The double-edged sword effect of indigo naturalis.

Indigo naturalis (IN) is a dried powder derived from plants such as Baphicacanthus cusia (Neeks) Bremek., Polygonum tinctorium Ait. and Isatis indigotica Fork. It has a historical application as a dye in ancient India, Egypt, Africa and China. Over time, it has been introduced to China and Japan for treatment of various ailments including hemoptysis, epistaxis, chest discomfort, and aphtha. Clinical and pre-clinical studies have widely demonstrated its promising effects on autoimmune diseases like psoriasis and Ulcerative colitis (UC). Despite the documented efficacy of IN in UC patients, concerns have been raised on the development of adverse effects with long term consumption, prompting a closer examination of its safety and tolerability in these contexts. This review aims to comprehensively assess the efficacy of IN in both clinical and pre-clinical settings, with a detailed exploration of the mechanisms of action involved. Additionally, it summarizes the observed potential toxicity of IN in animal and human settings was summarized. This review will deepen our understanding on the beneficial and detrimental effects of IN in UC, providing valuable insights for its future application in patients with this condition.

Enhancing intracellular mRNA precise imaging-guided photothermal therapy with a nucleic acid-based polydopamine nanoprobe.

Despite significant advancements in cancer research, real-time monitoring and effective treatment of cancer through non-invasive techniques remain a challenge. Herein, a novel polydopamine (PDA) nucleic acid nanoprobe has been developed for imaging signal amplification of intracellular mRNA and precise photothermal therapy guidance in cancer cells. The PDA nucleic acid nanoprobe (PDA@DNA) is constructed by assembling an aptamer hairpin (H1) labeled with the Cy5 fluorophore and another nucleic acid recognition hairpin (H2) onto PDA nanoparticles (PDA NPs), which have exceptionally high fluorescence quenching ability and excellent photothermal conversion properties. The nanoprobe could facilitate cellular uptake of DNA molecules and their protection from nuclease degradation. Upon recognition and binding to the intracellular mRNA target, a catalytic hairpin assembly (CHA) reaction occurs. The stem of H1 unfolds upon binding, allowing the exposed H1 to hybridize with H2, forming a flat and sturdy DNA double-stranded structure that detaches from the surface of PDA NPs. At the same time, the target mRNA is displaced and engages in a new cyclic reaction, resulting in the recovery and significant amplification of Cy5 fluorescence. Using thymidine kinase1 (TK1) mRNA as a model mRNA, this nanoprobe enables the analysis of TK1 mRNA with a detection limit of 9.34 pM, which is at least two orders of magnitude lower than that of a non-amplifying imaging nucleic acid probe. Moreover, with its outstanding performance for in vitro detection, this nanoprobe excels in precisely imaging tumor cells. Through live-cell TK1 mRNA imaging, it can accurately distinguish between tumor cells and normal cells. Furthermore, when exposed to 808-nm laser irradiation, the nanoprobe fully harnesses exceptional photothermal conversion properties of PDA NPs. This results in a localized temperature increase within tumor cells, which ultimately triggers apoptosis in these tumor cells. The integration of PDA@DNA presents innovative prospects for tumor diagnosis and image-guided tumor therapy, offering the potential for high-precision diagnosis and treatment of tumors.

Deep Learning Approach to Parse Eligibility Criteria in Dietary Supplements Clinical Trials Following OMOP Common Data Model.

Dietary supplements (DSs) have been widely used in the U.S. and evaluated in clinical trials as potential interventions for various diseases. However, many clinical trials face challenges in recruiting enough eligible patients in a timely fashion, causing delays or even early termination. Using electronic health records to find eligible patients who meet clinical trial eligibility criteria has been shown as a promising way to assess recruitment feasibility and accelerate the recruitment process. In this study, we analyzed the eligibility criteria of 100 randomly selected DS clinical trials and identified both computable and non-computable criteria. We mapped annotated entities to OMOP Common Data Model (CDM) with novel entities (e.g., DS). We also evaluated a deep learning model (Bi-LSTM-CRF) for extracting these entities on CLAMP platform, with an average F1 measure of 0.601. This study shows the feasibility of automatic parsing of the eligibility criteria following OMOP CDM for future cohort identification.