Human Invasion by Lesser Known Medically Important Trematodes in the Proximity of Tshwane,Gauteng Province,South Africa

It is well-documented that digenetic trematodes exhibit complex life cycles where the definitive hosts are vertebrates—mostly birds,other mammals and reptiles.It is also known that digenetic trematodes are host specific,but changes in our feeding habits, agricultural and social practices,have led to human beings infected with parasites that were previously regarded to be of no or insignificant medical importance. The objective of this study was to identify the less known digenetic parasites in the water bodies around Tshwane that may result in possible

Current Attitudes of Anesthesiologists towards Medically Futile Care

Purpose: To explore anesthesiologists’ perceptions of the reasons underlying why physicians continue to provide care that they consider futile. Methods: A qualitative study was conducted utilizing a grounded theory approach. Four separate focus groups (2 resident physician groups and 2 attending physician groups) were conducted over a three week span. An interview guide was used consisting of a proposed definition of futility and five open-ended questions. Responses to the five open-ended questions were used to guide follow up questions. Transcribed audio recordings were then analyzed. Results: With data reduction, we were able to separate responses into definitions of futility, stories of cases where futile care was provided, and opinions as to the underlying causes of continuing to provide futile care. A variety of opinions was obtained, suggesting the possibility that different groups (surgeons, anesthesiologists, family members) view questions of futility differently. Conclusions: Complete agreement on a definition of futility does not exist. Even when some agreement exists, there is great difficulty in predicting outcomes in individual cases. Future quantitative studies may provide more evidence of trends in underlying reasons for providing futile care. Focused education efforts may then lead to more agreement between all involved.

Mixed-decomposed convolutional network:A lightweight yet efficient convolutional neural network for ocular disease recognition

Eye health has become a global health concern and attracted broad attention.Over the years,researchers have proposed many state-of-the-art convolutional neural networks(CNNs)to assist ophthalmologists in diagnosing ocular diseases efficiently and precisely.However,most existing methods were dedicated to constructing sophisticated CNNs,inevitably ignoring the trade-off between performance and model complexity.To alleviate this paradox,this paper proposes a lightweight yet efficient network architecture,mixeddecomposed convolutional network(MDNet),to recognise ocular diseases.In MDNet,we introduce a novel mixed-decomposed depthwise convolution method,which takes advantage of depthwise convolution and depthwise dilated convolution operations to capture low-resolution and high-resolution patterns by using fewer computations and fewer parameters.We conduct extensive experiments on the clinical anterior segment optical coherence tomography(AS-OCT),LAG,University of California San Diego,and CIFAR-100 datasets.The results show our MDNet achieves a better trade-off between the performance and model complexity than efficient CNNs including MobileNets and MixNets.Specifically,our MDNet outperforms MobileNets by 2.5%of accuracy by using 22%fewer parameters and 30%fewer computations on the AS-OCT dataset.

An Automatic Implementation of Oropharyngeal Swab Sampling for Diagnosing Respiratory Infectious Diseases via Soft Robotic End-Effectors

The most widely adopted method for diagnosing respiratory infectious diseases is to conduct polymerase chain reaction(PCR)assays on patients’respiratory specimens,which are collected through either nasal or oropharyngeal swabs.The manual swab sampling process poses a high risk to the examiner and may cause false-negative results owing to improper sampling.In this paper,we propose a pneumatically actuated soft end-effector specifically designed to achieve all of the tasks involved in swab sampling.The soft end-effector utilizes circumferential instability to ensure grasping stability,and exhibits several key properties,including high load-to-weight ratio,error tolerance,and variable swab-tip stiffness,leading to successful automatic robotic oropharyngeal swab sampling,from loosening and tightening the transport medium tube cap,holding the swab,and conducting sampling,to snapping off the swab tail and sterilizing itself.Using an industrial collaborative robotic arm,we integrated the soft end-effector,force sensor,camera,lights,and remote-control stick,and developed a robotic oropharyngeal swab sampling system.Using this swab sampling system,we conducted oropharyngeal swab-sampling tests on 20 volunteers.Our Digital PCR assay results(RNase P RNA gene absolute copy numbers for the samples)revealed that our system successfully collected sufficient numbers of cells from the pharyngeal wall for respiratory disease diagnosis.In summary,we have developed a pharyngeal swab-sampling system based on an“enveloping”soft actuator,studied the sampling process,and imple-mented whole-process robotic oropharyngeal swab-sampling.

Erratum to Treatment strategies for patients with HER2-positive gastric cancer

For the affiliation information,the affiliation for author Feixue Wang should be Department of GI Medical Oncology,Tianjin Medical University Cancer Institute and Hospital,National Clinical Research Center for Cancer,Tianjin's Clinical Research Center for Cancer,Tianjin Key Laboratory of Digestive Cancer,Key Laboratory of Cancer Prevention and Therapy,Tianjin Medical University,Tianjin 300060,China.

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.

ThyroidNet:A Deep Learning Network for Localization and Classification of Thyroid Nodules

Aim:This study aims to establish an artificial intelligence model,ThyroidNet,to diagnose thyroid nodules using deep learning techniques accurately.Methods:A novel method,ThyroidNet,is introduced and evaluated based on deep learning for the localization and classification of thyroid nodules.First,we propose the multitask TransUnet,which combines the TransUnet encoder and decoder with multitask learning.Second,we propose the DualLoss function,tailored to the thyroid nodule localization and classification tasks.It balances the learning of the localization and classification tasks to help improve the model’s generalization ability.Third,we introduce strategies for augmenting the data.Finally,we submit a novel deep learning model,ThyroidNet,to accurately detect thyroid nodules.Results:ThyroidNet was evaluated on private datasets and was comparable to other existing methods,including U-Net and TransUnet.Experimental results show that ThyroidNet outperformed these methods in localizing and classifying thyroid nodules.It achieved improved accuracy of 3.9%and 1.5%,respectively.Conclusion:ThyroidNet significantly improves the clinical diagnosis of thyroid nodules and supports medical image analysis tasks.Future research directions include optimization of the model structure,expansion of the dataset size,reduction of computational complexity and memory requirements,and exploration of additional applications of ThyroidNet in medical image analysis.

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.

一种基于血液检测的诊断和预测HBV相关疾病的弹性方法

Chronic hepatitis B virus(HBV)infection,which threatens global public health,is a major contributor to liver-related morbidity and mortality.Examinations for liver diseases related to chronic HBV infection-including laboratory tests,ultrasounds,computed tomography(CT),and liver biopsies-may take up medical resources,particularly since they overlap in most instances.Thus,there is an urgent need to establish an economical and effective diagnosis method in order to streamline the medical process for HBV-related disea ses.Using complex network models constructed based on clinical blood tests,we provide such a method by defining the novel measure of functional resilience to assess patients’liver conditions.By combining network models and dynamics,we discovered the pivotal items and their corresponding thresholds,which can guide further research on preventing disease deterioration in critical states of these diseases.The macro-averaged precision of our method,functional resilience,is84.74%,whereas the macro-averaged precision of physicians’experience without assistance from imaging or biopsy is 55.63%.From an economic perspective,our approach could save the equivalent of at least30 USD per visit for most Chinese patients and at least 400 USD per visit for most US patients,compared with general diagnostic methods.Globally,this will add to savings of at least 10.5 billion USD annually.Our method can comprehensively evaluate the condition of patients’livers and help avert the waste of medical resources during the diagnosis of liver disease by reducing excessive imaging exams.

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.

Nonlinear Registration of Brain Magnetic Resonance Images with Cross Constraints of Intensity and Structure

Many deep learning-based registration methods rely on a single-stream encoder-decoder network for computing deformation fields between 3D volumes.However,these methods often lack constraint information and overlook semantic consistency,limiting their performance.To address these issues,we present a novel approach for medical image registration called theDual-VoxelMorph,featuring a dual-channel cross-constraint network.This innovative network utilizes both intensity and segmentation images,which share identical semantic information and feature representations.Two encoder-decoder structures calculate deformation fields for intensity and segmentation images,as generated by the dual-channel cross-constraint network.This design facilitates bidirectional communication between grayscale and segmentation information,enabling the model to better learn the corresponding grayscale and segmentation details of the same anatomical structures.To ensure semantic and directional consistency,we introduce constraints and apply the cosine similarity function to enhance semantic consistency.Evaluation on four public datasets demonstrates superior performance compared to the baselinemethod,achieving Dice scores of 79.9%,64.5%,69.9%,and 63.5%for OASIS-1,OASIS-3,LPBA40,and ADNI,respectively.

A multi-feature-based intelligent redundancy elimination scheme for cloud-assisted health systems

Redundancy elimination techniques are extensively investigated to reduce storage overheads for cloud-assisted health systems.Deduplication eliminates the redundancy of duplicate blocks by storing one physical instance referenced by multiple duplicates.Delta compression is usually regarded as a complementary technique to deduplication to further remove the redundancy of similar blocks,but our observations indicate that this is disobedient when data have sparse duplicate blocks.In addition,there are many overlapped deltas in the resemblance detection process of post-deduplication delta compression,which hinders the efficiency of delta compression and the index phase of resemblance detection inquires abundant non-similar blocks,resulting in inefficient system throughput.Therefore,a multi-feature-based redundancy elimination scheme,called MFRE,is proposed to solve these problems.The similarity feature and temporal locality feature are excavated to assist redundancy elimination where the similarity feature well expresses the duplicate attribute.Then,similarity-based dynamic post-deduplication delta compression and temporal locality-based dynamic delta compression discover more similar base blocks to minimise overlapped deltas and improve compression ratios.Moreover,the clustering method based on block-relationship and the feature index strategy based on bloom filters reduce IO overheads and improve system throughput.Experiments demonstrate that the proposed method,compared to the state-of-the-art method,improves the compression ratio and system throughput by 9.68%and 50%,respectively.

Learning Discriminatory Information for Object Detection on Urine Sediment Image

In clinical practice,the microscopic examination of urine sediment is considered an important in vitro examination with many broad applications.Measuring the amount of each type of urine sediment allows for screening,diagnosis and evaluation of kidney and urinary tract disease,providing insight into the specific type and severity.However,manual urine sediment examination is labor-intensive,time-consuming,and subjective.Traditional machine learning based object detection methods require hand-crafted features for localization and classification,which have poor generalization capabilities and are difficult to quickly and accurately detect the number of urine sediments.Deep learning based object detection methods have the potential to address the challenges mentioned above,but these methods require access to large urine sediment image datasets.Unfortunately,only a limited number of publicly available urine sediment datasets are currently available.To alleviate the lack of urine sediment datasets in medical image analysis,we propose a new dataset named UriSed2K,which contains 2465 high-quality images annotated with expert guidance.Two main challenges are associated with our dataset:a large number of small objects and the occlusion between these small objects.Our manuscript focuses on applying deep learning object detection methods to the urine sediment dataset and addressing the challenges presented by this dataset.Specifically,our goal is to improve the accuracy and efficiency of the detection algorithm and,in doing so,provide medical professionals with an automatic detector that saves time and effort.We propose an improved lightweight one-stage object detection algorithm called Discriminatory-YOLO.The proposed algorithm comprises a local context attention module and a global background suppression module,which aid the detector in distinguishing urine sediment features in the image.The local context attention module captures context information beyond the object region,while the global background suppression module emphasizes objects in uninformative backgrounds.We comprehensively evaluate our method on the UriSed2K dataset,which includes seven categories of urine sediments,such as erythrocytes(red blood cells),leukocytes(white blood cells),epithelial cells,crystals,mycetes,broken erythrocytes,and broken leukocytes,achieving the best average precision(AP)of 95.3%while taking only 10 ms per image.The source code and dataset are available at https://github.com/binghuiwu98/discriminatoryyolov5.

Symptom presentation and evolution in the first 48 hours after injury are associated with return to play after concussion in elite Rugby Union

Background:Return to play(RTP)in elite rugby is managed using a 6-stage graduated RTP protocol,which can result in clearance to play within 1 week of injury.We aimed to explore how symptom,cognitive,and balance presentation and evolution during concussion screens 2 h(head injury assessment(HIA2)and 48 h(HIA3)after injury were associated with time to RTP)to identify whether a more conservative graduated RTP may be appropriate.Methods:A retrospective cohort study was conducted in 380 concussed rugby players from elite men’s rugby over 3 consecutive seasons.Players were classified as shorter or longer returns,depending on whether RTP occurred within 7 days(allowing them to be considered to play the match 1 week after injury)or longer than 8 days,respectively.Symptom,cognitive,and balance performance during screens was assessed relative to baseline(normal or abnormal)and to the preceding screen(improving or worsening).Associations between sub-test abnormalities and RTP time were explored using odds ratios(OR,longer vs.shorter).Median day absence was compared between players with abnormal or worsening results and those whose results were normal or improving.Results:Abnormal symptom results during screens 2 h and 48 h after concussion were associated with longer return time(HIA2:OR=2.21,95%confidence interval(95%CI):1.39-3.50;HIA3:OR=3.30,95%CI:1.89-5.75).Worsening symptom number or severity from the time of injury to 2 h and 48 h post-injury was associated with longer return(HIA2:OR=2.49,95%CI:1.36-4.58;HIA3:OR=3.34,95%CI:1.10-10.15).Median days absence was greater in players with abnormal symptom results at both HIA2 and HIA3.Cognitive and balance performance were not associated with longer return and did not affect median days absence.Conclusion:Symptom presentation and evolution within 48 h of concussion were associated with longer RTP times.This may guide a more conservative approach to RTP,while still adhering to individualized concussion management principles.

美国《保健物理》(Health Physics)杂志英文摘要(2023年126卷第1期)

Radiation Protection Considerations for Cancer Patients with End-stage Renal Disease Receiving 131 I Treatment Matthew Louis1,Emmanuel M.Mate-Kole1,Landon Aziz2,Shaheen A.Dewji1(1.Nuclear and Radiological Engineering and Medical Physics Programs,Georgia Institute of Technology,770 State Street NW,Atlanta,GA 30332-0405;2.Houston Methodist Hospital,6565 Fannin St,Ste.SM539,Houston,TX 77030).

Enhanced Temporal Correlation for Universal Lesion Detection

Universal lesion detection(ULD)methods for computed tomography(CT)images play a vital role in the modern clinical medicine and intelligent automation.It is well known that single 2D CT slices lack spatial-temporal characteristics and contextual information compared to 3D CT blocks.However,3D CT blocks necessitate significantly higher hardware resources during the learning phase.Therefore,efficiently exploiting temporal correlation and spatial-temporal features of 2D CT slices is crucial for ULD tasks.In this paper,we propose a ULD network with the enhanced temporal correlation for this purpose,named TCE-Net.The designed TCE module is applied to enrich the discriminate feature representation of multiple sequential CT slices.Besides,we employ multi-scale feature maps to facilitate the localization and detection of lesions in various sizes.Extensive experiments are conducted on the DeepLesion benchmark demonstrate that thismethod achieves 66.84%and 78.18%for FS@0.5 and FS@1.0,respectively,outperforming compared state-of-the-art methods.

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.

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.

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.