Orgasmic Dysfunction after Radical Prostatectomy

In addition to urinary incontinence and erectile dysfunction, several other impairments of sexual function potentially occurring after radical prostatectomy (RP) have been described; as a whole, these less frequently assessed disorders are referred to as neglected side effects. In particular, orgasmic dysfunctions (ODs) have been reported in a non-negligible number of cases, with detrimental impacts on patients' overall sexual life. This review aimed to comprehensively discuss the prevalence and physiopathology of post-RP ODs, as well as potential treatment options. Orgasm-associated incontinence (climacturia) has been reported to occur in between 20% and 93% of patients after RP. Similarly, up to 19% of patients complain of postoperative orgasm-associated pain, mainly referred pain at the level of the penis. Moreover, impairment in the sensation of orgasm or even complete anorgasmia has been reported in 33% to 77% of patients after surgery. Clinical and surgical factors including age, the use of a nerve-sparing technique, and robotic surgery have been variably associated with the risk of ODs after RP, although robust and reliable data allowing for a proper estimation of the risk of postoperative orgasmic function impairment are still lacking. Likewise, little evidence regarding the management of postoperative ODs is currently available. In general, physicians should be aware of the prevalence of ODs after RP, in order to properly counsel all patients both preoperatively and immediately post-RP about the potential occurrence of bothersome and distressful changes in their overall sexual function

Transverse momentum dependent parton distributions in a light-cone quark model

The leading twist transverse momentum dependent parton distributions (TMDs) are studied in a light-cone description of the nucleon where the Fock expansion is truncated to consider only valence quarks. General analytic expressions are derived in terms of the six amplitudes needed to describe the three-quark sector of the nucleon light-cone wave function. Numerical calculations for the T-even TMDs are presented in a light-cone constituent quark model, and the role of the so-called pretzelosity is investigated to produce a nonspherical shape of the nucleon.Comment: references added and typos corrected; version to appear in Phys. Rev.

Shaping and Dilating the Fitness Landscape for Parameter Estimation in Stochastic Biochemical Models

The parameter estimation (PE) of biochemical reactions is one of the most challenging tasks in systems biology given the pivotal role of these kinetic constants in driving the behavior of biochemical systems. PE is a non-convex, multi-modal, and non-separable optimization problem with an unknown fitness landscape; moreover, the quantities of the biochemical species appearing in the system can be low, making biological noise a non-negligible phenomenon and mandating the use of stochastic simulation. Finally, the values of the kinetic parameters typically follow a log-uniform distribution; thus, the optimal solutions are situated in the lowest orders of magnitude of the search space. In this work, we further elaborate on a novel approach to address the PE problem based on a combination of adaptive swarm intelligence and dilation functions (DFs). DFs require prior knowledge of the characteristics of the fitness landscape; therefore, we leverage an alternative solution to evolve optimal DFs. On top of this approach, we introduce surrogate Fourier modeling to simplify the PE, by producing a smoother version of the fitness landscape that excludes the high frequency components of the fitness function. Our results show that the PE exploiting evolved DFs has a performance comparable with that of the PE run with a custom DF. Moreover, surrogate Fourier modeling allows for improving the convergence speed. Finally, we discuss some open problems related to the scalability of our methodology

Elective surgery for colorectal cancer in the aged: a clinical-economical evaluation.

A series of 56 consecutive patients, referred for surgery to a specialized institute, had elective laparotomies with various surgical procedures aimed at curing locoregional colorectal cancer. Data defining patient and tumour-related preoperative, operative and postoperative variables, including costs, were collected. The study group was divided into two age groups (< 65 vs > or = 65 years), which were similar in terms of patient- and tumour-related variables. Differences were not statistically significant (Pounds 440; 95% exact CI; Pounds -50; 1800). There is no evidence to suggest that there are any total charge differences in treating the two age groups, as confirmed by the cost analysis

Biochemical parameter estimation vs. benchmark functions: A comparative study of optimization performance and representation design

© 2019 Elsevier B.V. Computational Intelligence methods, which include Evolutionary Computation and Swarm Intelligence, can efficiently and effectively identify optimal solutions to complex optimization problems by exploiting the cooperative and competitive interplay among their individuals. The exploration and exploitation capabilities of these meta-heuristics are typically assessed by considering well-known suites of benchmark functions, specifically designed for numerical global optimization purposes. However, their performances could drastically change in the case of real-world optimization problems. In this paper, we investigate this issue by considering the Parameter Estimation (PE) of biochemical systems, a common computational problem in the field of Systems Biology. In order to evaluate the effectiveness of various meta-heuristics in solving the PE problem, we compare their performance by considering a set of benchmark functions and a set of synthetic biochemical models characterized by a search space with an increasing number of dimensions. Our results show that some state-of-the-art optimization methods – able to largely outperform the other meta-heuristics on benchmark functions – are characterized by considerably poor performances when applied to the PE problem. We also show that a limiting factor of these optimization methods concerns the representation of the solutions: indeed, by means of a simple semantic transformation, it is possible to turn these algorithms into competitive alternatives. We corroborate this finding by performing the PE of a model of metabolic pathways in red blood cells. Overall, in this work we state that classic benchmark functions cannot be fully representative of all the features that make real-world optimization problems hard to solve. This is the case, in particular, of the PE of biochemical systems. We also show that optimization problems must be carefully analyzed to select an appropriate representation, in order to actually obtain the performance promised by benchmark results

MedGA: A novel evolutionary method for image enhancement in medical imaging systems

Medical imaging systems often require the application of image enhancement techniques to help physicians in anomaly/abnormality detection and diagnosis, as well as to improve the quality of images that undergo automated image processing. In this work we introduce MedGA, a novel image enhancement method based on Genetic Algorithms that is able to improve the appearance and the visual quality of images characterized by a bimodal gray level intensity histogram, by strengthening their two underlying sub-distributions. MedGA can be exploited as a pre-processing step for the enhancement of images with a nearly bimodal histogram distribution, to improve the results achieved by downstream image processing techniques. As a case study, we use MedGA as a clinical expert system for contrast-enhanced Magnetic Resonance image analysis, considering Magnetic Resonance guided Focused Ultrasound Surgery for uterine fibroids. The performances of MedGA are quantitatively evaluated by means of various image enhancement metrics, and compared against the conventional state-of-the-art image enhancement techniques, namely, histogram equalization, bi-histogram equalization, encoding and decoding Gamma transformations, and sigmoid transformations. We show that MedGA considerably outperforms the other approaches in terms of signal and perceived image quality, while preserving the input mean brightness. MedGA may have a significant impact in real healthcare environments, representing an intelligent solution for Clinical Decision Support Systems in radiology practice for image enhancement, to visually assist physicians during their interactive decision-making tasks, as well as for the improvement of downstream automated processing pipelines in clinically useful measurements

Subcutaneous Trastuzumab (scT) and metronomic oral Vinorelbine (mVRL) combination in HER2 + ve advanced breast cancer (ABC) patients (pts): a pilot evaluation of toxicity. Preliminary results of the VICTOR-4 study

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Biological Characteristics and Medical Treatment of Breast Cancer in Young Women—A Featured Population: Results from the NORA Study

Background. The present paper described the biological characteristics and clinical behavior of young women in the cohort NORA study Patients and Methods. From 2000–2002, patients (N > 3500) were enrolled at 77 Italian hospitals. Women aged ≤50 years (N = 1013) were stratified into age groups (≤35, 36–40, 41–45, and 46–50 years). The relationship between age and patient characteristics, cancer presentation, and treatment was analyzed. Results. Younger women more frequently had tumors with ER/PgR-negative(χ2 = 7.07; P = .008), HER2 amplification (χ2 = 5.76; P = .01), and high (≥10%) Ki67 labelling index (χ2 = 9.53; P = .002). Positive nodal status, large tumors, and elevated Ki67 all associated with the choice for chemotherapy followed by endocrine therapy in hormone receptor-positive patients (P < .0001). At univariate analysis, ER-ve status, chemotherapy and age resulted as the only statistically significant variables (HR = 2.02, P = .004, and >40 versus ≤40, P < .0001, resp.). At multivariate analysis, after adjustment for significant clinical and pathological factors, age remains a significant prognostic variable (HR = 0.93, P = .0021). Conclusion. This cohort study suggests that age per sè is an important prognostic factor. The restricted role of early diagnosis and the aggressive behavior of cancer in this population make necessary the application of targeted medical strategies crucial