Unveiling the voices: Exploring perspectives and experiences of women, donors, recipient mothers and healthcare professionals in human milk donation: A systematic review of qualitative studies.

OBJECTIVES: This study aims to examine and synthesise the views and experiences of women, donors, recipient mothers and healthcare professionals regarding human milk donation or sharing. METHODS: The Joanna Briggs Institute (JBI) meta-aggregative approach to systematic reviews of qualitative studies was adopted. Six databases, MEDLINE, CINAHL, Embase, PsycINFO, Web of Science and Scopus were searched. English written qualitative studies from database inception to February 2024 were included. The JBI Critical Appraisal Checklist for Qualitative Research was used to appraise the collected research evidence. RESULTS: A total of 629 papers were screened, and 41 studies were included in the review. Six key findings were synthesised. (i) Donors, recipients and their families all benefit from milk donation. (ii) Motivation to receive or donate breast milk. (iii) Awareness and participation are affected by formal vs. informal sharing, mothers' personal experiences and external factors. (iv) Concerns about disease transmission, jealousy, bonding and traits. (v) Challenges encountered by donors, recipient mothers, staff and milk banks (vi) Suggestions for promoting human milk donation. DISCUSSION: Stakeholders of human milk donation, including donors, recipient mothers, healthcare professionals, and human milk bank representatives, face various physical, mental and practical challenges. Informal sharing complements formal donations and contributes to improved breastfeeding rates. Advocacy and education efforts are still needed to increase participation and safety levels. The major limitation of the study is the inadequate search on views of immediate family members.

MyoFold: Joint myocardial tissue composition and wall motion quantification via a highly folded sequence.

PURPOSE: This study aims to develop and evaluate a novel cardiovascular MR sequence, MyoFold, designed for the simultaneous quantifications of myocardial tissue composition and wall motion. METHODS: MyoFold is designed as a 2D single breathing-holding sequence, integrating joint T1/T2 mapping and cine imaging. The sequence uses a 2-fold accelerated balanced SSFP (bSSFP) for data readout and incorporates electrocardiogram synchronization to align with the cardiac cycle. MyoFold initially acquires six single-shot inversion-recovery images, completed during the diastole of six successive heartbeats. T2 preparation (T2-prep) is applied to introduce T2 weightings for the last three images. Subsequently, over the following six heartbeats, segmented bSSFP is performed for the movie of the entire cardiac cycle, synchronized with an electrocardiogram. A neural network trained using numerical simulations of MyoFold is used for T1 and T2 calculations. MyoFold was validated through phantom and in vivo experiments, with comparisons made against MOLLI, SASHA, T2-prep bSSFP, and the conventional cine. RESULTS: In phantom studies, MyoFold exhibited a 10% overestimation in T1 measurements, whereas T2 measurements demonstrated high accuracy. In vivo experiments revealed that MyoFold T1 had comparable accuracy to SASHA and precision similar to MOLLI. MyoFold demonstrated good agreement with T2-prep bSSFP in myocardial T2 measurements. No significant differences were observed in the quantification of left-ventricle wall thickness and function between MyoFold and the conventional cine. CONCLUSION: MyoFold presents as a rapid, simple, and multitasking approach for quantitative cardiovascular MR examinations, offering simultaneous assessment of tissue composition and wall motion. The sequence's multitasking capabilities make it a promising tool for comprehensive cardiac evaluations in clinical settings.

Recent Advances in Electrospinning Techniques for Precise Medicine.

In the realm of precise medicine, the advancement of manufacturing technologies is vital for enhancing the capabilities of medical devices such as nano/microrobots, wearable/implantable biosensors, and organ-on-chip systems, which serve to accurately acquire and analyze patients' physiopathological information and to perform patient-specific therapy. Electrospinning holds great promise in engineering materials and components for advanced medical devices, due to the demonstrated ability to advance the development of nanomaterial science. Nevertheless, challenges such as limited composition variety, uncontrollable fiber orientation, difficulties in incorporating fragile molecules and cells, and low production effectiveness hindered its further application. To overcome these challenges, advanced electrospinning techniques have been explored to manufacture functional composites, orchestrated structures, living constructs, and scale-up fabrication. This review delves into the recent advances of electrospinning techniques and underscores their potential in revolutionizing the field of precise medicine, upon introducing the fundamental information of conventional electrospinning techniques, as well as discussing the current challenges and future perspectives.

A Preliminary Study on Quantitative Analysis of Collagen and Apoptosis Related Protein on 1064 nm Laser-Induced Skin Injury.

To investigate the associated factors concerning collagen and the expression of apoptosis-related proteins in porcine skin injuries induced by laser exposure, live pig skin was irradiated at multiple spots one time, using a grid-array method with a 1064 nm laser at different power outputs. The healing process of the laser-treated areas, alterations in collagen structure, and changes in apoptosis were continuously observed and analyzed from 6 h to 28 days post-irradiation. On the 28th day following exposure, wound contraction and recovery were notably sluggish in the medium-high dose group, displaying more premature and delicate type III collagen within the newly regenerated tissues. The collagen density in these groups was roughly 37-58% of that in the normal group. Between days 14 and 28 after irradiation, there was a substantial rise in apoptotic cell count in the forming epidermis and granulation tissue of the medium-high dose group, in contrast to the normal group. Notably, the expression of proapoptotic proteins Bax, caspase-3, and caspase-9 surged significantly 14 days after irradiation in the medium-high dose group and persisted at elevated levels on the 28th day. During the later stage of wound healing, augmented apoptotic cell population and insufficient collagen generation in the newly generated skin tissue of the medium-high dose group were closely associated with delayed wound recovery.

The State-of-the-Art and Perspectives of Laser Ablation for Tumor Treatment.

Tumors significantly impact individuals' physical well-being and quality of life. With the ongoing advancements in optical technology, information technology, robotic technology, etc., laser technology is being increasingly utilized in the field of tumor treatment, and laser ablation (LA) of tumors remains a prominent area of research interest. This paper presents an overview of the recent progress in tumor LA therapy, with a focus on the mechanisms and biological effects of LA, commonly used ablation lasers, image-guided LA, and robotic-assisted LA. Further insights and future prospects are discussed in relation to these aspects, and the paper proposed potential future directions for the development of tumor LA techniques.

The LMIT: Light-mediated minimally-invasive theranostics in oncology.

Minimally-invasive diagnosis and therapy have gradually become the trend and research hotspot of current medical applications. The integration of intraoperative diagnosis and treatment is a development important direction for real-time detection, minimally-invasive diagnosis and therapy to reduce mortality and improve the quality of life of patients, so called minimally-invasive theranostics (MIT). Light is an important theranostic tool for the treatment of cancerous tissues. Light-mediated minimally-invasive theranostics (LMIT) is a novel evolutionary technology that integrates diagnosis and therapeutics for the less invasive treatment of diseased tissues. Intelligent theranostics would promote precision surgery based on the optical characterization of cancerous tissues. Furthermore, MIT also requires the assistance of smart medical devices or robots. And, optical multimodality lay a solid foundation for intelligent MIT. In this review, we summarize the important state-of-the-arts of optical MIT or LMIT in oncology. Multimodal optical image-guided intelligent treatment is another focus. Intraoperative imaging and real-time analysis-guided optical treatment are also systemically discussed. Finally, the potential challenges and future perspectives of intelligent optical MIT are discussed.

Text messaging interventions for breastfeeding outcomes: A systematic review and meta-analysis.

BACKGROUND: Given the health benefits of breastfeeding for infants and mothers, breastfeeding has become a significant public health issue. The global growth of mobile phone usage has created new options for breastfeeding promotion, including text messaging. OBJECTIVE: We aimed to evaluate the efficacy of text messaging interventions on breastfeeding outcomes and to identify the efficacy moderators of such interventions. METHODS: Ten electronic databases were searched from the inception of the databases to 5 July 2023. Studies were included if they used randomized controlled trials or quasi-experimental designs to evaluate the effect of text messaging interventions on breastfeeding outcomes. Two reviewers screened the included studies, assessed the risk of bias, and extracted the data. Pooled results were obtained by the random-effects model, and subgroup analyses were conducted on intervention characteristics to identify potential moderators. The protocol of this study was registered on PROSPERO (ID: CRD42022371311). RESULTS: Sixteen studies were included. Text messaging interventions could improve the exclusive breastfeeding rate (at <3 months: OR = 2.04; 95 % CI: 1.60-2.60, P < 0.001; at 3-6 months: OR = 1.66; 95 % CI: 1.18-2.33, P = 0.004; at ≥6 months: OR = 2.13; 95 % CI: 1.47-3.08, P < 0.001), and the breastfeeding self-efficacy (SMD = 0.30, 95 % CI: 0.14-0.45, P < 0.001). Text messaging interventions that covered antenatal and postnatal periods, delivered weekly were most effective in improving the exclusive breastfeeding rate. CONCLUSIONS: Text messaging interventions may improve breastfeeding practice compared with no or general health information. We suggest text messaging conducted from the pre- to postnatal periods in a weekly manner can effectively increase exclusive breastfeeding rates and breastfeeding self-efficacy. Further studies should investigate the relation between new theories (such as the health action process approach and the theory of message-framing) and efficacy of breastfeeding interventions, using text components.

Exploring South Asian women's perspectives and experiences of maternity care services: A qualitative evidence synthesis.

BACKGROUND: The United Nations Women and other sources have highlighted the poor maternal and neonatal care experienced by South Asian women, emphasizing the need to understand the cultural factors and specific experiences that influence their health-seeking behavior. This understanding is crucial for achieving health equity and improving health outcomes for women and infants. OBJECTIVES: This study aims to examine and synthesize qualitative evidence on the perspectives and experiences of South Asian women regarding maternity care services in destination countries. METHODS: A systematic review was conducted using the Joanna Briggs Institute's approach. Eight databases were searched for studies capturing the qualitative views and experiences of South Asian women - Medline, EMBASE, CINAHL Plus, Global Health, Scopus, PsycInfo, British Nursing Index and the Applied Social Science Index and Abstracts. Qualitative and mixed method studies written in English are included. The methodological quality of the included studies was assessed using the JBI's QARI checklist for qualitative studies and the MMAT checklist for mixed-methods studies. RESULTS: Fourteen studies, including twelve qualitative and two mixed-methods studies, were identified and found to be of high methodological quality. The overarching theme that emerged was "navigating cross-cultural maternity care experiences." This theme encapsulates the challenges and complexities faced by South Asian women in destination countries, including ethnocultural and religious differences, communication and language barriers, understanding different medical systems, and the impact of migration on their maternity care experiences. CONCLUSIONS: South Asian migrant women often have expectations that differ from the services provided in destination countries, leading to challenges in their social relationships. Communication and language barriers pose additional obstacles that can be addressed through strategies promoting better communication and culturally sensitive care. To enhance the utilization of maternity healthcare services, it is important to address these factors and provide personalized, culturally sensitive care for South Asian migrant women.

Stroke analysis and recognition in functional near-infrared spectroscopy signals using machine learning methods.

Stroke is a high-incidence disease with high disability and mortality rates. It is a serious public health problem worldwide. Shortened onset-to-image time is very important for the diagnosis and treatment of stroke. Functional near-infrared spectroscopy (fNIRS) is a noninvasive monitoring tool with real-time, noninvasive, and convenient features. In this study, we propose an automatic classification framework based on cerebral oxygen saturation signals to identify patients with hemorrhagic stroke, patients with ischemic stroke, and normal subjects. The reflected fNIRS signals were used to detect the cerebral oxygen saturation and the relative value of oxygen and deoxyhemoglobin concentrations of the left and right frontal lobes. The wavelet time-frequency analysis-based features from these signals were extracted. Such features were used to analyze the differences in cerebral oxygen saturation signals among different types of stroke patients and healthy humans and were selected to train the machine learning models. Furthermore, an important analysis of the features was performed. The accuracy of the models trained was greater than 85%, and the accuracy of the models after data augmentation was greater than 90%, which is of great significance in distinguishing patients with hemorrhagic stroke or ischemic stroke. This framework has the potential to shorten the onset-to-diagnosis time of stroke.

CHSNet: Automatic lesion segmentation network guided by CT image features for acute cerebral hemorrhage.

Stroke is a cerebrovascular disease that can lead to severe sequelae such as hemiplegia and mental retardation with a mortality rate of up to 40%. In this paper, we proposed an automatic segmentation network (CHSNet) to segment the lesions in cranial CT images based on the characteristics of acute cerebral hemorrhage images, such as high density, multi-scale, and variable location, and realized the three-dimensional (3D) visualization and localization of the cranial lesions after the segmentation was completed. To enhance the feature representation of high-density regions, and capture multi-scale and up-down information on the target location, we constructed a convolutional neural network with encoding-decoding backbone, Res-RCL module, Atrous Spatial Pyramid Pooling, and Attention Gate. We collected images of 203 patients with acute cerebral hemorrhage, constructed a dataset containing 5998 cranial CT slices, and conducted comparative and ablation experiments on the dataset to verify the effectiveness of our model. Our model achieved the best results on both test sets with different segmentation difficulties, test1: Dice = 0.918, IoU = 0.853, ASD = 0.476, RVE = 0.113; test2: Dice = 0.716, IoU = 0.604, ASD = 5.402, RVE = 1.079. Based on the segmentation results, we achieved 3D visualization and localization of hemorrhage in CT images of stroke patients. The study has important implications for clinical adjuvant diagnosis.

DeepFittingNet: A deep neural network-based approach for simplifying cardiac T1 and T2 estimation with improved robustness.

PURPOSE: To develop and evaluate a deep neural network (DeepFittingNet) for T1 /T2 estimation of the most commonly used cardiovascular MR mapping sequences to simplify data processing and improve robustness. THEORY AND METHODS: DeepFittingNet is a 1D neural network composed of a recurrent neural network (RNN) and a fully connected (FCNN) neural network, in which RNN adapts to the different number of input signals from various sequences and FCNN subsequently predicts A, B, and Tx of a three-parameter model. DeepFittingNet was trained using Bloch-equation simulations of MOLLI and saturation-recovery single-shot acquisition (SASHA) T1 mapping sequences, and T2 -prepared balanced SSFP (T2 -prep bSSFP) T2 mapping sequence, with reference values from the curve-fitting method. Several imaging confounders were simulated to improve robustness. The trained DeepFittingNet was tested using phantom and in-vivo signals, and compared to the curve-fitting algorithm. RESULTS: In testing, DeepFittingNet performed T1 /T2 estimation of four sequences with improved robustness in inversion-recovery T1 estimation. The mean bias in phantom T1 and T2 between the curve-fitting and DeepFittingNet was smaller than 30 and 1 ms, respectively. Excellent agreements between both methods was found in the left ventricle and septum T1 /T2 with a mean bias <6 ms. There was no significant difference in the SD of both the left ventricle and septum T1 /T2 between the two methods. CONCLUSION: DeepFittingNet trained with simulations of MOLLI, SASHA, and T2 -prep bSSFP performed T1 /T2 estimation tasks for all these most used sequences. Compared with the curve-fitting algorithm, DeepFittingNet improved the robustness for inversion-recovery T1 estimation and had comparable performance in terms of accuracy and precision.

Abnormal white matter changes in Alzheimer's disease based on diffusion tensor imaging: A systematic review.

Alzheimer's disease (AD) is a degenerative neurological disease in elderly individuals. Subjective cognitive decline (SCD), mild cognitive impairment (MCI) and further development to dementia (d-AD) are considered to be major stages of the progressive pathological development of AD. Diffusion tensor imaging (DTI), one of the most important modalities of MRI, can describe the microstructure of white matter through its tensor model. It is widely used in understanding the central nervous system mechanism and finding appropriate potential biomarkers for the early stages of AD. Based on the multilevel analysis methods of DTI (voxelwise, fiberwise and networkwise), we summarized that AD patients mainly showed extensive microstructural damage, structural disconnection and topological abnormalities in the corpus callosum, fornix, and medial temporal lobe, including the hippocampus and cingulum. The diffusion features and structural connectomics of specific regions can provide information for the early assisted recognition of AD. The classification accuracy of SCD and normal controls can reach 92.68% at present. And due to the further changes of brain structure and function, the classification accuracy of MCI, d-AD and normal controls can reach more than 97%. Finally, we summarized the limitations of current DTI-based AD research and propose possible future research directions.

Designing biomimetic scaffolds for skin tissue engineering.

There is a general increase in the number of patients with non-healing skin wounds, imposing a huge social and economic burden on patients and healthcare systems. Severe skin injury is an important clinical challenge. There is a lack of skin donors, and skin defects and scarring after surgery can lead to impaired skin function and skin integrity. Researchers worldwide have made great efforts to create human skin organs but are limited by the lack of key biological structural features of the skin. Tissue engineering repairs damaged tissue by incorporating cells into biocompatible and biodegradable porous scaffolds. Skin tissue engineered scaffolds not only have appropriate physical and mechanical properties but also exhibit skin-like surface topography and microstructure, which can promote cell adhesion, proliferation, and differentiation. At present, skin tissue engineering scaffolds are being developed into clinical applications that can overcome the limitations of skin transplantation, promote the process of wound healing, and repair skin tissue damage. This provides an effective therapeutic option for the management of patients with skin lesions. This paper reviews the structure and function of skin tissue and the process of wound healing, and summarizes the materials and manufacturing methods used to fabricate skin tissue engineering scaffolds. Next, the design considerations of skin tissue engineering scaffolds are discussed. An extensive review of skin scaffolds and clinically approved scaffold materials is presented. Lastly, some important challenges in the construction of skin tissue engineering scaffolds are presented.

Vascular elasticity measurement of the great saphenous vein based on optical coherence elastography.

Vascular elasticity is important in physiological and clinical problems. The mechanical properties of the great saphenous vein (GSV) deserve attention. This research aims to measure the radial elasticity of ex vivo GSV using the optical coherence elasticity (OCE). The finite element model of the phantom is established, the displacement field is calculated, the radial mechanical characteristics of the simulation body are obtained. Furthermore, we performed OCE on seven isolated GSVs. The strain field is obtained by combining the relationship between strain and displacement to obtain the radial elastic modulus of GSVs. In the phantom experiment, the strain of the experimental region of interest is mainly between 0.1 and 0.4, while the simulation result is between 0.06 and 0.40. The radial elastic modulus of GSVs ranged from 3.83 kPa to 7.74 kPa. This study verifies the feasibility of the OCE method for measuring the radial elastic modulus of blood vessels.

Automatic Segmentation of Laser-Induced Injury OCT Images Based on a Deep Neural Network Model.

Optical coherence tomography (OCT) has considerable application potential in noninvasive diagnosis and disease monitoring. Skin diseases, such as basal cell carcinoma (BCC), are destructive; hence, quantitative segmentation of the skin is very important for early diagnosis and treatment. Deep neural networks have been widely used in the boundary recognition and segmentation of diseased areas in medical images. Research on OCT skin segmentation and laser-induced skin damage segmentation based on deep neural networks is still in its infancy. Here, a segmentation and quantitative analysis pipeline of laser skin injury and skin stratification based on a deep neural network model is proposed. Based on the stratification of mouse skins, a laser injury model of mouse skins induced by lasers was constructed, and the multilayer structure and injury areas were accurately segmented by using a deep neural network method. First, the intact area of mouse skin and the damaged areas of different laser radiation doses are collected by the OCT system, and then the labels are manually labeled by experienced histologists. A variety of deep neural network models are used to realize the segmentation of skin layers and damaged areas on the skin dataset. In particular, the U-Net model based on a dual attention mechanism is used to realize the segmentation of the laser-damage structure, and the results are compared and analyzed. The segmentation results showed that the Dice coefficient of the mouse dermis layer and injury area reached more than 0.90, and the Dice coefficient of the fat layer and muscle layer reached more than 0.80. In the evaluation results, the average surface distance (ASSD) and Hausdorff distance (HD) indicated that the segmentation results are excellent, with a high overlap rate with the manually labeled area and a short edge distance. The results of this study have important application value for the quantitative analysis of laser-induced skin injury and the exploration of laser biological effects and have potential application value for the early noninvasive detection of diseases and the monitoring of postoperative recovery in the future.

Reporter Genes for Brain Imaging Using MRI, SPECT and PET.

The use of molecular imaging technologies for brain imaging can not only play an important supporting role in disease diagnosis and treatment but can also be used to deeply study brain functions. Recently, with the support of reporter gene technology, optical imaging has achieved a breakthrough in brain function studies at the molecular level. Reporter gene technology based on traditional clinical imaging modalities is also expanding. By benefiting from the deeper imaging depths and wider imaging ranges now possible, these methods have led to breakthroughs in preclinical and clinical research. This article focuses on the applications of magnetic resonance imaging (MRI), single-photon emission computed tomography (SPECT), and positron emission tomography (PET) reporter gene technologies for use in brain imaging. The tracking of cell therapies and gene therapies is the most successful and widely used application of these techniques. Meanwhile, breakthroughs have been achieved in the research and development of reporter genes and their imaging probe pairs with respect to brain function research. This paper introduces the imaging principles and classifications of the reporter gene technologies of these imaging modalities, lists the relevant brain imaging applications, reviews their characteristics, and discusses the opportunities and challenges faced by clinical imaging modalities based on reporter gene technology. The conclusion is provided in the last section.

Classification of gastric cancerous tissues by a residual network based on optical coherence tomography images.

Optical coherence tomography (OCT) is a noninvasive, radiation-free, and high-resolution imaging technology. The intraoperative classification of normal and cancerous tissue is critical for surgeons to guide surgical operations. Accurate classification of gastric cancerous OCT images is beneficial to improve the effect of surgical treatment based on the deep learning method. The OCT system was used to collect images of cancerous tissues removed from patients. An intelligent classification method of gastric cancerous tissues based on the residual network is proposed in this study and optimized with the ResNet18 model. Four residual blocks are used to reset the model structure of ResNet18 and reduce the number of network layers to identify cancerous tissues. The model performance of different residual networks is evaluated by accuracy, precision, recall, specificity, F1 value, ROC curve, and model parameters. The classification accuracies of the proposed method and ResNet18 both reach 99.90%. Also, the model parameters of the proposed method are 44% of ResNet18, which occupies fewer system resources and is more efficient. In this study, the proposed deep learning method was used to automatically recognize OCT images of gastric cancerous tissue. This artificial intelligence method could help promote the clinical application of gastric cancerous tissue classification in the future.

Current research and future prospects of IVOCT imaging-based detection of the vascular lumen and vulnerable plaque.

Intravascular optical coherence tomography (IVOCT) is an imaging method that has developed rapidly in recent years and is useful in coronary atherosclerosis diagnosis. It is widely used in the assessment of vulnerable plaque. This review summarizes the main research methods used in recent years for blood vessel lumen boundary detection and segmentation and vulnerable plaque segmentation and classification. This article aims to comprehensively and systematically introduce the research progress on internal tissues of blood vessels based on IVOCT images. The characteristics and advantages of various methods have been summarized to provide theoretical ideas and methods for the reference of relevant researchers and scholars.

Identification of moyamoya disease based on cerebral oxygen saturation signals using machine learning methods.

Moyamoya is a cerebrovascular disease with a high mortality rate. Early detection and mechanistic studies are necessary. Near-infrared spectroscopy (NIRS) was used to study the signals of the cerebral tissue oxygen saturation index (TOI) and the changes in oxygenated and deoxygenated hemoglobin concentrations (HbO and Hb) in 64 patients with moyamoya disease and 64 healthy volunteers. The wavelet transforms (WT) of TOI, HbO and Hb signals, as well as the wavelet phase coherence (WPCO) of these signals from the left and right frontal lobes of the same subject, were calculated. Features were extracted from the spontaneous oscillations of TOI, HbO and Hb in five physiological activity-related frequency segments. Machine learning models based on support vector machine (SVM), random forest (RF) and extreme gradient boosting (XGBoost) have been built to classify the two groups. For 20-min signals, the 10-fold cross-validation accuracies of SVM, RF and XGBoost were 87%, 85% and 85%, respectively. For 5-min signals, the accuracies of the three methods were 88%, 88% and 84%, respectively. The method proposed in this article has potential for detecting and screening moyamoya with high proficiency. Evaluating the cerebral oxygenation with NIRS shows great potential in screening moyamoya diseases.