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Colorectal cancer (CRC) continues to be a major health issue even though screening methods have facilitated early detection. Despite the high sensitivity of white-light colonoscopy, it frequently overlooks invasive flat or depressed lesions, which can lead to the development of larger, advanced tumors. Fluorescence molecular imaging (FMI) offers a promising approach for early tumor detection by targeting specific molecular characteristics of lesions. CD24 is upregulated during the adenoma-to-CRC transition, providing a potential target for FMI. Here, we developed a second near-infrared window (NIR-II) fluorescent probe with a high affinity for CD24 and evaluated its efficacy and targeting ability in cellular models, murine models, and clinical samples of CRC. CD24 expression was elevated in 76% of adenomas and 80% of CRCs. In a colitis-associated cancer mouse model, NIR-II imaging with the CD24-targeted probe achieved a significantly higher tumor-to-background ratio compared to conventional NIR-I imaging. The probe demonstrated exceptional sensitivity (92%) and specificity (92%) for detecting CRC, including small lesions less than 1 mm in size. This led to the identification of precancerous lesions missed by white-light detection and lesions missed by NIR-I imaging. Moreover, ex vivo human tissue incubation with the probe supported the potential for intraprocedural lesion identification via topical probe application during colonoscopy. In conclusion, this study successfully demonstrates the potential of CD24-targeted NIR-II imaging for identifying colorectal neoplasia, highlighting its significance for early CRC detection in the gastrointestinal tract.

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Air pollution, particularly fine particulate matter (PM2.5) with <2.5 µm in diameter, is a major public health concern. Studies have consistently linked PM2.5 exposure to a heightened risk of cardiovascular diseases (CVDs) such as ischemic heart disease (IHD), heart failure (HF), and cardiac arrhythmias. Notably, individuals with pre-existing age-related cardiometabolic conditions appear more susceptible. However, the specific impact of PM2.5 on CVDs susceptibility in older adults remains unclear. Therefore, this review addresses this gap by discussing the factors that make the elderly more vulnerable to PM2.5-induced CVDs. Accordingly, we focused on physiological aging, increased susceptibility, cardiometabolic risk factors, CVDs, and biological mechanisms. This review concludes by examining potential interventions to reduce exposure and the adverse health effects of PM2.5 in the elderly population. The latter includes dietary modifications, medications, and exploration of the potential benefits of supplements. By comprehensively analyzing these factors, this review aims to provide a deeper understanding of the detrimental effects of PM2.5 on cardiovascular health in older adults. This knowledge can inform future research and guide strategies to protect vulnerable populations from the adverse effects of air pollution.

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The impact of mitochondrial dysfunction on the pathogenesis of cardiovascular disease is increasing. However, the precise underlying mechanism remains unclear. Mitochondria produce cellular energy through oxidative phosphorylation while regulating calcium homeostasis, cellular respiration, and the production of biosynthetic chemicals. Nevertheless, problems related to cardiac energy metabolism, defective mitochondrial proteins, mitophagy, and structural changes in mitochondrial membranes can cause cardiovascular diseases via mitochondrial dysfunction. Mitofilin is a critical inner mitochondrial membrane protein that maintains cristae structure and facilitates protein transport while linking the inner mitochondrial membrane, outer mitochondrial membrane, and mitochondrial DNA transcription. Researchers believe that mitofilin may be a therapeutic target for treating cardiovascular diseases, particularly cardiac mitochondrial dysfunctions. In this review, we highlight current findings regarding the role of mitofilin in the pathogenesis of cardiovascular diseases and potential therapeutic compounds targeting mitofilin.

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Robust and interpretable image reconstruction is central to imageology applications in clinical practice. Prevalent deep networks, with strong learning ability to extract implicit information from data manifold, are still lack of prior knowledge introduced from mathematics or physics, leading to instability, poor structure interpretability and high computation cost. As to this issue, we propose two prior knowledge-driven networks to combine the good interpretability of mathematical methods and the powerful learnability of deep learning methods. Incorporating different kinds of prior knowledge, we propose subband-adaptive wavelet iterative shrinkage thresholding networks (SWISTA-Nets), where almost every network module is in one-to-one correspondence with each step involved in the iterative algorithm. By end-to-end training of proposed SWISTA-Nets, implicit information can be extracted from training data and guide the tuning process of key parameters that possess mathematical definition. The inverse problems associated with two medical imaging modalities, i.e., electromagnetic tomography and X-ray computational tomography are applied to validate the proposed networks. Both visual and quantitative results indicate that the SWISTA-Nets outperform mathematical methods and state-of-the-art prior knowledge-driven networks, especially with fewer training parameters, interpretable network structures and well robustness. We assume that our analysis will support further investigation of prior knowledge-driven networks in the field of ill-posed image reconstruction.

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Fluorescence imaging has demonstrated great potential for malignant tissue inspection. However, poor imaging quality of medical fluorescent images inevitably brings challenges to disease diagnosis. Though improvement of image quality can be achieved by translating the images from low-quality domain to high-quality domain, fewer scholars have studied the spectrum translation and the prevalent cycle-consistent generative adversarial network (CycleGAN) is powerless to grasp local and semantic details, leading to produce unsatisfactory translated images. To enhance the visual quality by shifting spectrum and alleviate the under-constraint problem of CycleGAN, this study presents the design and construction of the perception-enhanced spectrum shift GAN (PSSGAN). Besides, by introducing the constraint of perceptual module and relativistic patch, the model learns effective biological structure details of image translation. Moreover, the interpolation technique is innovatively employed to validate that PSSGAN can vividly show the enhancement process and handle the perception-fidelity trade-off dilemma of fluorescent images. A novel no reference quantitative analysis strategy is presented for medical images. On the open data and collected sets, PSSGAN provided 15.32% ∼ 35.19% improvement in structural similarity and 21.55% ∼ 27.29% improvement in perceptual quality over the leading method CycleGAN. Extensive experimental results indicated that our PSSGAN achieved superior performance and exhibited vital clinical significance.

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Ischemic heart disease (IHD) is a high-risk disease in the middle-aged and elderly population. The ischemic heart may be further damaged after reperfusion therapy with percutaneous coronary intervention (PCI) and other methods, namely, myocardial ischemia-reperfusion injury (MIRI), which further affects revascularization and hinders patient rehabilitation. Therefore, the investigation of new therapies against MIRI has drawn great global attention. Within the long history of the prevention and treatment of MIRI, traditional Chinese medicine (TCM) has increasingly been recognized by the scientific community for its multi-component and multi-target effects. These multi-target effects provide a conspicuous advantage to the anti-MIRI of TCM to overcome the shortcomings of single-component drugs, thereby pointing toward a novel avenue for the treatment of MIRI. However, very few reviews have summarized the currently available anti-MIRI of TCM. Therefore, a systematic data mining of TCM for protecting against MIRI will certainly accelerate the processes of drug discovery and help to identify safe candidates with synergistic formulations. The present review aims to describe TCM-based research in MIRI treatment through electronic retrieval of articles, patents, and ethnopharmacology documents. This review reported the progress of research on the active ingredients, efficacy, and underlying mechanism of anti-MIRI in TCM and TCM formulas, provided scientific support to the clinical use of TCM in the treatment of MIRI, and revealed the corresponding clinical significance and development prospects of TCM in treating MIRI.

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A combinative method using high-performance liquid chromatography-electrochemical detection for fingerprinting and quantitative analysis was developed and successfully applied for the quality evaluation of Lophatherum gracile Brongn leaves collected from 21 geographical locations in China. In the fingerprint analysis, 18 common peaks were observed among the 21 samples, and 10 peaks were identified. Simultaneous quantification of the 10 components was conducted to interpret the variations in these compounds among the L. gracile Brongn leaves originating from different geographical locations. The correlation between the chromatograms and the antioxidant activities of the samples was further studied. The results indicated a linear correlation between the antioxidant activity and the total common peak areas of the fingerprints obtained by high-performance liquid chromatography-electrochemical detection. Importantly, it was found that high-performance liquid chromatography-electrochemical detection fingerprinting can not only determine the quantities of individual components present in such samples but also evaluate the antioxidant activities of the samples. The developed method is a valuable reference for the further study and development of L. gracile Brongn.

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Exhaled breath contains thousands of gaseous volatile organic compounds (VOCs) that could be used as non-invasive biomarkers of lung cancer. Breath-based lung cancer screening has attracted wide attention on account of its convenience, low cost and easy popularization. In this paper, the research of lung cancer detection and staging is conducted by the self-developed electronic nose (e-nose) system. In order to investigate the performance of the device in distinguishing lung cancer patients from healthy controls, two feature extraction methods and two different classification models were adopted. Among all the models, kernel principal component analysis (KPCA) combined with extreme gradient boosting (XGBoost) achieved the best results among 235 breath samples. The accuracy, sensitivity and specificity of e-nose system were 93.59%, 95.60% and 91.09%, respectively. Meanwhile, the device could innovatively classify stages of 90 lung cancer patients (i.e., 44 stage III and 46 stage IV). Experimental results indicated that the recognition accuracy of lung cancer stages was more than 80%. Further experiments of this research also showed that the combination of sensor array and pattern recognition algorithms could identify and distinguish the expiratory characteristics of lung cancer, smoking and other respiratory diseases.

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The electrocardiogram (ECG) is a non-invasive, inexpensive, and effective tool for myocardial infarction (MI) diagnosis. Conventional detection algorithms require solid domain expertise and rely heavily on handcrafted features. Although previous works have studied deep learning methods for extracting features, these methods still neglect the relationships between different leads and the temporal characteristics of ECG signals. To handle the issues, a novel multi-lead attention (MLA) mechanism integrated with convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU) framework (MLA-CNN-BiGRU) is therefore proposed to detect and locate MI via 12-lead ECG records. Specifically, the MLA mechanism automatically measures and assigns the weights to different leads according to their contribution. The two-dimensional CNN module exploits the interrelated characteristics between leads and extracts discriminative spatial features. Moreover, the BiGRU module extracts essential temporal features inside each lead. The spatial and temporal features from these two modules are fused together as global features for classification. In experiments, MI location and detection were performed under both intra-patient scheme and inter-patient scheme to test the robustness of the proposed framework. Experimental results indicate that our intelligent framework achieved satisfactory performance and demonstrated vital clinical significance.

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The electronic nose (e-nose) system is a newly developing detection technology for its advantages of non-invasiveness, simple operation, and low cost. However, lung cancer screening through e-nose requires effective pattern recognition frameworks. Existing frameworks rely heavily on hand-crafted features and have relatively low diagnostic sensitivity. To handle these problems, gated recurrent unit based autoencoder (GRU-AE) is adopted to automatically extract features from temporal and high-dimensional e-nose data. Moreover, we propose a novel margin and sensitivity based ordering ensemble pruning (MSEP) model for effective classification. The proposed heuristic model aims to reduce missed diagnosis rate of lung cancer patients while maintaining a high rate of overall identification. In the experiments, five state-of-the-art classification models and two popular dimensionality reduction methods were involved for comparison to demonstrate the validity of the proposed GRU-AE-MSEP framework, through 214 collected breath samples measured by e-nose. Experimental results indicated that the proposed intelligent framework achieved high sensitivity of 94.22%, accuracy of 93.55%, and specificity of 92.80%, thereby providing a new practical means for wide disease screening by e-nose in medical scenarios.

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An efficient enzymolysis-ultrasonic assisted extraction (EUAE) was developed and optimized for the extraction of polysaccharide from Momordica charabtia L. The single factor experiments and orthogonal experiments were used for the key experimental factors and their test range. Based on the preliminary experimental results, the response surface methodology (RSM) and Box-Behnken design (BBD) were applied for the optimization of EUAE conditions. Using the multiple regression analysis and analysis of variance (ANOVA), the experimental data were fitted to a second-order polynomial equation and were used to generate the mathematical model of optimization experiments. The optimal extraction conditions were as follows: a pH of 4.38, a extraction temperature of 52.02°C and a extraction time of 36.87 min. Under the optimal extraction conditions, the extraction yield of Momordica charabtia L. polysaccharides (MCP) was 29.75±0.48%, which was well matched with the predicted value (29.80%) of the BBD model.

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