Gelatin tannate for acute childhood gastroenteritis: a randomized, single-blind controlled trial

Background Oral rehydration therapy is the recommended treatment for acute childhood gastroenteritis. The aim of this study was to assess the efficacy and safety of gelatin tannate plus oral rehydration compared with oral rehydration alone. Methods We conducted a multicenter, parallel, randomized, controlled, single-blind, prospective, open-label trial. A central randomization center used computer generated tables to allocate treatments. The study was performed in two medical centers in Italy. Sixty patients 3–72 months of age with acute gastroenteritis were recruited (median age 18 months; age range 3–66 months): 29 received an oral rehydration solution (ORS) and 31 an ORS plus gelatin tannate (ORS ? G). The primary outcome was the number of bowel movements 48 and 72 h after initiating treatment. Secondary outcomes were: duration of diarrhea, stool characteristics and adverse events. Results No patient was lost at follow-up. No significant difference in the number of bowel movements after 48 h was reported (2.7 ± 1.3 ORS ? G; 3.2 ± 0.8 ORS; p = 0.06), although the ORS ? G group showed a significant improvement in stool consistency (3.7 ± 1.0 vs. 4.3 ± 0.8; p = 0.005). At 72 h, a significant reduction in bowel movements was reported in the ORS ? G group compared with the ORS group (1.0 ± 1.4 vs. 2.0 ± 1.7; p = 0.01). Mean duration of diarrhea was significantly lower in the ORS ? G group than in the ORS only group (76.8 ± 19.2 vs. 108 ± 24.0 h; p.0001). No adverse events were reported. Conclusions Gelatin tannate added to oral rehydration in children with acute diarrhea was associated with a significant decrease in bowel movements at 72 h, with an early improvement in the stool consistency and shorter disease duration

HWD: A Novel Evaluation Score for Styled Handwritten Text Generation

Styled Handwritten Text Generation (Styled HTG) is an important task in document analysis, aiming to generate text images with the handwriting of given reference images. In recent years, there has been significant progress in the development of deep learning models for tackling this task. Being able to measure the performance of HTG models via a meaningful and representative criterion is key for fostering the development of this research topic. However, despite the current adoption of scores for natural image generation evaluation, assessing the quality of generated handwriting remains challenging. In light of this, we devise the Handwriting Distance (HWD), tailored for HTG evaluation. In particular, it works in the feature space of a network specifically trained to extract handwriting style features from the variable-lenght input images and exploits a perceptual distance to compare the subtle geometric features of handwriting. Through extensive experimental evaluation on different word-level and line-level datasets of handwritten text images, we demonstrate the suitability of the proposed HWD as a score for Styled HTG. The pretrained model used as backbone will be released to ease the adoption of the score, aiming to provide a valuable tool for evaluating HTG models and thus contributing to advancing this important research area.Comment: Accepted at BMVC202

Compressed Volumetric Heatmaps for Multi-Person 3D Pose Estimation

In this paper we present a novel approach for bottom-up multi-person 3D human pose estimation from monocular RGB images. We propose to use high resolution volumetric heatmaps to model joint locations, devising a simple and effective compression method to drastically reduce the size of this representation. At the core of the proposed method lies our Volumetric Heatmap Autoencoder, a fully-convolutional network tasked with the compression of ground-truth heatmaps into a dense intermediate representation. A second model, the Code Predictor, is then trained to predict these codes, which can be decompressed at test time to re-obtain the original representation. Our experimental evaluation shows that our method performs favorably when compared to state of the art on both multi-person and single-person 3D human pose estimation datasets and, thanks to our novel compression strategy, can process full-HD images at the constant runtime of 8 fps regardless of the number of subjects in the scene

Dress Code: High-Resolution Multi-Category Virtual Try-On

Image-based virtual try-on strives to transfer the appearance of a clothing item onto the image of a target person. Existing literature focuses mainly on upper-body clothes (e.g. t-shirts, shirts, and tops) and neglects full-body or lower-body items. This shortcoming arises from a main factor: current publicly available datasets for image-based virtual try-on do not account for this variety, thus limiting progress in the field. In this research activity, we introduce Dress Code, a novel dataset which contains images of multi-category clothes. Dress Code is more than 3x larger than publicly available datasets for image-based virtual try-on and features high-resolution paired images (1024 x 768) with front-view, full-body reference models. To generate HD try-on images with high visual quality and rich in details, we propose to learn fine-grained discriminating features. Specifically, we leverage a semantic-aware discriminator that makes predictions at pixel-level instead of image- or patch-level. The Dress Code dataset is publicly available at https://github.com/aimagelab/dress-code

Domain Translation with Conditional GANs: from Depth to RGB Face-to-Face

Can faces acquired by low-cost depth sensors be useful to see some characteristic details of the faces? Typically the answer is not. However, new deep architectures can generate RGB images from data acquired in a different modality, such as depth data. In this paper we propose a new Deterministic Conditional GAN, trained on annotated RGB-D face datasets, effective for a face-to-face translation from depth to RGB. Although the network cannot reconstruct the exact somatic features for unknown individual faces, it is capable to reconstruct plausible faces; their appearance is accurate enough to be used in many pattern recognition tasks. In fact, we test the network capability to hallucinate with some Perceptual Probes, as for instance face aspect classification or landmark detection. Depth face can be used in spite of the correspondent RGB images, that often are not available for darkness of difficult luminance conditions. Experimental results are very promising and are as far as better than previous proposed approaches: this domain translation can constitute a new way to exploit depth data in new future applications

Dress Code: High-Resolution Multi-Category Virtual Try-On

Image-based virtual try-on strives to transfer the appearance of a clothing item onto the image of a target person. Prior work focuses mainly on upper-body clothes (e.g. t-shirts, shirts, and tops) and neglects full-body or lower-body items. This shortcoming arises from a main factor: current publicly available datasets for image-based virtual try-on do not account for this variety, thus limiting progress in the field. To address this deficiency, we introduce Dress Code, which contains images of multi-category clothes. Dress Code is more than 3x larger than publicly available datasets for image-based virtual try-on and features high-resolution paired images (1024 x 768) with front-view, full-body reference models. To generate HD try-on images with high visual quality and rich in details, we propose to learn fine-grained discriminating features. Specifically, we leverage a semantic-aware discriminator that makes predictions at pixel-level instead of image- or patch-level. Extensive experimental evaluation demonstrates that the proposed approach surpasses the baselines and state-of-the-art competitors in terms of visual quality and quantitative results. The Dress Code dataset is publicly available at https://github.com/aimagelab/dress-code.Comment: Dress Code - Video Demo: https://www.youtube.com/watch?v=qr6TW3uTHG

Effect of allogeneic intraoperative blood transfusion on survival in patients treated with radical cystectomy for nonmetastatic bladder cancer: Results from a single high-volume institution

Transfusion has been related to poor survival after surgery in several cancers. Recently, timing of transfusion has been proposed as crucial in the determination of poor survival expectanies after surgery, in fact, intra- operative but not postoperative transfusion were found to be related. We confirmed these findings in patients who underwent radical cystectomy because of bladder cancer; physicians should avoid use of transfusion intraoperatively. Background: Previous studies have demonstrated that perioperative blood transfusion (BT) is associated with a significantly increased risk of cancer recurrence and mortality after radical cystectomy (RC). Recently, it was shown for the first time that intraoperative transfusion has a detrimental effect on cancer survival. The aim of the current study was to validate this finding in a single European institution. Patients and Methods: The study focused on 1490 consecutive nonmetastatic bladder cancer patients treated with RC at a single tertiary care referral center between January 1990 and August 2013. KaplaneMeier analyses and Cox regression analyses were used to assess the effect of timing of BT administration (no transfusion vs. intraoperative transfusion vs. postoperative transfusion vs. intra- operative and postoperative transfusion) on cancer-specific mortality (CSM), overall mortality (OM), and disease recurrence. Results: Mean age at the time of RC was 67 years. Overall, 322 (21.6%) patients received intraoperative BT and 97 (6.5%) received postoperative BT. At a mean follow-up time of 125 months (median, 110 months), the 5- and 10-year CSM rate was 846 (58%) and 715 (48%), respectively. In multivariable analyses patients who received intraoperative BT had greater risk of disease recurrence (hazard ratio [HR], 1.24; P .2). Conclusion: Our study confirms that intraoperative, but not postoperative BT, are related to a detrimental effect on survival after RC. These results should be take into account by physicians to administer BT using the correct timing