Accurate prediction of pathological complete response (pCR) to neoadjuvant therapy in luminal breast cancer remains challenging, hindering precise treatment decisions. Here, we present the Lumi-Guide system, a novel multimodal framework that integrates deep-learning-based MRI analysis with clinical and genomic data to enable personalized treatment selection. In this multicenter study, we analyzed 1,097 patients with luminal breast cancer from 6 international datasets. We developed a Swin Transformer-based Lumi-I model using 3-plane dynamic contrast-enhanced MRI data and integrated it with clinical factors to construct the Lumi-CI model, which demonstrated robust performance across the validation and 2 external test sets, with areas under the receiver operating characteristic curves of 0.810, 0.819, and 0.864, respectively. Radiogenomic analysis revealed distinct biological characteristics: The Lumi-I high-score group exhibited immunologically active and proliferative microenvironments, while the low-score group showed estrogen-response-driven signaling. To further enhance predictive accuracy, a genomic model (Lumi-G) based on 22 established RNA biomarkers was further developed and integrated with the Lumi-CI model to create a multimodal Lumi-CIG model. We subsequently designed a Lumi-Guide 2-step triage system that prioritizes clinical-imaging information (Lumi-CI) while selectively incorporating genomic data (Lumi-CIG) when beneficial, thus optimizing resource allocation. Critically, by integrating Lumi-Guide system prestratification with response-predictive-subtype-recommended therapies, patients predicted to achieve pCR demonstrated substantially higher actual pCR rates than controls across 3 treatment patterns: 77.8% vs. 61.5% (optimal treatment), 57.6% vs. 24.7% (non-optimal treatment), and 35.3% vs. 4.7% (double-negative response-predictive subtype). This clinically practical and biologically interpretable framework transforms personalized neoadjuvant therapy in luminal breast cancer into a scalable reality.

Triple-negative breast cancer (TNBC) is recognized as the notoriously difficult molecular subtype of breast cancer to manage therapeutically, creating a pressing need for more effective treatment approaches. Immunochemotherapy is an emerging strategy with great clinical potential, but most focus on the tumor microenvironment itself and ignore the immune regulation of lymph nodes. Herewith, we report a dual-pronged approach of tumor-targeted paclitaxel-encapsulated nanoemulsion (PTX Emul, Phase II clinical trial) and lymph node-targeted chlorogenic acid-encapsulated self-emulsifying nanocarriers (CHA-SME) to elicit strong and selective immunogenic cell death (ICD) in the tumor and to activate immune cells in the lymph nodes, respectively, to achieve highly effective cancer immunotherapy. PTX Emul exhibited evident tumor targetability and superior tumor accumulation, induced potent ICD, and then efficiently stimulated the maturation of dendritic cells (DCs). CHA-SME demonstrates a substantial capacity to enhance drug accumulation within the mesenteric lymph nodes through the lymphatic transport pathway. Of note, PTX Emul combined with CHA-SME augmented the immunotherapeutic effects through highly efficient ICD induction within the tumor microenvironment of 4T1 orthotopic tumor, the potent DC maturation, and the effective activation of T cell-based antitumor immunity, resulting in a substantial enhancement in the inhibition of 4T1 orthotopic tumors and, notably, a reduction in lung metastasis. The dual-pronged approach of TNBC-targeted PTX Emul and lymph node-targeted CHA-SME by inducing ICD and activating lymphoid immune cells, which amplifies the systemic antitumor immune response, provides an interesting platform for potent immunochemotherapy of TNBC.

Holographic light fields offer a promising route toward high-throughput 3-dimensional (3D) nanofabrication. However, the fabrication uniformity remains limited by severe speckle noise present in many previously demonstrated hologram coding methods. Here, we address this challenge by developing a model-driven deep learning framework that enables speckle-free hologram generation with high uniformity. We demonstrate its practical feasibility for 3D nanofabrication using 2-photon lithography (TPL) and term this approach SMART HoloTPL. By establishing a polymerization model based on the broadband angular-spectrum method, self-supervised network training is guided to explore advanced hologram coding strategies without reliance on paired datasets. Tailored neural network architecture and loss functions are designed for high-uniformity hologram generation. A TPL fabrication platform powered by a femtosecond regenerative laser amplifier has been built. SMART HoloTPL achieves large-scale speckle-free 3D nanofabrication with a 120,000 voxels/s throughput and 120-nm resolution, addressing key challenges in fabrication rate and quality.

Renal cell carcinoma (RCC), a highly heterogeneous malignancy that arises from the renal tubular epithelium, has exhibited a rising incidence in recent years and has become the predominant urologic malignancy, posing a major threat to human health and life. Although our understanding of RCC has advanced, treatment remains challenging owing to low specificity and sensitivity, particularly with respect to postoperative recurrence and therapy-induced renal injury. Therefore, effective strategies that can improve therapeutic outcomes are urgently needed. In recent years, the expanding investigation of materials for biomedical applications has identified several constructs with promising clinical potential; compared with traditional drugs, these materials offer superior targeting, an improved safety profile, and a broader array of functions and synergistic effects that can effectively overcome RCC recurrence, drug resistance, and metastasis. Moreover, biomaterials can attenuate renal damage and inhibit renal fibrosis, thereby preserving renal function. In this review, we present a comprehensive examination of the current landscape and future directions of biomaterials for RCC and renal injury treatment. We propose an integrated antitumor and kidney protection strategy and establish an analytical framework rooted in the fundamental contradictions of material design, encompassing functional conflicts, stimuli-responsive activation, and metabolic safety. This approach provides novel insights into overcoming critical challenges in the field.

Periodontitis is a chronic bacterial inflammatory disease. M2 macrophage-derived exosomes (M2-exos) possess targeted immunomodulatory abilities, but their role in mitigating oxidative stress (key for periodontitis treatment) remains unclear. In this study, we engineered M2-exos loaded with indocyanine green (ICG) (ICG@M2-exos) for the treatment of periodontitis. The constructed ICG@M2-exos effectively facilitated macrophage reprogramming from the M1 to the M2 phenotype, thereby resolving chronic inflammation and enhancing periodontal tissue repair. Under near-infrared irradiation, ICG conferred potent antibacterial efficacy against Porphyromonas gingivalis (P. gingivalis). Simultaneously, the exosomes released from ICG@M2-exos mitigated oxidative stress and decreased the expression of proinflammatory factors in THP-1 cells through promoting M2 polarization. In a rat model of P. gingivalis-induced periodontitis, the sustained release of ICG@M2-exos markedly expedited periodontal bone regeneration, accompanied by elevated levels of anti-inflammatory cytokines. Collectively, ICG-engineered M2-exos represent a promising strategy for tackling inflammatory periodontal conditions. This study demonstrates the dual advantages of ICG@M2-exos in near-infrared-responsive antibacterial activity and immunomodulation that work synergistically, laying a solid foundation for future clinical applications.

Recently, immune checkpoint inhibitors (ICIs) have demonstrated substantial benefits and potential by enhancing antitumor immune responses via the normalization of host T cell activity. Nonetheless, a considerable proportion of patients were tolerant to ICI therapy, thereby restricting its broader application in cancer treatment. Identifying the key barriers responsible for resistance to ICI therapy has led to the development of combination therapies with ICIs as a promising approach to overcome these challenges, providing new directions for ICI-based cancer immunotherapy. Neutrophils within the tumor microenvironment are considered a critical factor contributing to poor responses to ICI treatment, and combining ICIs with neutrophil-targeted therapy has shown remarkable efficacy and promise. Consequently, this review discusses the diverse roles of neutrophils in cancer immunotherapy, outlines the synergistic mechanisms of targeting neutrophils in conjunction with ICIs, highlights relevant clinical trials of this combination strategy, and discusses current limitations and future directions. Overall, this review aims to provide new insights into effective strategies for ICI therapy.

To reduce mental disorder prevalence, the understanding of resilience to stress-related disorder and its neurobiological mechanisms has come into the focus of biomedical research to develop both biologically rooted prevention and innovative therapeutic approaches for stress-related disorder. While some resilience mechanisms have been exemplified on the molecular, cellular, and brain-regional level, evidence on the neural systems level is rather sparse. We present the first translational evidence of adaptive plasticity in visual microcircuits and top-down modulation onto the visual system as a neurobiological resilience mechanism at the neural systems level in both humans and mice. In humans, we demonstrate that this adaptive microcircuit plasticity is linked to interactions between neurocognitive domains—executive and perceptual—and between brain regions—frontal and occipital—in specific oscillatory frequencies (β band in frontal inferior frontal gyrus and γ band in occipital V2). Additionally, expanding upon prior resilience research, our findings offer further evidence that phenotypic resilience is associated not only with macro- and microcircuit plasticity but also with better performance in neurocognitive functions central to resilience, i.e., perceptual discrimination in mice and cognitive control in humans. In mice, using awake 2-photon calcium imaging, we observed distinct resilient and susceptible network phenotypes in mouse visual cortex. Resilient animals surpassed both susceptible animals and nonstressed controls in their ability to encode visual afferents. This suggests an improved performance supporting the concepts of posttraumatic growth and stress inoculation on a neurobiological level. Resilience at the neural systems level involves active, dynamic processes rather than being merely passive responses to stress and constitutes a first example that neural network states of resilience are metastable, self-stabilizing, and noncontinuous entities that could serve as a target for new neural network interventions for fostering resilience.

Intraoperative cancer diagnosis using Raman spectroscopy is a growing area of research, but clinical adoption is held back by relatively slow single point data acquisition. Its application to glioblastoma is particularly attractive because of the distinct spectra of malignant and healthy brain tissues, which allow detection of even diffusely invasive glioblastoma with high accuracy. Current clinical practice for intraoperative guidance uses indocyanine green (ICG) for wide-field localization, but its nonspecific accumulation beyond tumor margins leads to false-positive guidance. Critically, the strong fluorescence background from ICG under 785-nm Raman excitation overwhelms the inherently weak Raman signal, thereby preventing concurrent use of the 2 modalities. Here, we overcome this barrier by introducing a signal-processing algorithm that suppresses ICG-derived interference, enabling coregistered acquisition. We present an integrated “search-and-confirm” workflow, where ICG first identifies regions of interest and subsequent signal processing suppresses fluorescence interference, thereby enabling machine-learning-based Raman diagnosis within 3 s and achieving 85% to 90% identification accuracy in vivo. By enabling spatial correlation, we demonstrate that ICG systematically overestimates tumor extent, while Raman signatures match histopathological margins, thereby transforming Raman spectroscopy into a real-time corrective filter for clinical fluorescence guidance and offering a practical path toward precision glioblastoma resection.