Abstract Introduction: Tumor size constitutes a very important staging factor among patients afflicted by solid tumors, and was closely associated with the overall survival (OS). Nonetheless, the prognostic import of tumor size in gastric cancer (GC) remains shrouded in uncertainty. Method: A multivariable-adjusted hazard ratio (HR) along with a 95% confidence interval (CI) was computed for GC using Cox proportional hazard regression models. To assess the non-linear association between tumor size and OS, we employed a restricted cubic spline regression analysis. Additionally, a two-piece-wise Cox proportional hazard model was utilized to determine the threshold effect. The efficacy prediction of tumor size was tested by ROC curve. Results: A cohort comprising 2,012 gastric cancer patients who had undergone gastrectomy was included in our secondary analysis, sourced from a multicenter study conducted in Korea. Also, we found a nonlinear association between tumor size and OS at the turning point as 5.7 (95%CI: 5.1, 6.8). The HR for mortality was 1.50 (95%CI: 1.38, 1.64) for tumors size <5.7, while 1.09 (95%CI: 1.05, 1.13) with size ≥ 5.7. There was still a significant non-linear relationship between OS and size after adjusting for potential confounding factors (P=0.001). In addition, Besides, a significantly higher HR was found in young GC patients(≤45: 1.33; 95%CI:1.24, 1.41; >45, 1.16; 95%CI: 1.13,1.19; P for interaction = 0.0004 ). Conclusions: Tumor size was non-linear associated with survival for patients receiving gastrectomy. It might have the higher predictive power in young GC.
Higher-resolution biopsy slice images reveal many details, which are widely used in medical practice. However, taking high-resolution slice images is more costly than taking low-resolution ones. In this paper, we propose a joint framework containing a novel transfer learning strategy and a deep super-resolution framework to generate high-resolution slice images from low-resolution ones. The super-resolution framework called SRFBN+ is proposed by modifying a state-of-the-art framework SRFBN. Specifically, the structure of the feedback block of SRFBN was modified to be more flexible. Besides, it is challenging to use typical transfer learning strategies directly for the tasks on slice images, as the patterns on different types of biopsy slice images are varying. To this end, we propose a novel transfer learning strategy, called Channel Fusion Transfer Learning (CF-Trans). CF-Trans builds a middle domain by fusing the data manifolds of the source domain and the target domain, serving as a springboard for knowledge transfer. Thus, in the transfer learning setting, SRFBN+ can be trained on the source domain and then the middle domain and finally the target domain. Experiments on biopsy slice images validate SRFBN+ works well in generating super-resolution slice images, and CF-Trans is an efficient transfer learning strategy.
Aspergillus is a genus of filamentous, ubiquitous, and opportunistic pathogenic fungi.Patients with underlying lung disease are susceptible to Aspergillus due to their immunodeficiency, but the symptoms become atypical as other lung diseases are contracted (1-3).Aspergillus fumigatus, A. flavus, and A. niger are the primary pathogenic species in most literatures (4, 5).The principal forms of pulmonary aspergillosis (PA) are allergic bronchopulmonary aspergillosis (ABPA), chronic (and saprophytic) pulmonary aspergillosis (CPA), and invasive pulmonary aspergillosis (IPA).In non-neutropenic patients, various forms are considered a semi-continuous spectrum of aspergillosis (4).The analysis of the clinical features can deepen our understanding of the spectrum of clinical manifestations of Aspergillus infection and provide references for its diagnosis.
Abstract Background Aortic diameter is a critical parameter for the diagnosis of aortic dilated diseases. Aortic dilation has some common risk factors with cardiovascular diseases. This study aimed to investigate potential influence of traditional cardiovascular risk factors and the measures of subclinical atherosclerosis on aortic diameter of specific segments among adults. Methods Four hundred and eight patients with cardiovascular risk factors were prospectively recruited in the observational study. Comprehensive transthoracic M-mode, 2-dimensional Doppler echocardiographic studies were performed using commercial and clinical diagnostic ultrasonography techniques. The aortic dimensions were assessed at different levels: (1) the annulus, (2) the mid-point of the sinuses of Valsalva, (3) the sinotubular junction, (4) the ascending aorta at the level of its largest diameter, (5) the transverse arch (including proximal arch, mid arch, distal arch), (6) the descending aorta posterior to the left atrium, and (7) the abdominal aorta just distal to the origin of the renal arteries. Multivariable linear regression analysis was used for evaluating aortic diameter-related risk factors, including common cardiovascular risk factors, co-morbidities, subclinical atherosclerosis, lipid profile, and hematological parameters. Results Significant univariate relations were found between aortic diameter of different levels and most traditional cardiovascular risk factors. Carotid intima-media thickness was significantly correlated with diameter of descending and abdominal aorta. Multivariate linear regression showed potential effects of age, sex, body surface area and some other cardiovascular risk factors on aortic diameter enlargement. Among them, high-density lipoprotein cholesterol had a significantly positive effect on the diameter of ascending and abdominal aorta. Diastolic blood pressure was observed for the positive associations with diameters of five thoracic aortic segments, while systolic blood pressure was only independently related to mid arch diameter. Conclusion Aortic segmental diameters were associated with diastolic blood pressure, high-density lipoprotein cholesterol, atherosclerosis diseases and other traditional cardiovascular risk factors, and some determinants still need to be clarified for a better understanding of aortic dilation diseases.
Glutathione peroxidase 4 (GPX4) is a promising target to induce ferroptosis for the treatment of triple-negative breast cancer (TNBC). We designed and synthesized a novel series of covalent GPX4 inhibitors based on RSL3 and ML162 by structural integration and simplification strategies. Among them, compound C18 revealed a remarkable inhibitory activity against TNBC cells and significantly inhibited the activity of GPX4 compared to RSL3 and ML162. Moreover, it was identified that C18 could notably induce ferroptosis with high selectivity by increasing the accumulation of lipid peroxides (LPOs) in cells. Further study demonstrated that C18 covalently bound to the Sec46 of GPX4. Surprisingly, C18 exhibited an outstanding potency of tumor growth inhibition in the MDA-MB-231 xenograft model with a TGI value of 81.0%@20 mg/kg without obvious toxicity. Overall, C18 could be a promising GPX4 covalent inhibitor to induce ferroptosis for the treatment of TNBC.
An improved Retinex fusion image enhancement algorithm is proposed for the traditional image denoising methods and problems of halo enlargement and image overexposure after image enhancement caused by the existing Retinex algorithm. First, a homomorphic filtering algorithm is used to enhance each RGB component of the underground coal mine surveillance image and convert the image from RGB space to HSV space. Second, bilateral filtering and multi-scale retinex with color restoration (MSRCR) fusion algorithms are used to enhance the luminance V component while keeping the hue H component unchanged. Third, adaptive nonlinear stretching transform is used for the saturation S-component. Last, the three elements are combined and converted back to RGB space. MATLAB simulation experiments verify the superiority of the improved algorithm. Based on the same dataset and experimental environment, the improved algorithm has a more uniform histogram distribution than the multi-scale Retinex (msr) algorithm and MSRCR algorithm through comparative experiments. At the same time, the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), standard deviation, average gradient, mean value, and colour picture information entropy of the images were improved by 8.28, 0.15, 4.39, 7.38, 52.92 and 2.04, respectively, compared to the MSR algorithm, and 3.97, 0.02, 34.33, 60.46, 26.21, and 1.33, respectively, compared to the MSRCR algorithm. The experimental results show that the image quality, brightness and contrast of the images enhanced by the improved Retinex algorithm are significantly enhanced, and the amount of information in the photos increases, the halo and overexposure in the images are considerably reduced, and the anti-distortion performance is also improved.
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