Notes

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4% lobectomy and 13.6% pneumonectomy) with mediastinal lymph node dissection. At a median follow-up of 68.4 months, a low SII (<1260) at diagnosis was independently associated with an improved OS (HR 0.448, p=0.004), DFS (HR 0.366, p<0.001), and FFR (HR 0.325, p=0.002).

We identified that a low SII was associated with improved OS, DFS, and FFR in patients undergoing trimodality therapy for stage III NSCLC. The interplay of the immune system and lung cancer outcomes remains an active area of investigation for which further study is warranted.
We identified that a low SII was associated with improved OS, DFS, and FFR in patients undergoing trimodality therapy for stage III NSCLC. The interplay of the immune system and lung cancer outcomes remains an active area of investigation for which further study is warranted.
Arteriovenous fistula (AVF) failure due to thrombosis is a major cause of morbidity in patients undergoing regular hemodialysis (HD). Advanced glycation end products (AGEs) and their receptor (RAGE) might contribute to inflammation, neointimal hyperplasia, and thrombosis. RAGE has a C-truncated secretory receptor form, called soluble RAGE (sRAGE). In this study, we aimed to evaluate the association of serum sRAGE with AVF failure due to thrombosis in HD patients.

Eighty-eight prevalent HD patients with functional AVF were included in the study. The presence of stenosis, clinical and laboratory data, and serum sRAGE was evaluated at inclusion. sRAGE concentration was measured by a competitive enzyme-linked immunosorbent assay, and stenosis was detected by ultrasound. Patients were prospectively followed up for 36 months. During this period, AVF failure (defined as the absence of blast or palpable thrill and impossible cannulation with 2 needles because of complete thrombosis) was noted and thrombosis was certified by ultrasound examination.

During follow-up, 16 (18.18%) patients lost their vascular access due to thrombosis. In multivariate Cox regression analysis, sRAGE was a significant predictor of vascular access thrombosis (hazard ratio = 1.15, 95% confidence interval 1.03-1.25, p = 0.012). Kaplan-Meier analysis showed a significantly lower AVF patency time in patients with sRAGE >16.78 ng/mL than those with sRAGE <16.78 ng/mL (p = 0.02). In the subgroup of patients with stenosis at baseline, sRAGE, serum albumin, obesity, and ischemic heart disease were associated with thrombosis.

In our study, baseline, systemic sRAGE is associated with the occurrence of thrombosis of AVF, and this marker has a significant impact on AVF survival.
In our study, baseline, systemic sRAGE is associated with the occurrence of thrombosis of AVF, and this marker has a significant impact on AVF survival.
The aim of the study was to compare the performances of cardiothoracic diameter ratio (CTR) and middle cerebral artery peak systolic velocity (MCA-PSV) in predicting fetal hemoglobin (Hb) Bart's disease and identify the best CTR cut-off for each gestational period.

Pregnancies at risk of fetal Hb Bart's disease (gestational ages of 12-36 weeks) were prospectively recruited to undergo ultrasound examination. The measurements of CTR and MCA-PSV were performed and recorded before invasive diagnosis.

During the study period (2005-2019), a total of 1,717 pregnancies at risk of fetal Hb Bart's disease met the inclusion criteria and were available for analysis, including 329 (19.2%) fetuses with Hb Bart's disease. The mean gestational age at the time of diagnosis was 19.30 ± 5.6 weeks, ranging from 12 to 36 weeks. The overall performance of CTR Z-scores is superior to that of MCA-PSV multiple of median (MoM) values; area under curve of 0.866 versus 0.711, p value <0.001. The diagnostic indices of CTR and MCA-PSV are increased with gestational age. Based on receiver operating characteristic curves of CTR Z-scores, the best cut-off points of CTR at 12-14, 15-17, 18-20, 21-23, and ≥24 weeks are 0.48, 0.49, 0.50, 0.51, and 0.54, respectively. The best cut-off of MCA-PSV is 1.3 MoM, giving the best performance at 21-23 weeks with a sensitivity of 91.8% and specificity of 85.5%.

The performance of CTR is much better than MCA-PSV in predicting fetal anemia caused by Hb Bart's disease. Nevertheless, whether this can be reproduced in anemia due to other causes, like isoimmunization, is yet to be explored.
The performance of CTR is much better than MCA-PSV in predicting fetal anemia caused by Hb Bart's disease. Nevertheless, whether this can be reproduced in anemia due to other causes, like isoimmunization, is yet to be explored.
Studies described an increased frequency of hypertensive disorders of pregnancy (HDP) after a COVID-19 episode. There is limited evidence about SARS-CoV-2 viral load in placenta. This study aimed to investigate the relationship between SARS-CoV-2 viral load in the placenta and clinical development of HDP after COVID-19 throughout different periods of gestation.

This is a case-control study in women with and without gestational hypertensive disorders after SARS-CoV-2 infection diagnosed by RT-PCR during pregnancy. Patients were matched by gestational age at the moment of COVID-19 diagnosis. We performed an analysis of SARS-CoV-2 RNA levels in placenta.

A total of 28 women were enrolled. Sixteen patients were diagnosed with COVID-19 during the third trimester and the remaining 12 patients in the other trimesters. Ten placentas (35.7%) were positive for SARS-CoV-2, 9 of them (9/14, 64.3%) belonged to the HDP group versus 1 (1/14, 7.2%) in the control group (p = 0.009). Those cases with the highest loads ofrough persistent placental infection and resulting placental damage.Magnetic resonance imaging (MRI) allows accurate and reliable organ delineation for many disease sites in radiation therapy because MRI is able to offer superb soft-tissue contrast. Manual organ-at-risk delineation is labor-intensive and time-consuming. This study aims to develop a deep-learning-based automated multi-organ segmentation method to release the labor and accelerate the treatment planning process for head-and-neck (HN) cancer radiotherapy. A novel regional convolutional neural network (R-CNN) architecture, namely, mask scoring R-CNN, has been developed in this study. In the proposed model, a deep attention feature pyramid network is used as a backbone to extract the coarse features given by MRI, followed by feature refinement using R-CNN. The final segmentation is obtained through mask and mask scoring networks taking those refined feature maps as input. With the mask scoring mechanism incorporated into conventional mask supervision, the classification error can be highly minimized in conventional mask R-CNN architecture. A cohort of 60 HN cancer patients receiving external beam radiation therapy was used for experimental validation. Five-fold cross-validation was performed for the assessment of our proposed method. The Dice similarity coefficients of brain stem, left/right cochlea, left/right eye, larynx, left/right lens, mandible, optic chiasm, left/right optic nerve, oral cavity, left/right parotid, pharynx, and spinal cord were 0.89 ± 0.06, 0.68 ± 0.14/0.68 ± 0.18, 0.89 ± 0.07/0.89 ± 0.05, 0.90 ± 0.07, 0.67 ± 0.18/0.67 ± 0.10, 0.82 ± 0.10, 0.61 ± 0.14, 0.67 ± 0.11/0.68 ± 0.11, 0.92 ± 0.07, 0.85 ± 0.06/0.86 ± 0.05, 0.80 ± 0.13, and 0.77 ± 0.15, respectively. After the model training, all OARs can be segmented within 1 min.Over the last few decades, breath analysis using electronic nose (eNose) technology has become a topic of intense research, as it is both non-invasive and painless, and is suitable for point-of-care use. To date, however, only a few studies have examined nasal air. selleck compound As the air in the oral cavity and the lungs differs from the air in the nasal cavity, it is unknown whether aspirated nasal air could be exploited with eNose technology. Compared to traditional eNoses, differential mobility spectrometry uses an alternating electrical field to discriminate the different molecules of gas mixtures, providing analogous information. This study reports the collection of nasal air by aspiration and the subsequent analysis of the collected air using a differential mobility spectrometer. We collected nasal air from ten volunteers into breath collecting bags and compared them to bags of room air and the air aspirated through the device. Distance and dissimilarity metrics between the sample types were calculated and statistical significance evaluated with Kolmogorov-Smirnov test. After leave-one-day-out cross-validation, a shrinkage linear discriminant classifier was able to correctly classify 100% of the samples. The nasal air differed (p less then 0.05) from the other sample types. The results show the feasibility of collecting nasal air by aspiration and subsequent analysis using differential mobility spectrometry, and thus increases the potential of the method to be used in disease detection studies.Objective.Subtype classification plays a guiding role in the clinical diagnosis and treatment of non-small-cell lung cancer (NSCLC). However, due to the gigapixel of whole slide images (WSIs) and the absence of definitive morphological features, most automatic subtype classification methods for NSCLC require manually delineating the regions of interest (ROIs) on WSIs.Approach.In this paper, a weakly supervised framework is proposed for accurate subtype classification while freeing pathologists from pixel-level annotation. With respect to the characteristics of histopathological images, we design a two-stage structure with ROI localization and subtype classification. We first develop a method called multi-resolution expectation-maximization convolutional neural network (MR-EM-CNN) to locate ROIs for subsequent subtype classification. The EM algorithm is introduced to select the discriminative image patches for training a patch-wise network, with only WSI-wise labels available. A multi-resolution mechanism is designed for fine localization, similar to the coarse-to-fine process of manual pathological analysis. In the second stage, we build a novel hierarchical attention multi-scale network (HMS) for subtype classification. HMS can capture multi-scale features flexibly driven by the attention module and implement hierarchical features interaction.Results.Experimental results on the 1002-patient Cancer Genome Atlas dataset achieved an AUC of 0.9602 in the ROI localization and an AUC of 0.9671 for subtype classification.Significance.The proposed method shows superiority compared with other algorithms in the subtype classification of NSCLC. The proposed framework can also be extended to other classification tasks with WSIs.We study the non-adiabatic dynamics of a typical symmetry-protected topological (SPT) phase-the Haldane insulator (HI) phase with broken bond-centered inversion. By continuously breaking the middle chain, we find the gap closes at a critical point in the deep HI regime with a change of particle number partition of the left or right system. The adiabatic evolution fails at this critical point and we show how to predict the dynamics of the entanglement entropy near this point using a two-level model. These results show that one can find a critical regime where the entanglement measurement is relatively robust against perturbation that breaks the protecting symmetries in the HI. This is in contrast to the common belief that the SPT phases are fragile without the protecting symmetries.
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