BACKGROUND Drug utilization research has an important role in assisting the healthcare administration to know,compute,and refine the prescription whose principal objective is to enable the rational use of drugs.Resear...BACKGROUND Drug utilization research has an important role in assisting the healthcare administration to know,compute,and refine the prescription whose principal objective is to enable the rational use of drugs.Research in developing nations relating to the cost of treatment is scarce when compared with developed countries.Thus,the drug utilization research studies from developing nations are most needed,and their number has been growing.AIM To evaluate patterns of utilization of antipsychotic drugs and direct medical cost analysis in patients newly diagnosed with schizophrenia.METHODS The present study was observational in type and based on a retrospective cohort to evaluate patterns of utilization of antipsychotic drugs using World Health Organization(WHO)core prescribing indicators and anatomical therapeutic chemical/defined daily dose indicators.We also calculated direct medical costs for a period of 6 months.RESULTS This study has found that atypical antipsychotics are the mainstay of treatment for schizophrenia in every age group and subcategories of schizophrenia.The evaluation based on WHO prescribing indicators showed a low average number of drugs per prescription and low prescribing frequency of antipsychotics from the National List of Essential Medicines 2015 and the WHO Essential Medicines List 2019.The total mean drug cost of our study was 1396 Indian rupees.The total mean cost due to the investigation in our study was 1017.34 Indian rupees.Therefore,the total mean direct medical cost incurred on patients in our study was 4337.28 Indian rupees.CONCLUSION The information from the present study can be used for reviewing and updating treatment policy at the institutional level.展开更多
The rapid growth of biomedical data,particularly multi-omics data including genomes,transcriptomics,proteomics,metabolomics,and epigenomics,medical research and clinical decision-making confront both new opportunities...The rapid growth of biomedical data,particularly multi-omics data including genomes,transcriptomics,proteomics,metabolomics,and epigenomics,medical research and clinical decision-making confront both new opportunities and obstacles.The huge and diversified nature of these datasets cannot always be managed using traditional data analysis methods.As a consequence,deep learning has emerged as a strong tool for analysing numerous omics data due to its ability to handle complex and non-linear relationships.This paper explores the fundamental concepts of deep learning and how they are used in multi-omics medical data mining.We demonstrate how autoencoders,variational autoencoders,multimodal models,attention mechanisms,transformers,and graph neural networks enable pattern analysis and recognition across all omics data.Deep learning has been found to be effective in illness classification,biomarker identification,gene network learning,and therapeutic efficacy prediction.We also consider critical problems like as data quality,model explainability,whether findings can be repeated,and computational power requirements.We now consider future elements of combining omics with clinical and imaging data,explainable AI,federated learning,and real-time diagnostics.Overall,this study emphasises the need of collaborating across disciplines to advance deep learning-based multi-omics research for precision medicine and comprehending complicated disorders.展开更多
On October 18,2017,the 19th National Congress Report called for the implementation of the Healthy China Strategy.The development of biomedical data plays a pivotal role in advancing this strategy.Since the 18th Nation...On October 18,2017,the 19th National Congress Report called for the implementation of the Healthy China Strategy.The development of biomedical data plays a pivotal role in advancing this strategy.Since the 18th National Congress of the Communist Party of China,China has vigorously promoted the integration and implementation of the Healthy China and Digital China strategies.The National Health Commission has prioritized the development of health and medical big data,issuing policies to promote standardized applica-tions and foster innovation in"Internet+Healthcare."Biomedical data has significantly contributed to preci-sion medicine,personalized health management,drug development,disease diagnosis,public health monitor-ing,and epidemic prediction capabilities.展开更多
Welcome to the 4th volume of Biomedical Engineering Communications the first issue of 2025!Biomedical engineering is a rapidly evolving field that combines engineering principles with medical and biological sciences t...Welcome to the 4th volume of Biomedical Engineering Communications the first issue of 2025!Biomedical engineering is a rapidly evolving field that combines engineering principles with medical and biological sciences to create innovative healthcare technologies.Biomedical engineering brings an interdisciplinary,problem-solving approach to bioengineering,biology and medicine.This interdisciplinary field is essential for developing advanced medical devices,diagnostic tools,and therapeutic solutions that enhance patient care and improve health outcomes.It allows them to develop technologies and systems that directly contribute to diagnosing,treating and preventing diseases.展开更多
As the healthcare system advances and expands in its services,the challenges of remaining efficient become more important.Emergency medical services(EMS)are vital cornerstones of communities.In many countries,EMS is a...As the healthcare system advances and expands in its services,the challenges of remaining efficient become more important.Emergency medical services(EMS)are vital cornerstones of communities.In many countries,EMS is available for every individual,regardless of their social or insurance status,via a toll-free telephone number.Understanding the risk factors for busy days in EMSs might be helpful for improving the allocation of resources,which is the key to better care for all patients in the prehospital setting.[1]An important factor influencing ambulance call volume could be the interplay of public behavior and weather.展开更多
This paper reconstructs in detail the course leading to the inception of the Chinese material medica(CMM)research at the Peking Union Medical College(PUMC)in 1920.By analyzing the primary materials from several archiv...This paper reconstructs in detail the course leading to the inception of the Chinese material medica(CMM)research at the Peking Union Medical College(PUMC)in 1920.By analyzing the primary materials from several archives,it provides,for the first time,a historiographical account of the major events and key figures in the process.These include the China Medical Board(CMB)Commission to East Asia in 1915 that shaped the attitudes of Drs.William H.Welch and Simon Flexner,the PUMC’s chief scientific architects,toward CMM and its scientific investigation;the influence of medical missionaries and Japanese scientists on these attitudes;the medical leaders’decisive roles in recruiting Ralph G.Mills and Bernard E.Read,two of medical missionaries with strong interests in and actual studies on CMM,to the PUMC,which serendipitously made them central figures associated with the CMM research at the College;and finally the critical role of Mills and other medical missionaries in introducing CMM research,both concept and material,to the CMB executives and in their reconciliating the research subject with the institutional aims.The findings of the study contextualize the inception of CMM research at PUMC from the perspective of broader narrative of transnational circulation and recognition of medical knowledge and highlight the intermediatory roles played by medical missionaries that were critical in the intersection between traditional Chinese medicine(TCM)and scientific medicine.The study also reveals multiple serendipitous occurrences associated with the eventual inception of the program,thus offers a fresh interpretation of the beginning of the most impactful research program of scientizing TCM in the first half of the 20th century.展开更多
The eighteenth-century Vietnamese medical work Treatise on Medical Knowledge by Hai Thuong(Hai Thuong Y Tong Tam Linh海上医宗心领),authored by the eminent physician Le Huu Trac黎有卓,reflects the transmission,applicat...The eighteenth-century Vietnamese medical work Treatise on Medical Knowledge by Hai Thuong(Hai Thuong Y Tong Tam Linh海上医宗心领),authored by the eminent physician Le Huu Trac黎有卓,reflects the transmission,application,and innovation of traditional Chinese medicine(TCM)in Vietnam.It encompasses principles,methods,formulas,medicines,clinical specialties,and clinical medical cases.Its primary textual foundation derives from Feng’s Tips and Secret Records(Fengshi jinnang milu冯氏锦囊秘录)by Feng Zhaozhang冯兆张(late Ming–early Qing),supplemented by Ming-era works such as Zhang Jingyue’s张景岳Jingyue’s Complete Compendium(Jingyue quanshu景岳全书),Li Chan’s李梴The Gateway to Medicine(Yixue rumen医学入门),and Zhao Xianke’s赵献可Thorough Knowledge of Medicine(Yi guan医贯).By synthesizing,enriching,streamlining,and annotating the quintessence of Ming-Qing medical texts,Le Huu Trac established the framework of traditional Vietnamese medicine,innovating upon TCM.Treatise on Medical Knowledge by Hai Thuong emphasizes the concept of“congenital water and fire”(先天水火),attributing the patho-mechanism of critical illnesses to the depletion of true yin and true yang(真阴真阳亏损),while expanding the indications of classical TCM formulas such as the Six-Ingredient Pill(六味丸)and Eight-Ingredient Pill(八味丸)through variations in their constituents,thus attaining the further localization of TCM.It advances the view that“there are no cold damage patterns(伤寒症)in Lingnan,rendering the Ephedra Decoction(麻黄汤)and Cinnamon Twig Decoction(桂枝汤)inapplicable,”as a result devising“three exterior-resolving formulas”(解表三方)and“six interior-harmonizing formulas”(和里六方)tailored to Vietnam’s climate and patient constitutions.The text also incorporates southern medicines(南药)and formulas containing them,using Han-Nom bilingual names to enhance understanding and application by the Vietnamese.Its Yang Case Collection(阳案集)and Yin Case Collection(阴案集),documenting both successful and unsuccessful treatments of severe illnesses,exemplify Vietnamese physicians’local exploration and practice of Chinese medical knowledge,embodying a profound understanding and flexible application of TCM by Vietnamese physicians.展开更多
Objective:To explore the application value of the modified Peyton’s four-step teaching method in bridge experimental courses for undergraduate medical students.Methods:100 undergraduate medical students from Bethune ...Objective:To explore the application value of the modified Peyton’s four-step teaching method in bridge experimental courses for undergraduate medical students.Methods:100 undergraduate medical students from Bethune Hospital of Shanxi from July 2023 to July 2024 were selected and grouped using a random number method.The control group received a conventional training program,while the observation group received a modified Peyton’s four-step teaching and training program.The DOPS scores and teaching satisfaction scores of the two groups of undergraduate medical students were compared.Results:After intervention,the scores of each dimension of the DOPS for the undergraduate medical students in the observation group were higher than those in the control group.The teaching satisfaction scores of the undergraduate medical students after teaching were lower in the control group than in the observation group.The differences between the two groups were statistically significant(P<0.05).Conclusion:The modified Peyton’s four-step teaching program developed in this study can promote teaching and learning methods for undergraduate medical students,improve teaching satisfaction levels,and help administrators stabilize the medical team.展开更多
Emergency medical services (EMS) are a vital element of the public healthcare system in China,^([1])providing an opportunity to respond to critical medical conditions and save people’s lives.^([2])The accessibility o...Emergency medical services (EMS) are a vital element of the public healthcare system in China,^([1])providing an opportunity to respond to critical medical conditions and save people’s lives.^([2])The accessibility of EMS has received considerable attention in health and transport geography studies.^([3])One of the optimal gauges for evaluating the accessibility of EMS is the response time,which is defined as the time from receiving an emergency call to the arrival of an ambulance.^([4])Beijing has already reduced the response time to approximately12 min,and the next goal is to ensure that the response time across Beijing does not exceed 12 min (the information comes from the Beijing Emergency Medical Center).展开更多
On April 26,2025,the Second Tsinghua Medicine Journal Innovation Conference convened in Beijing.Centered on the theme“AI-driven Academic:Shaping the Next Frontier”the Conference brought together journal editors,medi...On April 26,2025,the Second Tsinghua Medicine Journal Innovation Conference convened in Beijing.Centered on the theme“AI-driven Academic:Shaping the Next Frontier”the Conference brought together journal editors,medical researchers,and science policy experts to examine how data and artificial intelligence(AI)are reshaping scholarly publishing.Two keynote speeches set the stage:the first analyzed the opportunities for hospital-based research arising from new journal policies,data infrastructure,and enabling technologies;the second introduced the latest advances in general AI and their implications for academic publishing security and integrity.展开更多
Background:An urban medical group in Dapeng New District was established in 2017 with the objective of enhancing outcomes for common diseases and reinforcing primary care by integrating high‐level hospitals with prim...Background:An urban medical group in Dapeng New District was established in 2017 with the objective of enhancing outcomes for common diseases and reinforcing primary care by integrating high‐level hospitals with primary health services.This study aimed to evaluate the performance of the urban medical group using the triangular value chain framework.Methods:The evaluation was conducted using the Donabedian model,focusing on three key dimensions:safety and quality,accessibility,and affordability.Longitudinal data were collected from 2016 to 2022 through government annual reports,the medical insurance bureau,and hospital information systems.Preprogram and postprogram outcome measurements were compared to assess differences and trends,providing a clear picture of the program's effectiveness.Results:Accessibility improved significantly,with the number of hospital beds per 1000 residents increasing from 2.62 in 2017 to 3.76 in 2022.The availability of general practitioners(GPs)also rose markedly,from 0 per 10,000 residents in 2017 to 6.27 in 2022.Regarding safety and quality,the proportion of complex medical procedures conducted within the New District expanded substantially,from 7.35%in 2017 to 38.11%in 2021.Additionally,there was an enhancement in the standardized management rate of chronic diseases.Affordability assessments showed that the proportion of medical income derived from the medical insurance fund increased by nearly 22.81 percentage points between 2012 and 2021.By 2021,75.02%of medical patients were covered by medical insurance,representing an increase of approximately 44 percentage points from 31.19%in 2012.Conclusions:The implementation of the urban medical group in Dapeng New District has led to substantial improvements in healthcare accessibility,safety and quality,and affordability.Future initiatives will focus on advancing the“Dapeng Mode”to generate exemplary healthcare outcomes and minimize disparities in basic health services and health status between urban and rural populations.The reform agenda includes piloting payment reforms and innovative payment models within the Dapeng group,complemented by a health assessment and performance incentive system aimed at encouraging healthcare institutions to prioritize health management.展开更多
Background: Clinical decision support tools provide suggestions to support healthcare providers and clinicians, as they attend to patients. Clinicians use these tools to rapidly consult the evidence at the point of ca...Background: Clinical decision support tools provide suggestions to support healthcare providers and clinicians, as they attend to patients. Clinicians use these tools to rapidly consult the evidence at the point of care, a practice which has been found to reduce the time patients spend in hospitals, promote the quality of care and improve healthcare outcomes. Such tools include Medscape, VisualDx, Clinical Key, DynaMed, BMJ Best Practice and UpToDate. However, use of such tools has not yet been fully embraced in low-resource settings such as Uganda. Objective: This paper intends to collate data on the use and uptake of one such tool, UpToDate, which was provided at no cost to five medical schools in Uganda. Methods: Free access to UpToDate was granted through the IP addresses of five medical schools in Uganda in collaboration with Better Evidence at The Global Health Delivery Project at Harvard and Brigham and Women’s Hospital and Wolters Kluwer Health. Following the donation, medical librarians in the respective institutions conducted training sessions and created awareness of the tool. Usage data was aggregated, based on logins and content views, presented and analyzed using Excel tables and graphs. Results: The data shows similar trends in increased usage over the period of August 2022 to August 2023 across the five medical schools. The most common topics viewed, mode of access (using either the computer or the mobile app), total usage by institution, ratio of uses to eligible users by institution and ratio of uses to students by institution are shared. Conclusion: The study revealed that the tool was used by various user categories across the institutions with similar steady improved usage over the year. These results can inform the librarians as they encourage their respective institutions to continue using the tool to support uptake of point-of-care tools in clinical practice.展开更多
Additive manufacturing has emerged as a transformative technology for producing biomedical metals and implants,offering the potential to revolutionize patient care and treatment outcomes.This article reviews the recen...Additive manufacturing has emerged as a transformative technology for producing biomedical metals and implants,offering the potential to revolutionize patient care and treatment outcomes.This article reviews the recent advances in additive manufacturing(AM)of biomedical metal implants,especially load-bearing biomedical alloys,biodegradable alloys,novel metals,and 4D printing,whose properties are systematically assessed to facilitate material selection for specific medical applications.The applications of the most cutting-edge artificial intelligence in AM and surface functional modification are also presented.This article also explores the application of AM in various medical specialties,such as orthopedics,dentistry,cardiology,and neurosurgery,demonstrating its potential to solve complex clinical challenges and advance patient-centered healthcare solutions.Furthermore,it highlights the critical roles of AM in shaping the future of medical implant manufacturing.The optimistic outlook on the bright future of AM in medical metals delivers personalized,high-performance medical implants that improve patient treatment outcomes and well-being.展开更多
Brain-computer interfaces(BCIs)represent an emerging technology that facilitates direct communication between the brain and external devices.In recent years,numerous review articles have explored various aspects of BC...Brain-computer interfaces(BCIs)represent an emerging technology that facilitates direct communication between the brain and external devices.In recent years,numerous review articles have explored various aspects of BCIs,including their fundamental principles,technical advancements,and applications in specific domains.However,these reviews often focus on signal processing,hardware development,or limited applications such as motor rehabilitation or communication.This paper aims to offer a comprehensive review of recent electroencephalogram(EEG)-based BCI applications in the medical field across 8 critical areas,encompassing rehabilitation,daily communication,epilepsy,cerebral resuscitation,sleep,neurodegenerative diseases,anesthesiology,and emotion recognition.Moreover,the current challenges and future trends of BCIs were also discussed,including personal privacy and ethical concerns,network security vulnerabilities,safety issues,and biocompatibility.展开更多
Biomedical big data,characterized by its massive scale,multi-dimensionality,and heterogeneity,offers novel perspectives for disease research,elucidates biological principles,and simultaneously prompts changes in relat...Biomedical big data,characterized by its massive scale,multi-dimensionality,and heterogeneity,offers novel perspectives for disease research,elucidates biological principles,and simultaneously prompts changes in related research methodologies.Biomedical ontology,as a shared formal conceptual system,not only offers standardized terms for multi-source biomedical data but also provides a solid data foundation and framework for biomedical research.In this review,we summarize enrichment analysis and deep learning for biomedical ontology based on its structure and semantic annotation properties,highlighting how technological advancements are enabling the more comprehensive use of ontology information.Enrichment analysis represents an important application of ontology to elucidate the potential biological significance for a particular molecular list.Deep learning,on the other hand,represents an increasingly powerful analytical tool that can be more widely combined with ontology for analysis and prediction.With the continuous evolution of big data technologies,the integration of these technologies with biomedical ontologies is opening up exciting new possibilities for advancing biomedical research.展开更多
Convolutional neural network(CNN)with the encoder-decoder structure is popular in medical image segmentation due to its excellent local feature extraction ability but it faces limitations in capturing the global featu...Convolutional neural network(CNN)with the encoder-decoder structure is popular in medical image segmentation due to its excellent local feature extraction ability but it faces limitations in capturing the global feature.The transformer can extract the global information well but adapting it to small medical datasets is challenging and its computational complexity can be heavy.In this work,a serial and parallel network is proposed for the accurate 3D medical image segmentation by combining CNN and transformer and promoting feature interactions across various semantic levels.The core components of the proposed method include the cross window self-attention based transformer(CWST)and multi-scale local enhanced(MLE)modules.The CWST module enhances the global context understanding by partitioning 3D images into non-overlapping windows and calculating sparse global attention between windows.The MLE module selectively fuses features by computing the voxel attention between different branch features,and uses convolution to strengthen the dense local information.The experiments on the prostate,atrium,and pancreas MR/CT image datasets consistently demonstrate the advantage of the proposed method over six popular segmentation models in both qualitative evaluation and quantitative indexes such as dice similarity coefficient,Intersection over Union,95%Hausdorff distance and average symmetric surface distance.展开更多
Convolutional neural networks(CNNs)-based medical image segmentation technologies have been widely used in medical image segmentation because of their strong representation and generalization abilities.However,due to ...Convolutional neural networks(CNNs)-based medical image segmentation technologies have been widely used in medical image segmentation because of their strong representation and generalization abilities.However,due to the inability to effectively capture global information from images,CNNs can easily lead to loss of contours and textures in segmentation results.Notice that the transformer model can effectively capture the properties of long-range dependencies in the image,and furthermore,combining the CNN and the transformer can effectively extract local details and global contextual features of the image.Motivated by this,we propose a multi-branch and multi-scale attention network(M2ANet)for medical image segmentation,whose architecture consists of three components.Specifically,in the first component,we construct an adaptive multi-branch patch module for parallel extraction of image features to reduce information loss caused by downsampling.In the second component,we apply residual block to the well-known convolutional block attention module to enhance the network’s ability to recognize important features of images and alleviate the phenomenon of gradient vanishing.In the third component,we design a multi-scale feature fusion module,in which we adopt adaptive average pooling and position encoding to enhance contextual features,and then multi-head attention is introduced to further enrich feature representation.Finally,we validate the effectiveness and feasibility of the proposed M2ANet method through comparative experiments on four benchmark medical image segmentation datasets,particularly in the context of preserving contours and textures.展开更多
Image segmentation is attracting increasing attention in the field of medical image analysis.Since widespread utilization across various medical applications,ensuring and improving segmentation accuracy has become a c...Image segmentation is attracting increasing attention in the field of medical image analysis.Since widespread utilization across various medical applications,ensuring and improving segmentation accuracy has become a crucial topic of research.With advances in deep learning,researchers have developed numerous methods that combine Transformers and convolutional neural networks(CNNs)to create highly accurate models for medical image segmentation.However,efforts to further enhance accuracy by developing larger and more complex models or training with more extensive datasets,significantly increase computational resource consumption.To address this problem,we propose BiCLIP-nnFormer(the prefix"Bi"refers to the use of two distinct CLIP models),a virtual multimodal instrument that leverages CLIP models to enhance the segmentation performance of a medical segmentation model nnFormer.Since two CLIP models(PMC-CLIP and CoCa-CLIP)are pre-trained on large datasets,they do not require additional training,thus conserving computation resources.These models are used offline to extract image and text embeddings from medical images.These embeddings are then processed by the proposed 3D CLIP adapter,which adapts the CLIP knowledge for segmentation tasks by fine-tuning.Finally,the adapted embeddings are fused with feature maps extracted from the nnFormer encoder for generating predicted masks.This process enriches the representation capabilities of the feature maps by integrating global multimodal information,leading to more precise segmentation predictions.We demonstrate the superiority of BiCLIP-nnFormer and the effectiveness of using CLIP models to enhance nnFormer through experiments on two public datasets,namely the Synapse multi-organ segmentation dataset(Synapse)and the Automatic Cardiac Diagnosis Challenge dataset(ACDC),as well as a self-annotated lung multi-category segmentation dataset(LMCS).展开更多
【Objective】Medical imaging data has great value,but it contains a significant amount of sensitive information about patients.At present,laws and regulations regarding to the de-identification of medical imaging data...【Objective】Medical imaging data has great value,but it contains a significant amount of sensitive information about patients.At present,laws and regulations regarding to the de-identification of medical imaging data are not clearly defined around the world.This study aims to develop a tool that meets compliance-driven desensitization requirements tailored to diverse research needs.【Methods】To enhance the security of medical image data,we designed and implemented a DICOM format medical image de-identification system on the Windows operating system.【Results】Our custom de-identification system is adaptable to the legal standards of different countries and can accommodate specific research demands.The system offers both web-based online and desktop offline de-identification capabilities,enabling customization of de-identification rules and facilitating batch processing to improve efficiency.【Conclusions】This medical image de-identification system robustly strengthens the stewardship of sensitive medical data,aligning with data security protection requirements while facilitating the sharing and utilization of medical image data.This approach unlocks the intrinsic value inherent in such datasets.展开更多
Objective:The objective of this research is to thoroughly investigate the extent of mutual interference among clinical internships,postgraduate entrance examinations,and employment by examining engineering contradicti...Objective:The objective of this research is to thoroughly investigate the extent of mutual interference among clinical internships,postgraduate entrance examinations,and employment by examining engineering contradictions,thus offering theoretical insights and guidance for medical students to attain high-quality outcomes in clinical internships.Methods:A combination of literature reviews,questionnaires,interviews,and observations of internships was utilized,followed by a statistical analysis to assess the levels of interference among the three factors.Results:The senior participants achieved significantly higher scores than their junior counterparts in evaluations of comprehensive humanistic quality,understanding professional values,communication abilities,clinical skills,and attitudes towards learning,with differences that were statistically significant(p<0.05).After applying an interactive training approach that merges early clinical practice with foundational medical education,both groups displayed notable enhancements in activity content,formats,instructor attitudes,clinical performance,and the blending of theory with practice(p<0.05).Conclusion:By emphasizing‘early clinical’education,students are effectively engaged in clinical practice through active involvement,leading to feedback-oriented training.This strategy not only improves the overall quality of internships but also reduces the risk of scheduling conflicts with postgraduate entrance examinations and employment opportunities.展开更多
文摘BACKGROUND Drug utilization research has an important role in assisting the healthcare administration to know,compute,and refine the prescription whose principal objective is to enable the rational use of drugs.Research in developing nations relating to the cost of treatment is scarce when compared with developed countries.Thus,the drug utilization research studies from developing nations are most needed,and their number has been growing.AIM To evaluate patterns of utilization of antipsychotic drugs and direct medical cost analysis in patients newly diagnosed with schizophrenia.METHODS The present study was observational in type and based on a retrospective cohort to evaluate patterns of utilization of antipsychotic drugs using World Health Organization(WHO)core prescribing indicators and anatomical therapeutic chemical/defined daily dose indicators.We also calculated direct medical costs for a period of 6 months.RESULTS This study has found that atypical antipsychotics are the mainstay of treatment for schizophrenia in every age group and subcategories of schizophrenia.The evaluation based on WHO prescribing indicators showed a low average number of drugs per prescription and low prescribing frequency of antipsychotics from the National List of Essential Medicines 2015 and the WHO Essential Medicines List 2019.The total mean drug cost of our study was 1396 Indian rupees.The total mean cost due to the investigation in our study was 1017.34 Indian rupees.Therefore,the total mean direct medical cost incurred on patients in our study was 4337.28 Indian rupees.CONCLUSION The information from the present study can be used for reviewing and updating treatment policy at the institutional level.
文摘The rapid growth of biomedical data,particularly multi-omics data including genomes,transcriptomics,proteomics,metabolomics,and epigenomics,medical research and clinical decision-making confront both new opportunities and obstacles.The huge and diversified nature of these datasets cannot always be managed using traditional data analysis methods.As a consequence,deep learning has emerged as a strong tool for analysing numerous omics data due to its ability to handle complex and non-linear relationships.This paper explores the fundamental concepts of deep learning and how they are used in multi-omics medical data mining.We demonstrate how autoencoders,variational autoencoders,multimodal models,attention mechanisms,transformers,and graph neural networks enable pattern analysis and recognition across all omics data.Deep learning has been found to be effective in illness classification,biomarker identification,gene network learning,and therapeutic efficacy prediction.We also consider critical problems like as data quality,model explainability,whether findings can be repeated,and computational power requirements.We now consider future elements of combining omics with clinical and imaging data,explainable AI,federated learning,and real-time diagnostics.Overall,this study emphasises the need of collaborating across disciplines to advance deep learning-based multi-omics research for precision medicine and comprehending complicated disorders.
文摘On October 18,2017,the 19th National Congress Report called for the implementation of the Healthy China Strategy.The development of biomedical data plays a pivotal role in advancing this strategy.Since the 18th National Congress of the Communist Party of China,China has vigorously promoted the integration and implementation of the Healthy China and Digital China strategies.The National Health Commission has prioritized the development of health and medical big data,issuing policies to promote standardized applica-tions and foster innovation in"Internet+Healthcare."Biomedical data has significantly contributed to preci-sion medicine,personalized health management,drug development,disease diagnosis,public health monitor-ing,and epidemic prediction capabilities.
文摘Welcome to the 4th volume of Biomedical Engineering Communications the first issue of 2025!Biomedical engineering is a rapidly evolving field that combines engineering principles with medical and biological sciences to create innovative healthcare technologies.Biomedical engineering brings an interdisciplinary,problem-solving approach to bioengineering,biology and medicine.This interdisciplinary field is essential for developing advanced medical devices,diagnostic tools,and therapeutic solutions that enhance patient care and improve health outcomes.It allows them to develop technologies and systems that directly contribute to diagnosing,treating and preventing diseases.
文摘As the healthcare system advances and expands in its services,the challenges of remaining efficient become more important.Emergency medical services(EMS)are vital cornerstones of communities.In many countries,EMS is available for every individual,regardless of their social or insurance status,via a toll-free telephone number.Understanding the risk factors for busy days in EMSs might be helpful for improving the allocation of resources,which is the key to better care for all patients in the prehospital setting.[1]An important factor influencing ambulance call volume could be the interplay of public behavior and weather.
文摘This paper reconstructs in detail the course leading to the inception of the Chinese material medica(CMM)research at the Peking Union Medical College(PUMC)in 1920.By analyzing the primary materials from several archives,it provides,for the first time,a historiographical account of the major events and key figures in the process.These include the China Medical Board(CMB)Commission to East Asia in 1915 that shaped the attitudes of Drs.William H.Welch and Simon Flexner,the PUMC’s chief scientific architects,toward CMM and its scientific investigation;the influence of medical missionaries and Japanese scientists on these attitudes;the medical leaders’decisive roles in recruiting Ralph G.Mills and Bernard E.Read,two of medical missionaries with strong interests in and actual studies on CMM,to the PUMC,which serendipitously made them central figures associated with the CMM research at the College;and finally the critical role of Mills and other medical missionaries in introducing CMM research,both concept and material,to the CMB executives and in their reconciliating the research subject with the institutional aims.The findings of the study contextualize the inception of CMM research at PUMC from the perspective of broader narrative of transnational circulation and recognition of medical knowledge and highlight the intermediatory roles played by medical missionaries that were critical in the intersection between traditional Chinese medicine(TCM)and scientific medicine.The study also reveals multiple serendipitous occurrences associated with the eventual inception of the program,thus offers a fresh interpretation of the beginning of the most impactful research program of scientizing TCM in the first half of the 20th century.
文摘The eighteenth-century Vietnamese medical work Treatise on Medical Knowledge by Hai Thuong(Hai Thuong Y Tong Tam Linh海上医宗心领),authored by the eminent physician Le Huu Trac黎有卓,reflects the transmission,application,and innovation of traditional Chinese medicine(TCM)in Vietnam.It encompasses principles,methods,formulas,medicines,clinical specialties,and clinical medical cases.Its primary textual foundation derives from Feng’s Tips and Secret Records(Fengshi jinnang milu冯氏锦囊秘录)by Feng Zhaozhang冯兆张(late Ming–early Qing),supplemented by Ming-era works such as Zhang Jingyue’s张景岳Jingyue’s Complete Compendium(Jingyue quanshu景岳全书),Li Chan’s李梴The Gateway to Medicine(Yixue rumen医学入门),and Zhao Xianke’s赵献可Thorough Knowledge of Medicine(Yi guan医贯).By synthesizing,enriching,streamlining,and annotating the quintessence of Ming-Qing medical texts,Le Huu Trac established the framework of traditional Vietnamese medicine,innovating upon TCM.Treatise on Medical Knowledge by Hai Thuong emphasizes the concept of“congenital water and fire”(先天水火),attributing the patho-mechanism of critical illnesses to the depletion of true yin and true yang(真阴真阳亏损),while expanding the indications of classical TCM formulas such as the Six-Ingredient Pill(六味丸)and Eight-Ingredient Pill(八味丸)through variations in their constituents,thus attaining the further localization of TCM.It advances the view that“there are no cold damage patterns(伤寒症)in Lingnan,rendering the Ephedra Decoction(麻黄汤)and Cinnamon Twig Decoction(桂枝汤)inapplicable,”as a result devising“three exterior-resolving formulas”(解表三方)and“six interior-harmonizing formulas”(和里六方)tailored to Vietnam’s climate and patient constitutions.The text also incorporates southern medicines(南药)and formulas containing them,using Han-Nom bilingual names to enhance understanding and application by the Vietnamese.Its Yang Case Collection(阳案集)and Yin Case Collection(阴案集),documenting both successful and unsuccessful treatments of severe illnesses,exemplify Vietnamese physicians’local exploration and practice of Chinese medical knowledge,embodying a profound understanding and flexible application of TCM by Vietnamese physicians.
文摘Objective:To explore the application value of the modified Peyton’s four-step teaching method in bridge experimental courses for undergraduate medical students.Methods:100 undergraduate medical students from Bethune Hospital of Shanxi from July 2023 to July 2024 were selected and grouped using a random number method.The control group received a conventional training program,while the observation group received a modified Peyton’s four-step teaching and training program.The DOPS scores and teaching satisfaction scores of the two groups of undergraduate medical students were compared.Results:After intervention,the scores of each dimension of the DOPS for the undergraduate medical students in the observation group were higher than those in the control group.The teaching satisfaction scores of the undergraduate medical students after teaching were lower in the control group than in the observation group.The differences between the two groups were statistically significant(P<0.05).Conclusion:The modified Peyton’s four-step teaching program developed in this study can promote teaching and learning methods for undergraduate medical students,improve teaching satisfaction levels,and help administrators stabilize the medical team.
基金supported by National Key Research & Development Program of China (2022YFC3006201)。
文摘Emergency medical services (EMS) are a vital element of the public healthcare system in China,^([1])providing an opportunity to respond to critical medical conditions and save people’s lives.^([2])The accessibility of EMS has received considerable attention in health and transport geography studies.^([3])One of the optimal gauges for evaluating the accessibility of EMS is the response time,which is defined as the time from receiving an emergency call to the arrival of an ambulance.^([4])Beijing has already reduced the response time to approximately12 min,and the next goal is to ensure that the response time across Beijing does not exceed 12 min (the information comes from the Beijing Emergency Medical Center).
文摘On April 26,2025,the Second Tsinghua Medicine Journal Innovation Conference convened in Beijing.Centered on the theme“AI-driven Academic:Shaping the Next Frontier”the Conference brought together journal editors,medical researchers,and science policy experts to examine how data and artificial intelligence(AI)are reshaping scholarly publishing.Two keynote speeches set the stage:the first analyzed the opportunities for hospital-based research arising from new journal policies,data infrastructure,and enabling technologies;the second introduced the latest advances in general AI and their implications for academic publishing security and integrity.
文摘Background:An urban medical group in Dapeng New District was established in 2017 with the objective of enhancing outcomes for common diseases and reinforcing primary care by integrating high‐level hospitals with primary health services.This study aimed to evaluate the performance of the urban medical group using the triangular value chain framework.Methods:The evaluation was conducted using the Donabedian model,focusing on three key dimensions:safety and quality,accessibility,and affordability.Longitudinal data were collected from 2016 to 2022 through government annual reports,the medical insurance bureau,and hospital information systems.Preprogram and postprogram outcome measurements were compared to assess differences and trends,providing a clear picture of the program's effectiveness.Results:Accessibility improved significantly,with the number of hospital beds per 1000 residents increasing from 2.62 in 2017 to 3.76 in 2022.The availability of general practitioners(GPs)also rose markedly,from 0 per 10,000 residents in 2017 to 6.27 in 2022.Regarding safety and quality,the proportion of complex medical procedures conducted within the New District expanded substantially,from 7.35%in 2017 to 38.11%in 2021.Additionally,there was an enhancement in the standardized management rate of chronic diseases.Affordability assessments showed that the proportion of medical income derived from the medical insurance fund increased by nearly 22.81 percentage points between 2012 and 2021.By 2021,75.02%of medical patients were covered by medical insurance,representing an increase of approximately 44 percentage points from 31.19%in 2012.Conclusions:The implementation of the urban medical group in Dapeng New District has led to substantial improvements in healthcare accessibility,safety and quality,and affordability.Future initiatives will focus on advancing the“Dapeng Mode”to generate exemplary healthcare outcomes and minimize disparities in basic health services and health status between urban and rural populations.The reform agenda includes piloting payment reforms and innovative payment models within the Dapeng group,complemented by a health assessment and performance incentive system aimed at encouraging healthcare institutions to prioritize health management.
文摘Background: Clinical decision support tools provide suggestions to support healthcare providers and clinicians, as they attend to patients. Clinicians use these tools to rapidly consult the evidence at the point of care, a practice which has been found to reduce the time patients spend in hospitals, promote the quality of care and improve healthcare outcomes. Such tools include Medscape, VisualDx, Clinical Key, DynaMed, BMJ Best Practice and UpToDate. However, use of such tools has not yet been fully embraced in low-resource settings such as Uganda. Objective: This paper intends to collate data on the use and uptake of one such tool, UpToDate, which was provided at no cost to five medical schools in Uganda. Methods: Free access to UpToDate was granted through the IP addresses of five medical schools in Uganda in collaboration with Better Evidence at The Global Health Delivery Project at Harvard and Brigham and Women’s Hospital and Wolters Kluwer Health. Following the donation, medical librarians in the respective institutions conducted training sessions and created awareness of the tool. Usage data was aggregated, based on logins and content views, presented and analyzed using Excel tables and graphs. Results: The data shows similar trends in increased usage over the period of August 2022 to August 2023 across the five medical schools. The most common topics viewed, mode of access (using either the computer or the mobile app), total usage by institution, ratio of uses to eligible users by institution and ratio of uses to students by institution are shared. Conclusion: The study revealed that the tool was used by various user categories across the institutions with similar steady improved usage over the year. These results can inform the librarians as they encourage their respective institutions to continue using the tool to support uptake of point-of-care tools in clinical practice.
基金the financial supports from the ECU industrial Grant(No.G1006320)ECU DVC strategic research support fund(Grant Number 23965)National Natural Science Foundation of China under Grant Nos.52404382,52274387 and 52311530772。
文摘Additive manufacturing has emerged as a transformative technology for producing biomedical metals and implants,offering the potential to revolutionize patient care and treatment outcomes.This article reviews the recent advances in additive manufacturing(AM)of biomedical metal implants,especially load-bearing biomedical alloys,biodegradable alloys,novel metals,and 4D printing,whose properties are systematically assessed to facilitate material selection for specific medical applications.The applications of the most cutting-edge artificial intelligence in AM and surface functional modification are also presented.This article also explores the application of AM in various medical specialties,such as orthopedics,dentistry,cardiology,and neurosurgery,demonstrating its potential to solve complex clinical challenges and advance patient-centered healthcare solutions.Furthermore,it highlights the critical roles of AM in shaping the future of medical implant manufacturing.The optimistic outlook on the bright future of AM in medical metals delivers personalized,high-performance medical implants that improve patient treatment outcomes and well-being.
基金supported by the National Key R&D Program of China(2021YFF1200602)the National Science Fund for Excellent Overseas Scholars(0401260011)+3 种基金the National Defense Science and Technology Innovation Fund of Chinese Academy of Sciences(c02022088)the Tianjin Science and Technology Program(20JCZDJC00810)the National Natural Science Foundation of China(82202798)the Shanghai Sailing Program(22YF1404200).
文摘Brain-computer interfaces(BCIs)represent an emerging technology that facilitates direct communication between the brain and external devices.In recent years,numerous review articles have explored various aspects of BCIs,including their fundamental principles,technical advancements,and applications in specific domains.However,these reviews often focus on signal processing,hardware development,or limited applications such as motor rehabilitation or communication.This paper aims to offer a comprehensive review of recent electroencephalogram(EEG)-based BCI applications in the medical field across 8 critical areas,encompassing rehabilitation,daily communication,epilepsy,cerebral resuscitation,sleep,neurodegenerative diseases,anesthesiology,and emotion recognition.Moreover,the current challenges and future trends of BCIs were also discussed,including personal privacy and ethical concerns,network security vulnerabilities,safety issues,and biocompatibility.
基金supported by the National Natural Science Foundation of China(61902095).
文摘Biomedical big data,characterized by its massive scale,multi-dimensionality,and heterogeneity,offers novel perspectives for disease research,elucidates biological principles,and simultaneously prompts changes in related research methodologies.Biomedical ontology,as a shared formal conceptual system,not only offers standardized terms for multi-source biomedical data but also provides a solid data foundation and framework for biomedical research.In this review,we summarize enrichment analysis and deep learning for biomedical ontology based on its structure and semantic annotation properties,highlighting how technological advancements are enabling the more comprehensive use of ontology information.Enrichment analysis represents an important application of ontology to elucidate the potential biological significance for a particular molecular list.Deep learning,on the other hand,represents an increasingly powerful analytical tool that can be more widely combined with ontology for analysis and prediction.With the continuous evolution of big data technologies,the integration of these technologies with biomedical ontologies is opening up exciting new possibilities for advancing biomedical research.
基金National Key Research and Development Program of China,Grant/Award Number:2018YFE0206900China Postdoctoral Science Foundation,Grant/Award Number:2023M731204+2 种基金The Open Project of Key Laboratory for Quality Evaluation of Ultrasound Surgical Equipment of National Medical Products Administration,Grant/Award Number:SMDTKL-2023-1-01The Hubei Province Key Research and Development Project,Grant/Award Number:2023BCB007CAAI-Huawei MindSpore Open Fund。
文摘Convolutional neural network(CNN)with the encoder-decoder structure is popular in medical image segmentation due to its excellent local feature extraction ability but it faces limitations in capturing the global feature.The transformer can extract the global information well but adapting it to small medical datasets is challenging and its computational complexity can be heavy.In this work,a serial and parallel network is proposed for the accurate 3D medical image segmentation by combining CNN and transformer and promoting feature interactions across various semantic levels.The core components of the proposed method include the cross window self-attention based transformer(CWST)and multi-scale local enhanced(MLE)modules.The CWST module enhances the global context understanding by partitioning 3D images into non-overlapping windows and calculating sparse global attention between windows.The MLE module selectively fuses features by computing the voxel attention between different branch features,and uses convolution to strengthen the dense local information.The experiments on the prostate,atrium,and pancreas MR/CT image datasets consistently demonstrate the advantage of the proposed method over six popular segmentation models in both qualitative evaluation and quantitative indexes such as dice similarity coefficient,Intersection over Union,95%Hausdorff distance and average symmetric surface distance.
基金supported by the Natural Science Foundation of the Anhui Higher Education Institutions of China(Grant Nos.2023AH040149 and 2024AH051915)the Anhui Provincial Natural Science Foundation(Grant No.2208085MF168)+1 种基金the Science and Technology Innovation Tackle Plan Project of Maanshan(Grant No.2024RGZN001)the Scientific Research Fund Project of Anhui Medical University(Grant No.2023xkj122).
文摘Convolutional neural networks(CNNs)-based medical image segmentation technologies have been widely used in medical image segmentation because of their strong representation and generalization abilities.However,due to the inability to effectively capture global information from images,CNNs can easily lead to loss of contours and textures in segmentation results.Notice that the transformer model can effectively capture the properties of long-range dependencies in the image,and furthermore,combining the CNN and the transformer can effectively extract local details and global contextual features of the image.Motivated by this,we propose a multi-branch and multi-scale attention network(M2ANet)for medical image segmentation,whose architecture consists of three components.Specifically,in the first component,we construct an adaptive multi-branch patch module for parallel extraction of image features to reduce information loss caused by downsampling.In the second component,we apply residual block to the well-known convolutional block attention module to enhance the network’s ability to recognize important features of images and alleviate the phenomenon of gradient vanishing.In the third component,we design a multi-scale feature fusion module,in which we adopt adaptive average pooling and position encoding to enhance contextual features,and then multi-head attention is introduced to further enrich feature representation.Finally,we validate the effectiveness and feasibility of the proposed M2ANet method through comparative experiments on four benchmark medical image segmentation datasets,particularly in the context of preserving contours and textures.
基金funded by the National Natural Science Foundation of China(Grant No.6240072655)the Hubei Provincial Key Research and Development Program(Grant No.2023BCB151)+1 种基金the Wuhan Natural Science Foundation Exploration Program(Chenguang Program,Grant No.2024040801020202)the Natural Science Foundation of Hubei Province of China(Grant No.2025AFB148).
文摘Image segmentation is attracting increasing attention in the field of medical image analysis.Since widespread utilization across various medical applications,ensuring and improving segmentation accuracy has become a crucial topic of research.With advances in deep learning,researchers have developed numerous methods that combine Transformers and convolutional neural networks(CNNs)to create highly accurate models for medical image segmentation.However,efforts to further enhance accuracy by developing larger and more complex models or training with more extensive datasets,significantly increase computational resource consumption.To address this problem,we propose BiCLIP-nnFormer(the prefix"Bi"refers to the use of two distinct CLIP models),a virtual multimodal instrument that leverages CLIP models to enhance the segmentation performance of a medical segmentation model nnFormer.Since two CLIP models(PMC-CLIP and CoCa-CLIP)are pre-trained on large datasets,they do not require additional training,thus conserving computation resources.These models are used offline to extract image and text embeddings from medical images.These embeddings are then processed by the proposed 3D CLIP adapter,which adapts the CLIP knowledge for segmentation tasks by fine-tuning.Finally,the adapted embeddings are fused with feature maps extracted from the nnFormer encoder for generating predicted masks.This process enriches the representation capabilities of the feature maps by integrating global multimodal information,leading to more precise segmentation predictions.We demonstrate the superiority of BiCLIP-nnFormer and the effectiveness of using CLIP models to enhance nnFormer through experiments on two public datasets,namely the Synapse multi-organ segmentation dataset(Synapse)and the Automatic Cardiac Diagnosis Challenge dataset(ACDC),as well as a self-annotated lung multi-category segmentation dataset(LMCS).
基金CAMS Innovation Fund for Medical Sciences(CIFMS):“Construction of an Intelligent Management and Efficient Utilization Technology System for Big Data in Population Health Science.”(2021-I2M-1-057)Key Projects of the Innovation Fund of the National Clinical Research Center for Orthopedics and Sports Rehabilitation:“National Orthopedics and Sports Rehabilitation Real-World Research Platform System Construction”(23-NCRC-CXJJ-ZD4)。
文摘【Objective】Medical imaging data has great value,but it contains a significant amount of sensitive information about patients.At present,laws and regulations regarding to the de-identification of medical imaging data are not clearly defined around the world.This study aims to develop a tool that meets compliance-driven desensitization requirements tailored to diverse research needs.【Methods】To enhance the security of medical image data,we designed and implemented a DICOM format medical image de-identification system on the Windows operating system.【Results】Our custom de-identification system is adaptable to the legal standards of different countries and can accommodate specific research demands.The system offers both web-based online and desktop offline de-identification capabilities,enabling customization of de-identification rules and facilitating batch processing to improve efficiency.【Conclusions】This medical image de-identification system robustly strengthens the stewardship of sensitive medical data,aligning with data security protection requirements while facilitating the sharing and utilization of medical image data.This approach unlocks the intrinsic value inherent in such datasets.
基金Ministry of Education Industry-University Cooperative Education Program(Project No.:231002999080311)Xinxiang Medical University Education and Teaching Reform Research(Project No.:2021-XYJG-100)。
文摘Objective:The objective of this research is to thoroughly investigate the extent of mutual interference among clinical internships,postgraduate entrance examinations,and employment by examining engineering contradictions,thus offering theoretical insights and guidance for medical students to attain high-quality outcomes in clinical internships.Methods:A combination of literature reviews,questionnaires,interviews,and observations of internships was utilized,followed by a statistical analysis to assess the levels of interference among the three factors.Results:The senior participants achieved significantly higher scores than their junior counterparts in evaluations of comprehensive humanistic quality,understanding professional values,communication abilities,clinical skills,and attitudes towards learning,with differences that were statistically significant(p<0.05).After applying an interactive training approach that merges early clinical practice with foundational medical education,both groups displayed notable enhancements in activity content,formats,instructor attitudes,clinical performance,and the blending of theory with practice(p<0.05).Conclusion:By emphasizing‘early clinical’education,students are effectively engaged in clinical practice through active involvement,leading to feedback-oriented training.This strategy not only improves the overall quality of internships but also reduces the risk of scheduling conflicts with postgraduate entrance examinations and employment opportunities.
