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.

Robust zero-watermarking algorithm based on discrete wavelet transform and daisy descriptors for encrypted medical image

In the intricate network environment,the secure transmission of medical images faces challenges such as information leakage and malicious tampering,significantly impacting the accuracy of disease diagnoses by medical professionals.To address this problem,the authors propose a robust feature watermarking algorithm for encrypted medical images based on multi-stage discrete wavelet transform(DWT),Daisy descriptor,and discrete cosine transform(DCT).The algorithm initially encrypts the original medical image through DWT-DCT and Logistic mapping.Subsequently,a 3-stage DWT transformation is applied to the encrypted medical image,with the centre point of the LL3 sub-band within its low-frequency component serving as the sampling point.The Daisy descriptor matrix for this point is then computed.Finally,a DCT transformation is performed on the Daisy descriptor matrix,and the low-frequency portion is processed using the perceptual hashing algorithm to generate a 32-bit binary feature vector for the medical image.This scheme utilises cryptographic knowledge and zero-watermarking technique to embed watermarks without modifying medical images and can extract the watermark from test images without the original image,which meets the basic re-quirements of medical image watermarking.The embedding and extraction of water-marks are accomplished in a mere 0.160 and 0.411s,respectively,with minimal computational overhead.Simulation results demonstrate the robustness of the algorithm against both conventional attacks and geometric attacks,with a notable performance in resisting rotation attacks.

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.

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.

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.

Marine Predators Algorithm with Deep Learning-Based Leukemia Cancer Classification on Medical Images

In blood or bone marrow,leukemia is a form of cancer.A person with leukemia has an expansion of white blood cells(WBCs).It primarily affects children and rarely affects adults.Treatment depends on the type of leukemia and the extent to which cancer has established throughout the body.Identifying leukemia in the initial stage is vital to providing timely patient care.Medical image-analysis-related approaches grant safer,quicker,and less costly solutions while ignoring the difficulties of these invasive processes.It can be simple to generalize Computer vision(CV)-based and image-processing techniques and eradicate human error.Many researchers have implemented computer-aided diagnosticmethods andmachine learning(ML)for laboratory image analysis,hopefully overcoming the limitations of late leukemia detection and determining its subgroups.This study establishes a Marine Predators Algorithm with Deep Learning Leukemia Cancer Classification(MPADL-LCC)algorithm onMedical Images.The projectedMPADL-LCC system uses a bilateral filtering(BF)technique to pre-process medical images.The MPADL-LCC system uses Faster SqueezeNet withMarine Predators Algorithm(MPA)as a hyperparameter optimizer for feature extraction.Lastly,the denoising autoencoder(DAE)methodology can be executed to accurately detect and classify leukemia cancer.The hyperparameter tuning process using MPA helps enhance leukemia cancer classification performance.Simulation results are compared with other recent approaches concerning various measurements and the MPADL-LCC algorithm exhibits the best results over other recent approaches.

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.

DCFNet:An Effective Dual-Branch Cross-Attention Fusion Network for Medical Image Segmentation

Automatic segmentation of medical images provides a reliable scientific basis for disease diagnosis and analysis.Notably,most existing methods that combine the strengths of convolutional neural networks(CNNs)and Transformers have made significant progress.However,there are some limitations in the current integration of CNN and Transformer technology in two key aspects.Firstly,most methods either overlook or fail to fully incorporate the complementary nature between local and global features.Secondly,the significance of integrating the multiscale encoder features from the dual-branch network to enhance the decoding features is often disregarded in methods that combine CNN and Transformer.To address this issue,we present a groundbreaking dual-branch cross-attention fusion network(DCFNet),which efficiently combines the power of Swin Transformer and CNN to generate complementary global and local features.We then designed the Feature Cross-Fusion(FCF)module to efficiently fuse local and global features.In the FCF,the utilization of the Channel-wise Cross-fusion Transformer(CCT)serves the purpose of aggregatingmulti-scale features,and the Feature FusionModule(FFM)is employed to effectively aggregate dual-branch prominent feature regions from the spatial perspective.Furthermore,within the decoding phase of the dual-branch network,our proposed Channel Attention Block(CAB)aims to emphasize the significance of the channel features between the up-sampled features and the features generated by the FCFmodule to enhance the details of the decoding.Experimental results demonstrate that DCFNet exhibits enhanced accuracy in segmentation performance.Compared to other state-of-the-art(SOTA)methods,our segmentation framework exhibits a superior level of competitiveness.DCFNet’s accurate segmentation of medical images can greatly assist medical professionals in making crucial diagnoses of lesion areas in advance.

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

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

Feasibility of medical radioisotope production based on the proton beams at China Spallation Neutron Source

The utilization of a proton beam from the China Spallation Neutron Source(CSNS)for producing medical radioisotopes is appealing owing to its high current intensity and high energy.The medical isotope production based on the proton beam at the CSNS is significant for the development of future radiopharmaceuticals,particularly for theα-emitting radiopharmaceu-ticals.The production yield and activity of typical medical isotopes were estimated using the FLUKA simulation.The results indicate that the 300-MeV proton beam with a power of 100 kW at CSNS-II is highly suitable for proof-of-principle studies of most medical radioisotopes.In particular,this proton beam offers tremendous advantages for the large-scale production of alpha radioisotopes,such as 225Ac,whose theoretical production yield can reach approximately 57 Ci/week.Based on these results,we provide perspectives on the use of CSNS proton beams to produce radioisotopes for medical applications.

Reliable Data Collection Model and Transmission Framework in Large-Scale Wireless Medical Sensor Networks

Large-scale wireless sensor networks(WSNs)play a critical role in monitoring dangerous scenarios and responding to medical emergencies.However,the inherent instability and error-prone nature of wireless links present significant challenges,necessitating efficient data collection and reliable transmission services.This paper addresses the limitations of existing data transmission and recovery protocols by proposing a systematic end-to-end design tailored for medical event-driven cluster-based large-scale WSNs.The primary goal is to enhance the reliability of data collection and transmission services,ensuring a comprehensive and practical approach.Our approach focuses on refining the hop-count-based routing scheme to achieve fairness in forwarding reliability.Additionally,it emphasizes reliable data collection within clusters and establishes robust data transmission over multiple hops.These systematic improvements are designed to optimize the overall performance of the WSN in real-world scenarios.Simulation results of the proposed protocol validate its exceptional performance compared to other prominent data transmission schemes.The evaluation spans varying sensor densities,wireless channel conditions,and packet transmission rates,showcasing the protocol’s superiority in ensuring reliable and efficient data transfer.Our systematic end-to-end design successfully addresses the challenges posed by the instability of wireless links in large-scaleWSNs.By prioritizing fairness,reliability,and efficiency,the proposed protocol demonstrates its efficacy in enhancing data collection and transmission services,thereby offering a valuable contribution to the field of medical event-drivenWSNs.

Analyze the Performance of Electroactive Anticorrosion Coating of Medical Magnesium Alloy Using Deep Learning

Electroactive anticorrosion coatings are specialized surface treatments that prevent or minimize corrosion. Thestudy employs strategic thermodynamic equilibriumcalculations to pioneer a novel factor in corrosion protection.A first-time proposal, the total acidity (TA) potential of the hydrogen (pH) concept significantly shapes medicalmagnesium alloys. These coatings are meticulously designed for robust corrosion resistance, blending theoreticalinsights and practical applications to enhance our grasp of corrosion prevention mechanisms and establisha systematic approach to coating design. The groundbreaking significance of this study lies in its innovativeintegration of the TA/pH concept,which encompasses the TA/pH ratio of the chemical environment. This approachsurpasses convention by acknowledging the intricate interplay between the acidity and pH levels within thecoating formulation, thereby optimizing metal-phosphate-based conversion coatings and transforming corrosionmitigation strategies. To authenticate the TA/pH concept, the study comprehensively compares its findings withexisting research, rigorously validating the theoretical framework and reinforcing the correlates among TA/pHvalues and observed corrosion resistance in the coatings. The influence of mutations that occur naturally inthe detergent solution on persistent phosphorus changes is shown by empirical confirmation, which improvescorrosion resistance. This realization advances the field ofmaterials and the field’s knowledge of coated generation,particularly anticorrosion converter layers.

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.

Attitude and willingness on gamete donation among medical students:An experience from a state university in Sri Lanka

Objective:To assess the attitude and willingness of medical students of the Faculty of Medicine,University of Jaffna,regarding gamete donation.Methods:An institutional-based descriptive cross-sectional study was conducted at the Faculty of Medicine,University of Jaffna,from September 2022 to May 2023 among undergraduate medical students who gave their voluntary participation.A self-administered questionnaire was used as a study instrument to collect data regarding their attitude and willingness toward gamete donation.Results:A total of 345 participants were recruited and their sociodemographic data revealed that 56.8%of the participants were female,62.3%aged between 26 and 30 years,and 92.2%were unmarried.Many of them received information regarding gamete donations during their clinical appointments.Over half(67.8%)of them showed a negative attitude towards gamete donation.Regarding willingness,only 39.7%of participants had a positive approach for being a gamete donor;among them,84.7%preferred anonymous donations.Religion and ethnicity had a significant influence on their attitudes and willingness.In addition,male was also found to be more willing to donate gametes.Conclusions:Most medical students have negative views about gamete donation.Imparting awareness and knowledge of assisted reproductive technology and gamete donation within medical students'sociocultural and ethical backgrounds might facilitate a change in attitude towards gamete donation amongst future medical practitioners.

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.

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.

A review of medical ocular image segmentation

Deep learning has been extensively applied to medical image segmentation,resulting in significant advancements in the field of deep neural networks for medical image segmentation since the notable success of U Net in 2015.However,the application of deep learning models to ocular medical image segmentation poses unique challenges,especially compared to other body parts,due to the complexity,small size,and blurriness of such images,coupled with the scarcity of data.This article aims to provide a comprehensive review of medical image segmentation from two perspectives:the development of deep network structures and the application of segmentation in ocular imaging.Initially,the article introduces an overview of medical imaging,data processing,and performance evaluation metrics.Subsequently,it analyzes recent developments in U-Net-based network structures.Finally,for the segmentation of ocular medical images,the application of deep learning is reviewed and categorized by the type of ocular tissue.

Case Records as Medical Stories: A Song-dynasty Doctor’s Narration of His Own Medicine-Xu Shuwei (1080-1154)

The key point in studying or teaching the history of Chinese medicine is on the doctrines underlying it and on its perception of the body,physiology,pathology,and its treatment.Namely,there is often a tendency to focus on reading and analysing the classical canons and therapy-related texts including formularies and materia medica collections.However,focusing on these sources provides us with a one-sided presentation of Chinese medicine.These primary sources lack the clinical down-to-earth know-how that encompasses medical treatment,which are represented,for instance,in the clinical rounds of modern medical schools.Our traditional focus on the medical canons and formularies provides almost no clinical knowledge,leaving us with a one-sided narrative that ignores how medicine and healing are actually practiced in the field.This paper focuses on the latter aspect of medicine from a historical perspective.Using written and visual sources dating to the Song dynasty,clinical encounters between doctors and patients including their families are depicted based on case records recorded by a physician,members of the patient’s family,and bystanders.This array of case records or case stories will enable us to narrate the interaction between physicians and patients both from the clinical perspective and from the social interaction.This paper will also discuss visual depictions of the medical encounter to provide another perspective for narrating medicine during the Song dynasty.Medical case records and paintings depicting medical encounters are exemplary of the potential of Chinese primary sources for narrative medicine.

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.

The"4+4"Medical Doctor(MD)Pilot Program at PUMC:Implementation,Performance,and Prospects

Peking Union Medical College(PUMC)launched the"4+4"Medical Doctor(MD)pilot program in 2018,admitting students with non-medical backgrounds from top universities,aligning with national medical talent training policies to foster diverse and eager learners in medicine.On the occasion of the graduation of the first class of the"4+4"MD pilot class at PUMC in 2023,we reviewed the teaching reform in the pilot program and carried out a systematic survey and interviews with students,faculties,and management staff of the pilot class.This article reports on the measures taken by the pilot class at PUMC in enrollment and curriculum setting,and demonstrates the achievements of the pilot class in terms of student academic background structure,knowledge acquisition and skill learning,scientific research ability,and course evaluation.The results indicated that the pilot class had met the national demand for the"Medicine+X"talent training model.More specifically,with a diverse academic backgrounds,the pilot class graduates had academic levels comparable to the eight-year medical education graduates,and their scientific research abilities were satisfactory.The pilot program at PUMC will optimize the curriculum setting,strengthen the construction of faculty,learning resources,and teaching facilities,and reform the academic evaluation methods,thus deepening the reform of medical education and improving the"4+4"MD program as a novel medical education model.