Hasil untuk "Medical technology"

E. Cohen, D. Kuo, R. Agrawal et al.

R. Pethig

J. Ash, M. Berg, E. Coiera

Óscar Lucía, P. Maussion, E. Dede et al.

Yiwen Gao, He Li, Yan Luo

A. Marro, T. Bandukwala, Walter H Mak

M. Mirjalili, S. Zohoori

Electrospinning (electrostatic fiber spinning) is a modern and efficient method which uses electric field to produce fine fibers which their diameter can reduce to nanometers. These fibers have wide applications in industry such as filtration, composite materials, medical, membrane, etc. In this paper a review of electrospinning process, its products and applications are explained.

I. Liua, Eleni Strouliab, Ioanis Nikolaidisc et al.

Jiamin Tang, Jie Jia, Yuan Gao et al.

Abstract Background In the medical field, value co-creation involves patients’ active involvement. By collaborating with service providers, patients can contribute to the creation of more targeted and effective value. Patients’ self-efficacy and behavior are crucial in this process, as their active participation and support can enhance their service experience. This study investigated the impact of chronic disease patients’ self-efficacy and value co-creation behaviors on the outcomes of value co-creation. Methods Relevant data were acquired through a questionnaire survey using statistical methods, such as the t-test, analysis of variance, and stratified linear regression. This approach was used to examine the current conditions and factors influencing value co-creation outcomes among community-dwelling patients with chronic diseases. Additionally, a structural equation model was employed to systematically investigate and validate the impact pathways and mechanisms related to the influence of self-efficacy and value co-creation behaviors on value co-creation outcomes. We also explored the moderating effect of digital health technology application capabilities on the relationship between self-efficacy and value co-creation behaviors. Results Self-efficacy, information search, interactive collaboration, feedback provision, and shared decision-making exert significant positive influences on the value co-creation outcomes among individuals with chronic diseases. The path analysis of the structural equation model indicates that self-efficacy and value co-creation behaviors may directly impact value co-creation outcomes. Concurrently, value co-creation behaviors partially mediate the association between self-efficacy and value co-creation outcomes. Furthermore, the digital health technology application capability exhibits a negative moderating effect in the pathway from self-efficacy to value co-creation behaviors. Conclusions The implementation of health education and social support measures by healthcare institutions and communities may augment patient self-efficacy, facilitate doctor-patient interactions, and promote shared decision-making. These initiatives could enhance the value of chronic disease services and optimize patient experiences. Additionally, healthcare institution managers are encouraged to focus on optimizing internet hospital platforms, organizing digital health training for patients, and bolstering patients’ proficiency in digital health technology applications. This strategy aims to instill a sense of health responsibility among patients with chronic diseases by fostering positive behaviors in interactive collaboration, information search, feedback provision, and other dimensions.

Shaoqi He, Fei Yu, Rongyao Guo et al.

To model the response of neural networks to electromagnetic radiation in real-world environments, this study proposes a memristive dual-wing fractional-order Hopfield neural network (MDW-FOMHNN) model, utilizing a fractional-order memristor to simulate neuronal responses to electromagnetic radiation, thereby achieving complex chaotic dynamics. Analysis reveals that within specific ranges of the coupling strength, the MDW-FOMHNN lacks equilibrium points and exhibits hidden chaotic attractors. Numerical solutions are obtained using the Adomian Decomposition Method (ADM), and the system’s chaotic behavior is confirmed through Lyapunov exponent spectra, bifurcation diagrams, phase portraits, and time series. The study further demonstrates that the coupling strength and fractional order significantly modulate attractor morphologies, revealing diverse attractor structures and their coexistence. The complexity of the MDW-FOMHNN output sequence is quantified using spectral entropy, highlighting the system’s potential for applications in cryptography and related fields. Based on the polynomial form derived from ADM, a field programmable gate array (FPGA) implementation scheme is developed, and the expected chaotic attractors are successfully generated on an oscilloscope, thereby validating the consistency between theoretical analysis and numerical simulations. Finally, to link theory with practice, a simple and efficient MDW-FOMHNN-based encryption/decryption scheme is presented.

Mona Makita, Shinichi Kawamoto, Momoko Nagai-Tanima et al.

The ground reaction force (GRF) waveform during countermovement jumps (CMJs) is considered to reflect neuromuscular coordination strategies; yet the biomechanical mechanisms distinguishing unimodal from bimodal patterns remain unclear. This study investigated the influence of countermovement depth and velocity on GRF waveform shape and examined their relationship with joint moments and work. Twenty-six healthy young women (age: 22.1 ± 1.1 years; height: 160.8 ± 4.0 cm; body weight: 53.5 ± 5.6 kg) performed CMJs, and GRF waveforms were categorised as unimodal and bimodal patterns. Jump-related variables, joint moments, and work were analysed. Analysis of covariance (ANCOVA) was conducted using countermovement depth as a covariate, and correlation analyses examined the associations between depth and biomechanical parameters. Compared with the unimodal group, the bimodal group exhibited a significantly greater countermovement depth, with no significant difference in countermovement velocity. Before adjustment, knee joint work and ankle joint moments differed significantly between groups; however, these differences were no longer evident after adjusting for countermovement depth, indicating that it was a confounding factor. Correlation analyses demonstrated that a greater countermovement was associated with increased hip and knee joint work and reduced ankle joint contribution. These findings indicate that GRF waveform shape in CMJ is determined primarily by countermovement depth rather than velocity. The unimodal pattern reflected ankle-dominant simultaneous output, whereas the bimodal pattern reflected proximal joint-dominant sequential output. This study highlights the role of joint-specific coordination strategies and offers insight for developing individualised training and rehabilitation approaches.

Danyu Li, Yanlin Zhu, Changrong Yuan et al.

Abstract BackgroundMobile health (mHealth) for breast cancer care can greatly benefit patients’ symptom management. Although research supports the effectiveness of mHealth, older adult patients with breast cancer often face difficulties using it, hindering them from accessing effective symptom management possibilities. Understanding the preference for mHealth among this population is crucial for providing insights into effective mHealth design. ObjectiveThis study aimed to better understand the symptom management preference using mHealth for Chinese older adult patients with breast cancer and use the approach of personas to inform the mHealth design. MethodsThis was a descriptive qualitative study. In total, 17 patients with breast cancer aged 60 years and older were recruited from tertiary hospitals in Shanghai, China, using purposive sampling. Data were collected through one-on-one interviews. Content analysis was used to identify the factors that influence participants’ symptom management preference using mHealth. The categories of influencing factors of preference informed the persona template and guided the development of the persona. ResultsWe identified 3 major categories affecting participants’ preference for mHealth, including social interaction patterns, mHealth literacy, and symptoms. The following five personas were developed: (1) Positive Manager, (2) Dependent Parent, (3) Management Isolationist, (4) Image Manager, and (5) Clinician Dependent. We provide insights into how these personas can be used when designing and implementing mHealth for symptom management support. ConclusionsKey factors influencing symptom management preference using mHealth among Chinese older adult patients with breast cancer and personas developed based on that can foster a better understanding of this population and initiate future mHealth design and implementation.

Kerol Djoumessi, Philipp Berens

Convolutional neural networks (CNNs) are widely used for high-stakes applications like medicine, often surpassing human performance. However, most explanation methods rely on post-hoc attribution, approximating the decision-making process of already trained black-box models. These methods are often sensitive, unreliable, and fail to reflect true model reasoning, limiting their trustworthiness in critical applications. In this work, we introduce SoftCAM, a straightforward yet effective approach that makes standard CNN architectures inherently interpretable. By removing the global average pooling layer and replacing the fully connected classification layer with a convolution-based class evidence layer, SoftCAM preserves spatial information and produces explicit class activation maps that form the basis of the model's predictions. Evaluated on three medical datasets, SoftCAM maintains classification performance while significantly improving both the qualitative and quantitative explanation compared to existing post-hoc methods. Our results demonstrate that CNNs can be inherently interpretable without compromising performance, advancing the development of self-explainable deep learning for high-stakes decision-making. The code is available at https://github.com/kdjoumessi/SoftCAM

S. Khorasani, M. Danaei, M. R. Mozafari

Abstract Background The word “nutraceutical” can be defined as a food substance or part of it, which provides the body with medical or health benefits, including disease prevention and therapy. Scope and approach Nutraceuticals are a natural way to achieve therapeutic outcome with minimal or no side effects. However, they are subject to degradation resulting from exposure to environmental factors such as humidity, oxygen, heat, light and extreme pH values. Nanoliposome, or nanometric bilayer phospholipid vesicle, is a very promising encapsulation technology for the nutraceutical industry. Protection of sensitive bioactive molecules, storage stability, high loading capacity, enhanced bioavailability, and sustained-release mechanism are among the advantages offered by nanoliposome technology. They can encapsulate lipophilic and hydrophilic material at the same time providing synergistic effect. This article reviews various aspects of nanoliposome technology including their main physicochemical properties, generally employed preparation methods, targeting strategies and their application in food and nutraceutical industries. Key findings and conclusions The great potential of nanoliposomes in food and nutraceutical industries is being rapidly established due to unique properties of these nanocarriers. Considering the global health problems, their utilization for effective disease prevention and health promotion is of vital importance.

M. D. Di Prima, J. Coburn, David Hwang et al.

Additive manufacturing/3D printing of medical devices is becoming more commonplace, a 3D printed drug is now commercially available, and bioprinting is poised to transition from laboratory to market. Despite the variety of technologies enabling these products, the US Food and Drug Administration (FDA) is charged with protecting and promoting the public health by ensuring these products are safe and effective. To that end, we are presenting the FDA’s current perspective on additive manufacturing/3D printing of medical products ranging from those regulated by the Center for Devices and Radiological Health (CDRH), the Center for Drug Evaluation and Research (CDER), and the Center for Biologics Evaluation and Research (CBER). Each Center presents an overview of the additively manufactured products in their area and the specific concerns and thoughts on using this technology in those product spaces.

Yasir I. B. Ahmed, Hind S. Yagoub, Hind S. Yagoub et al.

Fan Wang, Shuai Pan, Xiao-wen Li et al.

PurposeThis study aims to explore the effectiveness of enhancing individual spatial cognitive abilities in alleviating the negative symptoms of visually induced motion sickness (VIMS). Additionally, it seeks to develop innovative intervention methods to improve spatial cognition and identify new treatment approaches for VIMS.MethodsThe study investigated the impact of innovative interventions on spatial cognitive abilities and their modulation of VIMS susceptibility. A total of 43 participants were recruited (23 in the experimental group and 20 in the control group). The experimental group underwent six sessions of spatial cognitive ability training, while the control group engaged in activities unrelated to spatial cognition.ResultsThe analysis revealed that the spatial cognitive ability scores of the experimental group significantly improved after the intervention. Furthermore, the experimental group exhibited significant differences in nausea, oculomotor, disorientation, and total SSQ scores before and after the intervention, indicating that the intervention effectively mitigated VIMS symptoms.ConclusionThis study developed a virtual reality training method that effectively enhances individual spatial cognitive abilities and significantly alleviates VIMS symptoms, providing a novel and effective approach for VIMS intervention and treatment.

Chang Su, Xinglin Liu, Can Wu et al.

Objective This study aimed to investigate the feasibility, efficacy, and safety of focused ultrasound (FUS) for the treatment of vulvar low-grade squamous intraepithelial lesions (VLSIL) with persistent symptoms.Methods This retrospective analysis included 24 VLSIL patients who underwent FUS treatment. At each follow-up visit, the clinical response was assessed including changes in symptoms and signs. In addition, the histological response was assessed based on the vulvar biopsy results of the 3rd follow-up. Clinical and histological response were assessed to elucidate the efficacy.Results A total of 22 patients completed follow-up and post-treatment pathological biopsies. After treatment, the clinical scores of itching decreased from 2.55 ± 0.51 to 0.77 ± 0.81 (p < 0.05). Furthermore, the clinical response rate and histological response rate were 86.4% and 81.8%, respectively. Only two cured patients indicated recurrence in the 3rd and 4th year during the follow-up period and achieved cure after re-treatment. In terms of adverse effects, only one patient developed ulcers after treatment, which healed after symptomatic anti-inflammatory treatment without scarring, and no other treatment complications were found in any patients. None of the patients developed a malignant transformation during the follow-up period.Conclusion This study revealed that FUS is feasible, effective, and safe for treating VLSIL patients with persistent symptoms, providing a new solution for the noninvasive treatment of symptomatic VLSIL.

Zheyuan Zhang, Ulas Bagci

Current medical image segmentation relies on the region-based (Dice, F1-score) and boundary-based (Hausdorff distance, surface distance) metrics as the de-facto standard. While these metrics are widely used, they lack a unified interpretation, particularly regarding volume agreement. Clinicians often lack clear benchmarks to gauge the "goodness" of segmentation results based on these metrics. Recognizing the clinical relevance of volumetry, we utilize relative volume prediction error (vpe) to directly assess the accuracy of volume predictions derived from segmentation tasks. Our work integrates theoretical analysis and empirical validation across diverse datasets. We delve into the often-ambiguous relationship between segmentation quality (measured by Dice) and volumetric accuracy in clinical practice. Our findings highlight the critical role of incorporating volumetric prediction accuracy into segmentation evaluation. This approach empowers clinicians with a more nuanced understanding of segmentation performance, ultimately improving the interpretation and utility of these metrics in real-world healthcare settings.

Zihan Li, Yuan Zheng, Dandan Shan et al.

Most recent scribble-supervised segmentation methods commonly adopt a CNN framework with an encoder-decoder architecture. Despite its multiple benefits, this framework generally can only capture small-range feature dependency for the convolutional layer with the local receptive field, which makes it difficult to learn global shape information from the limited information provided by scribble annotations. To address this issue, this paper proposes a new CNN-Transformer hybrid solution for scribble-supervised medical image segmentation called ScribFormer. The proposed ScribFormer model has a triple-branch structure, i.e., the hybrid of a CNN branch, a Transformer branch, and an attention-guided class activation map (ACAM) branch. Specifically, the CNN branch collaborates with the Transformer branch to fuse the local features learned from CNN with the global representations obtained from Transformer, which can effectively overcome limitations of existing scribble-supervised segmentation methods. Furthermore, the ACAM branch assists in unifying the shallow convolution features and the deep convolution features to improve model's performance further. Extensive experiments on two public datasets and one private dataset show that our ScribFormer has superior performance over the state-of-the-art scribble-supervised segmentation methods, and achieves even better results than the fully-supervised segmentation methods. The code is released at https://github.com/HUANGLIZI/ScribFormer.