Plastic pigtail vs lumen-apposing metal stents for drainage of walled-off necrosis (PROMETHEUS study): an open-label, multicenter randomized trial.

BACKGROUND: Lumen-apposing metal stents (LAMS) have displaced double-pigtail plastic stents (DPS) as the standard treatment for walled-off necrosis (WON),ß but evidence for exclusively using LAMS is limited. We aimed to assess whether the theoretical benefit of LAMS was superior to DPS. METHODS: This multicenter, open-label, randomized trial was carried out in 9 tertiary hospitals. Between June 2017, and Oct 2020, we screened 99 patients with symptomatic WON, of whom 64 were enrolled and randomly assigned to the DPS group (n = 31) or the LAMS group (n = 33). The primary outcome was short-term (4-weeks) clinical success determined by the reduction of collection. Secondary endpoints included long-term clinical success, hospitalization, procedure duration, recurrence, safety, and costs. Analyses were by intention-to-treat. CLINICALTRIALS: gov, NCT03100578. RESULTS: A similar clinical success rate in the short term (RR, 1.41; 95% CI 0.88-2.25; p = 0.218) and in the long term (RR, 1.2; 95% CI 0.92-1.58; p = 0.291) was observed between both groups. Procedure duration was significantly shorter in the LAMS group (35 vs. 45-min, p = 0.003). The hospital admission after the index procedure (median difference, - 10 [95% CI - 17.5, - 1]; p = 0.077) and global hospitalization (median difference - 4 [95% CI - 33, 25.51]; p = 0.82) were similar between both groups. Reported stent-related adverse events were similar for the two groups (36 vs.45% in LAMS vs. DPS), except for de novo fever, which was significantly 26% lower in LAMS (RR, 0.26 [0.08-0.83], p = 0.015). CONCLUSIONS: The clinical superiority of LAMS over DPS for WON therapy was not proved, with similar clinical success, hospital stay and similar safety profile between both groups, yet a significant reduction in procedure time was observed. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov, NCT03100578.

Mitigation of salt stress in lettuce by a biostimulant that protects the root absorption zone and improves biochemical responses.

Horticultural crops constantly face abiotic stress factors such as salinity, which have intensified in recent years due to accelerated climate change, significantly affecting their yields and profitability. Under these conditions, it has become necessary to implement effective and sustainable solutions to guarantee agricultural productivity and food security. The influence of BALOX®, a biostimulant of plant origin, was tested on the responses to salinity of Lactuca sativa L. var. longifolia plants exposed to salt concentrations up to 150 mM NaCl, evaluating different biometric and biochemical properties after 25 days of treatment. Control plants were cultivated under the same conditions but without the biostimulant treatment. An in situ analysis of root characteristics using a non-destructive, real-time method was also performed. The salt stress treatments inhibited plant growth, reduced chlorophyll and carotenoid contents, and increased the concentrations of Na+ and Cl- in roots and leaves while reducing those of Ca2+. BALOX® application had a positive effect because it stimulated plant growth and the level of Ca2+ and photosynthetic pigments. In addition, it reduced the content of Na+ and Cl- in the presence and the absence of salt. The biostimulant also reduced the salt-induced accumulation of stress biomarkers, such as proline, malondialdehyde (MDA), and hydrogen peroxide (H2O2). Therefore, BALOX® appears to significantly reduce osmotic, ionic and oxidative stress levels in salt-treated plants. Furthermore, the analysis of the salt treatments' and the biostimulant's direct effects on roots indicated that BALOX®'s primary mechanism of action probably involves improving plant nutrition, even under severe salt stress conditions, by protecting and stimulating the root absorption zone.

Siamese pyramidal deep learning network for strain estimation in 3D cardiac cine-MR.

Strain represents the quantification of regional tissue deformation within a given area. Myocardial strain has demonstrated considerable utility as an indicator for the assessment of cardiac function. Notably, it exhibits greater sensitivity in detecting subtle myocardial abnormalities compared to conventional cardiac function indices, like left ventricle ejection fraction (LVEF). Nonetheless, the estimation of strain poses considerable challenges due to the necessity for precise tracking of myocardial motion throughout the complete cardiac cycle. This study introduces a novel deep learning-based pipeline, designed to automatically and accurately estimate myocardial strain from three-dimensional (3D) cine-MR images. Consequently, our investigation presents a comprehensive pipeline for the precise quantification of local and global myocardial strain. This pipeline incorporates a supervised Convolutional Neural Network (CNN) for accurate segmentation of the cardiac muscle and an unsupervised CNN for robust left ventricle motion tracking, enabling the estimation of strain in both artificial phantoms and real cine-MR images. Our investigation involved a comprehensive comparison of our findings with those obtained from two commonly utilized commercial software in this field. This analysis encompassed the examination of both intra- and inter-user variability. The proposed pipeline exhibited demonstrable reliability and reduced divergence levels when compared to alternative systems. Additionally, our approach is entirely independent of previous user data, effectively eliminating any potential user bias that could influence the strain analyses.

Use of a Biostimulant to Mitigate the Effects of Excess Salinity in Soil and Irrigation Water in Tomato Plants.

Global warming is linked to progressive soil salinisation, which reduces crop yields, especially in irrigated farmland on arid and semiarid regions. Therefore, it is necessary to apply sustainable and effective solutions that contribute to enhanced crop salt tolerance. In the present study, we tested the effects of a commercial biostimulant (BALOX®) containing glycine betaine (GB) and polyphenols on the activation of salinity defense mechanisms in tomato. The evaluation of different biometric parameters and the quantification of biochemical markers related to particular stress responses (osmolytes, cations, anions, oxidative stress indicators, and antioxidant enzymes and compounds) was carried out at two phenological stages (vegetative growth and the beginning of reproductive development) and under different salinity conditions (saline and non-saline soil, and irrigation water), using two formulations (different GB concentrations) and two doses of the biostimulant. Once the experiments were completed, the statistical analysis revealed that both formulations and doses of the biostimulant produced very similar effects. The application of BALOX® improved plant growth and photosynthesis and assisted osmotic adjustment in root and leaf cells. The biostimulant effects are mediated by the control of ion transport, reducing the uptake of toxic Na+ and Cl- ions and favoring the accumulation of beneficial K+ and Ca2+ cations, and a significant increase in leaf sugar and GB contents. BALOX® significantly reduced salt-induced oxidative stress and its harmful effects, as evidenced by a decrease in the concentration of oxidative stress biomarkers, such as malondialdehyde and oxygen peroxide, which was accompanied by the reduction of proline and antioxidant compound contents and the specific activity of antioxidant enzymes with respect to the non-treated plants.

Direct health costs of amyotrophic lateral sclerosis in a multidisciplinary ALS unit in Catalonia (Spain).

Our research project computed the direct health costs of patients with amyotrophic lateral sclerosis (ALS) in a Spanish multidisciplinary unit and explored the main factors associated. Besides analyzing a context with universal health care provision, we used an administrative health care dataset from the most crucial center unit treating ALS in Catalonia (80% of total patients). Our results show that the direct health cost of caring for an ALS patient in our unit was 5,158€per patient/year. This cost was not influenced by the onset of the disease, sex or age, but it increased if the patient lived near our center since this facilitates the frequency of follow-up visits. Finally, the higher the educational level, the lower the direct health costs.

Results in mediastinal lymph node staging of surgical lung cancer: Data from the prospective cohort of the Spanish Video-Assisted Thoracic Surgery Group.

OBJECTIVES: The objective of this study was to assess the diagnostic performance of combined computerised tomography (CT) and positron emission tomography (PET) in mediastinal staging of surgical lung cancer based on data obtained from the prospective cohort of the Spanish Group for Video-Assisted Thoracic Surgery (GEVATS). METHODS: A total of 2782 patients underwent surgery for primary lung carcinoma. We analysed diagnostic success in mediastinal lymph node staging (cN2) using CT and PET. Bivariate and multivariate analyses were performed of the factors involved in this success. The risk of unexpected pN2 disease was analysed for cases in which an invasive testing is recommended: cN1, the tumour centrally located or the tumour diameter >3 cm. RESULTS: The overall success of CT together with PET was 82.9% with a positive predictive value of 0.21 and negative predictive value of 0.93. If the tumour was larger than 3 cm and for each unit increase in mediastinal SUVmax, the probability of success was lower with OR 0.59 (0.44-0.79) and 0.71 (0.66-0.75), respectively. In the video-assisted thoracic surgery (VATS) approach, the probability of success was higher with OR 2.04 (1.52-2.73). The risk of unexpected pN2 increased with the risk factors cN1, the tumour centrally located or the tumour diameter >3 cm: from 4.5% (0 factors) to 18.8% (3 factors) but did not differ significantly as a function of whether invasive testing was performed. CONCLUSIONS: CT and PET together have a high negative predictive value. The overall success of the staging is lower in the case of tumours >3 cm and high mediastinal SUVmax, and it is higher when VATS is performed. The risk of unexpected pN2 is higher if the disease is cN1, the tumour centrally located or the tumour diameter >3 cm but does not vary significantly as a function of whether patients have undergone invasive testing.

Automatic myocardium strain quantification in MR synthetic images with Deep Leaning.

Accurate quantification of myocardium strain in magnetic resonance images is important to correctly diagnose and monitor cardiac diseases. Currently, available methods to estimate motion are based on tracking brightness pattern differences between images. In cine-MR images, the myocardium interior presents an inhered homogeneity, which reduces the accuracy in estimated motion, and consequently strain. Neural networks have recently been shown to be an important tool for a variety of applications, including motion estimation. In this work, we investigate the feasibility of quantifying myocardium strain in cardiac resonance synthetic images using motion generated by a compact and powerful network called Pyramid, Warping, and Cost Volume (PWC). Using the motion generated by the neural network, the radial myocardium strain obtained presents a mean average error of 12.30% +- 6.50%, and in the circumferential direction 1.20% +-0.61 %, better than the two classical methods evaluated. Clinical Relevance- This work demonstrates the feasibility of estimating myocardium strain using motion estimated by a convolutional neural network.

Blockchain-Based Architecture Design for Personal Health Record: Development and Usability Study.

BACKGROUND: The importance of blockchain-based architectures for personal health record (PHR) lies in the fact that they are thought and developed to allow patients to control and at least partly collect their health data. Ideally, these systems should provide the full control of such data to the respective owner. In spite of this importance, most of the works focus more on describing how blockchain models can be used in a PHR scenario rather than whether these models are in fact feasible and robust enough to support a large number of users. OBJECTIVE: To achieve a consistent, reproducible, and comparable PHR system, we build a novel ledger-oriented architecture out of a permissioned distributed network, providing patients with a manner to securely collect, store, share, and manage their health data. We also emphasize the importance of suitable ledgers and smart contracts to operate the blockchain network as well as discuss the necessity of standardizing evaluation metrics to compare related (net)works. METHODS: We adopted the Hyperledger Fabric platform to implement our blockchain-based architecture design and the Hyperledger Caliper framework to provide a detailed assessment of our system: first, under workload, ranging from 100 to 2500 simultaneous record submissions, and second, increasing the network size from 3 to 13 peers. In both experiments, we used throughput and average latency as the primary metrics. We also created a health database, a cryptographic unit, and a server to complement the blockchain network. RESULTS: With a 3-peer network, smart contracts that write on the ledger have throughputs, measured in transactions per second (tps) in an order of magnitude close to 102 tps, while those contracts that only read have rates close to 103 tps. Smart contracts that write also have latencies, measured in seconds, in an order of magnitude close to 101 seconds, while that only read have delays close to 100 seconds. In particular, smart contracts that retrieve, list, and view history have throughputs varying, respectively, from 1100 tps to 1300 tps, 650 tps to 750 tps, and 850 tps to 950 tps, impacting the overall system response if they are equally requested under the same workload. Varying the network size and applying an equal fixed load, in turn, writing throughputs go from 102 tps to 101 tps and latencies go from 101 seconds to 102 seconds, while reading ones maintain similar values. CONCLUSIONS: To the best of our knowledge, we are the first to evaluate, using Hyperledger Caliper, the performance of a PHR blockchain architecture and the first to evaluate each smart contract separately. Nevertheless, blockchain systems achieve performances far below what the traditional distributed databases achieve, indicating that the assessment of blockchain solutions for PHR is a major concern to be addressed before putting them into a real production.

A small-molecule activator of the unfolded protein response eradicates human breast tumors in mice.

Metastatic estrogen receptor α (ERα)-positive breast cancer is presently incurable. Seeking to target these drug-resistant cancers, we report the discovery of a compound, called ErSO, that activates the anticipatory unfolded protein response (a-UPR) and induces rapid and selective necrosis of ERα-positive breast cancer cell lines in vitro. We then tested ErSO in vivo in several preclinical orthotopic and metastasis mouse models carrying different xenografts of human breast cancer lines or patient-derived breast tumors. In multiple orthotopic models, ErSO treatment given either orally or intraperitoneally for 14 to 21 days induced tumor regression without recurrence. In a cell line tail vein metastasis model, ErSO was also effective at inducing regression of most lung, bone, and liver metastases. ErSO treatment induced almost complete regression of brain metastases in mice carrying intracranial human breast cancer cell line xenografts. Tumors that did not undergo complete regression and regrew remained sensitive to retreatment with ErSO. ErSO was well tolerated in mice, rats, and dogs at doses above those needed for therapeutic responses and had little or no effect on normal ERα-expressing murine tissues. ErSO mediated its anticancer effects through activation of the a-UPR, suggesting that activation of a tumor protective pathway could induce tumor regression.

PCR-assisted impedimetric biosensor for colibactin-encoding pks genomic island detection in E. coli samples.

A fast PCR-assisted impedimetric biosensor was developed for the selective detection of the clbN gene from the polyketide synthase (pks) genomic island in real Escherichia coli samples. This genomic island is responsible for the production of colibactin, a harmful genotoxin that has been associated with colorectal cancer. The experimental protocol consisted of immobilizing the designated forward primer onto an Au electrode surface to create the sensing probe, followed by PCR temperature cycling in blank, positive, and negative DNA controls. Target DNA identification was possible by monitoring changes in the system's charge transfer resistance values (Rct) before and after PCR treatment through electrochemical impedance spectroscopy (EIS) analysis. Custom-made, flexible gold electrodes were fabricated using chemical etching optical lithography. A PCR cycle study determined the optimum conditions to be at 6 cycles providing fast results while maintaining a good sensitivity. EIS data for the DNA recognition process demonstrated the successful distinction between target interaction resulting in an increase in resistance to charge transfer (Rct) percentage change of 176% for the positive DNA control vs. 21% and 20% for the negative and non-DNA-containing controls, respectively. Results showed effective fabrication of a fast, PCR-based electrochemical biosensor for the detection of pks genomic island with a calculated limit of detection of 17 ng/µL.

Novel Chest Radiographic Biomarkers for COVID-19 Using Radiomic Features Associated with Diagnostics and Outcomes.

COVID-19 is a highly contagious disease that can cause severe pneumonia. Patients with pneumonia undergo chest X-rays (XR) to assess infiltrates that identify the infection. However, the radiographic characteristics of COVID-19 are similar to the other acute respiratory syndromes, hindering the imaging diagnosis. In this work, we proposed identifying quantitative/radiomic biomarkers for COVID-19 to support XR assessment of acute respiratory diseases. This retrospective study used different cohorts of 227 patients diagnosed with pneumonia; 49 of them had COVID-19. Automatically segmented images were characterized by 558 quantitative features, including gray-level histogram and matrices of co-occurrence, run-length, size zone, dependence, and neighboring gray-tone difference. Higher-order features were also calculated after applying square and wavelet transforms. Mann-Whitney U test assessed the diagnostic performance of the features, and the log-rank test assessed the prognostic value to predict Kaplan-Meier curves of overall and deterioration-free survival. Statistical analysis identified 51 independently validated radiomic features associated with COVID-19. Most of them were wavelet-transformed features; the highest performance was the small dependence matrix feature of "low gray-level emphasis" (area under the curve of 0.87, sensitivity of 0.85, [Formula: see text]). Six features presented short-term prognostic value to predict overall and deterioration-free survival. The features of histogram "mean absolute deviation" and size zone matrix "non-uniformity" yielded the highest differences on Kaplan-Meier curves with a hazard ratio of 3.20 ([Formula: see text]). The radiomic markers showed potential as quantitative measures correlated with the etiologic agent of acute infectious diseases and to stratify short-term risk of COVID-19 patients.

Hospitalization-related economic impact of patients with cardiogenic shock in a high-complexity reference centre.

AIMS: Cardiogenic shock (CS) is associated with high mortality. Current guidelines strongly recommend centralizing the care of these patients in high-complexity centres. We described the hospitalization-related economic cost and its main determinants in patients with CS in a high-complexity reference centre. METHODS AND RESULTS: This is a single-centre, retrospective study. All patients with CS (2015-17) were included. Hospitalization-related cost per patient was calculated by analytical accountability method, including hospital stay-related expenditures, interventions, and consumption of devices. Expenditure was expressed in 2018 euros. All-cause mortality during follow-up was registered. Ratio of cost per life-year gained (LYG) was also calculated. A total of 230 patients were included, with mean age of 63 years. In-hospital mortality was 88/230 (38.3%). Hospital stay was longer in patients surviving after the admission (21.7 vs. 7.5 days, P < 0.001). Total economic cost for the overall cohort was 3 947 118€ (mean/patient 17 161€). Most of this cost was attributable to hospital stay (81.1%). The rest of the expenditure was due to in-hospital procedures (13.1%) and the use of devices (5.8%). Most of hospital stay-related costs (79.8%) were due to Critical Care Unit stay. Mean follow-up was 651 days. Total LYG was 409.77 years for the whole series. The observed ratio of cost per LYG was 9632.52 €/LYG. CONCLUSIONS: Management of CS in a reference centre is associated to a significant economic cost, but with a low ratio of cost per LYG. Most of this cost is attributable to hospital stay, specifically in critical care units.

Improving the generalization of deep learning methods to segment the left ventricle in short axis MR images.

Cardiovascular disease is one of the major health problems worldwide. In clinical practice, cardiac magnetic resonance imaging (CMR) is considered the gold-standard imaging modality for the evaluation of the function and structure of the left ventricle (LV). More recently, deep learning methods have been used to segment LV with impressive results. On the other hand, this kind of approach is prone to overfit the training data, and it does not generalize well between different data acquisition centers, thus creating constraints to the use in daily routines. In this paper, we explore methods to improve the generalization in the segmentation performed by a convolutional neural network. We applied a U-net based architecture and compared two different pre-processing methods to improve uniformity in the image contrast between five cross-dataset training and testing. Overall, we were able to perform the segmentation of the left ventricle using multiple cross-dataset combinations of train and test, with a mean endocardium dice score of 0.82.Clinical Relevance- This work improves the result between the cross-dataset evaluation of the left ventricle segmentation, reducing the constraints for daily clinical adoption of a fully-automatic segmentation method.

Fully Automated Quantification of Cardiac Indices from Cine MRI Using a Combination of Convolution Neural Networks.

Cardiovascular magnetic resonance imaging (CMRI) is one of the most accurate non-invasive modalities for evaluation of cardiac function, especially the left ventricle (LV). In this modality, the manual or semi-automatic delineation of LV by experts is currently the standard clinical practice for chambers segmentation. Despite these efforts, global quantification of LV remains a challenge. In this work, a combination of two convolutional neural network (CNN) architectures for quantitative evaluation of the LV is described, which estimates the cavity and the myocardium areas, endocardial cavity dimensions in three directions, and the myocardium regional wall thickness in six radial directions. The method was validated in CMRI exams of 56 patients (LVQuan19 dataset) and evaluated by metrics Dice Index, Mean Absolute Error, and Correlation with superior performance compared to the state-of-the-art methods. The combination of the CNN architectures provided a simpler yet fully automated approach, requiring no specialist interaction.Clinical Relevance- With the proposed method, it is possible to perform automatically the full quantification of regional clinically relevant parameters of the left ventricle in short-axis CMRI images with superior performance compared to state-of-the-art methods.

Multi-View Ensemble Convolutional Neural Network to Improve Classification of Pneumonia in Low Contrast Chest X-Ray Images.

Pneumonia is one of the leading causes of childhood mortality worldwide. Chest x-ray (CXR) can aid the diagnosis of pneumonia, but in the case of low contrast images, it is important to include computational tools to aid specialists. Deep learning is an alternative because it can identify patterns automatically, even in low-resolution images. We propose herein a convolutional neural network (CNN) architecture with different training strategies towards detecting pneumonia on CXRs and distinguishing its subforms of bacteria and virus. We also evaluated different image pre-processing methods to improve the classification. This study used CXRs from pediatric patients from a public pneumonia CXR dataset. The pre-processing methods evaluated were image cropping and histogram equalization. To classify the images, we adopted the VGG16 CNN and replaced its fully-connected layers with a customized multilayer perceptron. With this architecture, we proposed and evaluated four different training strategies: original CXR image (baseline), chest-cavity-cropped image (A), and histogram-equalized segmented image (B). The last strategy method (C) implemented is based on ensemble between strategies A and B. The performance was assessed by the area under the ROC curve (AUC) with 95% confidence interval (CI), accuracy, sensitivity, specificity, and F1-score. The ensemble model C yielded the highest performances: AUC of 0.97 (CI: 0.96-0.99) to classify pneumonia vs. normal, and AUC of 0.91 (CI: 0.88-0.94) to classify bacterial vs. viral cases. All models that used pre-processed images showed higher AUC than baseline, which used the original CXR image. Image cropping and histogram equalization reduced irrelevant information from the exam, enhanced contrast, and was able to identify fine CXR texture details. The proposed ensemble model increased the representation of inflammatory patterns from bacteria and viruses with few epochs to train the deep CNNs.Clinical relevance- Deep learning can identify complex radiographic patterns in low contrast images due to pneumonia and distinguish its subforms of bacteria and virus. The correlation of imaging with lab results could accelerate the adoption of complementary exams to confirm the disease's cause.

Hotspots of Sequence Variability in Gut Microbial Genes Encoding Pro-Inflammatory Factors Revealed by Oligotyping.

The gut microbiota has been implicated in a number of normal and disease biological processes. Recent studies have identified a subset of gut bacterial genes as potentially involved in inflammatory processes. In this work, we explore the sequence variability for some of these bacterial genes using a combination of deep sequencing and oligotyping, a data analysis application that identifies mutational hotspots in short stretches of DNA. The genes for pks island, tcpC and usp, all harbored by certain strains of E. coli and all implicated in inflammation, were amplified by PCR directly from stool samples and subjected to deep amplicon sequencing. For comparison, the same genes were amplified from individual bacterial clones. The amplicons for pks island and tcpC from stool samples showed minimal levels of heterogeneity comparable with the individual clones. The amplicons for usp from stool samples, by contrast, revealed the presence of five distinct oligotypes in two different regions. Of these, the oligotype GT was found to be present in the control uropathogenic clinical isolate and also detected in stool samples from individuals with colorectal cancer (CRC). Mutational hotspots were mapped onto the USP protein, revealing possible substitutions around Leu110, Glu114, and Arg115 in the middle of the pyocin domain (Gln110, Gln114, and Thr115 in most healthy samples), and also Arg218 in the middle of the nuclease domain (His218 in the uropathogenic strain). All of these results suggest that a level of variability within bacterial pro-inflammatory genes could explain differences in bacterial virulence and phenotype.

The Presence of Gut Microbial Genes Encoding Bacterial Genotoxins or Pro-Inflammatory Factors in Stool Samples from Individuals with Colorectal Neoplasia.

Gut bacterial toxins are thought to contribute to the development of colorectal cancer (CRC). This study examines the presence of specific gut bacterial toxin genes in stool samples from individuals with colorectal neoplasia (adenomas and/or CRC). The presence of bacterial genes encoding genotoxic or pro-inflammatory factors (pks, tcpC, gelE, cnf-1, AMmurB, and usp) was established by PCR of stool samples from individuals from mainland US (n = 30; controls = 10, adenoma = 10, CRC = 10) and from Puerto Rico (PR) (n = 33; controls = 13; adenomas = 8; CRC = 12). Logistic regression models and multinomial logistic regression models were used to estimate the magnitude of association. Distinct bacterial gene profiles were observed in each sample cohort. In individuals with CRC, AMmurB was detected more frequently in samples from the US and gelE in samples from PR. In samples from PR, individuals with ≥2 gut bacterial toxin genes in stool had higher odds of having colorectal neoplasia (OR = 11.0, 95%: CI 1.0⁻637.1): however, no significant association between bacterial genes and colorectal neoplasia was observed in the US cohort. Further analyses are warranted in a larger cohort to validate these preliminary findings, but these encouraging results highlight the importance of developing bacterial markers as tools for CRC diagnosis or risk stratification.

Fractal characterization of retinal microvascular network morphology during diabetic retinopathy progression.

OBJECTIVE: The study aimed to characterize morphological changes of the retinal microvascular network during the progression of diabetic retinopathy. METHODS: Publicly available retinal images captured by a digital fundus camera from DIARETDB1 and STARE databases were used. The retinal microvessels were segmented using the automatic method, and vascular network morphology was analyzed by fractal parametrization such as box-counting dimension, lacunarity, and multifractals. RESULTS: The results of the analysis were affected by the ability of the segmentation method to include smaller vessels with more branching generations. In cases where the segmentation was more detailed and included a higher number of vessel branching generations, increased severity of diabetic retinopathy was associated with increased complexity of microvascular network as measured by box-counting and multifractal dimensions, and decreased gappiness of retinal microvascular network as measured by lacunarity parameter. This association was not observed if the segmentation method included only 3-4 vessel branching generations. CONCLUSIONS: Severe stages of diabetic retinopathy could be detected noninvasively by using high resolution fundus photography and automatic microvascular segmentation to the high number of branching generations, followed by fractal analysis parametrization. This approach could improve risk stratification for the development of microvascular complications, cardiovascular disease, and dementia in diabetes.

The Presence of Genotoxic and/or Pro-inflammatory Bacterial Genes in Gut Metagenomic Databases and Their Possible Link With Inflammatory Bowel Diseases.

Background: The human gut microbiota is a dynamic community of microorganisms that mediate important biochemical processes. Differences in the gut microbial composition have been associated with inflammatory bowel diseases (IBD) and other intestinal disorders. In this study, we quantified and compared the frequencies of eight genotoxic and/or pro-inflammatory bacterial genes found in metagenomic Whole Genome Sequences (mWGSs) of samples from individuals with IBD vs. a cohort of healthy human subjects. Methods: The eight selected gene sequences were clbN, clbB, cif, cnf-1, usp, tcpC from Escherichia coli, gelE from Enterococcus faecalis and murB from Akkermansia muciniphila. We also included the sequences for the conserved murB genes from E. coli and E. faecalis as markers for the presence of Enterobacteriaceae or Enterococci in the samples. The gene sequences were chosen based on their previously reported ability to disrupt normal cellular processes to either promote inflammation or to cause DNA damage in cultured cells or animal models, which could be linked to a role in IBD. The selected sequences were searched in three different mWGS datasets accessed through the Human Microbiome Project (HMP): a healthy cohort (N = 251), a Crohn's disease cohort (N = 60) and an ulcerative colitis cohort (N = 17). Results: Firstly, the sequences for the murB housekeeping genes from Enterobacteriaceae and Enterococci were more frequently found in the IBD cohorts (32% E. coli in IBD vs. 12% in healthy; 13% E. faecalis in IBD vs. 3% in healthy) than in the healthy cohort, confirming earlier reports of a higher presence of both of these taxa in IBD. For some of the sequences in our study, especially usp and gelE, their frequency was even more sharply increased in the IBD cohorts than in the healthy cohort, suggesting an association with IBD that is not easily explained by the increased presence of E. coli or E. faecalis in those samples. Conclusion: Our results suggest a significant association between the presence of some of these genotoxic or pro-inflammatory gene sequences and IBDs. In addition, these results illustrate the power and limitations of the HMP database in the detection of possible clinical correlations for individual bacterial genes.

Patellar tracking after isolated medial patellofemoral ligament reconstruction: dynamic evaluation using computed tomography.

PURPOSE: Medial patellofemoral ligament (MPFL) reconstruction offers good clinical results with a very low rate of instability recurrence. However, its in vivo effect on patellar tracking is not clearly known. The aim of this study is to investigate the effects of MPFL reconstruction on patellar tracking using dynamic 320-detector-row CT. METHODS: Ten patients with patellofemoral instability referred to isolated MPFL reconstruction surgery were selected and subjected to dynamic CT before and ≥6 months after surgery. Patellar tilt angles and shift distance were analysed using computer software specifically designed for this purpose. Kujala and Tegner scores were applied, and the radiation of the CTs was recorded. Two protocols for imaging acquisition were compared: a tube potential of 80 kV and 50 mA versus a tube potential of 120 kV and 100 mA, both with a slice thickness of 0.5 mm and an acquisition duration of 10 s. RESULTS: There were no changes in patellar tracking after MPFL reconstruction. There was no instability relapse. Clinical scores improved from a mean of 51.9 (±15.6)-74.2 (±20.9) on the Kujala scale (p = 0.011) and from a median of 2 (range 0-4) to 4 (range 1-6) on the Tegner scale (p = 0.017). The imaging protocols produced a dose-length product (DLP) of 254 versus 1617 mGycm and a radiation effective estimated dose of 0.2 versus 1.3 mSv, respectively. Both protocols allowed the analysis of the studied parameters without loss of precision. CONCLUSIONS: Reconstruction of the MPFL produced no improvement in patellar tilt or shift in the population studied. The low-radiation protocol was equally effective in measuring changes in patellar tracking and is recommended. Although the procedure successfully stabilized the patella, knee surgeons should not expect patellar shift and tilt correction when performing isolated patellofemoral ligament reconstruction in patients with recurrent patellar instability. LEVEL OF EVIDENCE: IV.