Harnessing poly(ionic liquid)s for sensing applications

The interest in poly(ionic liquid)s for sensing applications is derived from their strong interactions to a variety of analytes. By combining the desirable mechanical properties of polymers with the physical and chemical properties of ILs, new materials can be created. The tunable nature of both ionic liquids and polymers allows for incredible diversity, which is exemplified in their broad applicability. In this article we examine the new field of poly(ionic liquid) sensors by providing a detailed look at the current state-of-the-art sensing devices for solvents, gases, biomolecules, pH, and anions

A "non-parametric" version of the naive Bayes classifier

Many algorithms have been proposed for the machine learning task of classication. One of the simplest methods, the naive Bayes classifyer, has often been found to give good performance despite the fact that its underlying assumptions (of independence and a Normal distribution of the variables) are perhaps violated. In previous work, we applied naive Bayes and other standard algorithms to a breast cancer database from Nottingham City Hospital in which the variables are highly non-Normal and found that the algorithm performed well when predicting a class that had been derived from the same data. However, when we then applied naive Bayes to predict an alternative clinical variable, it performed much worse than other techniques. This motivated us to propose an alternative method, based on naive Bayes, which removes the requirement for the variables to be Normally distributed, but retains the essential structure and other underlying assumptions of the method. We tested our novel algorithm on our breast cancer data and on three UCI datasets which also exhibited strong violations of Normality. We found our algorithm outperformed naive Bayes in all four cases and outperformed multinomial logistic regression (MLR) in two cases. We conclude that our method offers a competitive alternative to MLR and naive Bayes when dealing with data sets in which non-Normal distributions are observed

Cancer profiles by affinity propagation

The affinity propagation algorithm is applied to a problem of breast cancer subtyping using traditional biologic markers. The algorithm provides a procedure to determine the number of profiles to be considered. A well know breast cancer case series was used to compare the results of the affinity propagation with the results obtained with standard algorithms and indexes for the optimal choice of the number of clusters. Results from affinity propagation are consistent with the results already obtained having the advantage of providing an indication about the number of clusters

Effects of lung volume reduction surgery for emphysema on glycolipidic hormones

BACKGROUND: Pulmonary emphysema is associated with cachexia and disregulation of the hormones regulating the glycolipid metabolism, insulin resistance, and altered substrate utilization. This study aimed at identifying the effects of lung volume reduction surgery (LVRS) on glycolipidic hormones compared to respiratory rehabilitation (RR). METHODS: Thirty-three patients with moderate-to-severe emphysema who were undergoing video-assisted thoracoscopic LVRS were compared to 31 similar patients who refused the operation and followed a standardized RR program. All patients were evaluated before and 12 months after treatment for respiratory function, body composition, glycolipidic hormones, metabolic parameters, and insulin resistance, which was calculated using the homeostatic model assessment index for insulin resistance (HOMA-IR). These groups were compared to a matched healthy control population. RESULTS: Only after LVRS significant improvements were obtained in respiratory function (FEV1, +25.2%; p<0.0001; residual volume, -19.5%; p<0.0001), metabolic parameters (total cholesterol, +13.1%; p<0.01; high-density lipoprotein cholesterol, +11.2%; p<0.01; triglycerides, +18.4; p<0.001; nonesterified fatty acid, -19.7%; p<0.001), and body composition (fat-free mass [FFM], +6.5%; p<0.01; fat mass [FM], +11.9%; p<0.01). The leptin/FM ratio (-6.1%; p<0.01) and resistin/FM ratio (-5.6%; p<0.01) decreased, whereas the adiponectin/FM ratio (+6.9%; p<0.01) and ghrelin (+9.2%; p<0.01) increased, together with reductions in glycemia (-8.8%; p<0.01), insulin level (-20.4%; p<0.001), and HOMA-IR (-27.2%; p<0.0001). The decrement in residual volume was correlated with increment of FFM (rho=-0.49; p<0.02), FM (rho=-0.55; p<0.009), and ghrelin (rho=-0.52; p<0.01), and also with decreases in leptin corrected for FM (rho=0.50; p<0.02) and, marginally, HOMA-IR (rho=0.35; p=0.07). CONCLUSIONS: After LVRS, glycolipidic hormone levels and nutritional status significantly improved, along with insulin resistance reduction and more physiologic utilization of substrates. Correlations between residual volume and body composition as well as glycolipidic hormone levels suggest that postoperative recovery in respiratory dynamics may induce favorable clinical changes when compared to RR

Cancer profiles by Affinity Propagation

The Affinity Propagation algorithm is applied to various problems of breast and cutaneous tumours subtyping using traditional biologic markers. The algorithm provides a procedure to determine the number of profiles to be considered. Well know breast cancer case series and cutaneous melanoma were used to compare the results of the Affinity Propagation with the results obtained with standard algorithms and indexes for the optimal choice of the number of clusters.Results from Affinity Propagation are consistent with the results already obtained having the advantage of providing an indication about the number of clusters

Exercise-induced mitral regurgitation and right ventricle to pulmonary circulation uncoupling across the heart failure phenotypes

Exercise-induced mitral regurgitation (Ex-MR) is one of the mechanisms that contribute to reduced functional capacity in heart failure (HF). Its prevalence is not well defined across different HF subtypes. The aim of the present study was to describe functional phenotypes and cardiac response to exercise in HFrEF, HFmrEF, and HFpEF, according to Ex-MR prevalence. A total of 218 patients with HF [146 men, 68 (59–78) yr], 137 HFrEF, 41 HFmrEF, 40 HFpEF, and 23 controls were tested with cardiopulmonary exercise test combined with exercise echocardiography. Ex-MR was defined as development of at least moderate (≥2+/4+) regurgitation during exercise. Ex-MR was highly prevalent in the overall population (52%) although differed in the subgroups as follows: 82/137 (60%) in HFrEF, 17/41 (41%) in HFmrEF, and 14/40 (35%) in HFpEF (P < 0.05). Ex-MR was associated with a high rate of ventilation (VE) to carbon dioxide production (VCO2) in all HF subtypes [31.2 (26.6–35.6) vs. 33.4 (29.6–40.5), P = 0.004; 28.1 (24.5–31.9) vs. 34.4 (28.2–36.7), P = 0.01; 28.8 (26.6–32.4) vs. 32.2 (29.2–36.7), P = 0.01] and with lower peak VO2 in HFrEF and HFmrEF. Exercise right ventricle to pulmonary circulation (RV-PC) uncoupling was observed in HFrEF and HFpEF patients with Ex-MR [peak TAPSE/SPAP: HFrEF 0.40 (0.30–0.57) vs. 0.29 (0.23–0.39), P = 0.006; HFpEF 0.44 (0.28–0.62) vs. 0.31 (0.27–0.33), P = 0.05]. HFpEF with Ex-MR showed a distinct phenotype characterized by better chronotropic reserve and peripheral O2 extraction

Clustering breast cancer data by consensus of different validity indices

Clustering algorithms will, in general, either partition a given data set into a pre-specified number of clusters or will produce a hierarchy of clusters. In this paper we analyse several different clustering techniques and apply them to a particular data set of breast cancer data. When we do not know a priori which is the best number of groups, we use a range of different validity indices to test the quality of clustering results and to determine the best number of clusters. While for the K-means method there is not absolute agreement among the indices as to which is the best number of clusters, for the PAM algorithm all the indices indicate 4 as the best cluster number

First-line imatinib vs second- and third-generation TKIs for chronic-phase CML: a systematic review and meta-analysis

Imatinib, the first tyrosine kinase inhibitor (TKI) for the treatment of chronic myeloid leukemia (CML), improves overall survival (OS), but the introduction of newer TKIs requires the definition of the optimal first-line TKI for newly diagnosed Philadelphia chromosome-positive (Ph+) chronic-phase (CP) CML. This systematic review of randomized controlled trials (RCTs) compares the efficacy and safety of imatinib vs second-generation (dasatinib, nilotinib, bosutinib) and third-generation TKIs (ponatinib) in adults with newly diagnosed Ph+ CP CML, concentrating on OS, progression-free survival (PFS), and hematological and nonhematological adverse events. The quality of the evidence was assessed using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) method. Seven RCTs published between 1990 and 2019 (involving 3262 participants) satisfied the eligibility criteria. Two RCTs (imatinib vs nilotinib and imatinib vs dasatinib) found no difference in 5-year OS or PFS. Second- and third-generation TKIs improved 3-month major molecular responses (relative risk [RR], 4.28; 95% confidence interval [CI], 2.20-8.32) and other efficacy outcomes, decreased accelerated/blastic-phase transformations (RR, 0.44; 95% CI, 0.26-0.74), but were associated with more cases of thrombocytopenia (RR, 1.57; 95% CI, 1.20-2.05), cardiovascular events (RR, 2.54; 95% CI, 1.49-4.33), and pancreatic (RR, 2.29; 95% CI, 1.32-3.96) and hepatic effects (RR, 3.51; 95% CI 1.55-7.92). GRADE showed that the certainty of the evidence ranged from high to moderate. This study shows that, in comparison with imatinib, second- and third-generation TKIs improve clinical responses, but the safer toxicity profile of imatinib may make it a better option for patients with comorbidities