Localized Microrobotic Delivery of Enzyme-Responsive Hydrogel-Immobilized Therapeutics to Suppress Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC), characterized by its aggressive metastatic propensity and lack of effective targeted therapeutic options, poses a major challenge in oncological management. A proof-of-concept neoadjuvant strategy aimed at inhibiting TNBC tumor growth and mitigating metastasis through a localized delivery of chemotherapeutics is reported in this paper. This approach addresses the limitations in payload capacity and stimuli responsiveness commonly associated with microrobotics in oncology.

UPAMNet: A unified network with deep knowledge priors for photoacoustic microscopy.

Photoacoustic microscopy (PAM) has gained increasing popularity in biomedical imaging, providing new opportunities for tissue monitoring and characterization. With the development of deep learning techniques, convolutional neural networks have been used for PAM image resolution enhancement and denoising. However, there exist several inherent challenges for this approach.

A 3D fast MR elastography sequence with interleaved multislab acquisition and Hadamard encoding.

Purposes: To enhance the functional capability of MRI, this study aims to develop a novel MR elastography (MRE) sequence that achieves rapid acquisition without distortion artifacts.

Methods: A displacement-encoded stimulated echo (DENSE) with multiphase acquisition scheme was used to capture wave images. A center-out golden-angle stack-of-stars sampling pattern was introduced for improved SNR and data incoherence.

Synthesis and antiproliferative evaluation of new hybrids of piperine and acylhydrazone.

Piperine, a natural amide isolated from the genus of , serves as a pharmacophore in medicinal chemistry. In this study, we synthesised and evaluated 18 novel piperine-acylhydrazone hybrids (-) for their antiproliferative activities . The structures of these hybrids were validated using H,C NMR, and HR-ESI-MS data.

Indirect Shear Wave Excitation for Brain Magnetic Resonance Elastography With Minimal Cerebral Blood Flow Alteration.

Unlabelled: Magnetic resonance elastography (MRE) of brain relies on inducing and measuring shear waves in the brain. However, studies have shown vibration could induce changes in cerebral blood flow (CBF), which has a modulation effect and can affect the biomechanical properties measured.

Objective: This work demonstrates the initial prototype of the indirect excitation method, which can generate shear waves in the brain with minimal changes in CBF.

A Comparative Study of Three Systems for Liver Magnetic Resonance Elastography.

Background: Different MR elastography (MRE) systems may produce different stiffness measurements, making direct comparison difficult in multi-center investigations.

Purpose: To assess the repeatability and reproducibility of liver stiffness measured by three typical MRE systems.

Study Type: Prospective.

Magnetic soft microfiberbots for robotic embolization.

Cerebral aneurysms and brain tumors are leading life-threatening diseases worldwide. By deliberately occluding the target lesion to reduce the blood supply, embolization has been widely used clinically to treat cerebral aneurysms and brain tumors. Conventional embolization is usually performed by threading a catheter through blood vessels to the target lesion, which is often limited by the poor steerability of the catheter in complex neurovascular networks, especially in submillimeter regions.

Chemical Constituents from var. Leioclada.

One previously undescribed naphthoquinone-benzisochromanquinone dimer berpolydiquinone A (), along with two previously undescribed naphthoquinone-anthraquinone dimers berpolydiquinones B and C (-), and one previously undescribed dimeric naphthalene berpolydinaphthalene A (), were isolated from the stems and leaves of var. leioclada. The chemical structures of these compounds were determined using high-resolution electrospray ionization mass spectroscopy (HR-ESI-MS), spectroscopic data, the exciton chirality method (ECM), and quantum chemical calculation.

Fiberbots: Robotic fibers for high-precision minimally invasive surgery.

Precise manipulation of flexible surgical tools is crucial in minimally invasive surgical procedures, necessitating a miniature and flexible robotic probe that can precisely direct the surgical instruments. In this work, we developed a polymer-based robotic fiber with a thermal actuation mechanism by local heating along the sides of a single fiber. The fiber robot was fabricated by highly scalable fiber drawing technology using common low-cost materials.

Micro/nanosystems for controllable drug delivery to the brain.

Drug delivery to the brain is crucial in the treatment for central nervous system disorders. While significant progress has been made in recent years, there are still major challenges in achieving controllable drug delivery to the brain. Unmet clinical needs arise from various factors, including controlled drug transport, handling large drug doses, methods for crossing biological barriers, the use of imaging guidance, and effective models for analyzing drug delivery.

Anthraquinone-benzisochromanquinone dimers from the stems and leaves of .

Three new anthraquinone-benzisochromanquinone dimers polyphylldiquinones A-C (), along with three known analogs floribundiquinone A-B (-) and 7-dehydroxyventiloquinone H (), were isolated from the stems and leaves of . The chemical structures and absolute configurations of these compounds were determined using HR-ESI-MS, spectroscopic data, and electronic circular dichroism. Notably, compounds (-) are dimeric quinones that share the same benzisochromanquinone moiety, specifically identified as 7-dehydroxyventiloquinone H (), which was the first time to report as a natural product.

A deep unrolled neural network for real-time MRI-guided brain intervention.

Accurate navigation and targeting are critical for neurological interventions including biopsy and deep brain stimulation. Real-time image guidance further improves surgical planning and MRI is ideally suited for both pre- and intra-operative imaging. However, balancing spatial and temporal resolution is a major challenge for real-time interventional MRI (i-MRI).

Noise-Factorized Disentangled Representation Learning for Generalizable Motor Imagery EEG Classification.

Motor Imagery (MI) Electroencephalography (EEG) is one of the most common Brain-Computer Interface (BCI) paradigms that has been widely used in neural rehabilitation and gaming. Although considerable research efforts have been dedicated to developing MI EEG classification algorithms, they are mostly limited in handling scenarios where the training and testing data are not from the same subject or session. Such poor generalization capability significantly limits the realization of BCI in real-world applications.

Beyond Supervised Learning for Pervasive Healthcare.

The integration of machine/deep learning and sensing technologies is transforming healthcare and medical practice. However, inherent limitations in healthcare data, namely scarcity, quality, and heterogeneity, hinder the effectiveness of supervised learning techniques which are mainly based on pure statistical fitting between data and labels. In this article, we first identify the challenges present in machine learning for pervasive healthcare and we then review the current trends beyond fully supervised learning that are developed to address these three issues.

Navigation with minimal occupation volume for teleoperated snake-like surgical robots: MOVE.

Master-Slave control is a common mode of operation for surgical robots as it ensures that surgeons are always in control and responsible for the procedure. Most teleoperated surgical systems use low degree-of-freedom (DOF) instruments, thus facilitating direct mapping of manipulator position to the instrument pose and tip location (tip-to-tip mapping). However, with the introduction of continuum and snake-like robots with much higher DOF supported by their inherent redundant architecture for navigating through curved anatomical pathways, there is a need for developing effective kinematic methods that can actuate all the joints in a controlled fashion.

Deep anatomy learning for lung airway and artery-vein modeling with contrast-enhanced CT synthesis.

Purpose: Endobronchial intervention requires detailed modeling of pulmonary anatomical substructure, such as lung airway and artery-vein maps, which are commonly extracted from non-contrast computed tomography (NCCT) independently using automatic segmentation approaches. We aim to make the first attempt to jointly train a CNN-based model for airway and artery-vein segmentation along with synthetic contrast-enhanced CT (CECT) generation.

Methods: A multi-task framework is proposed to simultaneously generate three segmentation maps and synthesize CECTs.