Exploring the Spectral Prior for Hyperspectral Image Super-Resolution.

In recent years, many single hyperspectral image super-resolution methods have emerged to enhance the spatial resolution of hyperspectral images without hardware modification. However, existing methods typically face two significant challenges. First, they struggle to handle the high-dimensional nature of hyperspectral data, which often results in high computational complexity and inefficient information utilization. Second, they have not fully leveraged the abundant spectral information in hyperspectral images. To address these challenges, we propose a novel hyperspectral super-resolution network named SNLSR, which transfers the super-resolution problem into the abundance domain. Our SNLSR leverages a spatial preserve decomposition network to estimate the abundance representations of the input hyperspectral image. Notably, the network acknowledges and utilizes the commonly overlooked spatial correlations of hyperspectral images, leading to better reconstruction performance. Then, the estimated low-resolution abundance is super-resolved through a spatial spectral attention network, where the informative features from both spatial and spectral domains are fully exploited. Considering that the hyperspectral image is highly spectrally correlated, we customize a spectral-wise non-local attention module to mine similar pixels along spectral dimension for high-frequency detail recovery. Extensive experiments demonstrate the superiority of our method over other state-of-the-art methods both visually and metrically. Our code is publicly available at https://github.com/HuQ1an/SNLSR.

Progressive Learning With Cross-Window Consistency for Semi-Supervised Semantic Segmentation.

Semi-supervised semantic segmentation focuses on the exploration of a small amount of labeled data and a large amount of unlabeled data, which is more in line with the demands of real-world image understanding applications. However, it is still hindered by the inability to fully and effectively leverage unlabeled images. In this paper, we reveal that cross-window consistency (CWC) is helpful in comprehensively extracting auxiliary supervision from unlabeled data. Additionally, we propose a novel CWC-driven progressive learning framework to optimize the deep network by mining weak-to-strong constraints from massive unlabeled data. More specifically, this paper presents a biased cross-window consistency (BCC) loss with an importance factor, which helps the deep network explicitly constrain confidence maps from overlapping regions in different windows to maintain semantic consistency with larger contexts. In addition, we propose a dynamic pseudo-label memory bank (DPM) to provide high-consistency and high-reliability pseudo-labels to further optimize the network. Extensive experiments on three representative datasets of urban views, medical scenarios, and satellite scenes with consistent performance gain demonstrate the superiority of our framework. Our code is released at https://jack-bo1220.github.io/project/CWC.html.

PSD-ELGAN: A pseudo self-distillation based CycleGAN with enhanced local adversarial interaction for single image dehazing.

Compared to pixel-level content loss, domain-level style loss in CycleGAN-based dehazing algorithms just imposes relatively soft constraints on the intermediate translated images, resulting in struggling to accurately model haze-free features from real hazy scenes. Furthermore, globally perceptual discriminator may misclassify real hazy images with significant scene depth variations as clean style, thereby resulting in severe haze residue. To address these issues, we propose a pseudo self-distillation based CycleGAN with enhanced local adversarial interaction for image dehazing, termed as PSD-ELGAN. On the one hand, we leverage the characteristic of CycleGAN to generate pseudo image pairs during training. Knowledge distillation is employed in this unsupervised framework to transfer the informative high-quality features from the self-reconstruction network of real clean images to the dehazing generator of paired pseudo hazy images, which effectively improves its haze-free feature representation ability without increasing network parameters. On the other hand, in the output of dehazing generator, four non-uniform image patches severely affected by residual haze are adaptively selected as input samples. The local discriminator could easily distinguish their hazy style, thereby further compelling the dehazing generator to suppress haze residues in such regions, thus enhancing its dehazing performance. Extensive experiments show that our PSD-ELGAN can achieve promising results and better generality across various datasets.

Chemometric-based analysis and bioassay guided identification of potent compounds with intestinal motility promoting effects from Dalitong Granules.

ETHNOPHARMACOLOGICAL RELEVANCE: Dalitong Granules (DLT), a potent Traditional Chinese Medicine known for its ability to promote gastrointestinal motility, is widely used in clinical practice for the treatment of Functional Dyspepsia (FD). Despite the remarkable clinical efficacy of DLT, the specific components responsible for its effectiveness remains unclear. AIM OF THE STUDY: The study aimed to identify potential active ingredients of DLT for treating FD through spectrum-effect relationship analysis, multivariate statistical analysis and network pharmacology analysis. The efficacy of these identified compounds was subsequently validated using the zebrafish intestinal peristalsis model. MATERIALS AND METHODS: The fingerprints of various solvent-extracted DLT were analyzed using high performance liquid chromatography coupled with tandem high-resolution mass spectrometry. The intestinal motility-promoting activities of DLT extracted by different solvents were evaluated through an intestinal propulsion test in mice. Potential therapeutic substances in DLT for treating FD were screened via chemometric analysis based on spectrum-effect relationship analysis. The correlation between the intensity of common peaks in the total ion chromatogram and the pharmacodynamic indices was assessed using multivariate statistical analysis. Additionally, given the complexity of Traditional Chinese Medicine, which comprises multiple components and targets, a network pharmacology analysis was performed to investigate the potential active ingredients in DLT. Finally, the pharmacological effects of these compounds in DLT were validated using a zebrafish intestinal motility model. RESULTS: Through spectral-effect relationships analysis and network pharmacology analysis, it was determined that ten ingredients in DLT contribute to the promotion of intestinal motility. In a zebrafish intestinal motility model, it was observed that eight chemicals (excluding tetrahydropalmatine) demonstrate favorable activity of promoting gastrointestinal motility. These findings suggest that these ingredients may serve as potential therapeutic agents for improving gastric motility disorders. CONCLUSIONS: This study employed spectral-effect relationship and network pharmacology analysis to identify the active ingredients in DLT. The findings were then validated using a zebrafish intestinal peristalsis model. These results provide a scientific foundation for the clinical application of DLT as a key traditional herbal formula for managing FD.

Learning a Non-Locally Regularized Convolutional Sparse Representation for Joint Chromatic and Polarimetric Demosaicking.

Division of focal plane color polarization camera becomes the mainstream in polarimetric imaging for it directly captures color polarization mosaic image by one snapshot, so image demosaicking is an essential task. Current color polarization demosaicking (CPDM) methods are prone to unsatisfied results since it's difficult to recover missed 15 or 14 pixels out of 16 pixels in color polarization mosaic images. To address this problem, a non-locally regularized convolutional sparse regularization model, which is advantaged in denoising and edge maintaining, is proposed to recall more information for CPDM task, and the CPDM task is transformed into an energy function to be solved by ADMM optimization. Finally, the optimal model generates informative and clear results. The experimental results, including reconstructed synthetic and real-world scenes, demonstrate that our proposed method outperforms the current state-of-the-art methods in terms of quantitative measurements and visual quality. The source code is available at https://github.com/roydon-luo/NLCSR-CPDM.

U-Match: Exploring Hierarchy-aware Local Context for Two-view Correspondence Learning.

Rejecting outlier correspondences is one of the critical steps for successful feature-based two-view geometry estimation, and contingent heavily upon local context exploration. Recent advances focus on devising elaborate local context extractors whereas typically adopting explicit neighborhood relationship modeling at a specific scale, which is intrinsically flawed and inflexible, because 1) severe outliers often populated in putative correspondences and 2) the uncertainty in the distribution of inliers and outliers make the network incapable of capturing adequate and reliable local context from such neighborhoods, therefore resulting in the failure of pose estimation. This prospective study proposes a novel network called U-Match that has the flexibility to enable implicit local context awareness at multiple levels, naturally circumventing the aforementioned issues that plague most existing studies. Specifically, to aggregate multi-level local context implicitly, a hierarchy-aware graph representation module is designed to flexibly encode and decode hierarchical features. Moreover, considering that global context always works collaboratively with local context, an orthogonal local-and-global information fusion module is presented to integrate complementary local and global context in a redundancy-free manner, thus yielding compact feature representations to facilitate correspondence learning. Thorough experimentation across relative pose estimation, homography estimation, visual localization, and point cloud registration affirms U-Match's remarkable capabilities. Our code is publicly available at https://github.com/ZizhuoLi/U-Match.

Seed to Prune: A Seeded Graph Neural Network for Two-View Correspondence Learning.

We present a simple yet tough-to-beat method dubbed SGNNet, for correspondence learning. Instead of focusing on devising sophisticated geometric extractors to explore the global or local contextual information involving all sparse correspondences as most existing studies have done, which may be biased by heavy outliers, we propose to first delve into elaborate contextual information encoded in several specific reliable correspondences, and later leverage it to achieve per-correspondence representation updating. To this end, the proposed network contains three pivotal modules: 1) dynamic seeding module, which aims to dynamically sample a set of reliable matches from the putative set as seeds to guide the network learning; 2) intraseed attention module (ISAM), which intends to capture the geometrical relations among seed matches and further leverage them to enhance seed features; and 3) dynamic unseeding module, which is designed to sufficiently aggregate favorable contextual information from seed matches and broadcast it back to features of original matches. With all the aforementioned components, the proposed SGNNet is capable of rejecting outliers from putative correspondences effectively. Extensive experiments indicate that our method beats current solid baselines and sets new SOTA scores across multiple domains and datasets. Notably, SGNNet attains an AUC@ 5° of 56.43% on YFCC100M without RANSAC, surpassing the most cutting-edge model by 4.51 absolute percentage points and exceeding the 55% AUC@ 5° bar for the first time. Project page: https://github.com/ZizhuoLi/SGNNet.

Mitochondrial DNA Leakage Promotes Persistent Pancreatic Acinar Cell Injury in Acute Pancreatitis via the cGAS-STING-NF-κB Pathway.

Previous research has shown that the activation of the cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) pathway in macrophages can promote severe acute pancreatitis through the release of inflammatory factors. The role of this pathway in pancreatic acinar cells, however, has not been studied, and understanding its mechanism could be crucial. We analysed plasma from 50 acute pancreatitis (AP) patients and 10 healthy donors using digital PCR, which links mitochondrial DNA (mtDNA) levels to the severity of AP. Single-cell sequencing of the pancreas during AP revealed differentially expressed genes and pathways in acinar cells. Experimental studies using mouse and cell models, which included mtDNA staining and quantitative PCR, revealed mtDNA leakage and the activation of STING-related pathways, indicating potential inflammatory mechanisms in AP. In conclusion, our study revealed that the mtDNA-STING-nuclear factor κB(NF-κB) pathway in pancreatic acinar cells could be a novel pathogenic factor in AP.

Arthroscopic dual-bone tunnel repair for palmer type IB injuries of the triangular fibrocartilage complex.

BACKGROUND: Triangular fibrocartilage complex (TFCC) injuries, especially Palmer type IB, pose surgical management challenges due to associated distal radial ulnar joint (DRUJ) instability. Traditional surgeries entail risks of complications. Arthroscopic repair presents advantages but lacks consensus on optimal techniques. To evaluate arthroscopic dual-bone tunnel repair in patients with Palmer type IB TFCC injuries of the wrist. METHODS: In this retrospective case series, grip strength ratio, joint range of motion, pain visual analogue scale (VAS), modified Mayo wrist score, and Disabilities of the Arm, Shoulder, and Hand (DASH) scores were assessed before and 12 months after surgery. RESULTS: The cohort consisted of 45 patients. At 12 months, the grip strength ratio improved from 0.71 ± 0.08 to 0.93 ± 0.05 (P < 0.001), and wrist joint rotation increased from 126.78 ± 13.28° to 145.76 ± 8.52° (P < 0.001). VAS (1.60 ± 0.58 vs. 6.33 ± 0.91, P < 0.001), DASH (12.96 ± 3.18 vs. 46.87 ± 6.62, P < 0.001), and modified Mayo wrist (88.11 ± 4.43 vs. 63.78 ± 7.99, P < 0.001) scores all improved after surgery. The overall complication rate was 4.44%. CONCLUSION: Arthroscopic dual-bone tunnel repair appears to be an effective intervention for alleviating wrist pain, restoring stability, and enhancing joint function in patients with TFCC Palmer type IB injuries.

T-Net++: Effective Permutation-Equivariance Network for Two-View Correspondence Pruning.

We propose a conceptually novel, flexible, and effective framework (named T-Net++) for the task of two-view correspondence pruning. T-Net++ comprises two unique structures: the "-'' structure and the "|'' structure. The "-'' structure utilizes an iterative learning strategy to process correspondences, while the "|'' structure integrates all feature information of the "-'' structure and produces inlier weights. Moreover, within the "|'' structure, we design a new Local-Global Attention Fusion module to fully exploit valuable information obtained from concatenating features through channel-wise and spatial-wise relationships. Furthermore, we develop a Channel-Spatial Squeeze-and-Excitation module, a modified network backbone that enhances the representation ability of important channels and correspondences through the squeeze-and-excitation operation. T-Net++ not only preserves the permutation-equivariance manner for correspondence pruning, but also gathers rich contextual information, thereby enhancing the effectiveness of the network. Experimental results demonstrate that T-Net++ outperforms other state-of-the-art correspondence pruning methods on various benchmarks and excels in two extended tasks. Our code will be available at https://github.com/guobaoxiao/T-Net.

Diterpenoid alkaloids from Delphinium sherriffii.

Three new diterpenoid alkaloids (1, 2, 3) and seventeen known (4-20) compounds were isolated from the whole plant of Delphinium sherriffii Munz (Ranunculaceae). Their structures were elucidated by various spectroscopic analyses, including IR, HR-ESI-MS, 1D and 2D NMR spectra. All compounds were evaluated for the inhibitory activity of Sf9 cells and compound 5 exhibited the strongest cytotoxicity (IC50 = 8.97 µM) against Sf9 cell line.

A predictive model combining connectomics and entropy biomarkers to discriminate long-term vagus nerve stimulation efficacy for pediatric patients with drug-resistant epilepsy.

AIMS: To predict the vagus nerve stimulation (VNS) efficacy for pediatric drug-resistant epilepsy (DRE) patients, we aim to identify preimplantation biomarkers through clinical features and electroencephalogram (EEG) signals and thus establish a predictive model from a multi-modal feature set with high prediction accuracy. METHODS: Sixty-five pediatric DRE patients implanted with VNS were included and followed up. We explored the topological network and entropy features of preimplantation EEG signals to identify the biomarkers for VNS efficacy. A Support Vector Machine (SVM) integrated these biomarkers to distinguish the efficacy groups. RESULTS: The proportion of VNS responders was 58.5% (38/65) at the last follow-up. In the analysis of parieto-occipital α band activity, higher synchronization level and nodal efficiency were found in responders. The central-frontal θ band activity showed significantly lower entropy in responders. The prediction model reached an accuracy of 81.5%, a precision of 80.1%, and an AUC (area under the receiver operating characteristic curve) of 0.838. CONCLUSION: Our results revealed that, compared to nonresponders, VNS responders had a more efficient α band brain network, especially in the parieto-occipital region, and less spectral complexity of θ brain activities in the central-frontal region. We established a predictive model integrating both preimplantation clinical and EEG features and exhibited great potential for discriminating the VNS responders. This study contributed to the understanding of the VNS mechanism and improved the performance of the current predictive model.

Incrementally Adapting Pretrained Model Using Network Prior for Multi-Focus Image Fusion.

Multi-focus image fusion can fuse the clear parts of two or more source images captured at the same scene with different focal lengths into an all-in-focus image. On the one hand, previous supervised learning-based multi-focus image fusion methods relying on synthetic datasets have a clear distribution shift with real scenarios. On the other hand, unsupervised learning-based multi-focus image fusion methods can well adapt to the observed images but lack the general knowledge of defocus blur that can be learned from paired data. To avoid the problems of existing methods, this paper presents a novel multi-focus image fusion model by considering both the general knowledge brought by the supervised pretrained backbone and the extrinsic priors optimized on specific testing sample to improve the performance of image fusion. To be specific, the Incremental Network Prior Adaptation (INPA) framework is proposed to incrementally integrate features extracted from the pretrained strong baselines into a tiny prior network (6.9% parameters of the backbone network) to boost the performance for test samples. We evaluate our method on both synthetic and real-world public datasets (Lytro, MFI-WHU, and Real-MFF) and show that our method outperforms existing supervised learning-based methods and unsupervised learning based methods.

Adipose-derived stem cell exosomes loaded with icariin attenuated M1 polarization of macrophages via inhibiting the TLR4/Myd88/NF-κB signaling pathway.

Abnormal macrophage polarization is one of the common pathological bases of various inflammatory diseases. The current research focus involves targeting macrophages to remodel their phenotype as a treatment approach for inflammatory diseases. Notably, exosomes can be delivered to specific types of cells or tissues or inflammatory area to realize targeted drug delivery. Although icariin (ICA) exhibits regulatory potential in macrophage polarization, the practical application of ICA is impeded by its water insolubility, poor permeability, and low bioavailability. Exploiting the inherent advantages of exosomes as natural drug carriers, we introduce a novel drug delivery system-adipose-derived stem cells-exosomes (ADSCs-EXO)-ICA. High-performance liquid chromatography analysis confirmed a loading rate of 92.7 ± 0.01 % for ADSCs-EXO-ICA, indicating the successful incorporation of ICA. As demonstrated by cell counting kit-8 assays, ADSCs-EXO exerted a significantly higher promotion effect on macrophage proliferation. The subsequent experimental results revealed the superior anti-inflammatory effect of ADSCs-EXO-ICA compared to individual treatments with EXO or ICA in the lipopolysaccharide + interferon-gamma-induced M1 inflammation model. Additionally, results from enzyme-linked immunosorbent assay, quantitative polymerase chain reaction, and western blot analyses revealed that ADSCs-EXO-ICA effectively inhibited macrophage polarization toward the M1-type and concurrently promoted polarization toward the M2-type. The underlying mechanism involved the modulation of macrophage polarization through inhibition of the Toll-like receptor 4/myeloid differentiation factor 88/nuclear transcription factor-kappa B signaling pathway, thereby mitigating inflammation. These findings underscore the potential therapeutic value of ADSCs-EXO-ICA as a novel intervention for inflammatory diseases.

GhHAM regulates GoPGF-dependent gland development and contributes to broad-spectrum pest resistance in cotton.

Cotton is a globally cultivated crop, producing 87% of the natural fiber used in the global textile industry. The pigment glands, unique to cotton and its relatives, serve as a defense structure against pests and pathogens. However, the molecular mechanism underlying gland formation and the specific role of pigment glands in cotton's pest defense are still not well understood. In this study, we cloned a gland-related transcription factor GhHAM and generated the GhHAM knockout mutant using CRISPR/Cas9. Phenotypic observations, transcriptome analysis, and promoter-binding experiments revealed that GhHAM binds to the promoter of GoPGF, regulating pigment gland formation in cotton's multiple organs via the GoPGF-GhJUB1 module. The knockout of GhHAM significantly reduced gossypol production and increased cotton's susceptibility to pests in the field. Feeding assays demonstrated that more than 80% of the cotton bollworm larvae preferred ghham over the wild type. Furthermore, the ghham mutants displayed shorter cell length and decreased gibberellins (GA) production in the stem. Exogenous application of GA3 restored stem cell elongation but not gland formation, thereby indicating that GhHAM controls gland morphogenesis independently of GA. Our study sheds light on the functional differentiation of HAM proteins among plant species, highlights the significant role of pigment glands in influencing pest feeding preference, and provides a theoretical basis for breeding pest-resistant cotton varieties to address the challenges posed by frequent outbreaks of pests.

Evaluation of the mechanism of Gong Ying San activity on dairy cows mastitis by network pharmacology and metabolomics analysis.

OBJECTIVES: The goal of this investigation was to identify the main compounds and the pharmacological mechanism of the traditional Chinese medicine formulation, Gong Ying San (GYS), by infrared spectral absorption characteristics, metabolomics, network pharmacology, and molecular-docking analysis for mastitis. The antibacterial and antioxidant activities were determined in vitro. METHODS: The chemical constituents of GYS were detected by ultra-high-performance liquid chromatography Q-extractive mass spectrometry (UHPLC-QE-MS). Related compounds were screened from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP, http://tcmspw.com/tcmsp.php) and the Encyclopedia of Traditional Chinese Medicine (ETCM, http://www.tcmip.cn/ETCM/index.php/Home/) databases; genes associated with mastitis were identified in DisGENT. A protein-protein interaction (PPI) network was generated using STRING. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment screening was conducted using the R module. Molecular-docking analyses were performed with the AutoDockTools V1.5.6. RESULTS: Fifty-four possible compounds in GYS with forty likely targets were found. The compound-target-network analysis showed that five of the ingredients, quercetin, luteolin, kaempferol, beta-sitosterol, and stigmasterol, had degree values >41.6, and the genes TNF, IL-6, IL-1ß, ICAM1, CXCL8, CRP, IFNG, TP53, IL-2, and TGFB1 were core targets in the network. Enrichment analysis revealed that pathways associated with cancer, lipids, atherosclerosis, and PI3K-Akt signaling pathways may be critical in the pharmacology network. Molecular-docking data supported the hypothesis that quercetin and luteolin interacted well with TNF-α and IL-6. CONCLUSIONS: An integrative investigation based on a bioinformatics-network topology provided new insights into the synergistic, multicomponent mechanisms of GYS's anti-inflammatory, antibacterial, and antioxidant activities. It revealed novel possibilities for developing new combination medications for reducing mastitis and its complications.

General Hyperspectral Image Super-Resolution via Meta-Transfer Learning.

Recent advances in deep learning-based methods have led to significant progress in the hyperspectral super-resolution (SR). However, the scarcity and the high dimension of data have hindered further development since deep models require sufficient data to learn stable patterns. Moreover, the huge domain differences between hyperspectral image (HSI) datasets pose a significant challenge in generalizability. To address these problems, we present a general hyperspectral SR framework via meta-transfer learning (MTL). We randomly sample various spectral ranges for SR tasks during MTL, allowing the model to accumulate diverse task experiences. Additionally, we implement a task schedule to gradually expand the number of bands, bridging the significant domain differences between datasets. By leveraging multiple datasets, we are able to achieve better performance and greater generalizability, making it applicable under various circumstances. Meanwhile, as a general framework, our scheme can be applied to existing methods to obtain performance improvements. In addition, we design an advanced network architecture based on the multifusion features to further improve the performance. Experiments demonstrate that our method not only achieves superior performance in both qualitative and quantitative terms but also can adapt robustly to a new and difficult sample, where few epochs can yield quite considerable results.

Triple-task mutual consistency for semi-supervised 3D medical image segmentation.

Semi-supervised deep learning algorithm is an effective means of medical image segmentation. Among these methods, multi-task learning with consistency regularization has achieved outstanding results. However, most of the existing methods usually simply embed the Signed Distance Map (SDM) task into the network, which underestimates the potential ability of SDM in edge awareness and leads to excessive dependence between tasks. In this work, we propose a novel triple-task mutual consistency (TTMC) framework to enhance shape and edge awareness capabilities, and overcome the task dependence problem underestimated in previous work. Specifically, we innovatively construct the Signed Attention Map (SAM), a novel fusion image with attention mechanism, and use it as an auxiliary task for segmentation to enhance the edge awareness ability. Then we implement a triple-task deep network, which jointly predicts the voxel-wise classification map, the Signed Distance Map and the Signed Attention Map. In our proposed framework, an optimized differentiable transformation layer associates SDM with voxel-wise classification map and SAM prediction, while task-level consistency regularization utilizes unlabeled data in an unsupervised manner. Evaluated on the public Left Atrium dataset and NIH Pancreas dataset, our proposed framework achieves significant performance gains by effectively utilizing unlabeled data, outperforming recent state-of-the-art semi-supervised segmentation methods. Code is available at https://github.com/Saocent/TTMC.

Custom-Design of Multi-Stimuli-Responsive Degradable Silica Nanoparticles for Advanced Cancer-Specific Chemotherapy.

Chemotherapy is crucial in oncology for combating malignant tumors but often encounters obatacles such as severe adverse effects, drug resistance, and biocompatibility issues. The advantages of degradable silica nanoparticles in tumor diagnosis and treatment lie in their ability to target drug delivery, minimizing toxicity to normal tissues while enhancing therapeutic efficacy. Moreover, their responsiveness to both endogenous and exogenous stimuli opens up new possibilities for integrating multiple treatment modalities. This review scrutinizes the burgeoning utility of degradable silica nanoparticles in combination with chemotherapy and other treatment modalities. Commencing the elucidation of degradable silica synthesis and degradation mechanisms, emphasis is placed on the responsiveness of these materials to endogenous (e.g., pH, redox reactions, hypoxia, and enzymes) and exogenous stimuli (e.g., light and high-intensity focused ultrasound). Moreover, this exploration delves into strategies harnessing degradable silica nanoparticles in chemotherapy alone, coupled with radiotherapy, photothermal therapy, photodynamic therapy, gas therapy, immunotherapy, starvation therapy, and chemodynamic therapy, elucidating multimodal synergies. Concluding with an assessment of advances, challenges, and constraints in oncology, despite hurdles, future investigations are anticipated to augment the role of degradable silica in cancer therapy. These insights can serve as a compass for devising more efficacious combined tumor treatment strategies.