Prediction of B-cell Linear Epitopes with a Combination of Support Vector Machine Classification and Amino Acid Propensity Identification

Epitopes are antigenic determinants that are useful because they induce B-cell antibody production and stimulate T-cell activation. Bioinformatics can enable rapid, efficient prediction of potential epitopes. Here, we designed a novel B-cell linear epitope prediction system called LEPS, Linear Epitope Prediction by Propensities and Support Vector Machine, that combined physico-chemical propensity identification and support vector machine (SVM) classification. We tested the LEPS on four datasets: AntiJen, HIV, a newly generated PC, and AHP, a combination of these three datasets. Peptides with globally or locally high physicochemical propensities were first identified as primitive linear epitope (LE) candidates. Then, candidates were classified with the SVM based on the unique features of amino acid segments. This reduced the number of predicted epitopes and enhanced the positive prediction value (PPV). Compared to four other well-known LE prediction systems, the LEPS achieved the highest accuracy (72.52%), specificity (84.22%), PPV (32.07%), and Matthews' correlation coefficient (10.36%)

Chronic hepatitis virus infection in patients with multiple myeloma: clinical characteristics and outcomes

OBJECTIVES: Cytotoxic agents and steroids are used to treat lymphoid malignancies, but these compounds may exacerbate chronic viral hepatitis. For patients with multiple myeloma, the impact of preexisting hepatitis virus infection is unclear. The aim of this study is to explore the characteristics and outcomes of myeloma patients with chronic hepatitis virus infection. METHODS: From 2003 to 2008, 155 myeloma patients were examined to determine their chronic hepatitis virus infection statuses using serologic tests for the hepatitis B (HBV) and C viruses (HCV). Clinical parameters and outcome variables were retrieved via a medical chart review. RESULTS: The estimated prevalences of chronic HBV and HCV infections were 11.0% (n = 17) and 9.0% (n = 14), respectively. The characteristics of patients who were hepatitis virus carriers and those who were not were similar. However, carrier patients had a higher prevalence of conventional cytogenetic abnormalities (64.3% vs. 25.0%). The cumulative incidences of grade 3-4 elevation of the level of alanine transaminase, 30.0% vs. 12.0%, and hyperbilirubinemia, 20.0% vs. 1.6%, were higher in carriers as well. In a Kaplan-Meier analysis, carrier patients had worse overall survival (median: 16.0 vs. 42.4 months). The prognostic value of carrier status was not statistically significant in the multivariate analysis, but an age of more than 65 years old, the presence of cytogenetic abnormalities, a beta-2-microglobulin level of more than 3.5 mg/L, and a serum creatinine level of more than 2 mg/ dL were independent factors associated with poor prognosis. CONCLUSION: Myeloma patients with chronic hepatitis virus infections might be a distinct subgroup, and close monitoring of hepatic adverse events should be mandatory

Antitumor Agents. 272. Structure−Activity Relationships and In Vivo Selective Anti-Breast Cancer Activity of Novel Neo-tanshinlactone Analogues

Neo-tanshinlactone (1) and its previously reported analogs, such as 2, are potent and selective in vitro anti-breast cancer agents. The synthetic pathway to 2 was optimized from seven to five steps, with a better overall yield. Structure–activity relationships studies on these compounds revealed some key molecular determinants for this family of anti-breast agents. Several derivatives (19-21 and 24) exerted potent and selective anti-breast cancer activity with IC50 values of 0.3, 0.2, 0.1 and 0.1 μg/mL, respectively, against the ZR-75-1 cell lines. Compound 24 was two- to three-fold more potent than 1 against SK-BR-3 and ZR-75-1. Importantly, 21 exhibited high selectivity; it was 23 times more active against ZR-75-1 than MCF-7. Compound 20 had an approximately 12-fold ratio of SK-BR-3/MCF-7 selectivity. In addition, analog 2 showed potent activity against a ZR-75-1 xenograft model, but not PC-3 and MDA-MB-231 xenografts, as well as high selectivity against breast cancer cell line compared with normal breast tissue-derived cell lines. Further development of lead compounds 19-21 and 24 as clinical trial candidates is warranted

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Chang H: Prediction of B-cell linear epitopes with a combination of support vector machine classification and amino acid propensity identification

Epitopes are antigenic determinants that are useful because they induce B-cell antibody production and stimulate T-cell activation. Bioinformatics can enable rapid, efficient prediction of potential epitopes. Here, we designed a novel B-cell linear epitope prediction system called LEPS, Linear Epitope Prediction by Propensities and Support Vector Machine, that combined physicochemical propensity identification and support vector machine (SVM) classification. We tested the LEPS on four datasets: AntiJen, HIV, a newly generated PC, and AHP, a combination of these three datasets. Peptides with globally or locally high physicochemical propensities were first identified as primitive linear epitope (LE) candidates. Then, candidates were classified with the SVM based on the unique features of amino acid segments. This reduced the number of predicted epitopes and enhanced the positive prediction value (PPV). Compared to four other well-known LE prediction systems, the LEPS achieved the highest accuracy (72.52%), specificity (84.22%), PPV (32.07%), and Matthews' correlation coefficient (10.36%)