Smart Drug Delivery Systems (SDDSs) are advanced platforms enabling controlled and stimuli-responsive drug release. Among external stimuli, near-infrared (NIR) light and magnetic fields are particularly attractive due to their non-invasive activation and tunable energy conversion. Multifunctional electrospun scaffolds (PCI) based on poly(butylene succinate) (PBS), ciprofloxacin (CPX), and iron oxide nanopowder (INPs, d < 50 nm) as a low-cost superparamagnetic component has been exhaustively explored, reporting the use of INP as photothermal agent in electrospun scaffolds, enabling high nanoparticle loading and NIR-triggered drug release, a strategy still scarcely explored in electrospun systems. This powder-based strategy simplifies formulation compared to stabilized SPION dispersions while preserving magnetic and photothermal functionalities. The resulting mats exhibited uniform, defect-free fibers with tunable mechanical properties and sustained CPX release (≈40% over 11 days), which increased to ≈60% under NIR irradiation. INPs imparted superparamagnetic behavior, NIR responsiveness, and MRI and X-ray detectability, while CPX improved fiber morphology and surface wettability. High cell viability (>85%) confirmed the cytocompatibility of the scaffolds. Comprehensive characterization included morphology, wettability, thermal and mechanical properties, NIR response, and drug release. Magnetic properties were evaluated using a cost-effective sensor-based approach (Hall sensors and fluxgate magnetometer), confirming superparamagnetic behavior. By coupling NIR-triggered drug release with MRI detectability, PCI scaffolds provide a compact theranostic platform for localized biomedical applications. Overall, PCI scaffolds represent a scalable and cost-effective multifunctional platform integrating controlled drug delivery, photothermal responsiveness, and imaging capability, showing strong potential for advanced biomedical applications.

A generative adversarial network with multi-scale structural features for sparse-view photoacoustic tomography reconstruction.

Timely and accurate identification of brain tumors is crucial for optimizing patient outcomes, guiding surgical planning, and determining appropriate treatment strategies. Despite the advancements in MRI-based tumor detection and artificial intelligence, developing reliable and clinically applicable models remains challenging, particularly in contexts where robustness, interpretability, and consistency are critical. Existing approaches often lack advanced 3D imaging capabilities and robust Explainable AI (XAI) techniques, which limit their diagnostic utility and clinical adoption. To address these limitations, this study proposes a robust deep learning architecture that incorporates the improved EfficientNet B7, Xception, and ResNet152 architectures. We further proposed a novel classification model incorporating enhanced data augmentation and histogram equalization to automatically classify brain MRI images into four diagnostic categories. Three distinct datasets, one segmented dataset, and a merged dataset combining all sources were used to train and evaluate the models. The proposed models achieved accuracies in the range of 97–99%, demonstrating consistent and strong performance across datasets. To enhance diagnostic transparency, XAI techniques, including Grad-CAM and LIME, were employed to provide visual insights into the system’s decision-making processes. Additionally, this study incorporated a novel 3D reconstruction approach across the sagittal, coronal, and axial planes, providing a comprehensive view that supports improved diagnostic accuracy and precise clinical interpretation. Overall, this study demonstrates a novel unified framework integrating classification, segmentation, XAI, current gaps in brain tumor diagnostics and holding significant potential to enhance clinical reliability, transparency, and precision, ultimately improvinge clinical reliability, interpretability, and precision, ultimately supporting better patient outcomes.

Novel Acousto-Optic Sensor for Needle Tip Tracking and Internal Ultrasound Imaging from the Tip for Biopsy and Thermal Ablation.

Major improvements in spaceborne early fire detection and small-fire FRP retrieval with the meteosat third generation flexible combined imager

Ultrasound-responsive renal-targeted nanoparticles deliver TAK-242 to inhibit NF-κB/NLRP3 signaling and attenuate sepsis-associated acute kidney injury.

A near-infrared dual-channel fluorescent probe for ultrasensitive detection of Zn2+ and Cd2+ in food safety and bioimaging applications.

Microfluidic fabrication of peptide modified carrier-free self-assembled crizotinib-metal nanodrugs for NIR fluorescence imaging and dual-pathway therapy of non-small cell lung cancer.

Triple-negative breast cancer (TNBC) is highly aggressive with limited diagnostic and therapeutic options. ADAM9 is overexpressed in TNBC and may serve as a theranostic target. Therefore, this study aims to develop [177Lu]Lu-DOTA-48HZ810, an ADAM9-targeted probe, and evaluate its targeting specificity, imaging capability, efficacy, and safety in TNBC models. ADAM9 expression in TNBC cells was assessed by flow cytometry, and the probe was constructed by conjugating 177Lu to the ADAM9-specific antibody 48HZ810 via DOTA, after which binding specificity, affinity, and internalization were evaluated using radioligand binding, immunoreactive fraction, and internalization assays. In vivo targeting and pharmacokinetics were further analyzed by SPECT/CT imaging and biodistribution studies, while efficacy, safety, and mechanisms were assessed via tumor growth, body weight, survival, hematology, histopathology, and immunofluorescence. High ADAM9 expression was confirmed in BT-549 cells, and [177Lu]Lu-DOTA-48HZ810 showed >99% radiochemical purity and stability, high affinity (Kd: 3.75 ± 0.72 nmol/L), and specificity (Bmax: 422.5 ± 17.85 nmol). The tumor was clearly visualized at 72 h, with peak uptake at 96 h (8.56 ± 0.69%ID/g), and the probe significantly inhibited tumor growth (TGI: 46.04% vs antibody: 18.14%) and improved survival (70% vs antibody: 50% vs control: 0%), with no significant toxicity observed. The antitumor activity was mediated by DNA damage, apoptosis induction, and proliferation suppression. Overall, the first ADAM9-targeted theranostic probe, [177Lu]Lu-DOTA-48HZ810, was successfully developed, demonstrating excellent tumor targeting, antitumor efficacy, and safety in TNBC models, and validating ADAM9 as a viable theranostic target.

Developing a DNA nanobooster-constructed aptasensing platform that enables high-fidelity imaging in living biosamples is of importance for detecting disease-correlated marker molecules. Here, we explore a direct-lock near-infrared (NIR) light-controlled strategy under apurinic/apyrimidinic endonuclease 1 (APE1) gating, which is then incorporated with a sensitive auto-reinforcing iterative cascaded DNA nanobooster. In one respect, the sensing system gains forceful bio-targeting proficiency by applying endogenous APE1 in biological media as a sensor gating, followed by implementing a direct-lock NIR light-controlled strategy through the combination of ultraviolet-emitting upconversion nanoparticles and a 7-(Diethylamino)coumarin-4-ylmethyl-group linkage at the apurinic/apyrimidinic site of one DNA strand. In the other aspect, a first-stage hybridization chain reaction (HCR) amplification and a later-stage iterative HCR cycle amplification triggered by a self-propelled Mn2+-dependent DNAzyme are integrated to devise a sensitive auto-reinforcing cascade. Upon appointing adenosine triphosphate (ATP)-a small molecule with dysregulated contents in various diseases like cancer-as a model target, our sensor is confirmed to have high sensitivity and exceptional specificity. Crucially, this aptasensing platform showcases high-fidelity imaging capability for low-abundance ATP from living cells to nude mouse bodies, contributing to an efficient tool for bioassays.

Nanomedicine offers powerful opportunities to overcome the pharmacokinetic and microenvironmental limitations of conventional chemotherapy, particularly in aggressive breast cancer subtypes. Among emerging nanocarriers, calcium carbonate (CaCO₃) nanoparticles, have gained prominence owing to their excellent biocompatibility, full biodegradability into endogenous ions, low-cost fabrication, and intrinsic pH-responsiveness to the weakly acidic tumor microenvironment. Stable at physiological pH yet dissolving under tumor and endo/lysosomal acidity, these platforms enable site-specific release of chemotherapeutics, nucleic acids and photosensitizers while buffering extracellular acidosis that drives chemoresistance and immune evasion. This review synthesizes advances in CaCO₃ nanoparticle design, covering precipitation-, carbonation-, gas-diffusion-, emulsion- and polymer-mediated routes, and links process parameters to polymorph control, morphology, surface functionalization and drug-loading behavior relevant for tumor targeting. We critically discuss preclinical applications in monotherapy and multimodal regimens, including chemo-photodynamic and gene-immunotherapy strategies, with emphasis on breast cancer models where enhanced permeability and retention and active targeting are exploited to improve intratumoral accumulation and therapeutic index. In addition, we highlight theranostic CaCO₃ constructs that integrate imaging capabilities for real-time tracking of biodistribution and treatment response, and outline translational challenges in scale-up, stability, safety and regulation. Finally, we discuss how artificial intelligence-guided formulation and design frameworks could accelerate clinical translation of calcite-based nanomedicines for precision oncology.

Semiconducting single-walled carbon nanotubes (SWCNTs) are emerging as powerful near-infrared probes for deep-tissue imaging. While SWCNT samples involving multiple chiralities can be seen as a collection of spectrally resolved fluorophores, their multiplex imaging capabilities remain underexplored. This work presents a filter-resolved evaluation of DSPE-mPEG-coated CoMoCAT SWCNTs integrating optical characterization, biodistribution, pharmacokinetics, and toxicity studies in BALB/c mice. Band-pass filters centered at 1000, 1050, and 1100 nm are used to isolate emission from the dominant (6,5) species and coexisting (7,5) and (7,6) chiralities within the NIR-II window. Following intraperitoneal administration, longitudinal whole-body imaging (0.5-24 h) illustrates rapid systemic spread and preferential uptake in reticuloendothelial organs. Ex vivo organ analysis reveals a hierarchy of emission intensities (liver >> spleen >> lung > heart > kidney > brain > thymus) preserved across all three detection channels, suggesting a negligible influence of SWCNT chirality on biodistribution. Serum kinetics show monoexponential decay with an apparent elimination half-life of ∼2.69 h. In a 21-day toxicity study, treated animals maintain stable body mass and normal behavior, organ-level fluorescence returns to the baseline, and H&E sections of the liver, kidney, and lung reveal preserved architecture without inflammatory or fibrotic lesions. Collectively, this work defines an initial in vivo tolerability profile, safe dosing, and imaging conditions for DSPE-mPEG/CoMoCAT SWCNTs and demonstrates that filter-resolved NIR-II fluorescence can be exploited for multiplex tracking of nanotube probes in vivo, providing a foundation for future biosensing and image-guided therapeutic applications.

Confocal microscopy provides optical sectioning and micrometer-scale resolution but remains constrained by mechanical axial scanning and bulky optics. Leveraging the intrinsic chromatic dispersion of a custom-designed metalens, we demonstrate parallel confocal depth imaging over a 1.3 mm axial range within a 200 nm visible bandwidth by encoding wavelengths into different axial positions to eliminate mechanical axial scanning, which yields an axial space-bandwidth product (ASBP) of ≈68. A single-mode fiber functions simultaneously as an illumination/collection probe and confocal pinhole, forming a compact design. The system achieves an axial resolution of 11.4 μm (axial sensitivity of 0.15 μm) and a lateral resolution of 2.19 μm near the optimal wavelength of 530 nm. The parallel confocal imaging capability across an extended depth is further validated by depth-resolved imaging and surface profile measurement. This compact, label-free platform underscores the potential of meta-optical chromatic confocal imaging for large-depth, high-resolution microscopic applications.

Triple-negative breast cancer (TNBC) is one of the most aggressive breast cancer subtypes with high metastatic and recurrence rates. However there is still a lack of effective targeting nanoprobes (NPs) which is leading to suboptimal targeted therapy. Cell membrane-encapsulation methods are one of the ideal targeting strategy, which could enhance the biocompatibility through endogenous membrane components while enabling functional integration of targeting and imaging capabilities. In this study, fluorescent dye-clinical indocyanine green (ICG) was successfully encapsulated by TNBC cell membranes via dual biolayer method to form M-ICG NPs. The M-ICG NPs exhibited well cellular uptake for TNBC tumor cells via active targeting capability after homologous cancer cell membrane coating. Meanwhile, strong near-infrared (NIR) fluorescence signals were detected at the tumor sites from mice post-injection of M-ICG NPs at 24 h, indicating that their effective targeted tumor accumulation enables good tumor-specific labeling with strong NIR fluorescence emission. Furthermore, the M-ICG NPs retain the excellent optical and photothermal properties of ICG. Under 808 nm laser irradiation, via infrared imaging, the process of temperature monitoring up to 55°C within 12 min was recorded, exhibiting a strong photothermal effect, which lays a foundation for constructing a multifunctional nanoplatform with tumor-specific targeting, excellent biocompatibility and structural stability.

Accurate and early diagnosis of cancer is critical for determining effective treatment strategies and improving patient survival rates. However, automated multi-class cancer detection remains an enormous clinical and computational challenge due to the high visual heterogeneity within specific cancer classes and the morphological similarities across different types of malignancies. Deep convolutional neural networks (CNNs), particularly the VGG-16 architecture, offer robust feature extraction capabilities for medical imaging; yet, their diagnostic performance is heavily restricted by suboptimal hyperparameter tuning and inefficient feature utilization. To solve this problem, this article proposes a comprehensive, dual-strategy deep learning framework that integrates both pre-trained and fine-tuned VGG-16 models with six nature-inspired metaheuristic optimization algorithms. By employing the Whale Optimization Algorithm (WOA), Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Modified PSO (MPSO), the framework autonomously optimizes critical hyperparameters to maximize classification accuracy. The proposed methodology was rigorously evaluated on two complex imaging datasets: a five-class dataset for cervical cancer (a leading global cause of female cancer-related mortality) and a three-class dataset for lymphoma (a complex malignancy of the lymphatic system). The experimental results demonstrated that integrating pre-trained VGG-16 networks with metaheuristic optimizers significantly outperformed baseline models across both datasets. Notably, the Whale Optimization Algorithm (WOA) exhibited superior performance, achieving up to 100% in accuracy, precision, recall, and specificity during the testing phase for both datasets. These findings confirm that optimizing deep CNNs with metaheuristic algorithms provides a highly adaptable, reliable, and precise framework capable of resolving the complexities of high-dimensional multi-class cancer diagnosis.

Ultrasound Computed Tomography (USCT) represents a paradigm shift in medical imaging, offering quantitative, high-resolution tissue characterization for diverse anatomical regions including breast, musculoskeletal system, brain, and lungs. By capturing the full ultrasonic wavefield through dedicated transducer arrays, USCT enables reconstruction of intrinsic tissue properties such as sound speed, attenuation, and acoustic impedance. Unlike conventional ultrasound, USCT provides three fundamental advantages: full-angle tomographic reconstruction, quantitative multi-parameter imaging capabilities, and operator-independent standardized acquisition-all while maintaining ultrasound's inherent safety and cost-effectiveness. While clinical adoption is still evolving, the technology has achieved significant milestones with several commercially available systems receiving regulatory approval for breast imaging. This review synthesizes recent advances across five critical domains: system hardware design, reflection imaging, quantitative multi-parameter reconstruction (particularly through full-waveform inversion), precision calibration methodologies, and expanding clinical applications. Additionally, we have offered a comprehensive review of the application of deep learning-related technologies in USCT. By comprehensively analyzing current challenges and emerging trends, this work provides researchers and clinicians with an essential reference for understanding the state of the art and identifying pivotal pathways toward widespread clinical implementation of USCT technology.

Development and clinical application of targeted alpha therapy using astatine (211At).

Breast cancer holds a horrifying picture of being one of the most common cancers across the world and with it, comes an even more troublesome morbidity and mortality scenario, especially for high-grade malignancy, such as triple-negative breast cancer. The JAK/STAT3 signaling pathway is one of the well-established oncogenic drivers for breast cancer in regard to tumor cell proliferation, survival, metastasis, immune evasion, and resistance to therapy. Natural phytochemicals, including resveratrol, show promising STAT3 inhibitory activity but have a limited clinical application due to their poor solubility, low bioavailability, and metabolic instability. Nanotechnology may be the best system to solve these problems by improving the pharmacokinetic properties and tumor-targeting abilities of phytochemicals. This review describes the pathophysiological role of STAT3 in breast cancer and discusses the chances of therapeutic exploitation via natural compounds, especially resveratrol, delivered via advanced nanoformulations; polymeric, lipid-based, and magnetic nanoparticles. A special approach was taken with respect to magnetic nanoparticles loaded with resveratrol, which offer a dual benefit of targeted therapy with imaging capabilities. Although preclinical results have been encouraging, transferring to successful clinical development will involve hurdles inherent to formulation complexity, regulatory compliance, safety, and patient-specific optimization approaches. Quality-by-design principles along with biomarker-driven strategies may lead to better advances in phytochemical-loaded nanoparticles becoming clinically promising STAT3-targeted breast cancer treatments.

Musculoskeletal magnetic resonance imaging has evolved substantially, driven by advances in hardware, image acquisition, and reconstruction techniques. Improvements in gradient performance and dedicated radiofrequency coils have enhanced spatial resolution and scan efficiency across field strengths. Image acceleration strategies, including parallel imaging, simultaneous multislice acquisition, and compressed sensing, now enable high-quality two-dimensional and three-dimensional magnetic resonance imaging with markedly reduced examination times and facilitate the time-neutral incorporation of advanced metal artifact reduction techniques into clinical magnetic resonance imaging protocols. Deep learning-based reconstruction and super-resolution augmentation methods have further expanded achievable acceleration and image quality. Emerging techniques such as synthetic magnetic resonance imaging, magnetic resonance neurography, kinematic magnetic resonance imaging, and zero echo time magnetic resonance imaging expand the capabilities of musculoskeletal magnetic resonance imaging. At the same time, renewed interest in low-field magnetic resonance imaging provides intriguing opportunities to improve accessibility and sustainability. Ultra-high field magnetic resonance imaging provides unprecedented spatial resolution and quantitative insights in selected applications. These developments are redefining musculoskeletal magnetic resonance imaging practice and broadening its clinical value.

Both in clinical practice and translational research, cell differentiation of leukocytes provides important diagnostic information and insights into pathophysiological mechanisms. The current gold-standard method for bronchoalveolar lavage fluid (BALF) analysis involves histochemical staining of cytospins, followed by manual morphological quantification. This approach however is labor-intensive, time-consuming, and highly operator-dependent, limiting its efficiency and throughput. This study proposes a deep learning framework for rapid, automated 3D leukocyte differentiation using label-free higher harmonic generation microscopy (HHGM). 3D leukocyte imaging was performed with label-free HHGM in a few minutes. Two deep learning models, ResNet 3D-50 and Vision Transformer (ViT) 3D, were trained, validated and tested for leucocyte differentiation on both BALF and blood fraction samples from 16 interstitial lung disease (ILDs) and 19 acute respiratory distress syndrome (ARDS) patients. Deep-learning model-prediction and cytospin analysis were performed by separate investigators. The results were compared using Bland-Altman analysis. The proposed framework achieved accuracies above 86% for BALF and above 96% for blood samples under five-fold cross-validation. The approach shows close agreement with gold-standard cytological analyses, with mean differences of <5% across leukocyte subpopulations. By integrating the label-free imaging capabilities of HHGM with deep learning, this study established a fast, accurate and high-throughput leukocyte differentiation in fresh BALF and blood samples. By significantly improving efficiency and reproducibility, this technology has the potential to transform clinical workflows and advance precision medicine.