Based on the phase separation phenomenon in micro-droplets, polymer-lipid Janus particles were prepared on a microfluidic flow focusing chip. Phase separation of droplets was caused by solvent volatilization and Janus morphology was formed under the action of interfacial tension. Because phase change from solid to liquid of the lipid hemisphere could be triggered by physiological temperature, the lipid hemisphere could be used for rapid release of drugs. While the polymer we selected was pH sensitive that the polymer hemisphere could degrade under acidic conditions, making it possible to release drugs in a specific pH environment, such as tumor tissues. Janus particles with different structures were obtained by changing the experimental conditions. To widen the application range of the particles, fatty alcohol and fatty acid-based phase change materials were also employed to prepare the particles, such as 1-tetradecanol, 1-hexadecanol and lauric acid. The melting points of these substances are higher than the physiological temperature, which can be applied in fever triggered drug release or in thermotherapy. The introduction of poly (lactic-co-glycolic acid) enabled the formation of multicompartment particles with three distinct materials. With different degradation properties of each compartment, the particles generated in this work may find applications in programmed and sequential drug release triggered by multiple stimuli.
Abstract Treatment planning for chronic diseases is a critical task in medical artificial intelligence, particularly in traditional Chinese medicine (TCM). However, generating optimized sequential treatment strategies for patients with chronic diseases in different clinical encounters remains a challenging issue that requires further exploration. In this study, we proposed a TCM herbal prescription planning framework based on deep reinforcement learning for chronic disease treatment (PrescDRL). PrescDRL is a sequential herbal prescription optimization model that focuses on long-term effectiveness rather than achieving maximum reward at every step, thereby ensuring better patient outcomes. We constructed a high-quality benchmark dataset for sequential diagnosis and treatment of diabetes and evaluated PrescDRL against this benchmark. Our results showed that PrescDRL achieved a higher curative effect, with the single-step reward improving by 117% and 153% compared to doctors. Furthermore, PrescDRL outperformed the benchmark in prescription prediction, with precision improving by 40.5% and recall improving by 63%. Overall, our study demonstrates the potential of using artificial intelligence to improve clinical intelligent diagnosis and treatment in TCM.
This work aimed to develop a multiphasic Janus particle system for programmed drug delivery. A phase separation based one-step microfluidic preparation process was demonstrated to generate triple-phase Janus microparticles with different degradation properties in each phase. In this system, particles with a series of complicated structures were generated, and programmed degradation behaviors according to the structures were achieved. Partial degradation of the particles and cargo release triggered by change of ambient temperature or pH could be realized as well. Further, graphene nanosheets and silica nanospheres were modified in the microparticles by using the principle of Pickering emulsion to enrich the functions of the particles. To reveal the potential of the particles in drug delivery applications, doxorubicin and curcumin were co-loaded in triple-phase microparticles, and zonal drug loading was achieved. In vitro drug release profiles and tumor cell apoptosis study indicated that the particles provided programmed release behavior as well as enhanced tumor inhibition efficacy compared with free drug administration and monophasic particles. This study proposed a facile and one-step fabrication method of multiphasic particles possessing programmed and triggered drug release kinetics, and may be the first attempt to generate triple-phase particles with phase separation method in a droplet microfluidic chip.
CT examination utilizes computational functions to achieve tomography of the human body based on the basic characteristics of X-rays, thereby unavoidably producing ionizing radiation that can cause damage to the human body. So, it is not applicable to pregnant women and children; Repeated exposure to CT irradiation in a short period of time may cause leukocytosis, fatigue, dizziness, vomiting and other symptoms. In particular, pregnant women, neonates and patients with extreme weakness are more likely to develop malformation, cancers and other adverse effects after exposure to radiation. However, endoscopic examination will induce physical damage to a certain extent, leading to potential risks of inflammation, and its process will cause fear and discomfort to patients, among which children are more likely to show fear than adults. In addition, there are many practical operation problems for endoscopic examination. So, it is not an ideal method. The medical infrared thermal imaging instrument adopts the high-tech infrared detection technology, which has no radiation and does not touch the human body. When the human body is diseased, the heat balance of the diseased part will also be destroyed. The infrared thermal imaging captures this imbalance based on the infrared rays from the human body to form an infrared thermogram, which reflects the temperature characteristics of the human body and thus will not harm the human body. The instrument has now already passed the clinical verification. Infrared thermography can well reflect the presentation of sinusitis, especially performs well in distinguishing whether the inflammation is acute or chronic. And the expression on infrared thermography is better than CT. Combined with artificial intelligence imaging algorithms, it can achieve feature analysis at the level of a single pixel and provide doctors with more detailed and accurate reference data, so as to implement efficient auxiliary diagnosis. The instrument is suitable for various types of hospitals and medical institutions, and even for home medical diagnosis when it is combined with a remote auxiliary diagnosis system.
Background: The purpose of our research was to establish a gene signature and determine the prognostic value of m 6 A methylation regulators in cutaneous melanoma and WTAP as a protective gene in cutaneous melanoma prognosis, we also evaluated gene mutations in cutaneous melanoma. Methods: We downloaded the RNA-seq transcriptome data and the clinical information for cutaneous melanoma patients from the GTEx and TCGA databases. Consensus clustering analysis was applied to divide the samples into two groups. Then the least absolute shrinkage and selection operator (LASSO) analyses were conducted to construct a risk signature, and we use external and internal datasets to verify its predictive value. We further searched the cBioPortal tools to detect genomic alterations and WTAP mutations. Finally, WTAP was further identified as a prognostic factor, and the related mechanisms mediated by WTAP were predicted by gene set enrichment analysis (GSEA). Experimental validations and have been further carried out. Results: Notably, m 6 A RNA methylation regulators play significant roles in tumorigenesis and development. In total, we selected three subtypes of cutaneous melanoma according to consensus clustering of the m 6 A RNA methylation regulators, and the stage of cutaneous melanoma was proven to be related to the subtypes. The Cox regression and LASSO analyses built a risk signature including ELF3, ZC3H13 and WTAP. The prognostic value of the risk signature in internal and external datasets have been proven then. The whole-genome and selected gene WTAP mutations were further explored. WTAP as a single prognostic factor was also explored and found to serve as an independent protective prognostic factor. Conclusions: Our study constructed a stable risk signature composed of m 6 A RNA methylation regulators in cutaneous melanoma. Moreover, WTAP was identified as a valuable prognostic factor and potential molecular target for cutaneous melanoma treatment.
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