Pneumonia is a serious lung infection, and its early detection remains challenging. Infection regions vary among patients on computed tomography (CT) images and are difficult to distinguish. Moreover, image analysts overlook clinical descriptions when enhancing image representation and fail to provide a comprehensive representation of CT images. In this study, a modified U-Net model called MLEU-Net is used to segment infection areas in chest CT scans based on clinical description-based tuning of the network layers. These text-based tuning layers help segment subpleural, perihilar, and diffuse regions. The MedSeg COVID Dataset 2 was used. The MLEU-Net model consists of (i) an encoder-decoder structure with skip connections; (ii) an Atrous Spatial Pyramid Pooling (ASPP) module used to capture features at multiple scales; (iii) attention mechanisms combined to focus on important regions; and (iv) dropout layers added to improve stability during training. In addition, features from clinical descriptions are combined with image features to support improved interpretation of the results. The proposed framework was trained on one dataset and externally validated on another. The model shows good performance in detecting infection regions, particularly in subpleural (Dice 0.74) and diffuse areas (Dice 0.71). The perihilar region is harder to detect due to very limited annotated data for that class. The results demonstrate good generalization to unseen data. The proposed multimodal MLEU-Net combines image and clinical information within a boundary-aware segmentation framework. External validation demonstrates that the model generalizes well to unseen CT data and supports reliable pneumonia infection analysis. The proposed framework was externally validated on the MosMedData dataset, demonstrating stable generalization to unseen CT images.
This work presented the design and development of pH-sensitive nanoparticles fabricated by dispersion polymerization (PEG-surface-tagged polymeric nanoparticles) loaded with cisplatin and paclitaxel. In vitro drug release and In vitro drug efficacy of the dual-loaded nanoparticles were carried out vis-à-vis their suitability for the treatment of triple-negative breast cancer (TNBC). A pH-sensitive acetal crosslinking agent was synthesized followed by incorporation in polylactide, poly( ε -caprolactone and polylactide-poly( ε -caprolactone) (blend) nanoparticles to facilitate the hydrolysis of the nanoparticle matrix in the tumor acidic microenvironment and the release of the encapsulated drugs. The efficacy of paclitaxel and cisplatin, when combined, and the nature of their interaction were determined based on a combination index in an In vitro cell-based assay (antiproliferative studies) using a triple-negative breast cancer cell line (MDA-MB-231). Data show a synergistic interaction between paclitaxel and cisplatin: the two drugs work together to enhance efficacy. Combination Index values for the different formulations were less than 1, showing synergism. The drug combination demonstrated greater cytotoxicity at lower doses compared to higher doses of paclitaxel or cisplatin alone, indicating that this dual-loaded drug approach could maintain therapeutic efficacy while reducing adverse side effects commonly associated with chemotherapy at high doses. This novel strategy not only facilitates dose reduction of chemotherapeutic agents but also enables the simultaneous, site-specific delivery of both drugs, offering a promising advancement in targeted TNBC therapy. Confocal microscopy studies showed the internalization of the nanoparticles in the cytoplasm.
A brain tumor is an aberrant cell development that may be benign or malignant. Tor form, size, location, and overlapping elements of visual characteristics associated with multi-grade BT identification therefore vary. In order to tackle these problems, a model for Multi-Grade Brain Tumor Detection (MGBTD) called Latent Graph Encoder Coupled Triple Generative Adversarial Networks with Walk-Spread Algorithm (LGECTGA2Nets+WSA) is proposed. To improve image quality, MRI images from the BRATS 2018 & Figshare datasets are first pre-processed using the Hybrid Recursive Reversible Box Filter-Based Fast Adaptive Bilateral Filtering (HRRBF-FABF) approach. Then, robust spatial-frequency feature extraction is done using the Discrete Quaternion Quadratic Phase Fourier Transform (DQQPFT). Tumor segmentation is performed with a Convolution-Transformer (CT), followed by classification using LGECTGA2Nets. The Walk-Spread Algorithm has been used to obtain model optimization. Python-based system has an accuracy and sensitivity value of 99.9% and 99.8% respectively, which is better when compared to other systems used in detecting and classifying multiple grades of tumors.
Recent studies have highlighted the promising chemotherapeutic and antioxidant properties of phytochemicals, which offer the advantage of fewer side effects compared to conventional therapies. Glioblastoma multiforme (GBM) is an aggressive and fatal brain tumor with a poor prognosis and limited treatment options. Despite advances in surgery, radiotherapy, and chemotherapy, effective GBM treatments remain a major challenge, largely due to the restrictive nature of the blood–brain barrier and the severe side effects associated with conventional drugs. The integration of herbal-based compounds into nanocarrier-based multimodal drug delivery systems holds significant potential to enhance therapeutic efficacy and overcome current limitations. This review begins with an overview of GBM, including current treatment strategies and the challenges associated with drug delivery to the brain. We then explore various phytochemicals and medicinal plants used in GBM therapy and their incorporation into nanostructured delivery systems. Finally, we discuss ongoing clinical trials and the biological fate of nanostructures, emphasizing their potential in GBM treatment. The therapeutic efficacy of natural compounds can be markedly improved through nanocarrier-based delivery. There is a growing need for novel natural biomaterials and their combination with potent chemotherapeutics, given their desirable characteristics such as biodegradability, biocompatibility, accessibility, renewability, and low toxicity. Recent advancements underscore the potential of combining nanotechnology with phytotherapy as a promising approach for GBM treatment—an area where conventional therapies have often proven inadequate.
Human health has already been significantly influenced by rising atmospheric CO 2 and declining O 2 levels posing critical challenges for environmental sustainability and life-support technologies. One solution to this problem is to develop photocatalytic material systems capable of capturing sufficient amounts of CO 2 for its visible light conversion to O 2 from water. The hypothesis of this study is that high surface area, porous, nanomaterial systems will capture enough CO 2 from water for its conversion to O 2 using visible light. Due to their high surface area and high porosity, here, a metal—organic framework (MOF-210) is expected to adsorb ample CO 2 and release large quantities of O 2 after combination with bismuth vanadate (BiVO 4 ) nanoparticles and photocatalysis. To test this, BiVO 4 nanoparticles were precipitated onto MOF-210, and the resulting composites were dispersed in water. Different concentrations of MOF-210 and water were tested. They were then exposed to visible light, and the initial and final CO 2 and O 2 levels were measured using CO 2 sensors and pH test strips. For comparison, the MOF-210 higher oxygen-containing BiVO 4 nanosystem was evaluated against a lower surface area oxygen-containing, non-porous, BiVO 4 -titanium dioxide (TiO 2 ) nanosystem. Materials were characterized and confirmed for size (using Scanning Electron Microscopy and Dynamic Light Scattering) and chemistry (using Energy Dispersive Chemistry). Experimental results showed that the high surface area, highly porous, MOF-210 did indeed adsorb sufficient CO 2 and when combined with BiVO 4 nanoparticles formed sufficient amounts of O 2 . The control lower surface area, non-porous, TiO 2 nanoparticle system did not generate sufficient O 2 . These findings confirm that the high surface area, highly porous MOF–BiVO 4 nanocomposites can indeed adsorb and convert sufficient CO 2 into useful O 2 highlighting the potential of MOF-based photocatalysts as dual-function materials for mitigating greenhouse gases and regenerating oxygen, with broader implications for climate change remediation and closed-loop life support in space exploration necessary to improve human health.
