Quality of life and satisfaction of patients after oncoplastic or traditional breast-conserving surgery using the BREAST-Q (BCT module): a prospective study

INTRODUCTION

The oncoplastic conservative surgery was developed as a natural evolution of traditional surgery, attempting to improve the therapeutic and aesthetic outcomes where tumor resection could be followed by not-adequate results. Our primary aim is to evaluate how patient satisfaction and quality-of-life after conservative oncoplastic surgery, using BREAST-Q (BCT Module), change pre- and post-operatively. The secondary aim is to compare patient-reported outcome after oncoplastic or traditional conservative surgery.

PATIENTS AND METHODS

We enrolled 647 patients who underwent traditional conservative surgery or oncoplastic surgery from January 2020 to December 2022. Only 232 women (35.9%) completed the BREAST-Q questionnaire on a web-based platform, at the preoperative phase and 3 months after treatment.

RESULTS

The average score of "Psychosocial well-being" and "Satisfaction with Breasts" 3 months after surgery showed a statistically significant improvement, while the average score for "Physical well-being: Chest" at 3 months showed a worsening compared to the baseline. "Sexual well-being" did not show statistically significant change. A significant difference between the post-operative outcome of oncoplastic surgery and traditional surgery was observed only for Physical well-being (better for traditional surgery).

CONCLUSIONS

The study showed significant improvement in patient-reported outcomes 3 months after the surgery, except for physical discomfort that increases especially after oncoplastic surgery. Furthermore, our data, as well as many others, point to the appropriateness of using OCS where there is an effective indication, while the perspective of patients cannot find significant superiority over TCS in any of the areas analyzed.

DIAL: a web server for the pairwise alignment of two RNA three-dimensional structures using nucleotide, dihedral angle and base-pairing similarities

DIAL (dihedral alignment) is a web server that provides public access to a new dynamic programming algorithm for pairwise 3D structural alignment of RNA. DIAL achieves quadratic time by performing an alignment that accounts for (i) pseudo-dihedral and/or dihedral angle similarity, (ii) nucleotide sequence similarity and (iii) nucleotide base-pairing similarity.

DIAL provides access to three alignment algorithms: global (Needleman–Wunsch), local (Smith–Waterman) and semiglobal (modified to yield motif search). Suboptimal alignments are optionally returned, and also Boltzmann pair probabilities Pr(a_i,b_j) for aligned positions a_i , b_j from the optimal alignment. If a non-zero suboptimal alignment score ratio is entered, then the semiglobal alignment algorithm may be used to detect structurally similar occurrences of a user-specified 3D motif. The query motif may be contiguous in the linear chain or fragmented in a number of noncontiguous regions.

The DIAL web server provides graphical output which allows the user to view, rotate and enlarge the 3D superposition for the optimal (and suboptimal) alignment of query to target. Although graphical output is available for all three algorithms, the semiglobal motif search may be of most interest in attempts to identify RNA motifs. DIAL is available at http://bioinformatics.bc.edu/clotelab/DIAL.

DiANNA 1.1: an extension of the DiANNA web server for ternary cysteine classification

DiANNA is a recent state-of-the-art artificial neural network and web server, which determines the cysteine oxidation state and disulfide connectivity of a protein, given only its amino acid sequence. Version 1.0 of DiANNA uses a feed-forward neural network to determine which cysteines are involved in a disulfide bond, and employs a novel architecture neural network to predict which half-cystines are covalently bound to which other half-cystines. In version 1.1 of DiANNA, described here, we extend functionality by applying a support vector machine with spectrum kernel for the cysteine classification problem-to determine whether a cysteine is reduced (free in sulfhydryl state), half-cystine (involved in a disulfide bond) or bound to a metallic ligand. In the latter case, DiANNA predicts the ligand among iron, zinc, cadmium and carbon. Available at: http://bioinformatics.bc.edu/clotelab/DiANNA/.

BTW: a web server for Boltzmann time warping of gene expression time series

Dynamic time warping (DTW) is a well-known quadratic time algorithm to determine the smallest distance and optimal alignment between two numerical sequences, possibly of different length. Originally developed for speech recognition, this method has been used in data mining, medicine and bioinformatics. For gene expression time series data, time warping distance is arguably a more flexible tool to determine genes having similar temporal expression, hence possibly related biological function, than either Euclidean distance or correlation coefficient—especially since time warping accommodates sequences of different length. The BTW web server allows a user to upload two tab-separated text files A,B of gene expression data, each possibly having a different number of time intervals of different durations. BTW then computes time warping distance between each gene of A with each gene of B, using a recently developed symmetric algorithm which additionally computes the Boltzmann partition function and outputs Boltzmann pair probabilities. The Boltzmann pair probabilities, not available with any other existent software, suggest possible biological significance of certain positions in an optimal time warping alignment. Availability: .

DiANNA: a web server for disulfide connectivity prediction

Correctly predicting the disulfide bond topology in a protein is of crucial importance for the understanding of protein function and can be of great help for tertiary prediction methods. The web server http://clavius.bc.edu/~clotelab/DiANNA/ outputs the disulfide connectivity prediction given input of a protein sequence. The following procedure is performed. First, PSIPRED is run to predict the protein's secondary structure, then PSIBLAST is run against the non-redundant SwissProt to obtain a multiple alignment of the input sequence. The predicted secondary structure and the profile arising from this alignment are used in the training phase of our neural network. Next, cysteine oxidation state is predicted, then each pair of cysteines in the protein sequence is assigned a likelihood of forming a disulfide bond--this is performed by means of a novel architecture (diresidue neural network). Finally, Rothberg's implementation of Gabow's maximum weighted matching algorithm is applied to diresidue neural network scores in order to produce the final connectivity prediction. Our novel neural network-based approach achieves results that are comparable and in some cases better than the current state-of-the-art methods.

Disulfide connectivity prediction using secondary structure information and diresidue frequencies

MOTIVATION

We describe a stand-alone algorithm to predict disulfide bond partners in a protein given only the amino acid sequence, using a novel neural network architecture (the diresidue neural network), and given input of symmetric flanking regions of N-terminus and C-terminus half-cystines augmented with residue secondary structure (helix, coil, sheet) as well as evolutionary information. The approach is motivated by the observation of a bias in the secondary structure preferences of free cysteines and half-cystines, and by promising preliminary results we obtained using diresidue position-specific scoring matrices.

RESULTS

As calibrated by receiver operating characteristic curves from 4-fold cross-validation, our conditioning on secondary structure allows our novel diresidue neural network to perform as well as, and in some cases better than, the current state-of-the-art method. A slight drop in performance is seen when secondary structure is predicted rather than being derived from three-dimensional protein structures.

Protein surface similarities: a survey of methods to describe and compare protein surfaces

Many methods have been developed to analyse protein sequences and structures, although less work has been undertaken describing and comparing protein surfaces. Evolution can lead sequences to diverge or structures to change topology; nevertheless, surface determinants that are essential to protein function itself may be mantained. Moreover, different molecules could converge to similar functions by gaining specific surface determinants. In such cases, sequence or structure comparisons are likely to be inadequate in describing or identifying protein functions and evolutionary relationships among proteins. Surface analysis can identify function determinants that are independent of sequence or secondary structure and can therefore be a powerful tool to highlight cases of possible convergent or divergent evolution. This kind of approach can be useful for a better understanding of protein molecular and biochemical mechanisms of catalysis or interaction with a ligand, which are usually surface dependent. Protein surface comparison, when compared to sequence or structure comparison methods, is a hard computational challenge and evaluated methods allowing the comparison of protein surfaces are difficult to find. In this review, we will survey the current knowledge about protein surface similarity and the techniques to detect it.

Three-dimensional view of the surface motif associated with the P-loop structure: cis and trans cases of convergent evolution

Here we identify the determinants of the nucleotide-binding ability associated with the P-loop-containing proteins, inferring their functional importance from their structural convergence to a unique three- dimensional (3D) motif. (1) A new surface 3D pattern is identified for the P-loop nucleotide-binding region, which is more selective than the corresponding sequence pattern; (2) the signature displays one residue that we propose is the determinant for the guanine-binding ability (the residues aligned to ras D119; this residue is known to be important only in the G-proteins, we extend the prediction to all the other P-loop- containing proteins); and (3) two cases of convergent evolution at the molecular level are highlighted in the analysis of the active site: the positive charge aligned to ras K117 and the arginine residues aligned to the GAP arginine finger. The analysis of the residues conserved on protein surfaces allows one to identify new functional or evolutionary relationships among protein structures that would not be detectable by conventional sequence or structure comparison methods.