificial hibernation.At present,artificial hibernation technology is not mature,and research fa ces various challenges.Neve rtheless,the effort is wo rthwhile for the future development of medicine.

Exploration of Graduate Student Cultivation Mode of Landscape Architecture under the Background of“Artificial Intelligence+X”

Under the background of“artificial intelligence+X”,the development of landscape architecture industry ushers in new opportunities,and professional talents need to be updated to meet the social demand.This paper analyzes the cultivation demand of landscape architecture graduate students in the context of the new era,and identifies the problems by comparing the original professional graduate training mode.The new cultivation mode of graduate students in landscape architecture is proposed,including updating the target orientation of the discipline,optimizing the teaching system,building a“dualteacher”tutor team,and improving the“industry-university-research-utilization”integrated cultivation,so as to cultivate high-quality compound talents with disciplinary characteristics.

Explainable Artificial Intelligence(XAI)Model for Cancer Image Classification

The use of Explainable Artificial Intelligence(XAI)models becomes increasingly important for making decisions in smart healthcare environments.It is to make sure that decisions are based on trustworthy algorithms and that healthcare workers understand the decisions made by these algorithms.These models can potentially enhance interpretability and explainability in decision-making processes that rely on artificial intelligence.Nevertheless,the intricate nature of the healthcare field necessitates the utilization of sophisticated models to classify cancer images.This research presents an advanced investigation of XAI models to classify cancer images.It describes the different levels of explainability and interpretability associated with XAI models and the challenges faced in deploying them in healthcare applications.In addition,this study proposes a novel framework for cancer image classification that incorporates XAI models with deep learning and advanced medical imaging techniques.The proposed model integrates several techniques,including end-to-end explainable evaluation,rule-based explanation,and useradaptive explanation.The proposed XAI reaches 97.72%accuracy,90.72%precision,93.72%recall,96.72%F1-score,9.55%FDR,9.66%FOR,and 91.18%DOR.It will discuss the potential applications of the proposed XAI models in the smart healthcare environment.It will help ensure trust and accountability in AI-based decisions,which is essential for achieving a safe and reliable smart healthcare environment.

Advancements in Barrett's esophagus detection:The role of artificial intelligence and its implications

Artificial intelligence(AI)is making significant strides in revolutionizing the detection of Barrett's esophagus(BE),a precursor to esophageal adenocarcinoma.In the research article by Tsai et al,researchers utilized endoscopic images to train an AI model,challenging the traditional distinction between endoscopic and histological BE.This approach yielded remarkable results,with the AI system achieving an accuracy of 94.37%,sensitivity of 94.29%,and specificity of 94.44%.The study's extensive dataset enhances the AI model's practicality,offering valuable support to endoscopists by minimizing unnecessary biopsies.However,questions about the applicability to different endoscopic systems remain.The study underscores the potential of AI in BE detection while highlighting the need for further research to assess its adaptability to diverse clinical settings.

Artificial Intelligence Prediction of One-Part Geopolymer Compressive Strength for Sustainable Concrete

Alkali-activated materials/geopolymer(AAMs),due to their low carbon emission content,have been the focus of recent studies on ecological concrete.In terms of performance,fly ash and slag are preferredmaterials for precursors for developing a one-part geopolymer.However,determining the optimum content of the input parameters to obtain adequate performance is quite challenging and scarcely reported.Therefore,in this study,machine learning methods such as artificial neural networks(ANN)and gene expression programming(GEP)models were developed usingMATLAB and GeneXprotools,respectively,for the prediction of compressive strength under variable input materials and content for fly ash and slag-based one-part geopolymer.The database for this study contains 171 points extracted from literature with input parameters:fly ash concentration,slag content,calcium hydroxide content,sodium oxide dose,water binder ratio,and curing temperature.The performance of the two models was evaluated under various statistical indices,namely correlation coefficient(R),mean absolute error(MAE),and rootmean square error(RMSE).In terms of the strength prediction efficacy of a one-part geopolymer,ANN outperformed GEP.Sensitivity and parametric analysis were also performed to identify the significant contributor to strength.According to a sensitivity analysis,the activator and slag contents had the most effects on the compressive strength at 28 days.The water binder ratio was shown to be directly connected to activator percentage,slag percentage,and calcium hydroxide percentage and inversely related to compressive strength at 28 days and curing temperature.

Artificial intelligence in individualized retinal disease management

Owing to the rapid development of modern computer technologies,artificial intelligence(AI)has emerged as an essential instrument for intelligent analysis across a range of fields.AI has been proven to be highly effective in ophthalmology,where it is frequently used for identifying,diagnosing,and typing retinal diseases.An increasing number of researchers have begun to comprehensively map patients’retinal diseases using AI,which has made individualized clinical prediction and treatment possible.These include prognostic improvement,risk prediction,progression assessment,and interventional therapies for retinal diseases.Researchers have used a range of input data methods to increase the accuracy and dependability of the results,including the use of tabular,textual,or image-based input data.They also combined the analyses of multiple types of input data.To give ophthalmologists access to precise,individualized,and high-quality treatment strategies that will further optimize treatment outcomes,this review summarizes the latest findings in AI research related to the prediction and guidance of clinical diagnosis and treatment of retinal diseases.

Magnetic Nonreciprocity in a Hybrid Device of Asymmetric Artificial Spin-Ice-Superconductors

Controlling the size and distribution of potential barriers within a medium of interacting particles can unveil unique collective behaviors and innovative functionalities.We introduce a unique superconducting hybrid device using a novel artificial spin ice structure composed of asymmetric nanomagnets.This structure forms a distinctive superconducting pinning potential that steers unconventional motion of superconducting vortices,thereby inducing a magnetic nonreciprocal effect,in contrast to the electric nonreciprocal effect commonly observed in superconducting diodes.Furthermore,the polarity of the magnetic nonreciprocity is in situ reversible through the tunable magnetic patterns of artificial spin ice.Our findings demonstrate that artificial spin ice not only precisely modulates superconducting characteristics but also opens the door to novel functionalities,offering a groundbreaking paradigm for superconducting electronics.

European Union Issues World’s First Comprehensive Regulations for Artificial Intelligence

In March 2024,European Union(EU)lawmakers passed the world’s first comprehensive set of regulations governing the use of artificial intelligence(AI)[1].The EU’s AI Act,two and a half years in the making,was initially drawn up as a landmark bill to reduce harm in areas in which AI was thought to pose the biggest risks to people,such as in health care,education,and security,as well as banning uses that pose“unacceptable risks,”including manipulation of human behavior and evaluation of individuals’trustworthiness based on personal characteristics.According to the regulations,which will go into effect in stages over the next two years,“high-risk”AI systems will require risk-mitigation strategies,high-quality data sets,transparency,better documentation,and human supervision.The most common current AI uses,such as augmenting recommendation engines and email spam filters,will see far less oversight.

Single-cell pan-omics, environmental neurology, and artificial intelligence:the time for holistic brain health research

The brain,with its trillions of neural connections,different cellular types,and molecular complexities,presents a formidable challenge for researchers aiming to comprehend the multifaceted nature of neural health.As traditional methods have provided valuable insights,emerging technologies offer unprecedented opportunities to delve deeper into the underpinnings of brain function.In the everevolving landscape of neuroscience,the quest to unravel the mysteries of the human brain is bound to take a leap forward thanks to new technological improvements and bold interpretative frameworks.

A Special Issue:“Artificial Intelligence Applied in New-Type Power System”for Global Energy Interconnection

In recent years,artificial intelligence(AI)has been widely used in the field of electricity,such as load prediction,fault diagnosis of the power equipment,intelligent scheduling of power grids.However,the application of latest AI technology still has many technical difficulties to be solved.In the process of upgrading from the traditional power system to the new-type power system,AC grids,DC grids and micro grids coexist.In addition,there are huge amount of power equipment and electronic devices,and the coupling relationship is very complicated.Moreover,the high proportion of clean energy and flexible loads connected to the grid leads to the enhancement of the stochastic characteristics of the system.And short-term and ultra-short-term forecasts are much more difficult.Therefore,the editorial office of Global Energy Interconnection has planned the special issue of“Artificial Intelligence Applied in New-Type Power System”.

Construction of a Cu@hollow TS-1 nanoreactor based on a hierarchical full-spectrum solar light utilization strategy for photothermal synergistic artificial photosynthesis

The artificial photosynthesis technology has been recognized as a promising solution for CO_(2) utilization.Photothermal catalysis has been proposed as a novel strategy to promote the efficiency of artificial photosynthesis by coupling both photochemistry and thermochemistry.However,strategies for maximizing the use of solar spectra with different frequencies in photothermal catalysis are urgently needed.Here,a hierarchical full-spectrum solar light utilization strategy is proposed.Based on this strategy,a Cu@hollow titanium silicalite-1 zeolite(TS-1)nanoreactor with spatially separated photo/thermal catalytic sites is designed to realize high-efficiency photothermal catalytic artificial photosynthesis.The space-time yield of alcohol products over the optimal catalyst reached 64.4μmol g−1 h−1,with the selectivity of CH3CH2OH of 69.5%.This rationally designed hierarchical utilization strategy for solar light can be summarized as follows:(1)high-energy ultraviolet light is utilized to drive the initial and difficult CO_(2) activation step on the TS-1 shell;(2)visible light can induce the localized surface plasmon resonance effect on plasmonic Cu to generate hot electrons for H2O dissociation and subsequent reaction steps;and(3)low-energy near-infrared light is converted into heat by the simulated greenhouse effect by cavities to accelerate the carrier dynamics.This work provides some scientific and experimental bases for research on novel,highly efficient photothermal catalysts for artificial photosynthesis.

The Variation of Plankton Community Structure in Artificial Reef Area and Adjacent Waters in Haizhou Bay

Plankton are an important component of marine protected areas(MPAs),and its communities would require much smaller interpatch distances to ensure connection among MPAs.According to the survey from MPAs dominated by artificial reefs and adjacent waters(estuary area(EA),aquaculture area(AA),artificial reef area(ARA),natural area(NA)and comprehensive effect area(CEA))in Haizhou Bay in spring and autumn,we analyzed phyto-zooplankton composition,abundance and biomass,and correlation with hydrologic variables to gain information about the forces that structure the plankton.The results showed that the dominant zooplankton were copepods(spring,98.9%;autumn,94.2%),while the phytoplankton were mainly composed of Bacillariophyta(spring,61.8%;autumn,95.6%).The RDA results showed that temperature,salinity and depth highly associated with the distribution and composition of plankton species among the habitats than other factors in spring;temperature,Chla and DO had the strongest influence in autumn.The zooplankton in the ARA and AA ecosystems basically contained the same species as those in other habitats,and each habitat also exhibited a relatively unique combination of plankton species.The structures of the EA zooplankton in spring and the EA phytoplankton in both seasons were much different than other habitats,which may have been caused by factors such as currents and tides.We concluded that there exists similarity of the plankton community between artificial reef area and adjacent waters,whereas the EAs may be relatively independent systems.Therefore,these interaction between plankton community should be considered when designing MPA networks,and ocean circulations should be considered more than the environmental factors.

Concept of Artificial Intelligence (AI) and Its Use in Orthopaedic Practice: Applications and Pitfalls: A Narrative Review

Background: The growth and use of Artificial Intelligence (AI) in the medical field is rapidly rising. AI is exhibiting a practical tool in the healthcare industry in patient care. The objective of this current review is to assess and analyze the use of AI and its use in orthopedic practice, as well as its applications, limitations, and pitfalls. Methods: A review of all relevant databases such as EMBASE, Cochrane Database of Systematic Reviews, MEDLINE, Science Citation Index, Scopus, and Web of Science with keywords of AI, orthopedic surgery, applications, and drawbacks. All related articles on AI and orthopaedic practice were reviewed. A total of 3210 articles were included in the review. Results: The data from 351 studies were analyzed where in orthopedic surgery. AI is being used for diagnostic procedures, radiological diagnosis, models of clinical care, and utilization of hospital and bed resources. AI has also taken a chunk of share in assisted robotic orthopaedic surgery. Conclusions: AI has now become part of the orthopedic practice and will further increase its stake in the healthcare industry. Nonetheless, clinicians should remain aware of AI’s serious limitations and pitfalls and consider the drawbacks and errors in its use.

Randomized Double-Blind Controlled Clinical Study of Ciprofol and Propofol in Patients with Painless Artificial Abortion

Background and Objectives: Propofol is a commonly used intravenous anesthetic for painless artificial abortion, but the injection pain and related adverse reactions such as those related to respiration and circulation it induces have also been criticized. We aimed to conduct a comparative study on the efficacy, safety and comfort of ciprofol and propofol applied in painless artificial abortion. Materials and Methods: A total of 140 early pregnant patients undergoing painless induced abortion were selected and randomly divided into the ciprofol combined with fentanyl group (Group C) and the propofol combined with fentanyl group (Group P), with 70 cases in each group. The anesthetic effect, depth of anesthesia sedation (NI), onset time, recovery time, recovery time of orientation, retention time in the anesthesia recovery room and total amount of intravenous anesthetic drug were recorded in both groups. The respiratory rate (RR), oxygen saturation (SpO2), mean arterial pressure (MAP), and heart rate (HR) at different time points were recorded. The occurrence of perioperative adverse events, injection pain, postoperative nausea and vomiting, and dizziness were compared. The pain score at 30 minutes after operation and the satisfaction of patients and surgeons with anesthesia were evaluated. Results: The success rate of anesthesia in both groups was 100%. There were no statistically significant differences in the NI value at each time point, intraoperative body movement, recovery time, recovery time of orientation, retention time in the anesthesia recovery room, and total dosage of sedative drugs (ml) between the two groups;the onset time in Group C was longer than that in Group P, with a statistically significant difference (P Conclusion: The efficacy of ciprofol in painless induced abortion is equivalent to that of propofol, and the incidence of adverse reactions is lower than that of propofol, with higher safety and comfort.

Status of clinical applications of artificial intelligence in echocardiography

Artificial intelligence(AI)technology has been increasingly used in medical field with its rapid developments.Echocardiography is one of the best imaging methods for clinical diagnosis of heart diseases,and combining with AI could further improve its diagnostic efficiency.Though the applications of AI in echocardiography remained at a relatively early stage,a variety of automated quantitative and analytical techniques were rapidly emerging and initially entered clinical practice.The status of clinical applications of AI in echocardiography were reviewed in this article.