Going the Distance A telemedicine company uses technology to meet Africa’s rural medical needs with prime health care

We have a scarcity of health care professionals in the rural areas and therefore we can offer medical services from a distance to the underserved rural population.

Pushing the frontiers of military medical excellence:updates,progress and future needs

Since its establishment in 2014,Military Medical Research has come a long way in becoming a premier journal for scientific articles from various different specialties,with a special emphasis on topics with military relevance.The field of military medicine may be obscure,and may not be readily encountered by the typical clinician on a day-today basis.This journal aims not only to pursue excellence in military research,but also to keep current with the latest advancements on general medical topics from each and every specialty.This editorial serves to recap and synthesize the existing progress,updates and future needs of military medical excellence,discussing foremostly the unique traits of literature published in this journal,and subsequently presenting the discourse regarding wartime and peacetime medicine,the role of the military in a public health emergency,as well as wound healing and organ regeneration.Special attention has been devoted to military topics to shed light on the effects of Chemical,Biological,Radiological and Explosive warfare,environmental medicine and military psychiatry,topics which rarely have a chance to be discussed elsewhere.The interconnectedness between military combat and soldier physical and mental well-being is intricate,and has been distorted by pandemics such as coronavirus disease 2019(COVID-19).This journal has come a long way since its first article was published,steadily contributing to the existing knowledge pool on general medical topics with a military slant.Only with continuous research and sharing,can we build upon the work of the scientific community,with hopes for the betterment of patient care.

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.

Prions:a threat to health security and the need for effective medical countermeasures

Prions are infectious conformations of certain naturally occurring proteins.These misfolded proteins can struc­turally alter healthy protein,creating misfolded copies that repeat the process and form protein aggregates that lead to neuronal cell death.Although years can pass from initial prion infection to clinical presentation of symp­toms,onset of symptoms is typically followed by rapid neurological decline resulting in death.Prion diseases have been characterized in animals ranging from sheep and cattle to cervids and humans,with notable cross-species infections such as the variant Creutzfeldt-Jakob disease.Thus,prions present a health risk with the potential to disrupt major food sources as well affect human health through animal to human and human to human trans­mission events.While human to human prion transmission is rare and the immediate risks for a prion-facilitated pandemic are low,prions are a class of pathogens for which we are underprepared.In addition,prions,and prion disease-like approaches,have also been discussed in the context of biological weapons and toxins,adding another layer of complexity surrounding biosecurity and biodefense.These threats underscore the need for increased scrutiny and research on prions.Here,pharmaceutical and nonpharmaceutical prion-specific interventions are discussed.Recent advances in prion therapeutic development are also briefly highlighted,and a set of policy rec­ommendations are given that aims to provide high level suggestions for the prevention and mitigation of prion diseases.

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.

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.

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.

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.

Feasibility study of the photonuclear reaction cross section of medical radioisotopes using a laser Compton scattering gamma source

In recent years,the gap between the supply and demand of medical radioisotopes has increased,necessitating new methods for producing medical radioisotopes.Photonuclear reactions based on gamma sources have unique advantages in terms of producing high specific activity and innovative medical radioisotopes.However,the lack of experimental data on reaction cross sections for photonuclear reactions of medical radioisotopes of interest has severely limited the development and production of photonuclear transmutation medical radioisotopes.In this study,the entire process of the generation,decay,and measurement of medical radioisotopes was simulated using online gamma activation and offline gamma measurements combined with a shielding gamma-ray spectrometer.Based on a quasi-monochromatic gamma beam from the Shanghai Laser Electron Gamma Source(SLEGS),the feasibility of this measurement of production cross section for surveyed medi-cal radioisotopes was simulated,and specific solutions for measuring medical radioisotopes with ultra-low production cross sections were provided.The feasibility of this method for high-precision measurements of the reaction cross section of medical radioisotopes was demonstrated.

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.

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.

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.

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.

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.

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.