Diagnosis of the type of glomerular disease that causes the nephrotic syndrome is necessary for appropriate treatment and typically requires a renal biopsy. The goal of this study was to identify candidate protein biomarkers to diagnose glomerular diseases. Proteomic methods and informatic analysis were used to identify patterns of urine proteins that are characteristic of the diseases. Urine proteins were separated by two-dimensional electrophoresis in 32 patients with FSGS, lupus nephritis, membranous nephropathy, or diabetic nephropathy. Protein abundances from 16 patients were used to train an artificial neural network to create a prediction algorithm. The remaining 16 patients were used as an external validation set to test the accuracy of the prediction algorithm. In the validation set, the model predicted the presence of the diseases with sensitivities between 75 and 86% and specificities from 92 to 67%. The probability of obtaining these results in the novel set by chance is 5 ؋ 10 ؊8 . Twenty-one gel spots were most important for the differentiation of the diseases. The spots were cut from the gel, and 20 were identified by mass spectrometry as charge forms of 11 plasma proteins: Orosomucoid, transferrin, ␣-1 microglobulin, zinc ␣-2 glycoprotein, ␣-1 antitrypsin, complement factor B, haptoglobin, transthyretin, plasma retinol binding protein, albumin, and hemopexin. These data show that diseases that cause nephrotic syndrome change glomerular protein permeability in characteristic patterns. The fingerprint of urine protein charge forms identifies the glomerular disease. The identified proteins are candidate biomarkers that can be tested in assays that are more amenable to clinical testing.

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Background Ileus is common after elective colorectal surgery, and is associated with increased adverse events and prolonged hospital stay. The aim was to assess the role of non‐steroidal anti‐inflammatory drugs (NSAIDs) for reducing ileus after surgery. Methods A prospective multicentre cohort study was delivered by an international, student‐ and trainee‐led collaborative group. Adult patients undergoing elective colorectal resection between January and April 2018 were included. The primary outcome was time to gastrointestinal recovery, measured using a composite measure of bowel function and tolerance to oral intake. The impact of NSAIDs was explored using Cox regression analyses, including the results of a centre‐specific survey of compliance to enhanced recovery principles. Secondary safety outcomes included anastomotic leak rate and acute kidney injury. Results A total of 4164 patients were included, with a median age of 68 (i.q.r. 57–75) years (54·9 per cent men). Some 1153 (27·7 per cent) received NSAIDs on postoperative days 1–3, of whom 1061 (92·0 per cent) received non‐selective cyclo‐oxygenase inhibitors. After adjustment for baseline differences, the mean time to gastrointestinal recovery did not differ significantly between patients who received NSAIDs and those who did not (4·6 versus 4·8 days; hazard ratio 1·04, 95 per cent c.i. 0·96 to 1·12; P = 0·360). There were no significant differences in anastomotic leak rate (5·4 versus 4·6 per cent; P = 0·349) or acute kidney injury (14·3 versus 13·8 per cent; P = 0·666) between the groups. Significantly fewer patients receiving NSAIDs required strong opioid analgesia (35·3 versus 56·7 per cent; P < 0·001). Conclusion NSAIDs did not reduce the time for gastrointestinal recovery after colorectal surgery, but they were safe and associated with reduced postoperative opioid requirement.

Acute kidney injury (AKI) is an important cause of death among hospitalized patients. The 2 most common causes of AKI are acute tubular necrosis (ATN) and prerenal azotemia (PRA). Appropriate diagnosis of the disease is important but often difficult. We analyzed urine proteins by 2-dimensional gel electrophoresis from 38 patients with AKI. Patients were randomly assigned to a training set, an internal test set, or an external validation set. Spot abundances were analyzed by artificial neural networks to identify biomarkers that differentiate between ATN and PRA. When the trained neural network algorithm was tested against the training data, it identified the diagnosis for 16 of 18 patients in the training set and all 10 patients in the internal test set. The accuracy was validated in the novel external set of patients where conditions of 9 of 10 patients were correctly diagnosed including 5 of 5 with ATN and 4 of 5 with PRA. Plasma retinol-binding protein was identified in 1 spot and a fragment of albumin and plasma retinol-binding protein in the other. These proteins are candidate markers for diagnostic assays of AKI.