Trajectories of hepatic steatosis and incidence of cardiovascular disease over a 29-year follow-up.

AIM: To examine the dynamic change in hepatic steatosis status during repeated assessments over time, and its potential impact on the risk of developing cardiovascular disease (CVD). METHODS: We assessed trajectories of hepatic steatosis and other metabolic disorders in 3134 middle-aged adults undergoing longitudinal assessment of ultrasonography during a pre-baseline period (1993-2009) in a population-based cohort study of liver health. Subsequently, we determined the association of hepatic steatosis trajectories with the incidence of CVD among 2185 CVD-free individuals, followed until 2021. Metabolic risk factors and cardiovascular events (including coronary heart disease and stroke) were determined through medical examination and linkage with nationwide health databases. RESULTS: We identified three discrete trajectories of hepatic steatosis according to changing pattern over time through group-based trajectory modeling: "stable, non-steatosis" (n = 1298), "intermittent" (n = 921), and "persistent steatosis" (n = 915). During the pre-baseline period, hepatic steatosis trajectories were associated with trajectories of developing diabetes and hypertension, and persistent steatosis (vs. other trajectories) was associated with higher risks and rapidly progressive disease patterns. At a median 13.6 years of follow-up, 629 CVD events occurred. A persistent (vs. non-steatosis: HR 1.44, 95% CI 1.17-1.76), but not intermittent, steatosis pattern predicted the future risk of CVD, after adjustment for age, sex, smoking, and obesity. This association was independent of genetic background, and remained after accounting for pre-baseline body-mass index, other cardiometabolic risk factors, Framingham risk score, medications, and hepatic fibrosis score. CONCLUSIONS: The persistence of hepatic steatosis is associated with trajectories of metabolic disorder development and increased risk of CVD. These data have important implications for practice and further research.

Multi-modal analysis for accurate prediction of preoperative stage and indications of optimal treatment in gastric cancer.

BACKGROUND: Accurate preoperative clinical staging of gastric cancer helps determine therapeutic strategies. However, no multi-category grading models for gastric cancer have been established. This study aimed to develop multi-modal (CT/EHRs) artificial intelligence (AI) models for predicting tumor stages and optimal treatment indication based on preoperative CT images and electronic health records (EHRs) in patients with gastric cancer. METHODS: This retrospective study enrolled 602 patients with a pathological diagnosis of gastric cancer from Nanfang hospital retrospectively and divided them into training (n = 452) and validation sets (n = 150). A total of 1326 features were extracted of which 1316 radiomic features were extracted from the 3D CT images and 10 clinical parameters were obtained from electronic health records (EHRs). Four multi-layer perceptrons (MLPs) whose input was the combination of radiomic features and clinical parameters were automatically learned with the neural architecture search (NAS) strategy. RESULTS: Two two-layer MLPs identified by NAS approach were employed to predict the stage of the tumor showed greater discrimination with the average ACC value of 0.646 for five T stages, 0.838 for four N stages than traditional methods with ACC of 0.543 (P value = 0.034) and 0.468 (P value = 0.021), respectively. Furthermore, our models reported high prediction accuracy for the indication of endoscopic resection and the preoperative neoadjuvant chemotherapy with the AUC value of 0.771 and 0.661, respectively. CONCLUSIONS: Our multi-modal (CT/EHRs) artificial intelligence models generated with the NAS approach have high accuracy for tumor stage prediction and optimal treatment regimen and timing, which could facilitate radiologists and gastroenterologists to improve diagnosis and treatment efficiency.

The regulatory roles of microRNAs toward pathogenesis and treatments in Huntington's disease.

Huntington's disease (HD) is one of neurodegenerative diseases, and is defined as a monogenetic disease due to the mutation of Huntingtin gene. This disease affects several cellular functions in neurons, and further influences motor and cognitive ability, leading to the suffering of devastating symptoms in HD patients. MicroRNA (miRNA) is a non-coding RNA, and is responsible for gene regulation at post-transcriptional levels in cells. Since one miRNA targets to several downstream genes, it may regulate different pathways simultaneously. As a result, it raises a potential therapy for different diseases using miRNAs, especially for inherited diseases. In this review, we will not only introduce the update information of HD and miRNA, but also discuss the development of potential miRNA-based therapy in HD. With the understanding toward the progression of miRNA studies in HD, we anticipate it may provide an insight to treat this devastating disease, even applying to other genetic diseases.

Increase in Akkermansiaceae in Gut Microbiota of Prostate Cancer-Bearing Mice.

Gut microbiota are reported to be associated with many diseases, including cancers. Several bacterial taxa have been shown to be associated with cancer development or response to treatment. However, longitudinal microbiota alterations during the development of cancers are relatively unexplored. To better understand how microbiota changes, we profiled the gut microbiota composition from prostate cancer-bearing mice and control mice at five different time points. Distinct gut microbiota differences were found between cancer-bearing mice and control mice. Akkermansiaceae was found to be significantly higher in the first three weeks in cancer-bearing mice, which implies its role in the early stage of cancer colonization. We also found that Bifidobacteriaceae and Enterococcaceae were more abundant in the second and last sampling week, respectively. The increments of Akkermansiaceae, Bifidobacteriaceae and Enterococcaceae were previously found to be associated with responses to immunotherapy, which suggests links between these bacteria families and cancers. Additionally, our function analysis showed that the bacterial taxa carrying steroid biosynthesis and butirosin and neomycin biosynthesis were increased, whereas those carrying naphthalene degradation decreased in cancer-bearing mice. Our work identified the bacteria taxa altered during prostate cancer progression and provided a resource of longitudinal microbiota profiles during cancer development in a mouse model.

United multi-task learning for abdominal contrast-enhanced CT synthesis through joint deformable registration.

Synthesizing abdominal contrast-enhanced computed tomography (CECT) images from non-enhanced CT (NECT) images is of great importance, in the delineation of radiotherapy target volumes, to reduce the risk of iodinated contrast agent and the registration error between NECT and CECT for transferring the delineations. NECT images contain structural information that can reflect the contrast difference between lesions and surrounding tissues. However, existing methods treat synthesis and registration as two separate tasks, which neglects the task collaborative and fails to address misalignment between images after the standard image pre-processing in training a CECT synthesis model. Thus, we propose an united multi-task learning (UMTL) for joint synthesis and deformable registration of abdominal CECT. Specifically, our UMTL is an end-to-end multi-task framework, which integrates a deformation field learning network for reducing the misalignment errors and a 3D generator for synthesizing CECT images. Furthermore, the learning of enhanced component images and the multi-loss function are adopted for enhancing the performance of synthetic CECT images. The proposed method is evaluated on two different resolution datasets and a separate test dataset from another center. The synthetic venous phase CECT images of the separate test dataset yield mean absolute error (MAE) of 32.78±7.27 HU, mean MAE of 24.15±5.12 HU on liver region, mean peak signal-to-noise rate (PSNR) of 27.59±2.45 dB, and mean structural similarity (SSIM) of 0.96±0.01. The Dice similarity coefficients of liver region between the true and synthetic venous phase CECT images are 0.96±0.05 (high-resolution) and 0.95±0.07 (low-resolution), respectively. The proposed method has great potential in aiding the delineation of radiotherapy target volumes.

miR-196a enhances polymerization of neuronal microfilaments through suppressing IMP3 and upregulating IGF2 in Huntington's disease.

Huntington's disease (HD) is one of the inheritable neurodegenerative diseases, and these diseases share several similar pathological characteristics, such as abnormal neuronal morphology. miR-196a is a potential target to provide neuroprotective functions, and has been reported to enhance polymerization of neuronal microtubules in HD. While microtubules and microfilaments are two important components of the neuronal cytoskeleton, whether miR-196a improves neuronal microfilaments is still unknown. Here, we identify insulin-like growth factor 2 mRNA binding protein 3 (IMP3), and show that miR-196a directly suppresses IMP3 to increase neurite outgrowth in neurons. In addition, IMP3 disturbs neurite outgrowth in vitro and in vivo, and worsens the microfilament polymerization. Moreover, insulin-like growth factor-II (IGF2) is identified as the downstream target of IMP3, and miR-196a downregulates IMP3 to upregulate IGF2, which increases microfilamental filopodia numbers and activates Cdc42 to increase neurite outgrowth. Besides, miR-196a increases neurite outgrowth through IGF2 in different HD models. Finally, higher expression of IMP3 and lower expression IGF2 are observed in HD transgenic mice and patients, and increase the formation of aggregates in the HD cell model. Taken together, miR-196a enhances polymerization of neuronal microfilaments through suppressing IMP3 and upregulating IGF2 in HD, supporting the neuroprotective functions of miR-196a through neuronal cytoskeleton in HD.

Image reconstruction in phase-contrast tomography exploiting the second-order statistical properties of the projection data.

X-ray phase-contrast tomography (PCT) methods seek to quantitatively reconstruct separate images that depict an object's absorption and refractive contrasts. Most PCT reconstruction algorithms generally operate by explicitly or implicitly performing the decoupling of the projected absorption and phase properties at each tomographic view angle by use of a phase-retrieval formula. However, the presence of zero-frequency singularity in the Fourier-based phase retrieval formulas will lead to a strong noise amplification in the projection estimate and the subsequent refractive image obtained using conventional algorithms like filtered backprojection (FBP). Tomographic reconstruction by use of statistical methods can account for the noise model and a priori information, and thereby can produce images with better quality over conventional filtered backprojection algorithms. In this work, we demonstrate an iterative image reconstruction method that exploits the second-order statistical properties of the projection data can mitigate noise amplification in PCT. The autocovariance function of the reconstructed refractive images was empirically computed and shows smaller and shorter noise correlation compared to those obtained using the FBP and unweighted penalized least-squares methods. Concepts from statistical decision theory are applied to demonstrate that the statistical properties of images produced by our method can improve signal detectability.