Potential therapeutic effects of branched-chain amino acids supplementation on resistance exercise-based muscle damage in humans

Branched-chain amino acids (BCAA) supplementation has been considered an interesting nutritional strategy to improve skeletal muscle protein turnover in several conditions. In this context, there is evidence that resistance exercise (RE)-derived biochemical markers of muscle soreness (creatine kinase (CK), aldolase, myoglobin), soreness, and functional strength may be modulated by BCAA supplementation in order to favor of muscle adaptation. However, few studies have investigated such effects in well-controlled conditions in humans. Therefore, the aim of this short report is to describe the potential therapeutic effects of BCAA supplementation on RE-based muscle damage in humans. The main point is that BCAA supplementation may decrease some biochemical markers related with muscle soreness but this does not necessarily reflect on muscle functionality.

Two-dimensional numerical approach for the vibration isolation analysis on thin walled wave barriers in poroelastic soils.

[EN]This paper is concerned with the vibration isolation efficiency analysis of total or partially buried thin walled wave barriers in poroelastic soils. A two-dimensional time harmonic model that treats soils and structures in a direct way by combining appropriately the conventional Boundary Element Method (BEM), the Dual BEM (DBEM) and the Finite Element Method es developed to this aim.

Graph-based analysis of brain resting-state fMRI data in nocturnal frontal lobe epileptic patients

Il lavoro che ho sviluppato presso l'unità di RM funzionale del Policlinico S.Orsola-Malpighi, DIBINEM, è incentrato sull'analisi dati di resting state - functional Magnetic Resonance Imaging (rs-fMRI) mediante l'utilizzo della graph theory, con lo scopo di valutare eventuali differenze in termini di connettività cerebrale funzionale tra un campione di pazienti affetti da Nocturnal Frontal Lobe Epilepsy (NFLE) ed uno di controlli sani. L'epilessia frontale notturna è una peculiare forma di epilessia caratterizzata da crisi che si verificano quasi esclusivamente durante il sonno notturno. Queste sono contraddistinte da comportamenti motori, prevalentemente distonici, spesso complessi, e talora a semiologia bizzarra. L'fMRI è una metodica di neuroimaging avanzata che permette di misurare indirettamente l'attività neuronale. Tutti i soggetti sono stati studiati in condizioni di resting-state, ossia di veglia rilassata. In particolare mi sono occupato di analizzare i dati fMRI con un approccio innovativo in campo clinico-neurologico, rappresentato dalla graph theory. I grafi sono definiti come strutture matematiche costituite da nodi e links, che trovano applicazione in molti campi di studio per la modellizzazione di strutture di diverso tipo. La costruzione di un grafo cerebrale per ogni partecipante allo studio ha rappresentato la parte centrale di questo lavoro. L'obiettivo è stato quello di definire le connessioni funzionali tra le diverse aree del cervello mediante l'utilizzo di un network. Il processo di modellizzazione ha permesso di valutare i grafi neurali mediante il calcolo di parametri topologici che ne caratterizzano struttura ed organizzazione. Le misure calcolate in questa analisi preliminare non hanno evidenziato differenze nelle proprietà globali tra i grafi dei pazienti e quelli dei controlli. Alterazioni locali sono state invece riscontrate nei pazienti, rispetto ai controlli, in aree della sostanza grigia profonda, del sistema limbico e delle regioni frontali, le quali rientrano tra quelle ipotizzate essere coinvolte nella fisiopatologia di questa peculiare forma di epilessia.

Population-based analysis of 4113 patients with acute cholecystitis: defining the optimal time-point for laparoscopic cholecystectomy

Objective: To compare clinical outcomes after laparoscopic cholecystectomy (LC) for acute cholecystitis performed at various time-points after hospital admission. Background: Symptomatic gallstones represent an important public health problem with LC the treatment of choice. LC is increasingly offered for acute cholecystitis, however, the optimal time-point for LC in this setting remains a matter of debate. Methods: Analysis was based on the prospective database of the Swiss Association of Laparoscopic and Thoracoscopic Surgery and included patients undergoing emergency LC for acute cholecystitis between 1995 and 2006, grouped according to the time-points of LC since hospital admission (admission day (d0), d1, d2, d3, d4/5, d ≥6). Linear and generalized linear regression models assessed the effect of timing of LC on intra- or postoperative complications, conversion and reoperation rates and length of postoperative hospital stay. Results: Of 4113 patients, 52.8% were female, median age was 59.8 years. Delaying LC resulted in significantly higher conversion rates (from 11.9% at d0 to 27.9% at d ≥6 days after admission, P < 0.001), surgical postoperative complications (5.7% to 13%, P < 0.001) and re-operation rates (0.9% to 3%, P = 0.007), with a significantly longer postoperative hospital stay (P < 0.001). Conclusions: Delaying LC for acute cholecystitis has no advantages, resulting in significantly increased conversion/re-operation rate, postoperative complications and longer postoperative hospital stay. This investigation—one of the largest in the literature—provides compelling evidence that acute cholecystitis merits surgery within 48 hours of hospital admission if impact on the patient and health care system is to be minimized.

Bilateral total extraperitoneal inguinal hernia repair (TEP) has outcomes similar to those for unilateral TEP: population-based analysis of prospective data of 6,505 patients

Whether bilateral total extraperitoneal (TEP) inguinal hernia repair is associated with worse outcomes than unilateral TEP continues to be a matter of debate. This study aimed to compare different outcomes of large cohorts of patients undergoing bilateral versus unilateral TEP.

TAPP or TEP? Population-based analysis of prospective data on 4,552 patients undergoing endoscopic inguinal hernia repair

Whether total extraperitoneal inguinal hernia repair (TEP) is associated with worse outcomes than transabdominal preperitoneal inguinal hernia repair (TAPP) continues to be a matter of debate. The objective of this large cohort study is to compare outcomes between patients undergoing TEP or TAPP.

The role of radiation therapy in resected T2 N0 esophageal cancer: a population-based analysis

The prognosis of even early-stage esophageal cancer is poor. Because there is not a consensus on how to manage T2 N0 disease, we examined survival after resection of T2 N0 esophageal cancer, with or without radiation therapy.

MODULARITY BASED FUZZY COMMUNITY DETECTION IN SOCIAL NETWORKS

Fuzzy community detection is to identify fuzzy communities in a network, which are groups of vertices in the network such that the membership of a vertex in one community is in [0,1] and that the sum of memberships of vertices in all communities equals to 1. Fuzzy communities are pervasive in social networks, but only a few works have been done for fuzzy community detection. Recently, a one-step forward extension of Newman’s Modularity, the most popular quality function for disjoint community detection, results into the Generalized Modularity (GM) that demonstrates good performance in finding well-known fuzzy communities. Thus, GMis chosen as the quality function in our research. We first propose a generalized fuzzy t-norm modularity to investigate the effect of different fuzzy intersection operators on fuzzy community detection, since the introduction of a fuzzy intersection operation is made feasible by GM. The experimental results show that the Yager operator with a proper parameter value performs better than the product operator in revealing community structure. Then, we focus on how to find optimal fuzzy communities in a network by directly maximizing GM, which we call it Fuzzy Modularity Maximization (FMM) problem. The effort on FMM problem results into the major contribution of this thesis, an efficient and effective GM-based fuzzy community detection method that could automatically discover a fuzzy partition of a network when it is appropriate, which is much better than fuzzy partitions found by existing fuzzy community detection methods, and a crisp partition of a network when appropriate, which is competitive with partitions resulted from the best disjoint community detections up to now. We address FMM problem by iteratively solving a sub-problem called One-Step Modularity Maximization (OSMM). We present two approaches for solving this iterative procedure: a tree-based global optimizer called Find Best Leaf Node (FBLN) and a heuristic-based local optimizer. The OSMM problem is based on a simplified quadratic knapsack problem that can be solved in linear time; thus, a solution of OSMM can be found in linear time. Since the OSMM algorithm is called within FBLN recursively and the structure of the search tree is non-deterministic, we can see that the FMM/FBLN algorithm runs in a time complexity of at least O (n2). So, we also propose several highly efficient and very effective heuristic algorithms namely FMM/H algorithms. We compared our proposed FMM/H algorithms with two state-of-the-art community detection methods, modified MULTICUT Spectral Fuzzy c-Means (MSFCM) and Genetic Algorithm with a Local Search strategy (GALS), on 10 real-world data sets. The experimental results suggest that the H2 variant of FMM/H is the best performing version. The H2 algorithm is very competitive with GALS in producing maximum modularity partitions and performs much better than MSFCM. On all the 10 data sets, H2 is also 2-3 orders of magnitude faster than GALS. Furthermore, by adopting a simply modified version of the H2 algorithm as a mutation operator, we designed a genetic algorithm for fuzzy community detection, namely GAFCD, where elite selection and early termination are applied. The crossover operator is designed to make GAFCD converge fast and to enhance GAFCD’s ability of jumping out of local minimums. Experimental results on all the data sets show that GAFCD uncovers better community structure than GALS.