Abstract Donor shortages for organ transplantations are a major clinical challenge worldwide. Potential risks that are inevitably encountered with traditional methods include complications, secondary injuries, and limited source donors. Three-dimensional (3D) printing technology holds the potential to solve these limitations; it can be used to rapidly manufacture personalized tissue engineering scaffolds, repair tissue defects in situ with cells, and even directly print tissue and organs. Such printed implants and organs not only perfectly match the patient’s damaged tissue, but can also have engineered material microstructures and cell arrangements to promote cell growth and differentiation. Thus, such implants allow the desired tissue repair to be achieved, and could eventually solve the donor-shortage problem. This review summarizes relevant studies and recent progress on four levels, introduces different types of biomedical materials, and discusses existing problems and development issues with 3D printing that are related to materials and to the construction of extracellular matrix in vitro for medical applications.
The isolation wards, institutional quarantine centers, and home quarantine are generating a huge amount of bio-medical waste (BMW) worldwide since the outbreak of novel coronavirus disease-2019 (COVID-19). The personal protective equipment, testing kits, surgical facemasks, and nitrile gloves are the major contributors to waste volume. Discharge of a new category of BMW (COVID-waste) is of great global concern to public health and environmental sustainability if handled inappropriately. It may cause exponential spreading of this fatal disease as waste acts as a vector for SARS-CoV-2, which survives up to 7 days on COVID-waste (like facemasks). Proper disposal of COVID-waste is therefore immediately requires to lower the threat of pandemic spread and for sustainable management of the environmental hazards. Henceforth, in the present article, disinfection technologies for handling COVID-waste from its separate collection to various physical and chemical treatment steps have been reviewed. Furthermore, policy briefs on the global initiatives for COVID-waste management including the applications of different disinfection techniques have also been discussed with some potential examples effectively applied to reduce both health and environmental risks. This article can be of great significance to the strategy development for preventing/controlling the pandemic of similar episodes in the future.
This article was migrated. The article was marked as recommended. Medical education across the world has experienced a major disruptive change as a consequence of the COVID-19 pandemic and technology has been rapidly and innovatively used to maintain teaching and learning. The future of medical education is uncertain after the pandemic resolves but several potential future scenarios are discussed to inform current decision-making about the future provision of teaching and learning. The use of emergent technology for education, such as artificial intelligence for adaptive learning and virtual reality, are highly likely to be essential components of the transformative change and the future of medical education. The benefits and challenges of the use of technology in medical education are discussed with the intention of informing all providers on how the changes after the pandemic can have a positive impact on both educators and students across the world.
Medical education must adapt to different health care contexts, including digitalized health care systems and a digital generation of students in a hyper-connected world. The aims of this study are to identify and synthesize the values that medical educators need to implement in the curricula and to introduce representative educational programs. An integrative review was conducted to combine data from various research designs. We searched for articles on PubMed, Scopus, Web of Science, and EBSCO ERIC between 2011 and 2017. Key search terms were “undergraduate medical education,” “future,” “twenty-first century,” “millennium,” “curriculum,” “teaching,” “learning,” and “assessment.” We screened and extracted them according to inclusion and exclusion criteria from titles and abstracts. All authors read the full texts and discussed them to reach a consensus about the themes and subthemes. Data appraisal was performed using a modified Hawker ‘s evaluation form. Among the 7616 abstracts initially identified, 28 full-text articles were selected to reflect medical education trends and suggest suitable educational programs. The integrative themes and subthemes of future medical education are as follows: 1) a humanistic approach to patient safety that involves encouraging humanistic doctors and facilitating collaboration; 2) early experience and longitudinal integration by early exposure to patient-oriented integration and longitudinal integrated clerkships; 3) going beyond hospitals toward society by responding to changing community needs and showing respect for diversity; and 4) student-driven learning with advanced technology through active learning with individualization, social interaction, and resource accessibility. This review integrated the trends in undergraduate medical education in readiness for the anticipated changes in medical environments. The detailed programs introduced in this study could be useful for medical educators in the development of curricula. Further research is required to integrate the educational trends into graduate and continuing medical education, and to investigate the status or effects of innovative educational programs in each medical school or environment.
Abstract Background: Artificial intelligence (AI) is a rapidly developing computer technology that has begun to be widely used in the medical field to improve the professional level and efficiency of clinical work, in addition to avoiding medical errors. In developing countries, the inequality between urban and rural health services is a serious problem, of which the shortage of qualified healthcare providers is the major cause of the unavailability and low quality of healthcare in rural areas. Some studies have shown that the application of computer-assisted or AI medical techniques could improve healthcare outcomes in rural areas of developing countries. Therefore, the development of suitable medical AI technology for rural areas is worth discussing and probing. Methods: This article reviews and discusses the literature concerning the prospects of medical AI technology, the inequity of healthcare, and the application of computer-assisted or AI medical techniques in rural areas of developing countries. Results: Medical AI technology not only could improve physicians' efficiency and quality of medical services, but other health workers could also be trained to use this technique to compensate for the lack of physicians, thereby improving the availability of healthcare access and medical service quality. This article proposes a multilevel medical AI service network, including a frontline medical AI system (basic level), regional medical AI support centers (middle levels), and a national medical AI development center (top level). Conclusion: The promotion of medical AI technology in rural areas of developing countries might be one means of alleviating the inequality between urban and rural health services. The establishment of a multilevel medical AI service network system may be a solution.
The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee (https://doi.org/10.2337/dc22-SPPC), are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction (https://doi.org/10.2337/dc22-SINT). Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.
The science and clinical practice of medical physics has been integral to the advancement of radiology and radiation therapy for over a century. In parallel, advances in surgery - including intraoperative imaging, registration, and other technologies within the expertise of medical physicists - have advanced primarily in connection to other disciplines, such as biomedical engineering and computer science, and via somewhat distinct translational paths. This review article briefly traces the parallel and convergent evolution of such scientific, engineering, and clinical domains with an eye to a potentially broader, more impactful role of medical physics in research and clinical practice of surgery. A review of image-guided surgery technologies is offered, including intraoperative imaging, tracking / navigation, image registration, visualization, and surgical robotics across a spectrum of surgical applications. Trends and drivers for research and innovation are traced, including federal funding and academic-industry partnership, and some of the major challenges to achieving major clinical impact are described. Opportunities for medical physicists to expand expertise and contribute to the advancement of surgery in the decade ahead are outlined, including research and innovation, data science approaches, improving efficiency through operations research and optimization, improving patient safety, and bringing rigorous quality assurance to technologies and processes in the circle of care for surgery. Challenges abound but appear tractable, including domain knowledge, professional qualifications, and the need for investment and clinical partnership.
Chanisra Suebbook, Raiwada Sanguantrakul Teeracharoensub, Pongtong Puranitee
et al.
Purpose To develop a culturally and locally validated and reliable questionnaire for clinical teacher evaluation containing constructs specific to the Thai resident learning context. Methods We followed seven steps for developing questionnaires for educational research. We generated a list of good clinical teacher attributes from a literature review and focus groups. The Delphi procedure was employed to identify the desirable characteristics for residents, involving three stakeholder groups. The content validity index (CVI) of each item was calculated. The average CVI across the items was greater than 0.8, indicating an acceptable level of reliability. Residents then underwent cognitive interviews before pilot testing of the questionnaire. Construct validity was examined using exploratory factor analysis. Reliability was measured using Cronbach’s alpha analysis. Results We identified 44 key clinical teacher characteristics through a literature review and focus groups. After two rounds of the Delphi procedure (35 panelists), 23 characteristics were selected. An initial 23-item questionnaire was developed with a high CVI score. A total of 216 completed questionnaires evaluating 36 clinical teachers were analyzed. Exploratory factor analysis yielded a two-factor model within a 20-item questionnaire. The clinical facilitator domain contained 14 items. The professional identity support domain included six items. Cronbach’s alpha of the model was 0.976. Conclusion A clinical teacher evaluation questionnaire for Thai residents was developed with robust validity and reliability. This validated tool not only allows systematic assessment and improvement of clinical teaching but also provides a replicable framework for developing culturally adapted teacher evaluation instruments in other settings.
Kaiwen Zhang,1,* Jiaoli Sun,1,* Caixia Zhang,2 Bo Jiao,1 Wencui Zhang,1 Shangchen Yu,1 Xueqin Cao,1 Zhiqiang Zhou,1 Guanglei Zhang,1 Xianwei Zhang1 1Department of Anesthesiology and Pain Medicine,Hubei Key Laboratory of Geriatric Anesthesia and Perioperative Brain Health, and Wuhan Clinical Research Center for Geriatric Anesthesia, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China; 2Department of Anesthesiology, Wuhan No. 1 Hospital, Wuhan, People’s Republic of China*These authors contributed equally to this workCorrespondence: Guanglei Zhang, Department of Anesthesiology and Pain Medicine, Hubei Key Laboratory of Geriatric Anesthesia and Perioperative Brain Health, and Wuhan Clinical Research Center for Geriatric Anesthesia, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095 Jiefang Avenue, Qiaokou District, Wuhan, Hubei Province, 430030, People’s Republic of China, Email zsytd528@163.com Xianwei Zhang, Department of Anesthesiology and Pain Medicine, Hubei Key Laboratory of Geriatric Anesthesia and Perioperative Brain Health, and Wuhan Clinical Research Center for Geriatric Anesthesia, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095 Jiefang Avenue, Qiaokou District, Wuhan, Hubei Province, 430030, People’s Republic of China, Email ourpain@163.comPurpose: Effective postoperative analgesia is essential for maternal recovery following cesarean delivery. Hydromorphone and nalbuphine are commonly used opioids with differing pharmacologic properties, but direct comparisons in postcesarean patient-controlled analgesia (PCA) are limited. This study aimed to compare the analgesic efficacy and safety of hydromorphone versus nalbuphine and to develop a predictive model for inadequate analgesia.Patients and Methods: In this prospective, randomized, double-blind clinical trial conducted from December 2024 to March 2025, 212 women undergoing elective cesarean section under spinal anesthesia were randomized (1:1) to receive hydromorphone (0.1 mg/mL) or nalbuphine (1 mg/mL) via standardized intravenous PCA. The primary outcome was the incidence of inadequate analgesia within 24 hours, defined as a numerical rating scale (NRS) score ≥ 4 at rest or during movement. Secondary outcomes included adverse events, PCA consumption, and recovery indicators. Multivariable logistic regression was used to identify predictors of inadequate analgesia, and a nomogram was constructed and internally validated.Results: Nalbuphine was associated with a significantly lower incidence of inadequate analgesia than hydromorphone (14.2% vs 26.4%; P = 0.026), as well as reduced nausea and vomiting (18.9% vs 32.1%; P = 0.001) and pruritus (0% vs 5.7%; P = 0.029). Independent predictors of inadequate analgesia included PCA regimen (OR, 0.30; P = 0.026), gestational diabetes mellitus (OR, 4.40; P = 0.007), blood type AB (OR, 3.80; P = 0.043), and preoperative anxiety (OR, 0.20; P = 0.044). The predictive model showed good discrimination (AUC = 0.754).Conclusion: Nalbuphine demonstrated superior analgesia and fewer adverse effects compared with hydromorphone for postcesarean PCA. The developed predictive model may support individualized pain management by identifying patients at risk for inadequate analgesia.Keywords: postoperative analgesia, patient-controlled analgesia, hydromorphone, nalbuphine, predictive model
Neil Barnes Abraham, Abhimanyu Pradhan, Suresh Sukumar
et al.
Background Breast cancer remains a significant global health concern, with early diagnosis and risk factor identification crucial for improving outcomes. Diffusion-Weighted Imaging (DWI) and Apparent Diffusion Coefficient (ADC) measurements have emerged as promising tools in breast cancer diagnostics. However, the influence of hormonal status on these measurements remains unclear. Objective This scoping review aims to synthesize current evidence on how hormonal changes in pre- and post-menopausal women influence ADC values of benign, malignant, and fibroglandular breast tissues. Method Following the Arksey and O’Malley framework, we conducted a comprehensive search of Scopus, Embase, and PubMed databases for relevant studies published between January 2000 and 2021. Inclusion criteria encompassed 1.5 Tesla MRI studies reporting ADC values in female subjects, considering menopausal status. Results Six studies meeting the inclusion criteria, involving 612 patients, were analyzed. Findings suggest that menopausal status may influence ADC values, with postmenopausal women generally showing lower ADC values in both normal fibroglandular tissue and breast lesions. The impact of menstrual cycle phases on ADC values was less consistent across studies. Conclusions This review highlights the potential influence of hormonal status on ADC values in breast tissues. While DWI with ADC mapping shows promise as a reliable diagnostic tool across different hormonal states, further research is needed to fully understand and account for hormonal influences on ADC measurements. Future studies should focus on longitudinal designs, standardization of DWI protocols, and integration of hormonal status information into breast cancer risk assessment models.
With the booming development of medical informatization and the ubiquitous connections in the fifth generation mobile communication technology (5G) era, the heterogeneity and explosive growth of medical data have brought huge challenges to data access, security and privacy, as well as information processing in Internet of Medical Things (IoMT). This article provides a comprehensive review of how to realize the timely processing and analysis of medical big data and the sinking of high-quality medical resources under the constraints of the existing medical environment and medical-related equipment. We mainly focus on the advantages brought by the cloud computing, edge computing and artificial intelligence technologies to the IoMT. We also explore how to rationalize the use of medical resources and the security and privacy of medical data, so that high-quality medical services can be provided to patients. Finally, we discuss the current challenges and possible future research directions in the edge-cloud computing and artificial intelligence related IoMT.
AbstractThe link between biological aging and prostate cancer (PCa) risk, particularly as indicated by elevated prostate-specific antigen (PSA) levels, remains uncertain. This study utilized data from the National Health and Nutrition Examination Survey (2001–2010) to explore this association. Biological age was assessed using Klemera-Doubal method age (KDMAge) and phenotypic age (PhenoAge). PCa was identified through self-reported diagnoses, and highly probable PCa was determined by PSA levels. We analyzed the prevalence of PCa and PSA-defined highly probable PCa across quartiles of biological age measures using weighted chi-square and linear trend tests. Associations were evaluated using weighted multiple logistic regression models. Among 7,209 and 6,682 males analyzed, the overall weighted prevalence of PCa was 2.86%, increasing to 9.60% in those aged 65 and above. A significant rise in PCa prevalence was observed with higher quartiles of KDMAge or PhenoAge (P for trend < 0.001), particularly in those under 65. In this younger group, higher PhenoAge acceleration quartiles were linked to increased PCa prevalence and higher risk of PCa (OR = 1.50, P = 0.015) as well as highly probable PCa in those without a diagnosis (OR = 1.28, P = 0.031). These findings suggest that accelerated biological aging is associated with an increased risk of PCa and may indicate early risk as signaled by PSA levels, even in those without a PCa diagnosis.
Background: Dengue virus (DENV) is the most widespread mosquito-borne virus, which can cause dengue fever with mild symptoms, or progress to fatal dengue hemorrhagic fever and dengue shock syndrome. As the main target cells of DENV, macrophages are responsible for the innate immune response against the virus. Methods: In this study, we investigated the role of pyroptosis in the pathogenic mechanism of dengue fever by examining the level of pyroptosis in DENV-1-infected macrophages and further screened differentially expressed microRNAs by high-throughput sequencing to predict microRNAs that could affect the pyroptosis of the macrophage. Results: Macrophages infected with DENV-1 were induced with decreased cell viability, decreased release of lactate dehydrogenase and IL-1β, activation of NLRP3 inflammasome and caspase-1, cleavage of GSDMD to produce an N-terminal fragment bound to cell membrane, and finally induced macrophage pyroptosis. MicroRNA expression profiles were obtained by sequencing macrophages from all periods of DENV-1 infection and comparing with the negative control. Sixty-three microRNAs differentially expressed in both the early and later stages of infection were also identified. In particular, miR-223-3p, miR-148a-3p, miR-125a-5p, miR-146a-5p and miR-34a-5p were recognized as small molecules that may be involved in the regulation of inflammation. Conclusions: In summary, this study aimed to understand the pathogenic mechanism of DENV through relevant molecular mechanisms and provide new targets for dengue-specific therapy.
Medical image analysis is essential to clinical diagnosis and treatment, which is increasingly supported by multi-modal large language models (MLLMs). However, previous research has primarily focused on 2D medical images, leaving 3D images under-explored, despite their richer spatial information. This paper aims to advance 3D medical image analysis with MLLMs. To this end, we present a large-scale 3D multi-modal medical dataset, M3D-Data, comprising 120K image-text pairs and 662K instruction-response pairs specifically tailored for various 3D medical tasks, such as image-text retrieval, report generation, visual question answering, positioning, and segmentation. Additionally, we propose M3D-LaMed, a versatile multi-modal large language model for 3D medical image analysis. Furthermore, we introduce a new 3D multi-modal medical benchmark, M3D-Bench, which facilitates automatic evaluation across eight tasks. Through comprehensive evaluation, our method proves to be a robust model for 3D medical image analysis, outperforming existing solutions. All code, data, and models are publicly available at: https://github.com/BAAI-DCAI/M3D.