Development Of Medical Technology Research Articles

A Stand-Off Laser-Induced Breakdown Spectroscopy (LIBS) System for Remote Bacteria Identification.

Bacteria are the primary cause of infectious diseases, making rapid and accurate identification crucial for timely pathogen diagnosis and disease control. However, traditional identification techniques such as polymerase chain reaction and loop-mediated isothermal amplification are complex, time-consuming, and pose infection risks. This study explores remote (~3 m) bacterial identification using laser-induced breakdown spectroscopy (LIBS) with a Cassegrain reflective telescope. Principal component analysis (PCA) was employed to reduce the dimensionality of the LIBS spectral data, and the accuracy of support vector machine (SVM) and Random Forest (RF) algorithms was compared. Multiple repeated experiments showed that the RF model achieved a classification accuracy, recall, precision, and F1-score of 99.81%, 99.80%, 99.79%, and 0.9979, respectively, outperforming the SVM model and providing more accurate remote bacterial identification. The method based on laser-induced plasma spectroscopy and machine learning has broad application prospects, supporting noncontact disease diagnosis, improving public health, and advancing medical research and technological development.

Shear Stress Induces a Time-Dependent Inflammatory Response in Human Monocyte-Derived Macrophages.

Macrophages are innate immune cells that are known for their extreme plasticity, enabling diverse phenotypes that lie on a continuum. In a simplified model, they switch between pro-inflammatory (M1) and anti-inflammatory (M2) phenotypes depending on surrounding microenvironmental cues, which have been implicated in disease outcomes. Although considerable research has been focused on macrophage response to biochemical cues and mechanical signals, there is a scarcity of knowledge surrounding their behavior in response to shear stress. In this study, we applied varying magnitudes of shear stress on human monocyte-derived macrophages (MDMs) using a cone-and-plate viscometer and evaluated changes in morphology, gene expression, protein expression, and cytokine secretion over time. MDMs exposed to shear stress exhibited a rounder morphology compared to statically-cultured controls. RT-qPCR results showed significant upregulation of TNF-α, and analysis of cytokine release revealed increased secretion of IL-8, IL-18, fractalkine, and other chemokines. The upregulation of pro-inflammatory factors was evident with both increasing magnitudes of shear and time. Taken together, these results indicate that prolonged shear exposure induced a pro-inflammatory phenotype in human MDMs. These findings have implications for medical technology development, such as in situ vascular graft design wherein macrophages are exposed to shear and have been shown to affect graft resorption, and in delineating disease pathophysiology, for example to further illuminate the role of macrophages in atherosclerosis where shear is directly related to disease outcome.

Lung Cancer Detection Using Convolutional Neural Networks

Lung cancer is among the most prevalent and deadly cancers worldwide. Accurate diagnosis and early detection are critical for improving lung cancer patient outcomes and survival rates. Thanks to developments in medical imaging technology, computer-aided diagnosis (CAD) systems have shown a great deal of promise in helping radiologists identify and diagnose lung cancer from medical images. Here, we present the use of convolutional neural networks (CNNs) to create an early detection system (CAD) for lung cancer. The suggested approach uses lung computed tomography (CT) scans as input and use a CNN architecture to extract high-level features from the pictures. We use transfer learning to enhance a CNN model trained on a large dataset of CT images. The CNN model has been taught to determine if a specific CT image contains lung cancer or not. We evaluate the performance of the proposed CAD system on a dataset of CT scans of the lungs from different institutions. The trial's results show that our CNN-based CAD system can reliably and precisely identify lung cancer from CT scans. We also show the comparative performance of our proposed system against the state-of-the-art machine learning methods for lung cancer prediction. In conclusion, the suggested CNN-based deep learning-based CAD system has produced encouraging results for lung cancer detection from CT scans. The approach might help radiologists identify and classify lung cancer early on, leading to better patient outcomes and survival rates. The viability and usefulness of the suggested approach in clinical practice require more study

Understanding disciplinary perspectives: a framework to develop skills for interdisciplinary research collaborations of medical experts and engineers

BackgroundHealth professionals need to be prepared for interdisciplinary research collaborations aimed at the development and implementation of medical technology. Expertise is highly domain-specific, and learned by being immersed in professional practice. Therefore, the approaches and results from one domain are not easily understood by experts from another domain. Interdisciplinary collaboration in medical research faces not only institutional, but also cognitive and epistemological barriers. This is one of the reasons why interdisciplinary and interprofessional research collaborations are so difficult. To explain the cognitive and epistemological barriers, we introduce the concept of disciplinary perspectives. Making explicit the disciplinary perspectives of experts participating in interdisciplinary collaborations helps to clarify the specific approach of each expert, thereby improving mutual understanding.MethodWe developed a framework for making disciplinary perspectives of experts participating in an interdisciplinary research collaboration explicit. The applicability of the framework has been tested in an interdisciplinary medical research project aimed at the development and implementation of diffusion MRI for the diagnosis of kidney cancer, where the framework was applied to analyse and articulate the disciplinary perspectives of the experts involved.ResultsWe propose a general framework, in the form of a series of questions, based on new insights from the philosophy of science into the epistemology of interdisciplinary research. We explain these philosophical underpinnings in order to clarify the cognitive and epistemological barriers of interdisciplinary research collaborations. In addition, we present a detailed example of the use of the framework in a concrete interdisciplinary research project aimed at developing a diagnostic technology. This case study demonstrates the applicability of the framework in interdisciplinary research projects.ConclusionInterdisciplinary research collaborations can be facilitated by a better understanding of how an expert’s disciplinary perspectives enables and guides their specific approach to a problem. Implicit disciplinary perspectives can and should be made explicit in a systematic manner, for which we propose a framework that can be used by disciplinary experts participating in interdisciplinary research project. Furthermore, we suggest that educators can explore how the framework and philosophical underpinning can be implemented in HPE to support the development of students’ interdisciplinary expertise.

Global, regional, and national burden of acute myeloid leukemia, 1990–2021: a systematic analysis for the global burden of disease study 2021

BackgroundAcute myeloid leukemia (AML), as the most common subtype of leukemia in adults, is characterised by rapid progression and poor prognosis. In the context of the rapid development of medical technology and the complexity of social factors, a detailed report describing the latest epidemiological patterns of AML is important for decision makers to allocate healthcare resources effectively.MethodsOur research utilized the latest data sourced from the Global Burden of Disease (GBD) 2021. To delineate the burden of AML, we comprehensively described the incidence, deaths, disability-adjusted life years (DALYs), and the associated age-standardized rates per 100,000 persons (ASR) spanning from 1990 to 2021 stratifies according to age, sex, socio-demographic index (SDI), and nationality. Additionally, we extracted and analyzed data about the risk factors that contribute to AML-related deaths and DALYs.ResultsAccording to our study, the incidence of AML has continued to rise globally from 79,372 in 1990 to 144,645 in 2021 and AML affected the male and the elderly populations disproportionately. Furthermore, there was a significant positive correlation between the burden of AML and the SDI value. Developed nations generally exhibited higher age-standardized incidence rate, age-standardized death rate, and age-standardized disability-adjusted life year rate than the developing nations. We also analyzed the prevalence of smoking, high body mass index, and occupational benzene and formaldehyde exposure in the AML population in different SDI regions. Moreover, smoking and high body mass index were more prevalent in developed countries, whereas occupational exposure to these chemicals was the predominant risk factor in developing countries.ConclusionThe global burden of AML has increased over the past 32 years, with rising morbidity and mortality. The incidence of AML is differentially distributed across different SDI countries or regions. AML incidence is higher in the elderly and in men. The proportions of smoking, high body mass index, and occupational exposure to benzene and formaldehyde varied by region. The findings highlight the need for region-specific prevention and call for future research on preventive strategies and new treatments to lower AML incidence and improve patient outcomes.

Novel Approaches and Applications of Nanotechnology in the Delivery of Topical Drugs for Psoriasis via Nanocarriers

Psoriasis is a non-contagious, continuing, auto-immune disease that mostly affects the skin, and about 2%-3% of the world's population suffers from it. In this review article, the primary focus is on the strategies involved in conventional therapies and the latest advances that have been recorded in metallic nano, polymer-based, and lipid-based formulations in the spectrum of anti-psoriatic drugs. Liposomes, ethosomes, solid lipid nanoparticles, micelles, and dendrimers are only some of the nanocarrier systems that have been extensively researched in relation to their potential use in nano formulations. This study incorporates patent applications that illustrate the nanoparticle's function in treating psoriasis. Hence, on the basis of an extensive literature survey, it is concluded that nano-formulations are a promising medium to treat a disease like psoriasis as they offer enhanced penetration, target-specific delivery, and improved efficacy. When applied to the study of biological systems and the development of novel medical technologies, nanobiotechnology offers potentially promising possibilities for the efficient use of nanoscale materials and processes. In this approach, nanotechnology and biotechnology are combined in order to develop nanoscale devices, materials, and systems that can be used for the diagnosis, treatment, and prevention of psoriasis. The future of the therapeutic effect of antipsoriatic drugs is dependent on both the benefits they have the ability to bring and the progress being made in the mass production of these carriers. Researching novel carrier systems or combination therapies is essential, but so is working to scale up existing technologies so they may be commercialised and used to benefit society at large.

Conditional generative adversarial network-assisted system for radiation-free evaluation of scoliosis using a single smartphone photograph: a model development and validation study

Adolescent idiopathic scoliosis (AIS) is the most common spinal disorder in children, characterized by insidious onset and rapid progression, which can lead to severe consequences if not detected in a timely manner. Currently, the diagnosis of AIS primarily relies on X-ray imaging. However, due to limitations in healthcare access and concerns over radiation exposure, this diagnostic method cannot be widely adopted. Therefore, we have developed and validated a screening system using deep learning technology, capable of generating virtual X-ray images (VXI) from two-dimensional Red Green Blue (2D-RGB) images captured by a smartphone or camera to assist spine surgeons in the rapid, accurate, and non-invasive assessment of AIS. We included 2397 patients with AIS and 48 potential patients with AIS who visited four medical institutions in mainland China from June 11th 2014 to November 28th 2023. Participants data included standing full-spine X-ray images captured by radiology technicians and 2D-RGB images taken by spine surgeons using a camera. We developed a deep learning model based on conditional generative adversarial networks (cGAN) called Swin-pix2pix to generate VXI on retrospective training (n=1842) and validation (n=100) dataset, then validated the performance of VXI in quantifying the curve type and severity of AIS on retrospective internal (n=100), external (n=135), and prospective test datasets (n=268). The prospective test dataset included 268 participants treated in Nanjing, China, from April 19th, 2023, to November 28th, 2023, comprising 220 patients with AIS and 48 potential patients with AIS. Their data underwent strict quality control to ensure optimal data quality and consistency. Our Swin-pix2pix model generated realistic VXI, with the mean absolute error (MAE) for predicting the main and secondary Cobb angles of AIS significantly lower than other baseline cGAN models, at 3.2° and 3.1° on prospective test dataset. The diagnostic accuracy for scoliosis severity grading exceeded that of two spine surgery experts, with accuracy of 0.93 (95% CI [0.91, 0.95]) in main curve and 0.89 (95% CI [0.87, 0.91]) in secondary curve. For main curve position and curve classification, the predictive accuracy of the Swin-pix2pix model also surpassed that of the baseline cGAN models, with accuracy of 0.93 (95% CI [0.90, 0.95]) for thoracic curve and 0.97 (95% CI [0.96, 0.98]), achieving satisfactory results on three external datasets as well. Our developed Swin-pix2pix model holds promise for using a single photo taken with a smartphone or camera to rapidly assess AIS curve type and severity without radiation, enabling large-scale screening. However, limited data quality and quantity, a homogeneous participant population, and rotational errors during imaging may affect the applicability and accuracy of the system, requiring further improvement in the future. National Key R&D Program of China, Natural Science Foundation of Jiangsu Province, China Postdoctoral Science Foundation, Nanjing Medical Science and Technology Development Foundation, Jiangsu Provincial Key Research and Development Program, and Jiangsu Provincial Medical Innovation Centre of Orthopedic Surgery.

Analyzing key health factors influencing life expectancy using machine learning

With the rapid development of global medical technology and the steady improvement of social living standards, life expectancy per capita has become one of the most important indicators of a country or region's level of development. It not only reflects the quality of life and health of the local people, but also profoundly affects the economic growth and social stability of the country. In underdeveloped countries, life expectancy at birth is generally lower than in more developed countries due to resource constraints and poor medical facilities. This phenomenon has triggered extensive research interest in whether there is some correlation between life expectancy per capita and economic growth, and how improving life expectancy can contribute to a country's economic transformation and development. The purpose of this paper is to explore the impact of life expectancy per capita on economic growth and how to promote the economic transformation and development of countries by increasing life expectancy. Through a comparative study of Asian countries such as China, Singapore and Japan, this paper analyzes the impact of different factors on life expectancy through technical methods such as visual analysis, hypothesis testing, regression modeling, and decision tree modeling, and explores how these factors play a role in economic growth. The results of this study may have some guiding significance for improving life expectancy.

Discussion on the Factors Affecting Psychological Resilience in School-Aged Children with Tumors

Under the background of the development of medical science and technology, the survival and cure rate of children with cancer has increased to a certain extent. In addition to the medical intervention and treatment measures, all sectors of society have gradually paid more attention to the living environment and mental health problems of children with cancer. To study the psychological elasticity in school-age children using life treatment and status, the main content, and how according to psychological elasticity factors to maintain the mental health level of children with school-age cancer, summarizes the domestic and foreign literature about the mental health development of children with school-age cancer, related to the influence of the psychological elasticity factors are summarized, discusses the main influence factors and suggested measures, to the school-age children with tumor psychological elasticity related research to provide reference.

Breast histopathological imaging using ultra-fast fluorescence confocal microscopy to identify cancer lesions at early stage.

Ultrafast fluorescent confocal microscopy is a hypothetical approach for breast cancer detection because of its potential to achieve instantaneous, high-resolution images of cellular-level tissue features. Traditional approaches such as mammography and biopsy are laborious, invasive, and inefficient; confocal microscopy offers many benefits over these approaches. However, confocal microscopy enables the exact differentiation of malignant cells, the expeditious examination of extensive tissue sections, and the optical sectioning of tissue samples into tiny slices. The primary goal should be to prevent cancer altogether, although detecting it early can help achieve that objective. This research presents a novel Breast Histopathology Convolutional Neural Network (BHCNN) for feature extraction and recursive feature elimination method for selecting the most significant features. The proposed approach utilizes full slide images to identify tissue in regions affected by invasive ductal carcinoma. In addition, a transfer learning approach is employed to enhance the performance and accuracy of the models in detecting breast cancer, while also reducing computation time by modifying the final layer of the proposed model. The results showed that the BHCNN model outperformed other models in terms of accuracy, achieving a testing accuracy of 98.42% and a training accuracy of 99.94%. The confusion matrix results show that the IDC positive (+) class achieved 97.44% accuracy and 2.56% inaccurate results, while the IDC negative (-) class achieved 98.73% accuracy and 1.27% inaccurate results. Furthermore, the model achieved less than 0.05 validation loss. RESEARCH HIGHLIGHTS: The objective is to develop an innovative framework using ultra-fast fluorescence confocal microscopy, particularly for the challenging problem of breast cancer diagnosis. This framework will extract essential features from microscopy and employ a gradient recurrent unit for detection. The proposed research offers significant potential in enhancing medical imaging through the provision of a reliable and resilient system for precise diagnosis of breast cancer, thereby propelling the progression of state-of-the-art medical technology. The most suitable feature was determined using BHRFE optimization techniques after retrieving the features by proposed model. Finally, the features chosen are integrated into a proposed methodology, which is then classified using a GRU deep model. The aforementioned research has significant potential to improve medical imaging by providing a complex and reliable system for precise evaluation of breast cancer, hence advancing the development of cutting-edge medical technology.

MENTORING IN THE HEALTHCARE SYSTEM: PROBLEMS AND PROSPECTS

ANNOTATION Rationale. During the period of rapid development of science and medical technologies, the professional and working environment is faced with the urgent task of finding mechanisms and tools for increasing the level of professional competencies and practical skills of medical specialists in the process of professional socialization. The sociological study conducted by the authors was focused on identifying factors of successful/unsuccessful professional socialization of young medical specialists, one of which is mentoring. Purpose of the study. Determining the role of the mentoring institution as a factor influencing the success/failure of professional socialization of medical specialists. Materials and methods. A survey was conducted among clinical residents of the Far Eastern State Medical University. A total of 184 respondents were interviewed using a questionnaire using a Google form. To solve the research problems, methods of analysis, comparison and generalization of the obtained data, analysis of regulatory documents, survey results and secondary data from sociological research were used. At the level of data processing and work with source material, descriptive statistics methods provided by SPSS and EXCEL software were used. Results. In the course of a sociological study conducted by the authors, the most pressing problems of introducing and implementing mentoring practices in the healthcare system were identified. The role of the mentoring institution as a socializing factor influencing the success/failure of professional socialization of medical specialists is also determined. The study formulates tasks for further research into the process of professional socialization of medical specialists from the point of view of identifying factors of a positive and destructive nature, and the role of socialization institutions in this process. Conclusion. Problems in the implementation of mentoring practices in healthcare related to the low mentoring activity of practicing medical specialists have been identified. A special need for the implementation of mentoring practices was identified at the stage of postgraduate training (clinical residency) by 83% of respondents and at the stage of starting independent professional work by 61% of respondents. Respondents expressed a positive attitude towards the introduction of mentoring in healthcare: 90% of respondents indicated that having a mentor when obtaining a medical specialty is mandatory.

Neural networks-based framework for recognizing streaming patterns in magnetized Maxwell–Oldroyd-B blood blended with tetra-hybrid nanoparticles and microbes over stenosis in an elastic artery

The proposed study delves into investigating blood flow patterns and thermal and solutal transport in an artery affected by stenosis. Specifically, it explores the behavior of blood ingrained with tetra-hybrid nanoparticles and gyrotactic microbes under magnetic effects. To capture the non-Newtonian nature of blood in such complex scenarios, the Maxwell-Oldroyd-B (MO) fluid model is employed. The pertinent nonlinear partial differential equations system accounts for similarity transformations and boundary layer assertions. Numerical results are assessed using a potent shooting approach with a fourth-order Runge–Kutta (RK4) method implemented through MATLAB’s bvp4c commands. The computational outcomes are visualized via graphical representation and tabulated data for in-depth analysis and illustration. The research yields significant insights into various model parameters. For instance, the Lorentz force is observed to exert a drag effect, hindering blood flow velocity, while the opposite trend is observed for blood temperature. Additionally, higher Peclet numbers correlate with reduced mass concentration levels, while gyrotactic microbes’ density profile exhibits an upsurge with elevated Lewis numbers. Notably, the Nusselt number for Newtonian blood is lower than MO blood, shedding light on the impact of blood properties on heat transport. Furthermore, this study proposes a technique based on artificial neural networks (ANNs) for accurately predicting skin friction coefficient, Nusselt number, Sherwood number, and microbes’ density number. The proposed algorithm achieves remarkable accuracy rates of 99.65% on the testing dataset and 99.99% during cross-validation for predicting the skin friction coefficient. The novel findings generated by this model hold promising implications for the treatment and monitoring of arterial diseases, as well as the development of cutting-edge medical devices and technologies.

Automated Lung Cancer Diagnosis Applying Butterworth Filtering, Bi-Level Feature Extraction, and Sparce Convolutional Neural Network to Luna 16 CT Images.

Accurate prognosis and diagnosis are crucial for selecting and planning lung cancer treatments. As a result of the rapid development of medical imaging technology, the use of computed tomography (CT) scans in pathology is becoming standard practice. An intricate interplay of requirements and obstacles characterizes computer-assisted diagnosis, which relies on the precise and effective analysis of pathology images. In recent years, pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection have witnessed the considerable potential of artificial intelligence, especially deep learning techniques. In this context, an artificial intelligence (AI)-based methodology for lung cancer diagnosis is proposed in this research work. As a first processing step, filtering using the Butterworth smooth filter algorithm was applied to the input images from the LUNA 16 lung cancer dataset to remove noise without significantly degrading the image quality. Next, we performed the bi-level feature selection step using the Chaotic Crow Search Algorithm and Random Forest (CCSA-RF) approach to select features such as diameter, margin, spiculation, lobulation, subtlety, and malignancy. Next, the Feature Extraction step was performed using the Multi-space Image Reconstruction (MIR) method with Grey Level Co-occurrence Matrix (GLCM). Next, the Lung Tumor Severity Classification (LTSC) was implemented by using the Sparse Convolutional Neural Network (SCNN) approach with a Probabilistic Neural Network (PNN). The developed method can detect benign, normal, and malignant lung cancer images using the PNN algorithm, which reduces complexity and efficiently provides classification results. Performance parameters, namely accuracy, precision, F-score, sensitivity, and specificity, were determined to evaluate the effectiveness of the implemented hybrid method and compare it with other solutions already present in the literature.

Improved Image Segmentation using Adaptive Threshold Morphology on CT-Scan Images for Brain Tumor Detection

Diagnosing disease by playing the role of image processing is one form of current medical technology development. The results of image processing performance have been able to provide accurate diagnoses to be used as material for decision-making. This research aims to carry out the process of detecting brain tumor objects in Computed Tomography (CT-Scan) images by developing a segmentation technique using the Adaptive Threshold Morphology (ATM) algorithm. The performance of the ATM algorithm in the segmentation process involves the Extended Adaptive Global Treshold (eAGT) function to produce an optimal threshold value. This research method involves several stages of the process in detecting tumor objects. The preprocessing stage is carried out using the cropping and filtering process which is optimized using the eAGT function. The next stage is the morphological segmentation process involving erosion and dilation operations. The final stage of the segmentation process using the ATM algorithm is labeling objects that have been detected. The research dataset used 187 Computed Tomography-Scan images from 10 brain tumor patients. The results of this study show that the accuracy rate for detecting brain tumor objects in Computed Tomography-Scan images is 93.47%. These results can provide an automatic and effective detection process based on the optimal threshold value that has been generated. Overall, this research contributes to the development of segmentation algorithms in image processing and can be used as an alternative solution in the treatment of brain tumor patients.

Revolutionary Point-of-Care Wearable Diagnostics for Early Disease Detection and Biomarker Discovery through Intelligent Technologies.

Early-stage disease detection, particularly in Point-Of-Care (POC) wearable formats, assumes pivotal role in advancing healthcare services and precision-medicine. Public benefits of early detection extend beyond cost-effectively promoting healthcare outcomes, to also include reducing the risk of comorbid diseases. Technological advancements enabling POC biomarker recognition empower discovery of new markers for various health conditions. Integration of POC wearables for biomarker detection with intelligent frameworks represents ground-breaking innovations enabling automation of operations, conducting advanced large-scale data analysis, generating predictive models, and facilitating remote and guided clinical decision-making. These advancements substantially alleviate socioeconomic burdens, creating a paradigm shift in diagnostics, and revolutionizing medical assessments and technology development. This review explores critical topics and recent progress in development of 1) POC systems and wearable solutions for early disease detection and physiological monitoring, as well as 2) discussing current trends in adoption of smart technologies within clinical settings and in developing biological assays, and ultimately 3) exploring utilities of POC systems and smart platforms for biomarker discovery. Additionally, the review explores technology translationfrom research labs to broader applications. It also addresses associated risks, biases, and challenges of widespread Artificial Intelligence (AI) integration in diagnostics systems, while systematically outlining potential prospects, current challenges, and opportunities.

Research on ultrasound-based radiomics: a bibliometric analysis.

A large number of studies related to ultrasound-based radiomics have been published in recent years; however, a systematic bibliometric analysis of this topic has not yet been conducted. In this study, we attempted to identify the hotspots and frontiers in ultrasound-based radiomics through bibliometrics and to systematically characterize the overall framework and characteristics of studies through mapping and visualization. A literature search was carried out in Web of Science Core Collection (WoSCC) database from January 2016 to December 2023 according to a predetermined search formula. Bibliometric analysis and visualization of the results were performed using CiteSpace, VOSviewer, R, and other platforms. Ultimately, 466 eligible papers were included in the study. Publication trend analysis showed that the annual publication trend of journals in ultrasound-based radiomics could be divided into three phases: there were no more than five documents published in this field in any year before 2018, a small yearly increase in the number of annual publications occurred between 2018 and 2022, and a high, stable number of publications appeared after 2022. In the analysis of publication sources, China was found to be the main contributor, with a much higher number of publications than other countries, and was followed by the United States and Italy. Frontiers in Oncology was the journal with the highest number of papers in this field, publishing 60 articles. Among the academic institutions, Fudan University, Sun Yat-sen University, and the Chinese Academy of Sciences ranked as the top three in terms of the number of documents. In the analysis of authors and cocited authors, the author with the most publications was Yuanyuan Wang, who has published 19 articles in 8 years, while Philippe Lambin was the most cited author, with 233 citations. Visualization of the results from the cocitation analysis of the literature revealed a strong centrality of the subject terms papillary thyroid cancer, biological behavior, potential biomarkers, and comparative assessment, which may be the main focal points of research in this subject. Based on the findings of the keyword analysis and cluster analysis, the keywords can be categorized into two major groups: (I) technological innovations that enable the construction of radiomics models such as machine learning and deep learning and (II) applications of predictive models to support clinical decision-making in certain diseases, such as papillary thyroid cancer, hepatocellular carcinoma (HCC), and breast cancer. Ultrasound-based radiomics has received widespread attention in the medical field and has been gradually been applied in clinical research. Radiomics, a relatively late development in medical technology, has made substantial contributions to the diagnosis, prediction, and prognostic evaluation of diseases. Additionally, the coupling of artificial intelligence techniques with ultrasound imaging has yielded a number of promising tools that facilitate clinical decision-making and enable the practice of precision medicine. Finally, the development of ultrasound-based radiomics requires multidisciplinary cooperation and joint efforts from the field biomedicine, information technology, statistics, and clinical medicine.

Philosophical and practical understanding of the quality of medical education in the Russian Federation: analysis of the opinions of students and practicing doctors

Introduction. The transformation of higher medical education, caused by the rapid development of medical technologies and growing social demands on the healthcare system, requires research into students’ ideas about quality education, their assessments and expectations, since students’ understanding of the learning process can have a significant impact on the development of student motivation and medical professionalism. The purpose of the study is to analyze the current system of higher medical education in the Russian Federation based on survey data from senior students, residents, and practicing doctors. Materials and methods. The survey involved 350 respondents (senior students, residents, practicing physicians) mainly from Moscow medical higher education institutions. Statistical analysis was carried out using the software StatTech v. 3.0.4. Methods of mathematical statistics: U-Mann-Whitney, Kruskal-Wallis, χ2 -Pearson tests. Results. It was revealed that only 52% of respondents consider the amount of theoretical knowledge acquired at a higher education institution under a specialty program to be sufficient. Students and graduates of the pediatric faculty more often consider the amount of theoretical training sufficient compared to respondents from the medical and dental faculties (p < 0.001). Only half of the respondents are satisfied with the quality of the education received. Students and graduates of the Faculty of Pediatrics more often stated that they use the knowledge and skills acquired during practical training in their professional activities (p < 0.001). Students and graduates of medical faculties of non-core Moscow universities and Russian National Research Medical University named after N.I. Pirogov are most satisfied with the amount of theoretical knowledge received within the framework of the specialty program compared to students and graduates of other higher education institutions (p < 0.001). Conclusion. The results of this study may be valuable for government agencies that determine strategies for the development of higher medical education in Russia, as well as for university leaders who plan targets for the development of educational activities with an emphasis on involving students in the educational process.

Applications of Medical Big Data and Personal Information Protection

In the current social environment, the generation and application of medical big data is closely related to the personal information security of every citizen. With the continuous development of medical technology, the application of medical big data has become increasingly widespread. On one hand, it can promote the development of medical technology, including drug development, personalized medicine, and precision treatment. On the other hand, medical big data also concerns the private information of citizens, which is a crucial part of personal information protection. However, in practice, there often exists a contradiction between medical big data and the protection of citizens' personal information rights. From the perspective of medical big data, there are overlaps and differences among citizens' personal information, privacy information, medical health information, and medical big data information. Relying solely on the “informed consent” rule for personal information cannot effectively protect personal information comprehensively or guarantee the application and development of medical big data. This article analyzes the issues surrounding Chinese medical big data in terms of personal information from a legal perspective, and proposes corresponding solutions to these issues. It is hoped that this research can contribute to advancing the legalization of medical data openness, re-examining patients' personal information in the context of medical big data, and balancing the conflicts between individual privacy interests, public interests, and economic benefits of medical data, thus better protecting individuals' personal information interests under social and private benefits.

Between Sharia, Gender, and Science in the Construction of ‘Iddah: The Response of Banyumas’ Ulamas

The development of science necessitates changes in various issues of human life, including matters of family law. One of the family law issues that often sparks debate is the issue of 'iddah. One of the reasons (reason) often found in the discussions of scholars is to ensure the cleanliness of the uterus from the fetus. If this argument is used as the main basis, it is refuted by the development of medical technology, which can ensure the cleanliness of the uterus in a relatively short time. Likewise, from a gender perspective, because the owner of the womb is only a woman, it cannot be exchanged. This study aims to analyze the extent to which Banyumas’ Ulama views respond to the development of science and gender discourse in the context of family law. This research is empirical legal research, analyzing the responses of Banyumas scholars. Researchers collected data from scholars who came from different religious organization backgrounds, namely Nahdlatul Ulama, Muhammadiyah, and al-Irsyad. These three are the organizations with the most followers in Banyumas. Banyumas became the locus of this research because there is a view that egalitarian principles are living values in society, as described in the chronicle historical texts (babad). The study concluded that the responses of the Banyumas Ulama were divided into two groups, namely textualists and semi-textualists. However, the semi-textualist group is more dominant among them. This research at the same time proves that the relationship between religion and science has not yet reached an integrative or even transdisciplinary level but is still at a dialogic level, and there is even a dualistic view.

Efficacy of laparoscopic low anterior resection for colorectal cancer patients with 3D-vascular reconstruction for left coronary artery preservation.

Laparoscopic low anterior resection (LLAR) has become a mainstream surgical method for the treatment of colorectal cancer, which has shown many advantages in the aspects of surgical trauma and postoperative rehabilitation. However, the effect of surgery on patients' left coronary artery and its vascular reconstruction have not been deeply discussed. With the development of medical imaging technology, 3D vascular reconstruction has become an effective means to evaluate the curative effect of surgery. To investigate the clinical value of preoperative 3D vascular reconstruction in LLAR of rectal cancer with the left colic artery (LCA) preserved. A retrospective cohort study was performed to analyze the clinical data of 146 patients who underwent LLAR for rectal cancer with LCA preservation from January to December 2023 in our hospital. All patients underwent LLAR of rectal cancer with the LCA preserved, and the intraoperative and postoperative data were complete. The patients were divided into a reconstruction group (72 patients) and a nonreconstruction group (74 patients) according to whether 3D vascular reconstruction was performed before surgery. The clinical features, operation conditions, complications, pathological results and postoperative recovery of the two groups were collected and compared. A total of 146 patients with rectal cancer were included in the study, including 72 patients in the reconstruction group and 74 patients in the nonreconstruction group. There were 47 males and 25 females in the reconstruction group, aged (59.75 ± 6.2) years, with a body mass index (BMI) (24.1 ± 2.2) kg/m2, and 51 males and 23 females in the nonreconstruction group, aged (58.77 ± 6.1) years, with a BMI (23.6 ± 2.7) kg/m2. There was no significant difference in the baseline data between the two groups (P > 0.05). In the submesenteric artery reconstruction group, 35 patients were type I, 25 patients were type II, 11 patients were type III, and 1 patient was type IV. There were 37 type I patients, 24 type II patients, 12 type III patients, and 1 type IV patient in the nonreconstruction group. There was no significant difference in arterial typing between the two groups (P > 0.05). The operation time of the reconstruction group was 162.2 ± 10.8 min, and that of the nonreconstruction group was 197.9 ± 19.1 min. Compared with that of the reconstruction group, the operation time of the two groups was shorter, and the difference was statistically significant (t = 13.840, P < 0.05). The amount of intraoperative blood loss was 30.4 ± 20.0 mL in the reconstruction group and 61.2 ± 26.4 mL in the nonreconstruction group. The amount of blood loss in the reconstruction group was less than that in the control group, and the difference was statistically significant (t = -7.930, P < 0.05). The rates of anastomotic leakage (1.4% vs 1.4%, P = 0.984), anastomotic hemorrhage (2.8% vs 4.1%, P = 0.672), and postoperative hospital stay (6.8 ± 0.7 d vs 7.0 ± 0.7 d, P = 0.141) were not significantly different between the two groups. Preoperative 3D vascular reconstruction technology can shorten the operation time and reduce the amount of intraoperative blood loss. Preoperative 3D vascular reconstruction is recommended to provide an intraoperative reference for laparoscopic low anterior resection with LCA preservation.