SPINAL DEFORMITY INDEX AND QUALITY OF LIFE OF PATIENTS WITH A DENSITOMETRIC DIAGNOSIS OF OSTEOPOROSIS

ABSTRACT Objective: To evaluate the existence of a possible significant correlation between the quality of life of outpatients with osteoporosis and the Spinal Deformity Index (SDI), a radiographic method for semiquantitative assessment of the spine that enables the identification of prevalent and incident fractures. Methods: A cross-sectional observational study carried out with female patients, Caucasians, over 50 years of age, with a densitometric diagnosis of osteoporosis and in an outpatient follow-up, who were submitted to the Oswestry Disability Index (ODI) and SF-36 questionnaires to measure the direct and indirect damage of vertebral fragility fractures on quality of life. The scores obtained in these questionnaires were correlated with the SDI scores, calculated from the radiographs of the lumbar and thoracic spine. Results: 48 patients completed the study, with a mean age of 69.6±6.7 years, mean body mass index (BMI) of 25.4±3.4 kg/m2, mean ODI of 25.1±17.9%, mean SF- 36 of 428.7±192.4 and mean SDI of 4.3±3. For the statistical analysis, Spearman's coefficient was used (p ≤ 0.05). Conclusion: There is no statistically significant correlation between the SDI and the scores obtained on the ODI and SF-36 quality of life questionnaires. Level of evidence: III. Study of non-consecutive patients, without gold standard, applied uniformly.

Semiquantitative Assessment of 99mTc-MIBI Uptake in Parathyroids of Secondary Hyperparathyroidism Patients from Chronic Renal Failure

Purpose Basing on semiquantitative assessment of 99mTc-MIBI uptake in parathyroids of secondary hyperparathyroidism (SHPT) patients from chronic renal failure, objective guidance could be given to improve the qualitative diagnosis accuracy of MIBI uptake.Methods MIBI uptake intensiveness was semiquantitatively calculated with software ImageJ. MIBI uptake intensiveness and clinical indices were compared in 3-level grouping method consisting of slight (group 1), medium (group 2) and high (group 3) MIBI uptake groups and 2-level grouping method consisting of insignificant and significant MIBI uptake groups.Results Patient age, renal failure course, hemodialysis vintage, glomerular filtration rate (GFR), serum parathyroid hormone (PTH) and alkaline phosphatase (AKP) were positively, but serum uric acid (UA) was negatively, significantly related to MIBI uptake intensiveness; patient age was negatively, but serum phosphorus (P) and calcium (Ca) ´ P were positively, significantly related to MIBI washout; oral administration of calcitriol and calcium would significantly reduce MIBI uptake and washout. MIBI uptake tendency might alter during specified course. In 3-level grouping method, such 7 indices as the MIBI uptake intensiveness, renal failure course, hemodialysis vintage, serum AKP, Ca, cysteine proteinase inhibitor C and PTH were comparable between group 1 and 2, but were significantly different between group 1 and 3, and between group 2 and 3. In 2-level grouping method, above 7 indices plus blood urine nitrogen (BUN)/Creatinine were all significantly different between insignificant and significant group with these indices except BUN/Creatinine being greater in significant group than in insignificant group. All above significant relations or differences were with p < 0.05.Conclusions Patient age, renal failure course, hemodialysis vintage, GFR, serum PTH, AKP, UA, phosphorus and Ca ´ P, oral administration of calcitriol and calcium, and parathyroids themselves could significantly influence MIBI uptake in parathyroids of SHPT patients. 2-level grouping method should be adopted to qualitatively diagnosis MIBI uptake.

PepMANDIS: A Peptide Selection Tool for Designing Function-Based Targeted Proteomic Assays in Complex Microbial Systems

The majority of studies focusing on microbial functioning in various environments are based on DNA or RNA sequencing techniques that have inherent limitations and usually provide a distorted picture about the functional status of the studied system. Untargeted proteomics is better suited for that purpose, but it suffers from low efficiency when applied in complex consortia. In practice, the scanning capabilities of the currently employed LC-MS/MS systems provide limited coverage of key-acting proteins, hardly allowing a semiquantitative assessment of the most abundant ones from most prevalent species. When particular biological processes of high importance are under investigation, the analysis of specific proteins using targeted proteomics is a more appropriate strategy as it offers superior sensitivity and comes with the added benefits of increased throughput, dynamic range and selectivity. However, the development of targeted assays requires a priori knowledge regarding the optimal peptides to be screened for each protein of interest. In complex, multi-species systems, a specific biochemical process may be driven by a large number of homologous proteins having considerable differences in their amino acid sequence, complicating LC-MS/MS detection. To overcome the complexity of such systems, we have developed an automated pipeline that interrogates UniProt database or user-created protein datasets (e.g. from metagenomic studies) to gather homolog proteins with a defined functional role and extract respective peptide sequences, while it computes several protein/peptide properties and relevant statistics to deduce a small list of the most representative, process-specific and LC-MS/MS-amenable peptides for the microbial enzymatic activity of interest.

Evaluation of dystrophin expression by immunohistochemistry as a prognostic factor in leiomyosarcomas (LMS).

e23525 Background: Leiomyosarcomas (LMS) are soft tissue sarcomas that derive from smooth muscle cells and can arise anywhere in the body (most frequently in extremities, uterus or retroperitoneum). The clinical prognostic factors are well established but molecular factors influencing prognostic are unknown. The DMD gene, which codifies for the dystrophin protein, has been proposed as tumour suppressor gene in LMS. The aim of our study is to evaluate dystrophin expression in LMS and its relation with the patient prognosis. Methods: A total of 103 patients (pts) with LMS were analysed. Clinical and pathological data were collected retrospectively from patients’ electronic board system. Dystrophin expression was analysed by immunohistochemistry (IHC) in paraffin embedded tissue LMS samples using the Novocastra NCL-DYS2 (Leica Biosystems). Semiquantitative assessment of the staining was classified from score 0 to 3 (0 = no dystrophin expression, 1 = low expression, 2 = moderate expression, 3 = high expression). Results: Of all 103 pts, 70 (68%) had localized disease and the majority of tumours were larger than 5 cm (75%). The most frequent locations of primary LMS were extremities (n = 31; 30.1%), uterus (n = 23; 23.2%) and retroperitoneum (n = 21; 20.4%). Most LMS were high grade (G): 7 pts G1 (6.8%), 35 pts G2 (34%) 2 and 53 pts G3 (51.5%). 50 of all grade LMS (48.6%) had loss of dystrophin expression (score 0), 17 (16.5%) had score 1, 23 (22.3%) score 2 and 10 (9.7%) score 3. Loss/low dystrophin expression measured as score 0 or 1, was more frequent in grade 3 LMS compared to grade 2 or grade 1 (77.4% vs. 57.1% vs. 16.7%; p = 0.005). There were no differences in dystrophin loss between localized or metastatic disease at diagnosis (64.2% vs. 72.2%; p > 0.05). In our series, loss or reduced dystrophin expression did not correlate with the risk of relapse in localized patients or overall survival in metastatic patients. Conclusions: Loss of dystrophin expression is a common event in LMS. Loss/low dystrophin expression is more frequent in grade 3 LMS compared to grade 2 and grade 1 LMS. Loss of dystrophin expression did not correlate with risk of relapse or overall survival in our series. Additional genetic evaluations to study DMD in LMS are underway.

Interoperator Reliability of Lung Ultrasound during the COVID-19 Pandemic

Aim Lung ultrasound (LUS) is a reliable, radiation-free, and bedside imaging technique used to assess several pulmonary diseases. Although COVID-19 is diagnosed with a nasopharyngeal swab, detection of pulmonary involvement is crucial for safe patient discharge. Computed tomography (CT) is currently the gold standard. To treat paucisymptomatic patients, we have implemented a “fast track” pathway in our emergency department, using LUS as a valid alternative. Minimal data is available in the literature about interobserver reliability and the level of expertise needed to perform a reliable examination. Our aim was to assess these. Materials and Methods This was a single-center prospective study. We enrolled 96 patients. 12 lung areas were explored in each patient with a semiquantitative assessment of pulmonary aeration loss in order to obtain the LUS score. Scans were performed by two different operators, an expert and a novice, who were blinded to their colleague’s results. Results 96 patients were enrolled. The intraclass correlation coefficient (ICC) showed excellent agreement between the expert and the novice operator (ICC 0.975; 0.962–0.983); demographic features (age, sex, and chronic pulmonary disease) did not influence the reproducibility of the method. The ICC was 0.973 (0.950–0.986) in males, 0.976 (0.959–0.986) in females; 0.965 (0.940–0.980) in younger patients (≤ 46 yrs), and 0.973 (0.952–0.985) in older (> 46 yrs) patients. The ICC was 0.967 (0.882–0.991) in patients with pulmonary disease and 0.975 (0.962–0.984) in the other patients. The learning curve showed an increase in interobserver agreement. Conclusion Our results confirm the feasibility and reproducibility of the method among operators with different levels of expertise, with a rapid learning curve.

Microscope-Based Automated Quantification of Liver Fibrosis in Mice Using a Deep Learning Algorithm

In preclinical studies that involve animal models for hepatic fibrosis, accurate quantification of the fibrosis is of utmost importance. The use of digital image analysis based on deep learning artificial intelligence (AI) algorithms can facilitate accurate evaluation of liver fibrosis in these models. In the present study, we compared the quantitative evaluation of collagen proportionate area in the carbon tetrachloride model of liver fibrosis in the mouse by a newly developed AI algorithm to the semiquantitative assessment of liver fibrosis performed by a board-certified toxicologic pathologist. We found an excellent correlation between the 2 methods of assessment, most evident in the higher magnification (×40) as compared to the lower magnification (×10). These findings strengthen the confidence of using digital tools in the toxicologic pathology field as an adjunct to an expert toxicologic pathologist.