Comparison of anti Chlamydia antibodies in tubal and non-tubal infertile patients

Background and purpose: Chlamydia trachomatis infection is one of the most prevalent bacterial sexually transmitted infection in most countries. This organism may stay in genital tract for long time and cause subtle yet progressive damage in fallopian tubes. In this study we evaluate the correlation between chlamydia antibodies and tubal and other factors of infertility.Materials and Methods: In this case control study, 28 patients with tubal factor infertility, 28 patients with non tubal factor infertility and 30 normal patients were enroled. Presence or absence of tubal factor was assessed by direct vision via laparscopy, then titres of IgA and IgG were evaluated in all of them using ELISA method in the same labratory. Data were recoded and analyzed using SPSS software and chi-square, Fisher's exact, T-test and Mann- Whitney test.Results: Positive titre of IgG was higher in tubal factor infertility but it was not statistically significant between three groups (p>0.294). Positive titres of IgA were more common in non tubal factor infertility (p=0.007). Though positive and negative titres of IgA (P=0.224) and IgG (P=0.273) were not statistically different in fertile and infertile patients. Positive and negative titres of IgA and IgG were also not statistically different in patients with or without PID (p>0.05).Conclusion: No correlation was found between the positive titres of IgG and IgA against Chlamydia and tubal factor infertility

Improved predictions of total kidney volume growth rate in ADPKD using two-parameter least squares fitting

Abstract Mayo Imaging Classification (MIC) for predicting future kidney growth in autosomal dominant polycystic kidney disease (ADPKD) patients is calculated from a single MRI/CT scan assuming exponential kidney volume growth and height-adjusted total kidney volume at birth to be 150 mL/m. However, when multiple scans are available, how this information should be combined to improve prediction accuracy is unclear. Herein, we studied ADPKD subjects ( $$n = 36$$ n = 36 ) with 8+ years imaging follow-up (mean = 11 years) to establish ground truth kidney growth trajectory. MIC annual kidney growth rate predictions were compared to ground truth as well as 1- and 2-parameter least squares fitting. The annualized mean absolute error in MIC for predicting total kidney volume growth rate was $$2.1\% \pm 2\%$$ 2.1 % ± 2 % compared to $$1.1\% \pm 1\%$$ 1.1 % ± 1 % ( $$p = 0.002$$ p = 0.002 ) for a 2-parameter fit to the same exponential growth curve used for MIC when 4 measurements were available or $$1.4\% \pm 1\%$$ 1.4 % ± 1 % ( $$p = 0.01$$ p = 0.01 ) with 3 measurements averaging together with MIC. On univariate analysis, male sex ( $$p = 0.05$$ p = 0.05 ) and PKD2 mutation ( $$p = 0.04$$ p = 0.04 ) were associated with poorer MIC performance. In ADPKD patients with 3 or more CT/MRI scans, 2-parameter least squares fitting predicted kidney volume growth rate better than MIC, especially in males and with PKD2 mutations where MIC was less accurate

Clinical Quality Control of MRI Total Kidney Volume Measurements in Autosomal Dominant Polycystic Kidney Disease

Total kidney volume measured on MRI is an important biomarker for assessing the progression of autosomal dominant polycystic kidney disease and response to treatment. However, we have noticed that there can be substantial differences in the kidney volume measurements obtained from the various pulse sequences commonly included in an MRI exam. Here we examine kidney volume measurement variability among five commonly acquired MRI pulse sequences in abdominal MRI exams in 105 patients with ADPKD. Right and left kidney volumes were independently measured by three expert observers using model-assisted segmentation for axial T2, coronal T2, axial single-shot fast spin echo (SSFP), coronal SSFP, and axial 3D T1 images obtained on a single MRI from ADPKD patients. Outlier measurements were analyzed for data acquisition errors. Most of the outlier values (88%) were due to breathing during scanning causing slice misregistration with gaps or duplication of imaging slices (n = 35), slice misregistration from using multiple breath holds during acquisition (n = 25), composing of two overlapping acquisitions (n = 17), or kidneys not entirely within the field of view (n = 4). After excluding outlier measurements, the coefficient of variation among the five measurements decreased from 4.6% pre to 3.2%. Compared to the average of all sequences without errors, TKV measured on axial and coronal T2 weighted imaging were 1.2% and 1.8% greater, axial SSFP was 0.4% greater, coronal SSFP was 1.7% lower and axial T1 was 1.5% lower than the mean, indicating intrinsic measurement biases related to the different MRI contrast mechanisms. In conclusion, MRI data acquisition errors are common but can be identified using outlier analysis and excluded to improve organ volume measurement consistency. Bias toward larger volume measurements on T2 sequences and smaller volumes on axial T1 sequences can also be mitigated by averaging data from all error-free sequences acquired

Deep Learning Automation of Kidney, Liver, and Spleen Segmentation for Organ Volume Measurements in Autosomal Dominant Polycystic Kidney Disease

Organ volume measurements are a key metric for managing ADPKD (the most common inherited renal disease). However, measuring organ volumes is tedious and involves manually contouring organ outlines on multiple cross-sectional MRI or CT images. The automation of kidney contouring using deep learning has been proposed, as it has small errors compared to manual contouring. Here, a deployed open-source deep learning ADPKD kidney segmentation pipeline is extended to also measure liver and spleen volumes, which are also important. This 2D U-net deep learning approach was developed with radiologist labeled T2-weighted images from 215 ADPKD subjects (70% training = 151, 30% validation = 64). Additional ADPKD subjects were utilized for prospective (n = 30) and external (n = 30) validations for a total of 275 subjects. Image cropping previously optimized for kidneys was included in training but removed for the validation and inference to accommodate the liver which is closer to the image border. An effective algorithm was developed to adjudicate overlap voxels that are labeled as more than one organ. Left kidney, right kidney, liver and spleen labels had average errors of 3%, 7%, 3%, and 1%, respectively, on external validation and 5%, 6%, 5%, and 1% on prospective validation. Dice scores also showed that the deep learning model was close to the radiologist contouring, measuring 0.98, 0.96, 0.97 and 0.96 on external validation and 0.96, 0.96, 0.96 and 0.95 on prospective validation for left kidney, right kidney, liver and spleen, respectively. The time required for manual correction of deep learning segmentation errors was only 19:17 min compared to 33:04 min for manual segmentations, a 42% time saving (p = 0.004). Standard deviation of model assisted segmentations was reduced to 7, 5, 11, 5 mL for right kidney, left kidney, liver and spleen respectively from 14, 10, 55 and 14 mL for manual segmentations. Thus, deep learning reduces the radiologist time required to perform multiorgan segmentations in ADPKD and reduces measurement variability

Efficient and selective polymer supported Mo(VI) catalyst for alkene epoxidation in batch and continuous reactors

The growing concern for the environment, increasingly stringent standards for the release of chemicals into the environment and economic competitiveness have prompted extensive efforts to improve chemical synthesis and manufacturing methods as well as development of new synthetic methodologies that minimise or completely eliminate pollutants. As a consequence, more and more attention has been focused on the use of safer chemicals through proper design of clean processes and products. Epoxides are key raw materials or intermediates in organic synthesis, particularly for the functionalisation of substrates and production of a wide variety of chemicals such as pharmaceuticals, plastics, paints, perfumes, food additives and adhesives. The conventional methods for the industrial production of epoxides employ either stoichiometric peracids or chlorohydrin as an oxygen source. However, both methods have serious environmental impact as the former produces an equivalent amount of acid waste, whilst the later yields chlorinated by-products and calcium chloride waste. There has been considerable effort to develop alternative alkene epoxidation methods by employing an oxidant such as tert-butyl hydroperoxide (TBHP) as it is environmentally benign, safer to handle and possesses good solubility in polar solvents. A notable industrial implementation of alkene epoxidation with TBHP was the Halcon process that employed soluble molybdenum(VI) as a catalyst for liquid phase epoxidation of propylene to propylene oxide. However, homogenous catalysed alkene epoxidation has several drawbacks including deposition of catalyst on the reactor walls and increased difficulties in separation of catalyst from the reaction mixture. In this work, an efficient and selective polystyrene 2-(aminomethyl)pyridine supported molybdenum complex (Ps.AMP.Mo) and a polybenzimidazole supported molybdenum complex (PBI.Mo) have been used as catalysts for epoxidation of 4-vinyl-1-cyclohexene (i.e. 4-VCH) using TBHP as an oxidant in batch and continuous reactors. An extensive assessment of the catalytic activity, stability and reusability of the catalysts has been conducted in a classical batch reactor. Experiments have been carried out to study the effect of reaction temperature, feed molar ratio of alkene to TBHP and catalyst loading on the yield of 1,2-epoxyhexane and 4-vinyl-1-cyclohexane 1,2-epoxide (4-VCH 1,2-epoxide) to optimise the reaction conditions in a batch reactor. A detailed evaluation of molybdenum (Mo) leaching from the polymer supported catalyst has been investigated by isolating any residue from reaction supernatant solutions and then using these residues as potential catalyst in epoxidation reactions. Furthermore, the efficiency of the heterogeneous catalyst for continuous epoxidation studies have been assessed using a FlowSyn continuous flow reactor by studying the effect of reaction temperature, feed molar ratio of alkene to TBHP and feed flow rate on the conversion of the oxidant and the yield of corresponding epoxide. The continuous flow epoxidation using FlowSyn reactor has shown considerable time savings, high reproducibility and selectivity along with remarkable improvements in catalyst stability compared to reactions carried out in a batch reactor

A Primer for Utilizing Deep Learning and Abdominal MRI Imaging Features to Monitor Autosomal Dominant Polycystic Kidney Disease Progression

Abdominal imaging of autosomal dominant polycystic kidney disease (ADPKD) has historically focused on detecting complications such as cyst rupture, cyst infection, obstructing renal calculi, and pyelonephritis; discriminating complex cysts from renal cell carcinoma; and identifying sources of abdominal pain. Many imaging features of ADPKD are incompletely evaluated or not deemed to be clinically significant, and because of this, treatment options are limited. However, total kidney volume (TKV) measurement has become important for assessing the risk of disease progression (i.e., Mayo Imaging Classification) and predicting tolvaptan treatment’s efficacy. Deep learning for segmenting the kidneys has improved these measurements’ speed, accuracy, and reproducibility. Deep learning models can also segment other organs and tissues, extracting additional biomarkers to characterize the extent to which extrarenal manifestations complicate ADPKD. In this concept paper, we demonstrate how deep learning may be applied to measure the TKV and how it can be extended to measure additional features of this disease