Effect of storage duration on carprofen concentration measurements in dog plasma.

BACKGROUND: Storage of samples may be necessary prior to testing drug levels in certain study designs; however, the effect of storage duration on measured drug levels is not known for all drugs. OBJECTIVES: The objective of this study was to evaluate the stability of carprofen in canine plasma when stored at -80°C for 6 months. METHODS: Six healthy dogs were enrolled (1-10 years old, 17-35 kg) and received compounded carprofen at 2.2 mg/kg orally every 12 h for 2 days. On the third day, blood was collected immediately before the morning dose (trough), then 1 and 6 h after the dose (sampling timepoint). Whole blood was immediately centrifuged, and plasma was stored at -80°C. Plasma carprofen concentration was measured at day 2, week 2 and then monthly for 6 months using reversed-phase high-performance liquid chromatography. The measured carprofen concentrations were analysed statistically using a linear mixed effects model. RESULTS: There was no effect of storage time over 6 months (p = 0.891) on measured carprofen levels. Although there was an effect of sampling timepoint (0, 1 and 6 h) (p < 0.001), the interaction between storage timepoint and sampling timepoint was not statistically significant (p = 1). CONCLUSIONS: Carprofen-laden canine plasma samples can be stored for up to 6 months before analysis with no degradation in carprofen concentrations expected.

Differentiating Colorectal Carcinoma From Diverticulitis With Computerised Tomography: Does Every Patient Need Follow-Up Colonoscopy After an Episode of Acute Diverticulitis?

Purpose To correlate computerised tomography (CT) and endoscopic follow-up (FU) in differentiating presentations of acute diverticulitis (AD) and colorectal carcinoma (CRC). Methods Patient's discharge summaries between April 2018 and September 2019, stating AD under-diagnosis were retrieved. Admission details, CT reports, endoscopic findings and histopathology results were retrospectively collected from prospectively maintained data. Results In our study period of 17 months, we identified 150 patients with an admission diagnosis of AD. In total, 134 patients had a CT confirmed diagnosis of AD; 61% had uncomplicated acute diverticulitis (UAD) and 39% complicated acute diverticulitis (CAD). The mean age of the patients was 64 years, and 59% were female. Of the 134, 15 patients were excluded, and 119 with AD were discharged with a plan to have FU endoscopy. Overall, 75% of the patients managed to undergo complete endoscopic investigation, 4% had incomplete endoscopy, and 21% failed to attend endoscopy. Follow-up (FU) endoscopic investigation found polyps in 20 patients; seven were reported as tubular adenomas with low-grade dysplasia and 10 as non-concerning hyperplastic or sessile polyps. One was inflammatory, and two were malignant. CT scans for two patients with malignant polyps were reported as CAD with suspicion of sigmoid cancer in one and right-sided perforated diverticulitis in the other. Both patients were female aged over 60 years. Conclusions This study demonstrates that a selective approach is more appropriate for endoscopic FU after an episode of AD. Risk stratification is required to allocate FU endoscopic investigation for patients at high risk for CRC.

PLIT: An alignment-free computational tool for identification of long non-coding RNAs in plant transcriptomic datasets.

Long non-coding RNAs (lncRNAs) are a class of non-coding RNAs which play a significant role in several biological processes. RNA-seq based transcriptome sequencing has been extensively used for identification of lncRNAs. However, accurate identification of lncRNAs in RNA-seq datasets is crucial for exploring their characteristic functions in the genome as most coding potential computation (CPC) tools fail to accurately identify them in transcriptomic data. Well-known CPC tools such as CPC2, lncScore, CPAT are primarily designed for prediction of lncRNAs based on the GENCODE, NONCODE and CANTATAdb databases. The prediction accuracy of these tools often drops when tested on transcriptomic datasets. This leads to higher false positive results and inaccuracy in the function annotation process. In this study, we present a novel tool, PLIT, for the identification of lncRNAs in plants RNA-seq datasets. PLIT implements a feature selection method based on L1 regularization and iterative Random Forests (iRF) classification for selection of optimal features. Based on sequence and codon-bias features, it classifies the RNA-seq derived FASTA sequences into coding or long non-coding transcripts. Using L1 regularization, 31 optimal features were obtained based on lncRNA and protein-coding transcripts from 8 plant species. The performance of the tool was evaluated on 7 plant RNA-seq datasets using 10-fold cross-validation. The analysis exhibited superior accuracy when evaluated against currently available state-of-the-art CPC tools.

Diagnostic dilemma of ocular lymphoma.

We report a very unusual presentation of primary intraocular lymphoma masquerading as anterior uveitis with atypical symptoms. A 68-year-old man, initially presented with a 4-day history of painless, left blurred vision. Examination revealed 2 or more anterior chamber cells, mutton-fat keratic precipitates and posterior synechiae. Treatment was initiated, the eye settled, and the patient was discharged. He re-presented 7 months later with hypopyon, a hazy cornea, fibrin deposits and a narrowed anterior chamber angle. Over the coming weeks, it became apparent that the temporal iris was thickened, involving the angle, and his intraocular pressure increased despite topical dorzolamide. Following advice from a tertiary centre and referral to a regional ocular oncology centre, a transscleral biopsy was performed and suggested a ciliary body melanoma. Enucleation was advised and performed, with histological examination revealing features consistent with an ocular diffuse large B-cell lymphoma not involving the optic nerve, sclera or cornea. This is the first ever published report of a case of ocular lymphoma masquerading in this way.

Head and neck nonmelanoma cutaneous malignancy treatment in a skin cancer referral center.

OBJECTIVE: The purpose of our study was to determine the level of accuracy provided by clinical diagnosis in head and neck cutaneous malignancy as confirmed by histopathologic examination of the specimen in a skin cancer referral center. Positive predictive value was selected as a reliable measure of diagnostic accuracy. We also aimed to audit our surgical outcomes and record and present the completeness of excision, the rates of recurrence and metastasis, and the reconstructive method. STUDY DESIGN: We reviewed the electronic charts of 867 consecutive patients with skin tumors and calculated the positive and negative predictive values of clinical diagnosis. RESULTS: The positive predictive value was 91% for basal cell carcinoma and 78.8% for squamous cell carcinoma. Of the 178 histologically diagnosed squamous cell carcinomas, 15 were metastatic. CONCLUSIONS: In a skin cancer referral center, expert clinical diagnosis may safely overcome the need for preoperative biopsy. Relying on clinical diagnosis to formalize a treatment plan for head and neck nonmelanoma skin cancer is safe and efficient. This is more reliable in cases of basal cell carcinoma compared with suspected squamous cell carcinomas.

Automatic screening and classification of diabetic retinopathy and maculopathy using fuzzy image processing.

Digital retinal imaging is a challenging screening method for which effective, robust and cost-effective approaches are still to be developed. Regular screening for diabetic retinopathy and diabetic maculopathy diseases is necessary in order to identify the group at risk of visual impairment. This paper presents a novel automatic detection of diabetic retinopathy and maculopathy in eye fundus images by employing fuzzy image processing techniques. The paper first introduces the existing systems for diabetic retinopathy screening, with an emphasis on the maculopathy detection methods. The proposed medical decision support system consists of four parts, namely: image acquisition, image preprocessing including four retinal structures localisation, feature extraction and the classification of diabetic retinopathy and maculopathy. A combination of fuzzy image processing techniques, the Circular Hough Transform and several feature extraction methods are implemented in the proposed system. The paper also presents a novel technique for the macula region localisation in order to detect the maculopathy. In addition to the proposed detection system, the paper highlights a novel online dataset and it presents the dataset collection, the expert diagnosis process and the advantages of our online database compared to other public eye fundus image databases for diabetic retinopathy purposes.