Associative Tasks Computing Offloading Scheme in Internet of Medical Things with Deep Reinforcement Learning

The Internet of Medical Things(Io MT) is regarded as a critical technology for intelligent healthcare in the foreseeable 6G era. Nevertheless, due to the limited computing power capability of edge devices and task-related coupling relationships, Io MT faces unprecedented challenges. Considering the associative connections among tasks, this paper proposes a computing offloading policy for multiple-user devices(UDs) considering device-to-device(D2D) communication and a multi-access edge computing(MEC)technique under the scenario of Io MT. Specifically,to minimize the total delay and energy consumption concerning the requirement of Io MT, we first analyze and model the detailed local execution, MEC execution, D2D execution, and associated tasks offloading exchange model. Consequently, the associated tasks’ offloading scheme of multi-UDs is formulated as a mixed-integer nonconvex optimization problem. Considering the advantages of deep reinforcement learning(DRL) in processing tasks related to coupling relationships, a Double DQN based associative tasks computing offloading(DDATO) algorithm is then proposed to obtain the optimal solution, which can make the best offloading decision under the condition that tasks of UDs are associative. Furthermore, to reduce the complexity of the DDATO algorithm, the cacheaided procedure is intentionally introduced before the data training process. This avoids redundant offloading and computing procedures concerning tasks that previously have already been cached by other UDs. In addition, we use a dynamic ε-greedy strategy in the action selection section of the algorithm, thus preventing the algorithm from falling into a locally optimal solution. Simulation results demonstrate that compared with other existing methods for associative task models concerning different structures in the Io MT network, the proposed algorithm can lower the total cost more effectively and efficiently while also providing a tradeoff between delay and energy consumption tolerance.

Reconstruction of Knowledge and Medical Images in the Convergence of Chinese and Western Medicine:Taking “Sweet Meat” as an Example

The pancreas is neither part of the five Zang organs(五脏) nor the six Fu organs(六腑).Thus,it has received little attention in Chinese medical literature.In the late 19th century,medical missionaries in China started translating and introducing anatomical and physiological knowledge about the pancreas.As for the word pancreas,an early and influential translation was “sweet meat”(甜肉),proposed by Benjamin Hobson(合信).The translation “sweet meat” is not faithful to the original meaning of “pancreas”,but is a term coined by Hobson based on his personal habits,and the word “sweet” appeared by chance.However,in the decades since the term “sweet meat” became popular,Chinese medicine practitioners,such as Tang Zonghai(唐宗海),reinterpreted it by drawing new medical illustrations for “sweet meat” and giving new connotations to the word “sweet”.This discussion and interpretation of “sweet meat” in modern China,particularly among Chinese medicine professionals,is not only a dissemination and interpretation of the knowledge of “pancreas”,but also a construction of knowledge around the term “sweet meat”.

Process,Material,and Regulatory Considerations for 3D Printed Medical Devices and Tissue Constructs

Three-dimensional(3D)printing is a highly automated platform that facilitates material deposition in a layer-by-layer approach to fabricate pre-defined 3D complex structures on demand.It is a highly promising technique for the fabrication of personalized medical devices or even patient-specific tissue constructs.Each type of 3D printing technique has its unique advantages and limitations,and the selection of a suitable 3D printing technique is highly dependent on its intended application.In this review paper,we present and highlight some of the critical processes(printing parameters,build orientation,build location,and support structures),material(batch-to-batch consistency,recycling,protein adsorption,biocompatibility,and degradation properties),and regulatory considerations(sterility and mechanical properties)for 3D printing of personalized medical devices.The goal of this review paper is to provide the readers with a good understanding of the various key considerations(process,material,and regulatory)in 3D printing,which are critical for the fabrication of improved patient-specific 3D printed medical devices and tissue constructs.

Advancements in medical treatment for pancreatic neuroendocrine tumors:A beacon of hope

This editorial highlights the remarkable advancements in medical treatment strategies for pancreatic neuroendocrine tumors(pan-NETs),emphasizing tailored approaches for specific subtypes.Cytoreductive surgery and somatostatin analogs(SSAs)play pivotal roles in managing tumors,while palliative options such as molecular targeted therapy,peptide receptor radionuclide therapy,and chemotherapy are reserved for SSA-refractory patients.Gastrinomas,insul-inomas,glucagonomas,carcinoid tumors and VIPomas necessitate distinct thera-peutic strategies.Understanding the genetic basis of pan-NETs and exploring immunotherapies could lead to promising avenues for future research.This review underscores the evolving landscape of pan-NET treatment,offering renewed hope and improved outcomes for patients facing this complex disease.

Curved Spine:The Shape of Spine in Taoist Body Diagrams and Its Influence on Medical Body Diagrams

From the early Taoist diagrams of the human body to the end of the Qing dynasty and the beginning of the Republic of China,Taoists exaggerated and deformed the human spine in a shape-shifting manner.It is likely that medical practitioners were influenced by this style of representation,and there are also numerous diagrams of the human body with the curved spine in the lateral-view diagrams of viscera and Ming Tang Tu(明堂图Acupuncture and Moxibustion Chart),which constantly show the human torso in an elliptical“egg shape”.No later than the Ming dynasty,medical practitioners began to depict the actual physiological spinal curve of the human body.By the Qing dynasty,the depiction of the spinal curve in medical diagrams of the human figure showed a tendency to part ways with the Taoist freehand style of the previous generation.Although the representation of the curve of the spine was very crude,later medical images of the human body at least gradually straightened the spine and no longer depicted it in a shape-shifting manner.However,the curved spine in Taoist diagrams of the human body continued to exist,and the presentation of the curved spine never changed.This way of depicting its appearance,which is very different from reality,is shaped by Taoism's special way of perceiving and viewing the body,and may also contain another form of truth.

Medical management of urolithiasis: Great efforts and limited progress

Objective:To provide a comprehensive review on the existing literature on medical management of urolithiasis.Methods:A thorough literature review was performed using Medline,PubMed/PMC,Embase,and the Cochrane Database of Systematic Reviews up to December 2022 to identify publications on the medical management of urolithiasis.Studies that assessed dietary and pharmacologic management of urolithiasis were reviewed;studies on medical expulsive therapy were not included in this review.Results:Medical management of urolithiasis ranges from the prophylactic management of kidney stone disease to dissolution therapies.While most treatment concepts have been long established,large randomized controlled trials are scarce.Dietary modification and increased fluid intake remain cornerstones in the conservative management of urolithiasis.A major limitation for medical management of urolithiasis is poor patient compliance.Conclusion:Medical management of urolithiasis is more important in patients with recurrent urolithiasis and patients with metabolic abnormalities putting them at higher risk of developing stones.Although medical management can be effective in limiting stone recurrence,medical interventions often fail due to poor compliance.

Importance-aware 3D volume visualization for medical content-based image retrieval-a preliminary study

Background A medical content-based image retrieval(CBIR)system is designed to retrieve images from large imaging repositories that are visually similar to a user′s query image.CBIR is widely used in evidence-based diagnosis,teaching,and research.Although the retrieval accuracy has largely improved,there has been limited development toward visualizing important image features that indicate the similarity of retrieved images.Despite the prevalence of 3D volumetric data in medical imaging such as computed tomography(CT),current CBIR systems still rely on 2D cross-sectional views for the visualization of retrieved images.Such 2D visualization requires users to browse through the image stacks to confirm the similarity of the retrieved images and often involves mental reconstruction of 3D information,including the size,shape,and spatial relations of multiple structures.This process is time-consuming and reliant on users'experience.Methods In this study,we proposed an importance-aware 3D volume visualization method.The rendering parameters were automatically optimized to maximize the visibility of important structures that were detected and prioritized in the retrieval process.We then integrated the proposed visualization into a CBIR system,thereby complementing the 2D cross-sectional views for relevance feedback and further analyses.Results Our preliminary results demonstrate that 3D visualization can provide additional information using multimodal positron emission tomography and computed tomography(PETCT)images of a non-small cell lung cancer dataset.

Mindfulness training in medical education as a means to improve resilience,empathy,and mental health in the medical profession

The high rates of depression,burnout,and increased risk of suicide among medical students,residents,and physicians in comparison with other careers signal a mental health crisis within our profession.We contend that this crisis coupled with the inadequate acquisition of interpersonal skills during medical education results from the interaction between a challenging environment and the mental capital of individuals.Additionally,we posit that mindfulness-based practices are instrumental for the development of major components of mental capital,such as resilience,flexibility of mind,and learning skills,while also serving as a pathway to enhance empathy,compassion,self-awareness,conflict resolution,and relational abilities.Importantly,the evidence base supporting the effectiveness of mindfulness-based interventions has been increasing over the years,and a growing number of medical schools have already integrated mindfulness into their curricula.While we acknowledge that mindfulness is not a panacea for all educational and mental health problems in this field,we argue that there is currently an unprecedented opportunity to gather momentum,spread and study mindfulness-based programs in medical schools around the world as a way to address some longstanding shortcomings of the medical profession and the health and educational systems upon which it is rooted.

EDSUCh:A robust ensemble data summarization method for effective medical diagnosis

Identifying rare patterns for medical diagnosis is a challenging task due to heterogeneity and the volume of data.Data summarization can create a concise version of the original data that can be used for effective diagnosis.In this paper,we propose an ensemble summarization method that combines clustering and sampling to create a summary of the original data to ensure the inclusion of rare patterns.To the best of our knowledge,there has been no such technique available to augment the performance of anomaly detection techniques and simultaneously increase the efficiency of medical diagnosis.The performance of popular anomaly detection algorithms increases significantly in terms of accuracy and computational complexity when the summaries are used.Therefore,the medical diagnosis becomes more effective,and our experimental results reflect that the combination of the proposed summarization scheme and all underlying algorithms used in this paper outperforms the most popular anomaly detection techniques.

Flexner's legacy and the future of medical education:Embracing challenge and opportunity

This editorial comments on the article by Alzerwi.We focus on the development course,present challenges,and future perspectives of medical education.Modern medical education is gradually undergoing significant and profound changes worldwide.The emergence of new ideas,methodologies,and techniques has created opportunities for medical education developments and brought new concerns and challenges,ultimately promoting virtuous progress in medical education reform.The sustainable development of medical education needs joint efforts and support from governments,medical colleges,hospitals,researchers,administrators,and educators.

Deep Forest-Based Fall Detection in Internet of Medical Things Environment

This article introduces a new medical internet of things(IoT)framework for intelligent fall detection system of senior people based on our proposed deep forest model.The cascade multi-layer structure of deep forest classifier allows to generate new features at each level with minimal hyperparameters compared to deep neural networks.Moreover,the optimal number of the deep forest layers is automatically estimated based on the early stopping criteria of validation accuracy value at each generated layer.The suggested forest classifier was successfully tested and evaluated using a public SmartFall dataset,which is acquired from three-axis accelerometer in a smartwatch.It includes 92781 training samples and 91025 testing samples with two labeled classes,namely non-fall and fall.Classification results of our deep forest classifier demonstrated a superior performance with the best accuracy score of 98.0%compared to three machine learning models,i.e.,K-nearest neighbors,decision trees and traditional random forest,and two deep learning models,which are dense neural networks and convolutional neural networks.By considering security and privacy aspects in the future work,our proposed medical IoT framework for fall detection of old people is valid for real-time healthcare application deployment.

Special Issue: The Global Cross-cultural Integration of TCM in the Mainstream Healthcare, Medical Education and the Practice of Medicine

Ioannis Solos Ph.D.,M.D.(China),L.Ac.currently serves as President and CEO at the Saint George Clinic and Research Institute,Scottsdale,AZ.,and Associate Editor for Chinese Medicine and Culture.Professor Solos has earned his Master of Medicine in Traditional Diagnosis at the Beijing University of Chinese Medicine,and his Medical Ph.D.in Chinese and Western Integrative Medicine at the Jinan University in Guangzhou.He practices and teaches integrative clinical medicine,Jing Fang(经方TCM formulas),martial lineage acupuncture,and his personalized style of“tendon and fascia reconditioning manipulations for bone and joint disease”.

Advances in Biomedical Applications of Hydrogels from Seaweed-Derived Sulfated Polysaccharides:Carrageenan,Fucoidan,and Ulvan

Sulfated polysaccharides extracted from seaweeds,including Carrageenan,Fucoidan and Ulvan,are crucial bioactive compounds known for their diverse beneficial properties,such as anti-inflammatory,antitumor,immunomodulatory,antiviral,and anticoagulant effects.These polysaccharides form hydrogels hold immense promise in biomedicine,particularly in tissue engineering,drug delivery systems and wound healing.This review comprehensively explores the sources and structural characteristics of the three important sulfated polysaccharides extracted from different algae species.It elucidates the gelation mechanisms of these polysaccharides into hydrogels.Furthermore,the biomedical applications of these three sulfated polysaccharide hydrogels in wound healing,drug delivery,and tissue engineering are discussed,highlighting their potential in the biomedicine.

Investigation on the Knowledge, Attitude and Practice of Oral Health among Medical College and University Undergraduate

Objectives: This study aims to investigate the status of knowledge, attitude and practice (KAP) of oral health among medical undergraduate students, and provide reference for implementing oral health interventions. Methods: A total of 528 undergraduate students enrolled in Fuzhou Medical College from February 2023 to September 2023 were selected as the research subjects. Their oral health KAP were investigated, and the oral health behavior habits of different types of medical students were compared, and possible influencing factors were analyzed. Results: The total awareness rate of oral health knowledge among medical students is 77.0%, with an average score of 3.85 ± 1.16 points. The overall positive rate of oral health attitudes among medical students is 80.0%, with an average score of 3.19 ± 0.72 points. The total qualified rate of oral health behavior is 65.9%, with an average score of 4.61 ± 1.23 points. The scores of oral health knowledge, attitudes, and behaviors among medical students are related to gender, major, smoking status, and oral health status. The frequency of brushing teeth in the female group was higher than that in the male group, while the habit of brushing teeth before bedtime and the frequency of timely replacement of toothbrushes when deformed were lower, with statistical significance (p 0.05). The frequency of timely replacement of toothbrushes varies among medical students from different majors, and the difference is statistically significant (p 0.05). People who have a habit of eating hot and cold food have a higher frequency of brushing their teeth every day, and the difference is statistically significant (p 0.05). Non smokers have a better habit of brushing their teeth before bedtime and a higher frequency of timely replacement when their toothbrush deforms, with a statistically significant difference (p 0.05). The frequency of using fluoride toothpaste or medicated toothpaste, having a habit of unilateral chewing, and timely replacement of toothbrushes when deformed in patients with existing oral problems is higher than that of those without oral problems, and the difference is statistically significant (p 0.05). Conclusion: The knowledge, attitude, and behavior of oral health among medical students in this school are above average. Students with different genders, dietary and smoking habits, and oral health status have different oral health behavioral habits. It is recommended to include oral health education in mandatory courses for various medical majors.

Knowledge of Hospital Acquired Infections (HAIs) among Medical Students in a Tertiary Hospital in Jos North Local Government Area, Plateau State, Nigeria

Background: Hospital Acquired Infections (HAIs) remain a common cause of death, functional disability, emotional suffering and economic burden among hospitalized patients. Knowledge of HAIs is important in its prevention and control. This study seeks to assess the knowledge of Hospital Acquired Infections (HAIs) among medical students in a Tertiary Hospital in Jos North Local Government Area, Plateau State, Nigeria. Methods: This was a descriptive cross-sectional study done in October 2019 among clinical medical students using a Multistage sampling technique. Data was collected using a self-administered structured questionnaire and analyzed using the IBM SPSS 20 (Statistical Package for the Social Sciences). Ethical approval was granted by Bingham University Teaching Hospital, Ethics Committee, Jos, Plateau State. Results: A total of 219 students in the clinical arm of the College of Medicine and Health Sciences were selected. A higher proportion (97.7%) of respondents knew about Hospital Acquired Infections and 85.4% knew that Hospital Acquired infections occur in the hospital, and (86.3%) considered patients contagious with half (58.9%) considered patients as the most important source of HAIs, followed by care givers (13.2%), then doctors including medical students and interns (10.0%) and lastly nurses (8.7%). The majority of respondents (70.8%) considered Surgical Wound Infections to be the most commonly occurring HAI, followed by UTIs (69.9%), RTIs (61.2%), BSIs (37.0%) and others (0.9%). The clinical thermometer was the instrument that most commonly transmits HAIs (82.6%), then followed by stethoscope (62.1%), white coats (53.9%), and blood pressure cuff (51.1%). Most respondents knew the infectious substances, like blood (96.3%), nasal discharge (82.6%), saliva (85.3%), and faeces (79.4%) transmitted HAIs, 72.6% of the respondents said that they were aware of the recommended hand washing techniques by WHO. Conclusion: The majority of students 91.3% had good knowledge while 8.7% had poor knowledge of HAIs. Lower classes had more respondents with poor knowledge. This finding was statistically significant (p = 0.002, Chi-square 12.819). Students are encouraged to keep up the level of knowledge they have about HAIs. These students can help improve the knowledge of those whose knowledge level is low. Government and NGOs should support sponsorship for capacity-building events targeted at HAIs for healthcare workers and medical students.

A novel medical image data protection scheme for smart healthcare system

The Internet of Multimedia Things(IoMT)refers to a network of interconnected multimedia devices that communicate with each other over the Internet.Recently,smart healthcare has emerged as a significant application of the IoMT,particularly in the context of knowledge‐based learning systems.Smart healthcare systems leverage knowledge‐based learning to become more context‐aware,adaptable,and auditable while maintain-ing the ability to learn from historical data.In smart healthcare systems,devices capture images,such as X‐rays,Magnetic Resonance Imaging.The security and integrity of these images are crucial for the databases used in knowledge‐based learning systems to foster structured decision‐making and enhance the learning abilities of AI.Moreover,in knowledge‐driven systems,the storage and transmission of HD medical images exert a burden on the limited bandwidth of the communication channel,leading to data trans-mission delays.To address the security and latency concerns,this paper presents a lightweight medical image encryption scheme utilising bit‐plane decomposition and chaos theory.The results of the experiment yield entropy,energy,and correlation values of 7.999,0.0156,and 0.0001,respectively.This validates the effectiveness of the encryption system proposed in this paper,which offers high‐quality encryption,a large key space,key sensitivity,and resistance to statistical attacks.

Research on Multi-Scale Feature Fusion Network Algorithm Based on Brain Tumor Medical Image Classification

Gliomas have the highest mortality rate of all brain tumors.Correctly classifying the glioma risk period can help doctors make reasonable treatment plans and improve patients’survival rates.This paper proposes a hierarchical multi-scale attention feature fusion medical image classification network(HMAC-Net),which effectively combines global features and local features.The network framework consists of three parallel layers:The global feature extraction layer,the local feature extraction layer,and the multi-scale feature fusion layer.A linear sparse attention mechanism is designed in the global feature extraction layer to reduce information redundancy.In the local feature extraction layer,a bilateral local attention mechanism is introduced to improve the extraction of relevant information between adjacent slices.In the multi-scale feature fusion layer,a channel fusion block combining convolutional attention mechanism and residual inverse multi-layer perceptron is proposed to prevent gradient disappearance and network degradation and improve feature representation capability.The double-branch iterative multi-scale classification block is used to improve the classification performance.On the brain glioma risk grading dataset,the results of the ablation experiment and comparison experiment show that the proposed HMAC-Net has the best performance in both qualitative analysis of heat maps and quantitative analysis of evaluation indicators.On the dataset of skin cancer classification,the generalization experiment results show that the proposed HMAC-Net has a good generalization effect.

Fractional-order heterogeneous memristive Rulkov neuronal network and its medical image watermarking application

This article proposes a novel fractional heterogeneous neural network by coupling a Rulkov neuron with a Hopfield neural network(FRHNN),utilizing memristors for emulating neural synapses.The study firstly demonstrates the coexistence of multiple firing patterns through phase diagrams,Lyapunov exponents(LEs),and bifurcation diagrams.Secondly,the parameter related firing behaviors are described through two-parameter bifurcation diagrams.Subsequently,local attraction basins reveal multi-stability phenomena related to initial values.Moreover,the proposed model is implemented on a microcomputer-based ARM platform,and the experimental results correspond to the numerical simulations.Finally,the article explores the application of digital watermarking for medical images,illustrating its features of excellent imperceptibility,extensive key space,and robustness against attacks including noise and cropping.

AI-Powered Image Security:Utilizing Autoencoders for Advanced Medical Image Encryption

With the rapid advancement in artificial intelligence(AI)and its application in the Internet of Things(IoT),intelligent technologies are being introduced in the medical field,giving rise to smart healthcare systems.The medical imaging data contains sensitive information,which can easily be stolen or tampered with,necessitating secure encryption schemes designed specifically to protect these images.This paper introduces an artificial intelligence-driven novel encryption scheme tailored for the secure transmission and storage of high-resolution medical images.The proposed scheme utilizes an artificial intelligence-based autoencoder to compress highresolution medical images and to facilitate fast encryption and decryption.The proposed autoencoder retains important diagnostic information even after reducing the image dimensions.The low-resolution images then undergo a four-stage encryption process.The first two encryption stages involve permutation and the next two stages involve confusion.The first two stages ensure the disruption of the structure of the image,making it secure against statistical attacks.Whereas the two stages of confusion ensure the effective concealment of the pixel values making it difficult to decrypt without secret keys.This encrypted image is then safe for storage or transmission.The proposed scheme has been extensively evaluated against various attacks and statistical security parameters confirming its effectiveness in securing medical image data.

Factors Affecting Cotrimoxazole Prophylactic Therapy Compliance in HIV Patients Attending a Care and Treatment Clinic at Bugando Medical Centre in Mwanza, Tanzania

Introduction: Cotrimoxazole Prophylactic Therapy (CPT) compliance lowers the risk of opportunistic infections and other Acquired Immune Deficiency Syndrome (AIDS)-related diseases. The aim of this study was to examine factors that influence compliance with CPT among HIV patients in the Care and Treatment Clinic (CTC) at Bugando Medical Centre (BMC) in Mwanza, Tanzania. Methods: A descriptive cross-sectional study was conducted at the BMC between April 1, 2021, and June 30, 2021. Data were collected using face-to-face interviews and a semi-structured questionnaire. Data are presented in frequency, percentages, and cross-tabulation tables. A P-value of less than 0.05 was considered statistically significant. Results: The prevalence of compliance with CPT by self-reported measurement was 158 (63.7%). Most CPT-compliant participants were more likely to have a spouse who is familiar with CPT, have a family member who is aware of their HIV status, and be aware of the benefits of CPT. The majority of participants who complied with CPT were more likely to have experienced counseling during refill, felt that the length of time spent seeing doctors for treatment was reasonable, and received accurate information from them. Conclusion: Most adult HIV patients attending CTC at BMC were reported to be in compliance with CPT. These findings suggest that improving social support and patient-provider communication may be effective strategies for improving compliance with CPT among HIV patients.