Development Of Medical Technology Research Articles

Innovative Research on Physical Data Management of Football Players Under the Background of Smart Medical Treatment

With the intensification of the competition for public medical resources and the rapid development of smart medical technology, the demand for in-depth analysis of athletes’ basic information and decision support is growing day by day. However, the practice of applying smart medicine to athletes’ physical information management is still relatively limited. This paper aims to explore how to use smart medical decision technology to build an integrated athlete physical information management and decision support platform. Taking football players as the research object, this paper integrates data mining technology into a smart medical decision-making model, to achieve accurate analysis and effective management of athletes’ physical fitness information. The core of the research is to develop a smart healthcare decision model that can identify key information and patterns on the platform through data mining technology. In the selection of the decision algorithm, this paper adopts cluster analysis and association rule mining algorithms, which can deeply dig into the potential rules and correlations in the data. Through the application of an algorithm, we can accurately evaluate the physical condition of football players, and provide scientific guidance and suggestions for their subsequent physical training. The algorithm in this study is not only innovative in theory, but also shows good adaptability and effectiveness in practical application.

Cosmetic and Reconstructive Facial Surgeries from the Perspective of Islam

Facial aesthetics and reconstructive surgery are among the prominent new phenomena of our time, which have become widespread with the development of medical science and technology. These types of surgeries are typically conducted to modify the external aesthetics of the body, address psychological issues stemming from mental health conditions, correct congenital deficiencies, or attain a desired physical appearance. In today's world, aesthetic surgeries, have become a cultural trend among the younger generation, even though they may not be medically or health-related necessities, and even the elderly are showing a tendency towards them. Consequently, the issue of the ruling on facial aesthetics and reconstructive surgery has become a significant matter within Islamic jurisprudence. This study has been carried out through an analytical-descriptive approach, utilizing authoritative Islamic references, modern legal texts, and fatwa compilations. This research demonstrates that the classical fiqh books have not addressed this issue at all; however, with the widespread prevalence of these surgeries in modern times Many Islamic scholars have studied this issue from a fiqh perspective and based on the principle that altering the divine creation is impermissible, they have referenced this as evidence to classify it as haram. In contrast, reconstructive surgeries performed for the treatment of accidents, illnesses, or congenital defects have not been prohibited and do not present any legal objection from an Islamic perspective. For this reason, considering specific conditions such as the existence of a genuine need for surgery, the likelihood of healing, the lesser harm of performing the surgery compared to not doing so, and the absence of any side effects during or after the surgery, Islamic scholars have deemed these surgeries permissible.

Evolution of surgical treatment for hepatolithiasis.

Hepatolithiasis is a common disease where stones are located in the intrahepatic bile duct. Hepatolithiasis is a disease with regional characteristics. The complication and postoperative recurrence rates of the disease are high. The intrahepatic cholangiocarcinoma and the incidence of liver cirrhosisare the main causes of death in patients with hepatolithiasis. Thus, it is difficult to treat. The majority of biliary stones are readily removed endoscopically, however complex intrahepatic or large refractory extrahepatic stones often require surgical or percutaneous interventions when standard endoscopic methods fail. At present, the main clinical treatment for hepatolithiasis is surgery, of which there are different methods depending on the patient's condition. With the continuous updates and development of medical technology, the treatment of hepatolithiasis has improved. In this paper, several mainstream surgical methods including partial hepatectomy, choledochojejunostomy, biliary tract exploration and lithotomy, percutaneous transhepatic chledochoscopic lithotripsy and liver transplantation used in the clinic are reviewed for clinicians' reference. Depending on the characteristics of each case, a suitable surgical method is chosen to obtain the best treatment effect.

Diagnostic value of 4D pelvic floor ultrasound for postpartum mild to moderate pelvic organ prolapse in primiparas based on medical thermal imaging monitoring

Traditional diagnostic methods often rely on the doctor’s experience, which can lead to missed diagnosis or misdiagnosis. With the development of wireless network medical technology, it provides a new possibility for the diagnosis of pelvic floor ultrasound. The purpose of this study was to evaluate the diagnostic value of 4D pelvic floor ultrasound based on wireless network medical technology in puerperal mild to moderate pelvic organ prolapse, and to explore its feasibility in clinical application. In this study, puerperal women were selected for 4D pelvic floor ultrasound examination after delivery, and image data was transmitted in real time to remote experts for analysis combined with wireless network technology. The sensitivity, specificity and application effect of the technique were evaluated by comparing the ultrasound results with the clinical diagnosis results. The results show that compared with traditional methods, wireless network medical technology can significantly improve the efficiency of image transmission and diagnosis accuracy. Therefore 4D pelvic floor ultrasound based on wireless network medical technology can effectively diagnose the postpartum mild and moderate pelvic organ prolapse of first-time partrients, which has high clinical application value, not only improves the diagnostic efficiency, but also provides a new development direction for telemedicine, and is worthy of promotion and application in clinical practice.

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.

Accounting support for innovative solutions in medical institutions

Intellectual property in medicine is the most important tool for stimulating innovation and protecting scientific achievements. Patents, copyrights and other forms of intellectual property contribute to the development of medical science and technology and create incentives for investment in research and development. This article is devoted to topical issues that reveal the features of accounting for intellectual property in healthcare institutions. The following methods were used in the study: analysis and synthesis, comparison, logical and systems approaches. As a result of the study, information was obtained on the rapid development of medical technologies, pharmaceuticals and digital solutions that significantly increase the role of intellectual property in healthcare. Accounting as the most important management function, along with regulation and planning, is inextricably linked with the protection of intellectual assets and is important as a tool for increasing the competitiveness of healthcare institutions, strengthening their financial stability and stimulating innovation. It is concluded that the reflection of intellectual property in the accounting system of medical institutions plays a key role in management, and it must be constantly developed and improved, taking into account modern challenges and technological innovations. Prospects for improving intellectual property accounting in healthcare are directly related to progress in digital technologies. The results reflected in this article can be used in the educational process of higher education institutions and in the practical work of accounting departments of modern organizations (including healthcare).

Multimodal separation and cross fusion network based on Raman spectroscopy and FTIR spectroscopy for diagnosis of thyroid malignant tumor metastasis

The diagnosis of cervical lymph node metastasis from thyroid cancer is an essential stage in the progression of thyroid cancer. The metastasis of cervical lymph nodes directly affects the prognosis and survival rate of patients. Therefore, timely and early diagnosis is crucial for effective treatment and can significantly improve patients’ survival rate and quality of life. Traditional diagnostic methods, such as ultrasonography and radionuclide scanning, have limitations, such as complex operations and high missed diagnosis rates. Raman spectroscopy and FTIR spectroscopy can well reflect the molecular information of samples, have characteristics such as sensitivity and specificity, and are simple to operate. They have been widely used in clinical research in recent years. With the development of intelligent medical diagnosis technology, medical data shows a multi-modal trend. Compared with single-modal data, multi-modal data fusion can achieve complementary information, provide more comprehensive and valuable diagnostic information, significantly enhance the richness of data features, and improve the modeling effect of the model, helping to achieve better results. Accurate disease diagnosis. Existing research mostly uses cascade processing, ignoring the important correlations between multi-modal data, and at the same time not making full use of the intra-modal relationships that are also beneficial to prediction. We developed a new multi-modal separation cross-fusion network (MSCNet) based on deep learning technology. This network fully captures the complementary information between and within modalities through the feature separation module and feature cross-fusion module and effectively integrates Raman spectrum and FTIR spectrum data to diagnose thyroid cancer cervical lymph node metastasis accurately. The test results on the serum vibrational spectrum data set of 99 cases of cervical lymph node metastasis showed that the accuracy and AUC of a single Raman spectrum reached 63.63% and 63.78% respectively, and the accuracy and AUC of a single FTIR spectrum reached 95.84% respectively and 96%. The accuracy and AUC of Raman spectroscopy combined with FTIR spectroscopy reached 97.95% and 98% respectively, which is better than existing diagnostic technology. The omics correlation verification obtained correlation pairs of 5 Raman frequency shifts and 84 infrared spectral bands. This study provides new ideas and methods for the early diagnosis of cervical lymph node metastasis of thyroid cancer.

Associations of HMGB1, sTNFR-1 and NLR with Premature Delivery Secondary to Infection in Pregnant Women Undergoing Cervical Cerclage

Background: Although cervical cerclage has improved with the rapid development of medical technology, there remains a distinct probability of adverse pregnancy outcomes. To investigate the associations of changes in the high mobility group box 1 (HMGB1), soluble tumor necrosis factor receptor 1 (sTNFR-1) and peripheral blood neutrophil-to-lymphocyte ratio (NLR) with premature delivery secondary to infection in pregnant patients undergoing cervical cerclage. Methods: Sixty-seven pregnant patients with premature delivery after cervical cerclage, who were treated at the Affiliated Matern & Child Care Hospital of Nantong University from January 2022 to October 2023, were enrolled, including 43 with premature delivery secondary to infection (infectious group) and 24 with idiopathic premature delivery (non-infectious group). The pre-delivery serum levels of HMGB1, sTNFR-1 and the peripheral blood level of NLR were compared between the 2 groups. Further, the clinical value of these 3 indicators in predicting premature delivery secondary to infection among pregnant patients undergoing cervical cerclage was assessed by receiver operating characteristic (ROC) curve analysis. Results: The infectious group exhibited significantly higher serum levels of HMGB1, sTNFR-1 and peripheral blood level of NLR compared to those in the non-infectious group, demonstrating significant differences (p < 0.05). Logistic regression analysis revealed that HMGB1 and NLR were independent influencing factors for premature delivery (p < 0.05). According to the ROC curve analysis results, the changes in HMGB1, sTNFR-1 and NLR levels may reflect the risk of premature delivery secondary to infection among pregnant patients undergoing cervical cerclage. The area under the curve (AUC), sensitivity and specificity of combined detection were all markedly higher than those of independent detection. Conclusions: HMGB1, sTNFR-1 and NLR levels are risk factors for third-trimester premature delivery among pregnant patients undergoing cervical cerclage. Timely combined detection of serum HMGB1, sTNFR-1 and peripheral blood NLR during the third trimester can improve the clinical diagnostic acumen, which enables early prevention to help lower the risk of premature delivery.

Lower urinary tract symptoms in men after surgery for benign prostatic hyperplasia

BACKGROUND: The development of medical technologies has resulted into a significant increase in the number of patients undergoing minimally invasive surgeries for benign prostatic hyperplasia. However, 29.9–41% of patients after surgical treatment are not satisfied with the surgical treatment results. AIM: To assess the rate and nature of lower urinary tract symptoms in men after surgical treatment of benign prostatic hyperplasia with various surgical techniques. MATERIALS AND METHODS: A telephone survey was carried out among 6117 men aged 59.57 ± 11.11 years (48–83 years), who underwent surgical treatment for benign prostatic hyperplasia with various surgical techniques and 5960 of these patients were surveyed using the IPSS questionnaire in the period 2012–2022. Moreover, to assess urination, patients kept their voiding diaries for 72 hours, a questionnaire to evaluate the symptoms of overactive bladder; ultrasound of the bladder, prostate and upper urinary tract, uroflowmetry, and, if indicated, a urodynamic study were performed. RESULTS: The study showed that 2413 (40.5%) patients were “absolutely satisfied” with the surgical treatment results, 829 (13.9%) patients were “partially satisfied” and noted improved urination, although its quality was assessed as unsatisfactory, and 2718 (45.6%) operated men were “absolutely dissatisfied”. Lower urinary tract symptoms persisted in 2655 (47.9%) men which underwent surgery, and in 3.3% of cases (87 patients) their severity increased in the postoperative period. CONCLUSIONS: The high rate of dissatisfaction with the surgical treatment results of patients with benign prostatic hyperplasia is caused by the persistence of irritative symptoms in the postoperative period and does not depend on the surgery performed. The origin of symptoms in most patients is not associated with benign prostatic hyperplasia, and the surgery indications are overestimated.

Delivery Methods of Radiopharmaceuticals: Exploring Global Strategies to Minimize Occupational Radiation Exposure.

In the world of nuclear medicine, health care professionals face the challenge of safeguarding themselves and their patients from occupational radiation exposure. As the field experiences exponential growth, driven by the surge in approvals of radiopharmaceuticals for diagnostic and therapeutic applications, it becomes vital to delve into the delivery methods of radiopharmaceuticals. Health care professionals take precautions during radiopharmaceutical administration, including maintaining distance from radioactive sources, using shielding, limiting exposure time, and monitoring radiation levels with badges. Regular evaluations provide compliance with recommended exposure limits, yet concerns persist, especially regarding the cumulative radiation exposure from manual injections over time. Understanding the long-term effects of radiation exposure has spurred the development of cutting-edge medical device technologies, such as autoinjectors, designed to administer radiopharmaceuticals accurately while minimizing total radiation dose to health care professionals. The U.S. Pharmacopeia 825 regulation refers to these devices as "direct infusion systems." Nuclear medicine technologists commonly refer to them as "autoinjectors," whereas device manufacturers may use terms such as injection system, radiopharmaceutical injector, or infusion system. Despite variations in terminology, these devices hold a pivotal role in shaping the future of radiopharmaceutical delivery. In an era of escalating demand for PET procedures worldwide, skilled health care professionals ensure the safe and precise dosing of radiopharmaceuticals. This article explores the state-of-the-art medical devices in radiopharmaceutical delivery, spotlighting transformative medical devices currently revolutionizing the nuclear medicine landscape in the global market.

Advancements in Engineering Tetrahedral Framework Nucleic Acids for Biomedical Innovations.

Tetrahedral framework nucleic acids (tFNAs) are renowned for their controllable self-assembly, exceptional programmability, and excellent biocompatibility, which have led to their widespread application in the biomedical field. Beyond these features, tFNAs demonstrate unique chemical and biological properties including high cellular uptake efficiency, structural bio-stability, and tissue permeability, which are derived from their distinctive 3D structure. To date, an extensive range of tFNA-based nanostructures are intelligently designed and developed for various biomedical applications such as drug delivery, gene therapy, biosensing, and tissue engineering, among other emerging fields. In addition to their role in drug delivery systems, tFNAs also possess intrinsic properties that render them highly effective as therapeutic agents in the treatment of complex diseases, including arthritis, neurodegenerative disorders, and cardiovascular diseases. This dual functionality significantly enhances the utility of tFNAs in biomedical research, presenting valuable opportunities for the development of next-generation medical technologies across diverse therapeutic and diagnostic platforms. Consequently, this review comprehensively introduces the latest advancements of tFNAs in the biomedical field, with a focus on their benefits and applications as drug delivery nanoplatforms, and their inherent capabilities as therapeutic agents. Furthermore, the current limitations, challenges, and future perspectives of tFNAs are explored.

CDC42 deficiency leads to endometrial stromal cell senescence in recurrent implantation failure.

Does the downregulation of cell division cycle 42 (CDC42) protein in endometrial stroma lead to endometrial senescence in patients with recurrent implantation failure (RIF), and what is the potential mechanism? CDC42 deficiency causes endometrial stromal senescence and decidualization defects, impairing uterine receptivity of RIF patients, via activation of Wnt signaling pathway. Uterine aging is unique due to the cyclic remodeling and decidualization of endometrial tissue. Several transcriptomic studies have reported increased senescence in the endometrium in young patients with RIF. Our previous transcriptomic sequencing study discovered that endometrium from women with RIF showed downregulation of CDC42, which is an essential molecule affected by various senescence-related diseases. The endometrial samples of a total of 71 fertile control patients and 37 RIF patients were collected to verify the association between CDC42 expression and endometrial senescence of RIF patients. Primary endometrial stromal cells (EnSCs) were isolated from endometrial biopsies taken from patients without any endometrial complications and planning to undergo IVF, then subjected to adenovirus-mediated CDC42 knockdown and decidualization induction to explore the detailed mechanism by which CDC42 governs stromal senescence and decidualization. Wnt inhibitor XAV-939 was used to correct the endometrial senescence and decidualization defect. Senescence was determined by cell cycle arrest markers (e.g. P16, P21, and P53), SASP molecules (e.g. IL6 and CXCL8), and SA-β-gal staining. Masson's staining and Sirius Red staining were used to detect the endometrial fibrosis. Decidualization was evaluated by the mRNA expression and protein secretion of PRL and IGFBP1, F-actin immunostaining, and the BeWo spheroids 'in vitro implantation' model. Methods used to assess cell function included adenovirus transduction, RNA-sequencing, bioinformatic analysis, western blotting, RT-qPCR, ELISA, and immunofluorescence. Here, we observed remarkably increased levels of stromal senescence and fibrosis, along with stromal CDC42 deficiency, in the endometrium of patients with RIF (P < 0.001). Knockdown of CDC42 effectively induced premature senescence in EnSCs, leading to aberrant accumulation of senescent EnSCs and collagen deposition during decidualization. CDC42 deficiency in EnSCs restrained the decidualization differentiation and receptivity to trophoblast cells. Transcriptomic analysis revealed Wnt signaling activation as a critical downstream alteration in CDC42-deficient EnSCs. Mechanistically, CDC42 interacted with AKT competitively to impede the binding of GSK3β to AKT. Knockdown of CDC42 increased AKT-mediated phosphorylation of GSK3β to inactivate the Axin-GSK3β destruction complex, leading to accumulation and nuclear translocation of β-catenin. Importantly, Wnt signaling inhibitors partially corrected the endometrial senescence caused by CDC42 deficiency, and improved both decidualization and trophoblast invasion. RNA-seq data sets generated in this study have been deposited at the NCBI database with BioProject accession number PRJNA1102745. The present study was based on in vitro cell cultures. Further studies involving CDC42-regulated endometrial senescence are needed in knockout mice model and human endometrial assembloids. In addition to uncovering endometrial senescence in RIF, our findings underscore the significance of CDC42 in modulating EnSC senescence to maintain the decidualization function, and suggest Wnt signaling inhibitors as potential therapeutic agents for alleviating endometrial senescence. This work was supported by the National Natural Science Foundation of China [82271698 (R.J.), 82030040 (H.S.), 82288102 (H.W.), and 82371680 (G.Y.)]; the Natural Science Foundation of Jiangsu Province [BK20231117 (R.J.)]; and the Medical Science and Technology Development Foundation of Nanjing Department of Health [YKK23097 (Y.Z.)]. The authors declare no potential conflicts of interest.

Medical image segmentation network based on feature filtering with low number of parameters

In recent years, the medical image segmentation method based on hybrid convolutional neural network (CNN) and Vision Transformer (ViT) has made great progress, but it still faces the challenge of unbalanced global and local modeling, and excessive parameters. In addition, ViT repeatedly uses the whole feature map to model the global information, thus generating irrelevant and weakly related information, which will weaken the performance of the model when facing small datasets and segmentation targets. Therefore, this paper proposes a feature screening network based on similarity, named Screening Feature (SF)-MixedNet. Specifically, this paper first proposes a new feature extractor, namely Correlation based Similarity Transformer (CSimFormer). On the basis of parameter pruning, it uses the Screening Feature Multi-head Self Attention (SF-MSA) to establish the remote dependency, and calculates the similarity between local elements through the Location-Sensitive Mechanism (LsM) to obtain the weight matrix. Then, the correlation between regional elements is mined by Region Matching and Selection (RMS) mechanism, and the obtained information is filtered according to the corresponding rules to reduce the side effects of redundant information. Extensive experiments on Synapse dataset, ACDC dataset and SegPC-2021 dataset show that the segmentation accuracy reaches 83.51%, 92.20% and 81.27% respectively. Especially in the Synapse dataset, our method is 6.31% higher than the baseline. The method proposed in this paper effectively improves the segmentation accuracy, provides more detailed information for medical diagnosis and promotes the development of medical artificial intelligence technology.

Exploring the Integration of the MimicIV Database with CRRT Technology to Optimize Treatment Strategies for Critically Ill Patients

With the rapid development of medical information technology, the application of big data in medical research is increasingly widespread. The MimicIV database, as an open resource containing extensive clinical information on Intensive Care Unit (ICU) patients, provides valuable data support for researchers. Continuous Renal Replacement Therapy (CRRT), as an effective renal support technique, plays a crucial role in the treatment of critically ill patients. This paper aims to explore how to utilize the information in the MimicIV database, combined with CRRT technology, to optimize treatment strategies for critically ill patients. By analyzing clinical data from the MimicIV database, this paper proposes a series of CRRT-based treatment strategies intended to enhance treatment effectiveness, reduce complications, and improve patient outcomes.

Fast‐MedNeXt: Accelerating the MedNeXt Architecture to Improve Brain Tumour Segmentation Efficiency

ABSTRACTWith the rapid development of medical imaging technology, 3D image segmentation technology has gradually become a mainstream method, especially in brain tumour detection and diagnosis showing its unique advantages. The technique makes full use of 3D spatial information to locate and analyze tumours more accurately, thus playing an important role in improving diagnostic accuracy, optimising treatment planning and promoting research. However, it also suffers from significant computational expenditure and delayed processing pace. In this paper, we propose an innovative optimisation scheme to address this problem. We thoroughly investigate the MedNeXt network and propose Fast‐MedNeXt, which aims to increase the processing speed while maintaining accuracy. First, we introduce the partial convolution (PConv) technique, which replaces the deep convolutional layers in the network. This improvement effectively reduces computation and memory requirements while maintaining efficient feature extraction. Second, based on PConv, we propose PConv‐Down and PConv‐Up modules, which are applied to the up‐sampling and down‐sampling modules to further optimise the network structure and improve efficiency. To confirm the efficacy of the approach, we carried out a sequence of tests in the multimodal brain tumour segmentation challenge 2021 (BraTS2021). By comparing with the MedNeXt series network, the Fast‐MedNeXt reduced the latency by 22.1%, 20.5%, 15.8%, and 11.4% respectively, while the average accuracy also increased by 0.475% and 0.2% respectively. These significant performance improvements demonstrate the effectiveness of Fast‐MedNeXt in 3D medical image segmentation tasks and provide a new and more efficient solution for the field.

Exploring the Etiology and Clinical Diagnosis and Treatment of Upper Gastrointestinal Bleeding

Upper gastrointestinal bleeding refers to bleeding caused by the digestive tract above the flexor ligament, including diseases of the esophagus, stomach, duodenum, pancreas, biliary tract, and lesions after gastrojejunostomy. It is a common and critical condition in internal medicine. In recent years, with the continuous development of medical technology and the improvement of treatment methods, the diagnosis and treatment level of this disease has been greatly improved. However, this disease has the characteristics of fast onset, complex condition, and certain mortality rate. Therefore, exploring and analyzing the etiology, causes, and treatment plans of this disease is of great significance for improving the cure rate and prognosis of patients with this disease. Due to inflammation, mechanical injury, vascular disease, or adjacent organ lesions, the choice of treatment plan varies depending on the cause. Based on the above situation, this article, The etiology and diagnosis and treatment plan of upper gastrointestinal bleeding have been explored, which can provide reference for improving the treatment level of this disease

Advancements in Vascular Closure Devices for Effective Hemostasis in Femoral Artery Interventions.

With medical technology development, endovascular intervention has been widely used in clinical practice, and the establishment of surgical access through the femoral artery, where most vascular interventions are performed, is a common method. Postoperative hemostasis at the femoral artery puncture site is a key part of interventional procedures and is particularly important to ensure the safety and effectiveness of hemostasis. Some patients undergoing interventional therapy also use anticoagulant and antiplatelet drugs preoperatively and undergo systemic heparin session intraoperatively, which leads to abnormal coagulation, thus increasing the difficulty of hemostasis at the puncture point postoperatively. Certain patients with specific conditions, such as combined vascular calcification, obesity, diabetes mellitus, and renal impairment, present more challenging cases for postoperative puncture point hemostasis. Femoral artery puncture site hemostasis methods include manual compression, arterial compression devices, and vascular closure devices, which are a kind of equipment that helps interventional doctors stop bleeding quickly at the femoral artery puncture site. From the 1990s to the present, vascular occluders with many different concepts and mechanisms have emerged. Based on different hemostatic principles and materials, the mechanisms and principles of action are varied and include sealant occlusion, collagen patch embolization, polyester suture closure, absorbable polyethanol embolic agents, nickel-titanium alloy clips, polydiethanol sealant embolization, and suture bioabsorbable patches. Many studies have compared the hemostatic effect of vascular closure devices with those of manual compression. In this article, we review the hemostatic effects of the 2 modalities and the advances in the use of vascular closure devices in vascular intervention.

Quantum classical hybrid convolutional neural networks for breast cancer diagnosis.

The World Health Organization states that early diagnosis is essential to increasing the cure rate for breast cancer, which poses a danger to women's health worldwide. However, the efficacy and cost limitations of conventional diagnostic techniques increase the possibility of misdiagnosis. In this work, we present a quantum hybrid classical convolutional neural network (QCCNN) based breast cancer diagnosis approach with the goal of utilizing quantum computing's high-dimensional data processing power and parallelism to increase diagnosis efficiency and accuracy. When working with large-scale and complicated datasets, classical convolutional neural network (CNN) and other machine learning techniques generally demand a large amount of computational resources and time. Their restricted capacity for generalization makes it challenging to maintain consistent performance across multiple data sets. To address these issues, this paper adds a quantum convolutional layer to the classical convolutional neural network to take advantage of quantum computing to improve learning efficiency and processing speed. Simulation experiments on three breast cancer datasets, GBSG, SEER and WDBC, validate the robustness and generalization of QCCNN and significantly outperform CNN and logistic regression models in classification accuracy. This study not only provides a novel method for breast cancer diagnosis but also achieves a breakthrough in breast cancer diagnosis and promotes the development of medical diagnostic technology.

A comprehensive review of deep learning for medical image segmentation

Medical image segmentation provides detailed mappings of regions of interest, facilitating precise identification of critical areas and greatly aiding in the diagnosis, treatment, and understanding of diverse medical conditions. However, conventional techniques frequently rely on hand-crafted feature-based approaches, posing challenges when dealing with complex medical images, leading to issues such as low accuracy and sensitivity to noise. Recent years have seen substantial research focused on the effectiveness of deep learning models for segmenting medical images. In this study, we present a comprehensive review of the various deep learning-based approaches for medical image segmentation and provide a detailed analysis of their contributions to the domain. These methods can be broadly categorized into five groups: CNN-based methods, Transformer-based methods, Mamba-based methods, semi-supervised learning methods, and weakly supervised learning methods. Convolutional Neural Networks (CNNs), with their efficient feature self-learning, have driven major advances in medical image segmentation. Subsequently, Transformers, leveraging self-attention mechanisms, have achieved performance on par with or surpassing Convolutional Neural Networks. Mamba-based methods, as a novel selective state-space model, are emerging as a promising direction. Furthermore, due to the limited availability of annotated medical images, research in weakly supervised and semi-supervised learning continues to evolve. This review covers common evaluation methods, datasets, and deep learning applications in diagnosing and treating skin lesions, hippocampus, tumors, and polyps. Finally, we identify key challenges such as limited data, diverse modalities, noise, and clinical applicability, and propose future research in zero-shot segmentation, transfer learning, and multi-modal techniques to advance the development of medical image segmentation technology.

Novel method to analyse and reconstruct optical constants of biological substances with application of Kramers–Kronig relations

Spectroscopic measurements of dielectric characteristics of biomolecules and tissues are a basis of many modern methods of medical diagnostics, research, and engineering. This requires accurate knowledge about the refraction index, n, and the extinction coefficient, k, at various frequencies, which are usually not available from the literature. Moreover, spectral data often need to be analysed, compared, or verified, especially if obtained for different samples and experimental conditions. The above problems can be tackled with the help of Kramers–Kronig relations, which are based on fundamental physical principles and can be generally used for arbitrary substances, including complex biological molecules and tissues. However, their direct application for analysis and reconstruction of the optical constants from experiment is hindered by the restricted frequency range of data, the presence of experimental errors, and the prevalence of qualitative rather than quantitative spectra. Our work focuses on the calibration of measured data and the reconstruction of missing spectra. We developed robust reconstruction techniques designed for cases, when disjoint intervals of absorbance data are available, commonly given in IR or/and UV ranges. The techniques hinge on a reference refractive index, given within a certain frequency window and preferably situated in proximity to the absorbance data. The proposed methods are applied to reconstruct the dielectric functions of the protein bovine serum albumine (BSA) and glucose across the entire frequency range. The acquired data are further employed to assess the feasibility of constructing a protein-based coating for implanted antennas. By incorporating spectral characteristics from different frequency ranges into one consistent picture, our methods supply the full information about dielectric response, which is associated with concentration, structure and environment of biomolecules. This provides a mathematical background for combined applications of spectroscopic instruments operating in different frequency ranges that will support fundamental research and stimulate the development of medical technologies.