Machine learning applications in breast cancer survival and therapeutic outcome prediction based on multi-omic analysis.

The high heterogeneity within and between breast cancer patients complicates treatment determination and prognosis assessment. Treatment decision-making is influenced by various factors, such as tumor subtype, histological grade, and genotype, necessitating personalized treatment strategies. Prognostic outcomes vary significantly depending on patient-specific conditions. As a critical branch of artificial intelligence, machine learning efficiently handles large datasets and automates decision-making processes. The introduction of machine learning offers new solutions for breast cancer treatment selection and prognosis assessment. In the field of cancer therapy, traditional methods for predicting treatment and survival outcomes often rely on single or few biomarkers, limiting their ability to capture the complexity of biological processes comprehensively. Machine learning analyzes patients' multi-omic data and the intricate patterns of variations during cancer initiation and progression to predict patients' survival and treatment outcomes. Consequently, it facilitates the selection of appropriate therapeutic interventions to implement early intervention and improve treatment efficacy for patients. Here, we first introduce common machine learning methods, and then elaborate on the application of machine learning in the field of survival prediction and prognosis from two aspects: evaluating survival and predicting treatment outcomes for breast cancer patients. The aim is to provide breast cancer patients with precise treatment strategies to improve therapeutic outcomes and quality of life.

The regulatory mechanisms by enhancers during cancer initiation and progression.

Enhancer is a DNA sequence, and mainly acts in cis to regulate gene transcription. Due to the uncertainty in both location and distance between enhancers and their target genes, it is more complex and difficult to study the underlying regulatory mechanism of enhancers. Accumulating evidences indicate that enhancers are closely associated with the occurrence and development of diseases, such as cancer. Therefore, the studies of enhancers in cancer will be helpful to deeply unravel cancer pathogenesis and to promote the development of antitumor drugs. The related research is with great social significance and economic value. Currently, the identification of enhancers is insufficient. The regulatory mechanisms by enhancers during the initiation and progression of cancer and other diseases have not been fully delineated. In this review, we provide an overview of enhancers, super enhancers and their properties, followed by a description of enhancer prediction and identification at the genome-wide level. Finally, we summarize the regulatory roles of enhancers during diseases such as cancer in recent years, thereby providing a reference for the future exploration on enhancer regulatory mechanisms as well as cancer diagnosis and treatment.

Simultaneous determination of primary particle size distribution and thermal accommodation coefficient of soot aggregates using low-fluence LII.

For the ill-posed inverse problem of LII-based nanoparticle size measurement, recovered primary particle size distribution (PPSD) is sensitive to the uncertainty of LII model parameters. In the absence of reliable prior knowledge, the thermal accommodation coefficient (TAC) and fractal-dependent shielding factor are often required to be inferred simultaneously with the PPSD. In the simplified LII model for low fluence regime, TAC and fractal-dependent shielding factor are combined to define a new fractal-dependent TAC. The present study theoretically verified the feasibility of inferring PPSD and fractal-dependent TAC from the normalized LII signals. Moreover, the inversion is independent of prior knowledge of most full LII model parameters, which is attributed to low laser fluence, normalized signal, and fractal-dependent TAC.

Retrieval of fractal dimension and size distribution of non-compact soot aggregates from relative intensities of multi-wavelength angular-resolved light scattering.

A new technique is developed to retrieve the fractal dimension and size distribution of soot aggregates simultaneously from the relative intensities of multi-wavelength angular-resolved light scattering. Compared with other techniques, the main advantage of this method is its independence of knowing complex refractive index, number density of aggregate, fractal prefactor and primary particle diameter. The forward light scattering procedure of soot aggregate is described by Rayleigh-Debye-Gans polydisperse fractal aggregate (RDG-PFA) scattering theory, and the retrieval process is performed by using the covariance matrix adaption-evolution strategy algorithm (CMA-ES). Three different measurement models, i.e. absolute scattering and transmittance, absolute scattering, relative scattering (RS), are investigated in present research. Numerical experiments have been performed to test the feasibility of the CMA-ES algorithm. Combined with the multi-wavelength RDG-PFA strategy, the retrieval accuracy of soot aggregate size distribution is proved to be more effectively by using the RS model. Satisfactory results under 10% Gaussian measurement noise have demonstrated the feasibility of the proposed method.

[Research progress of risk factors of adjacent segment degeneration after anterior cervical discectomy and fusion].

Anterior cervical discectomy and fusion (ACDF) has achieved good clinical results since it was used in clinic, and is considered as the gold standard for the treatment of cervical spondylosis. However, more and more attention has been paid to adjacent segment degeneration(ASDeg) after fusion, and the debate about its pathogenesis is mainly focused on the bio-machanical stress changes of adjacent segments caused by fusion and the result of the natural aging process. The occurrence of ASDeg after fusion seriously affect the med-and long-term outcome of surgery, and some patients even need secondary surgery. In order to reduce or even avoid the occurrence of ASDeg, many new techniques have emerged in clinic, such as artificial disc replacement with preservation of motor segments, emerging cell transplantation technology and so on, but the clinical effect still needs to be confirmed by a large number of studies. Therefore, finding the risk factors of ASDeg after fusion is of great significance for fusion surgery on the clinical work. At present, there is still no unified overview of the research on the risk factors of ASDeg. This article will review the research progress and corresponding countermeasures of the risk factors of ASDeg after ACDF, in order to guide the clinical application.