W2S: Microscopy Data with Joint Denoising and Super-Resolution for Widefield to SIM Mapping

In fluorescence microscopy live-cell imaging, there is a critical trade-off between the signal-to-noise ratio and spatial resolution on one side, and the integrity of the biological sample on the other side. To obtain clean high-resolution (HR) images, one can either use microscopy techniques, such as structured-illumination microscopy (SIM), or apply denoising and super-resolution (SR) algorithms. However, the former option requires multiple shots that can damage the samples, and although efficient deep learning based algorithms exist for the latter option, no benchmark exists to evaluate these algorithms on the joint denoising and SR (JDSR) tasks. To study JDSR on microscopy data, we propose such a novel JDSR dataset, Widefield2SIM (W2S), acquired using a conventional fluorescence widefield and SIM imaging. W2S includes 144,000 real fluorescence microscopy images, resulting in a total of 360 sets of images. A set is comprised of noisy low-resolution (LR) widefield images with different noise levels, a noise-free LR image, and a corresponding high-quality HR SIM image. W2S allows us to benchmark the combinations of 6 denoising methods and 6 SR methods. We show that state-of-the-art SR networks perform very poorly on noisy inputs. Our evaluation also reveals that applying the best denoiser in terms of reconstruction error followed by the best SR method does not necessarily yield the best final result. Both quantitative and qualitative results show that SR networks are sensitive to noise and the sequential application of denoising and SR algorithms is sub-optimal. Lastly, we demonstrate that SR networks retrained end-to-end for JDSR outperform any combination of state-of-the-art deep denoising and SR networksComment: ECCVW 2020. Project page: \<https://github.com/ivrl/w2s

The puzzle of self-reported weight gain in a month of fasting (Ramadan) among a cohort of Saudi families in Jeddah, Western Saudi Arabia

<p>Abstract</p> <p>Background</p> <p>During Ramadan fast, approximately one billion Muslims abstain from food and fluid between the hours of sunrise to sunset, and usually eat a large meal after sunset and another meal before sunrise. Many studies reported good health-related outcomes of fasting including weight loss. The objective of this study is to identify the local pattern of expenditure on food consumption, dietary habits during Ramadan and correlate that to self-reported weight gain after Ramadan in a group of families in Jeddah, Western Saudi Arabia.</p> <p>Methods</p> <p>A Cross-section study using a pre-designed questionnaire to identify the local pattern of expenditure on food consumption, dietary habits during Ramadan and correlate that to self-reported weight gain after Ramadan in a representative cohort of Saudis living in Jeddah. It was piloted on 173 nutrition students and administered by them to their families.</p> <p>Results</p> <p>A total of 173 Saudi families were interviewed. One out of 5 indicated that their expenditure increases during Ramadan. Approximately two thirds of the respondents (59.5%) reported weight gain after Ramadan. When asked about their perspective explanations for that: 40% attributed that to types of foods being rich in fat and carbohydrates particularly date in (Sunset meal) 97.7% and rice in (Dawn meal) 80.9%. One third (31.2%) indicated that it was due to relative lack of physical exercise in Ramadan and 14.5% referred that to increase in food consumption. Two thirds (65.2%) of those with increased expenditure reported weight gain.</p> <p>Conclusion</p> <p>Surprisingly weight gain and not weight loss was reported after Ramadan by Saudis which indicates timely needed life-style and dietary modification programs for a population which reports one of the highest prevalence rates of diabetes.</p

Treatment Response of Cystic Echinococcosis to Benzimidazoles: A Systematic Review

Over the past 30 years, benzimidazoles have increasingly been used to treat cystic echinococcosis (CE). The efficacy of benzimidazoles, however, remains unclear. We systematically searched MEDLINE, EMBASE, SIGLE, and CCTR to identify studies on benzimidazole treatment outcome. A large heterogeneity of methods in 23 reports precluded a meta-analysis of published results. Specialist centres were contacted to provide individual patient data. We conducted survival analyses for cyst response defined as inactive (CE4 or CE5 by the ultrasound-based World Health Organisation [WHO] classification scheme) or as disappeared. We collected data from 711 treated patients with 1,308 cysts from six centres (five countries). Analysis was restricted to 1,159 liver and peritoneal cysts. Overall, 1–2 y after initiation of benzimidazole treatment 50%–75% of active C1 cysts were classified as inactive/disappeared compared to 30%–55% of CE2 and CE3 cysts. Further in analyzing the rate of inactivation/disappearance with regard to cyst size, 50%–60% of cysts <6 cm responded to treatment after 1–2 y compared to 25%–50% of cysts >6 cm. However, 25% of cysts reverted to active status within 1.5 to 2 y after having initially responded and multiple relapses were observed; after the second and third treatment 60% of cysts relapsed within 2 y. We estimated that 2 y after treatment initiation 40% of cysts are still active or become active again. The overall efficacy of benzimidazoles has been overstated in the past. There is an urgent need for a pragmatic randomised controlled trial that compares standardized benzimidazole therapy on responsive cyst stages with the other treatment modalities

Hereditary breast cancer in Middle Eastern and North African (MENA) populations: identification of novel, recurrent and founder BRCA1 mutations in the Tunisian population

Germ-line mutations in BRCA1 breast cancer susceptibility gene account for a large proportion of hereditary breast cancer families and show considerable ethnic and geographical variations. The contribution of BRCA1 mutations to hereditary breast cancer has not yet been thoroughly investigated in Middle Eastern and North African populations. In this study, 16 Tunisian high-risk breast cancer families were screened for germline mutations in the entire BRCA1 coding region and exon–intron boundaries using direct sequencing. Six families were found to carry BRCA1 mutations with a prevalence of 37.5%. Four different deleterious mutations were detected. Three truncating mutations were previously described: c.798_799delTT (916 delTT), c.3331_3334delCAAG (3450 delCAAG), c.5266dupC (5382 insC) and one splice site mutation which seems to be specific to the Tunisian population: c.212 + 2insG (IVS5 + 2insG). We also identified 15 variants of unknown clinical significance. The c.798_799delTT mutation occurred at an 18% frequency and was shared by three apparently unrelated families. Analyzing five microsatellite markers in and flanking the BRCA1 locus showed a common haplotype associated with this mutation. This suggests that the c.798_799delTT mutation is a Tunisian founder mutation. Our findings indicate that the Tunisian population has a spectrum of prevalent BRCA1 mutations, some of which appear as recurrent and founding mutations

Analysis of mass spectrometry data from the secretome of an explant model of articular cartilage exposed to pro-inflammatory and anti-inflammatory stimuli using machine learning

Background: Osteoarthritis (OA) is an inflammatory disease of synovial joints involving the loss and degeneration of articular cartilage. The gold standard for evaluating cartilage loss in OA is the measurement of joint space width on standard radiographs. However, in most cases the diagnosis is made well after the onset of the disease, when the symptoms are well established. Identification of early biomarkers of OA can facilitate earlier diagnosis, improve disease monitoring and predict responses to therapeutic interventions. Methods: This study describes the bioinformatic analysis of data generated from high throughput proteomics for identification of potential biomarkers of OA. The mass spectrometry data was generated using a canine explant model of articular cartilage treated with the pro-inflammatory cytokine interleukin 1 β (IL-1β). The bioinformatics analysis involved the application of machine learning and network analysis to the proteomic mass spectrometry data. A rule based machine learning technique, BioHEL, was used to create a model that classified the samples into their relevant treatment groups by identifying those proteins that separated samples into their respective groups. The proteins identified were considered to be potential biomarkers. Protein networks were also generated; from these networks, proteins pivotal to the classification were identified. Results: BioHEL correctly classified eighteen out of twenty-three samples, giving a classification accuracy of 78.3% for the dataset. The dataset included the four classes of control, IL-1β, carprofen, and IL-1β and carprofen together. This exceeded the other machine learners that were used for a comparison, on the same dataset, with the exception of another rule-based method, JRip, which performed equally well. The proteins that were most frequently used in rules generated by BioHEL were found to include a number of relevant proteins including matrix metalloproteinase 3, interleukin 8 and matrix gla protein. Conclusions: Using this protocol, combining an in vitro model of OA with bioinformatics analysis, a number of relevant extracellular matrix proteins were identified, thereby supporting the application of these bioinformatics tools for analysis of proteomic data from in vitro models of cartilage degradation

Biomechanical evaluation of predictive parameters of progression in adolescent isthmic spondylolisthesis: a computer modeling and simulation study

<p>Abstract</p> <p>Background</p> <p>Pelvic incidence, sacral slope and slip percentage have been shown to be important predicting factors for assessing the risk of progression of low- and high-grade spondylolisthesis. Biomechanical factors, which affect the stress distribution and the mechanisms involved in the vertebral slippage, may also influence the risk of progression, but they are still not well known. The objective was to biomechanically evaluate how geometric sacral parameters influence shear and normal stress at the lumbosacral junction in spondylolisthesis.</p> <p>Methods</p> <p>A finite element model of a low-grade L5-S1 spondylolisthesis was constructed, including the morphology of the spine, pelvis and rib cage based on measurements from biplanar radiographs of a patient. Variations provided on this model aimed to study the effects on low grade spondylolisthesis as well as reproduce high grade spondylolisthesis. Normal and shear stresses at the lumbosacral junction were analyzed under various pelvic incidences, sacral slopes and slip percentages. Their influence on progression risk was statistically analyzed using a one-way analysis of variance.</p> <p>Results</p> <p>Stresses were mainly concentrated on the growth plate of S1, on the intervertebral disc of L5-S1, and ahead the sacral dome for low grade spondylolisthesis. For high grade spondylolisthesis, more important compression and shear stresses were seen in the anterior part of the growth plate and disc as compared to the lateral and posterior areas. Stress magnitudes over this area increased with slip percentage, sacral slope and pelvic incidence. Strong correlations were found between pelvic incidence and the resulting compression and shear stresses in the growth plate and intervertebral disc at the L5-S1 junction.</p> <p>Conclusions</p> <p>Progression of the slippage is mostly affected by a movement and an increase of stresses at the lumbosacral junction in accordance with spino-pelvic parameters. The statistical results provide evidence that pelvic incidence is a predictive parameter to determine progression in isthmic spondylolisthesis.</p