TdCCA with Dual-Modal Signal Fusion: Degenerated Occipital and Frontal Connectivity of Adult Moyamoya Disease for Early Identification.

Cognitive impairment in patients with moyamoya disease (MMD) manifests earlier than clinical symptoms. Early identification of brain connectivity changes is essential for uncovering the pathogenesis of cognitive impairment in MMD. We proposed a temporally driven canonical correlation analysis (TdCCA) method to achieve dual-modal synchronous information fusion from electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) for exploring the differences in brain connectivity between MMD and normal control groups.

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.

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.

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 T/T 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.

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 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.

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.

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.

Research on high proportion of clean energy grid-connected oscillation risk prediction technology based on CNN and trend feature analysis.

Oscillations, commonly known as a universal, propagative, and intricate event in the new power system, often give rise to generator tripping and load shedding, not only adversely affecting the power flow limit and the power angle stability but also posing threats to the lines of defense for stability and protection. Traditionally, emphasis has been laid on post-fault oscillation management, an emergency measure to deal with the impact and damage that have already affected the power grid. As such, this paper focuses on an oscillation prediction technique to detect oscillation energy early and intervene proactively to prevent further faults.

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.

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.

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.

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

Purpose: To develop and evaluate a deep neural network (DeepFittingNet) for T /T 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 T of a three-parameter model. DeepFittingNet was trained using Bloch-equation simulations of MOLLI and saturation-recovery single-shot acquisition (SASHA) T mapping sequences, and T -prepared balanced SSFP (T -prep bSSFP) T mapping sequence, with reference values from the curve-fitting method.

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.

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.

Self-supervised speckle noise reduction of optical coherence tomography without clean data.

Optical coherence tomography (OCT) is widely used in clinical diagnosis due to its non-invasive, real-time, and high-resolution characteristics. However, the inherent speckle noise seriously degrades the image quality, which might damage the fine structures in OCT, thus affecting the diagnosis results. In recent years, supervised deep learning-based denoising methods have shown excellent denoising ability.

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).

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.

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.

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.

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.

Quantitative investigation of laser ablation based on real-time temperature variations and OCT images for laser treatment applications.

Background And Objective: Lasers are widely employed in clinical applications. In vivo monitoring of real-time information about different-wavelength laser surgeries would provide important surgical feedback for surgeons or clinical therapy instruments. However, the quantitative effect of laser ablation or vaporization still needs to be further explored and investigated.

Evaluation of a 3.8-µm laser-induced skin injury and their repair with in vivo OCT imaging and noninvasive monitoring.

To explore a 3.8-µm laser-induced damage and wound healing effect, we propose using optical coherence tomography (OCT) and a noninvasive monitoring-based in vivo evaluation method to quantitatively and qualitatively analyze the time-dependent biological effect of a 3.8-µm laser.