Potentials of Natural Antioxidants in Reducing Inflammation and Oxidative Stress in Chronic Kidney Disease.

Chronic kidney disease (CKD) presents a substantial global public health challenge, with high morbidity and mortality. CKD patients often experience dyslipidaemia and poor glycaemic control, further exacerbating inflammation and oxidative stress in the kidney. If left untreated, these metabolic symptoms can progress to end-stage renal disease, necessitating long-term dialysis or kidney transplantation. Alleviating inflammation responses has become the standard approach in CKD management. Medications such as statins, metformin, and GLP-1 agonists, initially developed for treating metabolic dysregulation, demonstrate promising renal therapeutic benefits. The rising popularity of herbal remedies and supplements, perceived as natural antioxidants, has spurred investigations into their potential efficacy. Notably, lactoferrin, Boerhaavia diffusa, Amauroderma rugosum, and Ganoderma lucidum are known for their anti-inflammatory and antioxidant properties and may support kidney function preservation. However, the mechanisms underlying the effectiveness of Western medications and herbal remedies in alleviating inflammation and oxidative stress occurring in renal dysfunction are not completely known. This review aims to provide a comprehensive overview of CKD treatment strategies and renal function preservation and critically discusses the existing literature's limitations whilst offering insight into the potential antioxidant effects of these interventions. This could provide a useful guide for future clinical trials and facilitate the development of effective treatment strategies for kidney functions.

Co-ERA-Net: Co-Supervision and Enhanced Region Attention for Accurate Segmentation in COVID-19 Chest Infection Images.

Accurate segmentation of infected lesions in chest images remains a challenging task due to the lack of utilization of lung region information, which could serve as a strong location hint for infection. In this paper, we propose a novel segmentation network Co-ERA-Net for infections in chest images that leverages lung region information by enhancing supervised information and fusing multi-scale lung region and infection information at different levels. To achieve this, we introduce a Co-supervision scheme incorporating lung region information to guide the network to accurately locate infections within the lung region. Furthermore, we design an Enhanced Region Attention Module (ERAM) to highlight regions with a high probability of infection by incorporating infection information into the lung region information. The effectiveness of the proposed scheme is demonstrated using COVID-19 CT and X-ray datasets, with the results showing that the proposed schemes and modules are promising. Based on the baseline, the Co-supervision scheme, when integrated with lung region information, improves the Dice coefficient by 7.41% and 2.22%, and the IoU by 8.20% and 3.00% in CT and X-ray datasets respectively. Moreover, when this scheme is combined with the Enhanced Region Attention Module, the Dice coefficient sees further improvement of 14.24% and 2.97%, with the IoU increasing by 28.64% and 4.49% for the same datasets. In comparison with existing approaches across various datasets, our proposed method achieves better segmentation performance in all main metrics and exhibits the best generalization and comprehensive performance.

Targeted Sequencing Approach and Its Clinical Applications for the Molecular Diagnosis of Human Diseases.

The outbreak of COVID-19 has positively impacted the NGS market recently. Targeted sequencing (TS) has become an important routine technique in both clinical and research settings, with advantages including high confidence and accuracy, a reasonable turnaround time, relatively low cost, and fewer data burdens with the level of bioinformatics or computational demand. Since there are no clear consensus guidelines on the wide range of next-generation sequencing (NGS) platforms and techniques, there is a vital need for researchers and clinicians to develop efficient approaches, especially for the molecular diagnosis of diseases in the emergency of the disease and the global pandemic outbreak of COVID-19. In this review, we aim to summarize different methods of TS, demonstrate parameters for TS assay designs, illustrate different TS panels, discuss their limitations, and present the challenges of TS concerning their clinical application for the molecular diagnosis of human diseases.

Label-free metabolic imaging for sensitive and robust monitoring of anti-CD47 immunotherapy response in triple-negative breast cancer.

BACKGROUND: Immunotherapy is revolutionizing cancer treatment from conventional radiotherapies and chemotherapies to immune checkpoint inhibitors which use patients' immune system to recognize and attack cancer cells. Despite the huge clinical success and vigorous development of immunotherapies, there is a significant unmet need for a robust tool to identify responders to specific immunotherapy. Early and accurate monitoring of immunotherapy response is indispensable for personalized treatment and effective drug development. METHODS: We established a label-free metabolic intravital imaging (LMII) technique to detect two-photon excited autofluorescence signals from two coenzymes, NAD(P)H (reduced nicotinamide adenine dinucleotide (phosphate) hydrogen) and FAD (flavin adenine dinucleotide) as robust imaging markers to monitor metabolic responses to immunotherapy. Murine models of triple-negative breast cancer (TNBC) were established and tested with different therapeutic regimens including anti-cluster of differentiation 47 (CD47) immunotherapy to monitor time-course treatment responses using the developed metabolic imaging technique. RESULTS: We first imaged the mechanisms of the CD47-signal regulatory protein alpha pathway in vivo, which unravels macrophage-mediated antibody-dependent cellular phagocytosis and illustrates the metabolism of TNBC cells and macrophages. We further visualized the autofluorescence of NAD(P)H and FAD and found a significant increase during tumor growth. Following anti-CD47 immunotherapy, the imaging signal was dramatically decreased demonstrating the sensitive monitoring capability of NAD(P)H and FAD imaging for therapeutic response. NAD(P)H and FAD intravital imaging also showed a marked decrease after chemotherapy and radiotherapy. A comparative study with conventional whole-body bioluminescence and fluorescent glucose imaging demonstrated superior sensitivity of metabolic imaging. Flow cytometry validated metabolic imaging results. In vivo immunofluorescent staining revealed the targeting ability of NAD(P)H imaging mainly for tumor cells and a small portion of immune-active cells and that of FAD imaging mainly for immunosuppressive cells such as M2-like tumor-associated macrophages. CONCLUSIONS: Collectively, this study showcases the potential of the LMII technique as a powerful tool to visualize dynamic changes of heterogeneous cell metabolism of cancer cells and immune infiltrates in response to immunotherapy thus providing sensitive and complete monitoring. Leveraged on ability to differentiate cancer cells and immunosuppressive macrophages, the presented imaging approach provides particularly useful imaging biomarkers for emerged innate immune checkpoint inhibitors such as anti-CD47 therapy.

Current Developments of Artificial Intelligence in Digital Pathology and Its Future Clinical Applications in Gastrointestinal Cancers.

The implementation of DP will revolutionize current practice by providing pathologists with additional tools and algorithms to improve workflow. Furthermore, DP will open up opportunities for development of AI-based tools for more precise and reproducible diagnosis through computational pathology. One of the key features of AI is its capability to generate perceptions and recognize patterns beyond the human senses. Thus, the incorporation of AI into DP can reveal additional morphological features and information. At the current rate of AI development and adoption of DP, the interest in computational pathology is expected to rise in tandem. There have already been promising developments related to AI-based solutions in prostate cancer detection; however, in the GI tract, development of more sophisticated algorithms is required to facilitate histological assessment of GI specimens for early and accurate diagnosis. In this review, we aim to provide an overview of the current histological practices in AP laboratories with respect to challenges faced in image preprocessing, present the existing AI-based algorithms, discuss their limitations and present clinical insight with respect to the application of AI in early detection and diagnosis of GI cancer.