TransExION: a transformer based explainable similarity metric for comparing IONS in tandem mass spectrometry.

Small molecule identification is a crucial task in analytical chemistry and life sciences. One of the most commonly used technologies to elucidate small molecule structures is mass spectrometry. Spectral library search of product ion spectra (MS/MS) is a popular strategy to identify or find structural analogues. This approach relies on the assumption that spectral similarity and structural similarity are correlated. However, popular spectral similarity measures, usually calculated based on identical fragment matches between the MS/MS spectra, do not always accurately reflect the structural similarity. In this study, we propose TransExION, a Transformer based Explainable similarity metric for IONS. TransExION detects related fragments between MS/MS spectra through their mass difference and uses these to estimate spectral similarity. These related fragments can be nearly identical, but can also share a substructure. TransExION also provides a post-hoc explanation of its estimation, which can be used to support scientists in evaluating the spectral library search results and thus in structure elucidation of unknown molecules. Our model has a Transformer based architecture and it is trained on the data derived from GNPS MS/MS libraries. The experimental results show that it improves existing spectral similarity measures in searching and interpreting structural analogues as well as in molecular networking. SCIENTIFIC CONTRIBUTION: We propose a transformer-based spectral similarity metrics that improves the comparison of small molecule tandem mass spectra. We provide a post hoc explanation that can serve as a good starting point for unknown spectra annotation based on database spectra.

MIND4OLIGOS: Determining the Monoisotopic Mass of Oligonucleotides Observed in High-Resolution Mass Spectrometry.

Oligonucleotide therapeutics have emerged as an important class of drugs offering targeted therapeutic strategies that complement traditional modalities, such as monoclonal antibodies and small molecules. Their unique ability to precisely modulate gene expression makes them vital for addressing previously undruggable targets. A critical aspect of developing these therapies is characterizing their molecular composition accurately. This includes determining the monoisotopic mass of oligonucleotides, which is essential for identifying impurities, degradants, and modifications that can affect the drug efficacy and safety. Mass spectrometry (MS) plays a pivotal role in this process, yet the accurate interpretation of complex mass spectra remains challenging, especially for large molecules, where the monoisotopic peak is often undetectable. To address this issue, we have adapted the MIND algorithm, originally developed for top-down proteomics, for use with oligonucleotide data. This adaptation allows for the prediction of monoisotopic mass from the more readily detectable, most-abundant peak mass, enhancing the ability to annotate complex spectra of oligonucleotides. Our comprehensive validation of this modified algorithm on both in silico and real-world oligonucleotide data sets has demonstrated its effectiveness and reliability. To facilitate wider adoption of this advanced analytical technique, we have encapsulated the enhanced MIND algorithm in a user-friendly Shiny application. This online platform simplifies the process of annotating complex oligonucleotide spectra, making advanced mass spectrometry analysis accessible to researchers and drug developers. The application is available at https://valkenborg-lab.shinyapps.io/mind4oligos/.

Oncoplastic breast-conserving surgery improves cosmetic outcomes without increasing recurrence risk compared to modified radical mastectomy in early breast cancer patients: development and validation of a recurrence risk prediction model.

In the quest for effective treatment of early-stage breast cancer, this study aimed to compare the clinical efficacy of modified radical mastectomy (MRM) and oncoplastic breast-conserving surgery (OBCS). Breast cancer remains a major health concern globally, where early detection and effective treatment strategies are crucial for improving the outcomes of patients. MRM and OBCS are two primary treatment modalities for breast cancer, each with its distinct benefits and challenges. Through a retrospective analysis, we found that although the patients in the OBCS group experienced a longer operation time, they had significantly less intraoperative bleeding, postoperative drainage, and hospitalization time compared to the MRM group. Furthermore, patients in the OBCS group demonstrated higher subjective satisfaction and quality of life scores, along with better objective outcomes. In terms of postoperative complications and recurrence rates, no significant difference was identified between the two groups. However, our multivariate Cox regression analysis identified lymph node metastasis and molecular type as independent prognostic factors for disease-free survival (DFS). Subsequently, we constructed a risk model based on these variables, which was proven to be effective in predicting recurrence, with an area under the risk score curve for recurrence prediction being 0.852. The group with a lower risk score demonstrated a significantly higher DFS rate. Our study suggests that compared with MRM, OBCS can significantly reduce surgical incision, improve patient satisfaction, and does not increase the risk of complications or recurrence. Our risk model, developed using Cox regression, also demonstrated high clinical value in predicting breast cancer recurrence, thereby aiding in personalized patient management and treatment planning.

MeRgeION: a Multifunctional R Pipeline for Small Molecule LC-MS/MS Data Processing, Searching, and Organizing.

Small molecule structure elucidation using tandem mass spectrometry (MS/MS) plays a crucial role in life science, bioanalytical, and pharmaceutical research. There is a pressing need for increased throughput of compound identification and transformation of historical data into information-rich spectral databases. Meanwhile, molecular networking, a recent bioinformatic framework, provides global displays and system-level understanding of complex LC-MS/MS data sets. Herein we present meRgeION, a multifunctional, modular, and flexible R-based toolbox to streamline spectral database building, automated structural elucidation, and molecular networking. The toolbox offers diverse tuning parameters and the possibility to combine various algorithms in the same pipeline. As an open-source R package, meRgeION is ideally suited for building spectral databases and molecular networks from privacy-sensitive and preliminary data. Using meRgeION, we have created an integrated spectral database covering diverse pharmaceutical compounds that was successfully applied to annotate drug-related metabolites from a published nontargeted metabolomics data set as well as reveal the chemical space behind this complex data set through molecular networking. Moreover, the meRgeION-based processing workflow has demonstrated the usefulness of a spectral library search and molecular networking for pharmaceutical forced degradation studies. meRgeION is freely available at: https://github.com/daniellyz/meRgeION2.

Evaluation of indocyanine green combined with methylene blue staining in sentinel lymph node biopsy of breast cancer.

Background: Methylene blue as tracer used in sentinel lymph node biopsy (SLNB) have low detection rate and high false negative rate. Indocyanine green (ICG) can detect the flow of subcutaneous lymphatic vessels and the position of lymph nodes dynamically. This study sought to evaluate the efficacy of ICG combined with methylene blue staining in SLNB of breast cancer. Methods: One hundred and fifty-six early breast cancer patients treated at our hospital from July 2020 to May 2022 were enrolled in this study. SLNB was performed by ICG combined with methylene blue staining under the guidance of the fluorescent tracer navigation system FLI-10B. Standard axillary lymph node dissection (ALND) was performed in patients with sentinel lymph node (SLN) metastasis confirmed by intraoperative frozen pathology, while low ALND was performed in patients with negative SLNs. According to the staining condition, the SLNs were divided into: (I) the combined group (SLNs with methylene blue staining and/or ICG luminescence); (II) the methylene blue group (SLNs with methylene blue staining alone); and (III) the ICG group (SLNs with ICG luminescence alone). The detection rate, accuracy, sensitivity, and false negative rate of SLNB were compared among the 3 groups. Results: A total of 592 SLNs were detected in the combined group (average 3.8 SLNs), yielding a detection rate of 97.4%; the accuracy, sensitivity, and false negative rates were 97.4%, 92.7%, and 7.3%. In the methylene blue group, 390 SLNs were detected (average 2.5 SLNs), yielding a detection rate of 84.6%; the accuracy, sensitivity, and false negative rates were 83.3%, 89.1%, and 10.9%. A total of 483 SLNs were detected in the ICG group (average 3.1 SLNs), the detection rate was 92.9%; the accuracy, sensitivity and false negative rates were 91.7%, 90.9%, and 9.1%. The average number of detected SLNs, detection rate and accuracy rate in the combined group were higher than those in the methylene blue group (P<0.05), and the accuracy rate of the combined group was higher than that of the ICG group (P<0.05). Conclusions: ICG combined with methylene blue staining is a promising and effective tracing strategy in the SLNB of breast cancer with high detection and accuracy rates.

Feature Selection Pipelines with Classification for Non-targeted Metabolomics Combining the Neural Network and Genetic Algorithm.

Non-targeted metabolomics via high-resolution mass spectrometry methods, such as direct infusion Fourier transform-ion cyclotron resonance mass spectrometry (DI-FT-ICR MS), produces data sets with thousands of features. By contrast, the number of samples is in general substantially lower. This disparity presents challenges when analyzing non-targeted metabolomics data sets and often requires custom methods to uncover information not always accessible via classical statistical techniques. In this work, we present a pipeline that combines a convolutional neural network with traditional statistical approaches and an adaptation of a genetic algorithm. The developed method was applied to a lifestyle intervention cohort data set, where subjects at risk of type 2 diabetes underwent an oral glucose tolerance test. Feature selection is the final result of the pipeline, achieved through classification of the data set via a neural network, with a precision-recall score of over 0.9 on the test set. The features most relevant for the described classification were then chosen via a genetic algorithm. The output of the developed pipeline encompasses approximately 200 features with high predictive scores, providing a fingerprint of the metabolic changes in the prediabetic class on the data set. Our framework presents a new approach which allows to apply complex modeling based on convolutional neural networks for the analysis of high-resolution mass spectrometric data.

A Compositional Model to Predict the Aggregated Isotope Distribution for Average DNA and RNA Oligonucleotides.

Structural modifications of DNA and RNA molecules play a pivotal role in epigenetic and posttranscriptional regulation. To characterise these modifications, more and more MS and MS/MS- based tools for the analysis of nucleic acids are being developed. To identify an oligonucleotide in a mass spectrum, it is useful to compare the obtained isotope pattern of the molecule of interest to the one that is theoretically expected based on its elemental composition. However, this is not straightforward when the identity of the molecule under investigation is unknown. Here, we present a modelling approach for the prediction of the aggregated isotope distribution of an average DNA or RNA molecule when a particular (monoisotopic) mass is available. For this purpose, a theoretical database of all possible DNA/RNA oligonucleotides up to a mass of 25 kDa is created, and the aggregated isotope distribution for the entire database of oligonucleotides is generated using the BRAIN algorithm. Since this isotope information is compositional in nature, the modelling method is based on the additive log-ratio analysis of Aitchison. As a result, a univariate weighted polynomial regression model of order 10 is fitted to predict the first 20 isotope peaks for DNA and RNA molecules. The performance of the prediction model is assessed by using a mean squared error approach and a modified Pearson's χ2 goodness-of-fit measure on experimental data. Our analysis has indicated that the variability in spectral accuracy contributed more to the errors than the approximation of the theoretical isotope distribution by our proposed average DNA/RNA model. The prediction model is implemented as an online tool. An R function can be downloaded to incorporate the method in custom analysis workflows to process mass spectral data.

Current and future deep learning algorithms for tandem mass spectrometry (MS/MS)-based small molecule structure elucidation.

RATIONALE: Structure elucidation of small molecules has been one of the cornerstone applications of mass spectrometry for decades. Despite the increasing availability of software tools, structure elucidation from tandem mass spectrometry (MS/MS) data remains a challenging task, leaving many spectra unidentified. However, as an increasing number of reference MS/MS spectra are being curated at a repository scale and shared on public servers, there is an exciting opportunity to develop powerful new deep learning (DL) models for automated structure elucidation. ARCHITECTURES: Recent early-stage DL frameworks mostly follow a "two-step approach" that translates MS/MS spectra to database structures after first predicting molecular descriptors. The related architectures could suffer from: (1) computational complexity because of the separate training of descriptor-specific classifiers, (2) the high dimensional nature of mass spectral data and information loss due to data preprocessing, (3) low substructure coverage and class imbalance problem of predefined molecular fingerprints. Inspired by successful DL frameworks employed in drug discovery fields, we have conceptualized and designed hypothetical DL architectures to tackle the above issues. For (1), we recommend multitask learning to achieve better performance with fewer classifiers by grouping structurally related descriptors. For (2) and (3), we introduce feature engineering to extract condensed and higher-order information from spectra and structure data. For instance, encoding spectra with subtrees and pre-calculated spectral patterns add peak interactions to the model input. Encoding structures with graph convolutional networks incorporates connectivity within a molecule. The joint embedding of spectra and structures can enable simultaneous spectral library and molecular database search. CONCLUSIONS: In principle, given enough training data, adapted DL architectures, optimal hyperparameters and computing power, DL frameworks can predict small molecule structures, completely or at least partially, from MS/MS spectra. However, their performance and general applicability should be fairly evaluated against classical machine learning frameworks.

MESSAR: Automated recommendation of metabolite substructures from tandem mass spectra.

Despite the increasing importance of non-targeted metabolomics to answer various life science questions, extracting biochemically relevant information from metabolomics spectral data is still an incompletely solved problem. Most computational tools to identify tandem mass spectra focus on a limited set of molecules of interest. However, such tools are typically constrained by the availability of reference spectra or molecular databases, limiting their applicability of generating structural hypotheses for unknown metabolites. In contrast, recent advances in the field illustrate the possibility to expose the underlying biochemistry without relying on metabolite identification, in particular via substructure prediction. We describe an automated method for substructure recommendation motivated by association rule mining. Our framework captures potential relationships between spectral features and substructures learned from public spectral libraries. These associations are used to recommend substructures for any unknown mass spectrum. Our method does not require any predefined metabolite candidates, and therefore it can be used for the hypothesis generation or partial identification of unknown unknowns. The method is called MESSAR (MEtabolite SubStructure Auto-Recommender) and is implemented in a free online web service available at messar.biodatamining.be.

Metabolic diversity conveyed by the process leading to glutathione accumulation in inactivated dry yeast: A synthetic media study.

Glutathione-rich inactivated dry yeasts (GSH-IDY) are purported to accumulate glutathione intracellularly and then released into the must. Glutathione is beneficial for wine quality, but research has highlighted that GSH-IDYs have a synergic antioxidant effect similar to that of molecular GSH. Combination of negative mode ultra-high-resolution Fourrier-Transform Ion-Cyclotron-Resonance Mass Spectrometry ((-)FT-ICR-MS), ultra-high-performance liquid chromatography coupled to a Quadrupole-Time of Flight mass spectrometer (UHPLC-Q-ToF-MS) and HPLC/Diode Detector Array (DAD)-Fluorescence spectroscopy was applied to three inactivated dry yeasts soluble fractions, with increasing intracellular glutathione concentration, in order to explore the chemical diversity released in different synthetic media. Using the mean of size exclusion chromatography/DAD and fluorescence detection we report than most of the signals detected were below the 5-75 kDa-calibrated region of the chromatogram, indicating that most of the soluble protein fraction is composed of low molecular weight soluble peptides. In light of these results, high-resolution mass spectrometry was used to scan and annotate the low molecular weight compounds from 50 to 1500 Da and showed that GSH level of enrichment in IDYs was correlated to a discriminant chemical diversity of the corresponding soluble fractions. Our results clearly show an impact of the GSH accumulation process not only visible on the glutathione itself, but also on the global diversity of compounds. Within the 1674 ions detected by (-)FT-ICR-MS, the ratio of annotated elemental formulas containing carbon, hydrogen, oxygen, nitrogen and sulfur (CHONS) to annotated elemental formulas containing carbon, hydrogen, oxygen (CHO) increased from 0.2 to 2.1 with the increasing levels of IDYs GSH content and 36 unique CHONS annotated formulas were unique to the IDY with the highest concentration of GSH. Amongst the 1674 detected ions 193 were annotated as potential peptides (from 2 to 5 residues), 61 ions were annotated as unique amino acid combinations and 46% of which being significantly more intense in GSH-rich IDY. Thus, the process leading to the accumulation of glutathione also involves other metabolic pathways which contribute to an increase in CHONS containing compounds potentially released in wine, notably peptides.

The Antioxidant Potential of White Wines Relies on the Chemistry of Sulfur-Containing Compounds: An Optimized DPPH Assay.

The DPPH (2,2-Diphenyl-1-picrylhydrazyl) assay is an easy and efficient method commonly used to determine the antioxidant capacity of many food matrices and beverages. In contrast with red wines, white wines are poorer in antioxidant polyphenolics, and the more hydrophilic sulfur-containing compounds in them may contribute significantly to their antioxidant capacity. The modification of the classical DPPH method, with a methanol-buffer and the measure of EC20 (quantity of sample needed to decrease the initial DPPH concentration by 20%) has shown that sulfur-containing compounds such as cysteine (0.037 ± 0.003), glutathione (0.054 ± 0.003) or methanethiol (0.104 ± 0.003) appeared to bear antioxidant capacity comparable to well known phenolic compounds, such as catechin (0.035 ± 0.003), caffeic acid (0.057 ± 0.003) and ferulic acid (0.108 ± 0.003), respectively. In the case of white wines, the comparison with REDOX-sensory scores showed that results from this modified DPPH assay are strongly correlated with sensory attributes (r = 0.73, p < 0.1). These results provide an unprecedented illustration of the important contribution of these sulfur-containing compounds to the radical quenching ability of white wines.

Non-Saccharomyces Yeasts Nitrogen Source Preferences: Impact on Sequential Fermentation and Wine Volatile Compounds Profile.

Nitrogen sources in the must are important for yeast metabolism, growth, and performance, and wine volatile compounds profile. Yeast assimilable nitrogen (YAN) deficiencies in grape must are one of the main causes of stuck and sluggish fermentation. The nitrogen requirement of Saccharomyces cerevisiae metabolism has been described in detail. However, the YAN preferences of non-Saccharomyces yeasts remain unknown despite their increasingly widespread use in winemaking. Furthermore, the impact of nitrogen consumption by non-Saccharomyces yeasts on YAN availability, alcoholic performance and volatile compounds production by S. cerevisiae in sequential fermentation has been little studied. With a view to improving the use of non-Saccharomyces yeasts in winemaking, we studied the use of amino acids and ammonium by three strains of non-Saccharomyces yeasts (Starmerella bacillaris, Metschnikowia pulcherrima, and Pichia membranifaciens) in grape juice. We first determined which nitrogen sources were preferentially used by these yeasts in pure cultures at 28 and 20°C (because few data are available). We then carried out sequential fermentations at 20°C with S. cerevisiae, to assess the impact of the non-Saccharomyces yeasts on the availability of assimilable nitrogen for S. cerevisiae. Finally, 22 volatile compounds were quantified in sequential fermentation and their levels compared with those in pure cultures of S. cerevisiae. We report here, for the first time, that non-Saccharomyces yeasts have specific amino-acid consumption profiles. Histidine, methionine, threonine, and tyrosine were not consumed by S. bacillaris, aspartic acid was assimilated very slowly by M. pulcherrima, and glutamine was not assimilated by P. membranifaciens. By contrast, cysteine appeared to be a preferred nitrogen source for all non-Saccharomyces yeasts. In sequential fermentation, these specific profiles of amino-acid consumption by non-Saccharomyces yeasts may account for some of the interactions observed here, such as poorer performances of S. cerevisiae and volatile profile changes.

Digging into the low molecular weight peptidome with the OligoNet web server.

Bioactive peptides play critical roles in regulating many biological processes. Recently, natural short peptides biomarkers are drawing significant attention and are considered as "hidden treasure" of drug candidates. High resolution and high mass accuracy provided by mass spectrometry (MS)-based untargeted metabolomics would enable the rapid detection and wide coverage of the low-molecular-weight peptidome. However, translating unknown masses (<1 500 Da) into putative peptides is often limited due to the lack of automatic data processing tools and to the limit of peptide databases. The web server OligoNet responds to this challenge by attempting to decompose each individual mass into a combination of amino acids out of metabolomics datasets. It provides an additional network-based data interpretation named "Peptide degradation network" (PDN), which unravels interesting relations between annotated peptides and generates potential functional patterns. The ab initio PDN built from yeast metabolic profiling data shows a great similarity with well-known metabolic networks, and could aid biological interpretation. OligoNet allows also an easy evaluation and interpretation of annotated peptides in systems biology, and is freely accessible at https://daniellyz200608105.shinyapps.io/OligoNet/ .

Wine microbiome: A dynamic world of microbial interactions.

Most fermented products are generated by a mixture of microbes. These microbial consortia perform various biological activities responsible for the nutritional, hygienic, and aromatic qualities of the product. Wine is no exception. Substantial yeast and bacterial biodiversity is observed on grapes, and in both must and wine. The diverse microorganisms present interact throughout the winemaking process. The interactions modulate the hygienic and sensorial properties of the wine. Many studies have been conducted to elucidate the nature of these interactions, with the aim of establishing better control of the two fermentations occurring during wine processing. However, wine is a very complex medium making such studies difficult. In this review, we present the current state of research on microbial interactions in wines. We consider the different kinds of interactions between different microorganisms together with the consequences of these interactions. We underline the major challenges to obtaining a better understanding of how microbes interact. Finally, strategies and methodologies that may help unravel microbe interactions in wine are suggested.

MetICA: independent component analysis for high-resolution mass-spectrometry based non-targeted metabolomics.

BACKGROUND: Interpreting non-targeted metabolomics data remains a challenging task. Signals from non-targeted metabolomics studies stem from a combination of biological causes, complex interactions between them and experimental bias/noise. The resulting data matrix usually contain huge number of variables and only few samples, and classical techniques using nonlinear mapping could result in computational complexity and overfitting. Independent Component Analysis (ICA) as a linear method could potentially bring more meaningful results than Principal Component Analysis (PCA). However, a major problem with most ICA algorithms is the output variations between different runs and the result of a single ICA run should be interpreted with reserve. RESULTS: ICA was applied to simulated and experimental mass spectrometry (MS)-based non-targeted metabolomics data, under the hypothesis that underlying sources are mutually independent. Inspired from the Icasso algorithm, a new ICA method, MetICA was developed to handle the instability of ICA on complex datasets. Like the original Icasso algorithm, MetICA evaluated the algorithmic and statistical reliability of ICA runs. In addition, MetICA suggests two ways to select the optimal number of model components and gives an order of interpretation for the components obtained. CONCLUSIONS: Correlating the components obtained with prior biological knowledge allows understanding how non-targeted metabolomics data reflect biological nature and technical phenomena. We could also extract mass signals related to this information. This novel approach provides meaningful components due to their independent nature. Furthermore, it provides an innovative concept on which to base model selection: that of optimizing the number of reliable components instead of trying to fit the data. The current version of MetICA is available at https://github.com/daniellyz/MetICA.

[Clinical and biological features of familial nonmedullary thyroid carcinoma].

OBJECTIVE: To analyze the clinical and biological features of familial nonmedullary thyroid carcinoma (FNMTC). METHODS: Clinical data of 66 FNMTC cases of 32 pedigrees was retrospectively analyzed, compared with that of 182 control cases taken randomly from the patients with sporadic papillary thyroid carcinoma (SPTC), who were diagnosed and treated in Tianjin Cancer Hospital between January 2008 and November 2012. The features of FNMTC of the first and second generations were objectively analyzed. Some data quoted from the literature were also used for the analysis. RESULTS: The median age at diagnosis of all the 66 FNMTC patients was 44 years, and 57 (86.4%) were females. Moreover, 71.2% (47 patients, 23 pedigrees) of the FNMTC patients exhibited a sibling relationship, and 28.8% (19 patients, 9 pedigrees) of the FNMTC patients exhibited a parent-offspring relationship, and 9 cases in the first generation and 10 cases in the second generation. There were significant differences between the FNMTC group and SPTC group in terms of tumor multicentricity, tumor bilaterality, lymph node metastasis, central lymph node metastasis, concomitant chronic thyroiditis and recurrence (P < 0.05). Compared with SPTC, sibling FNMTC presented a higher rate of central lymph node metastasis, while parent-offspring FNMTC showed frequent tumor bilaterality and a higher rate of recurrence (P < 0.05). Besides, patients in the second generation were diagnosed at an earlier age and had a higher male rate, the tumors were more frequently multifocal and bilateral, and had a higher rate of lymph node metastasis. CONCLUSIONS: FNMTC may be more aggressive than SPTC and patients in the second generation may exhibit the "anticipation" phenomenon. It's necessary to make sufficient detailed interrogation and long-term follow-up of the patients and their family for providing individual recommendations for clinical management.

[Harmonic scalpel versus conventional resection in thyroid surgery: a meta analysis on the safety outcomes].

OBJECTIVE: To compare the safety between harmonic scalpel and conventional resection in total or near total thyroidectomy with meta-analysis. METHODS: The prospective randomized controlled studies were searched for in electronic databases (MEDLINE, EMBASE, Cochrane Library). Meta analysis of acquired data was performed through the use of RevMan 5.2 software. RESULTS: According to the inclusion criterion, 13 articles were enrolled which compared on the safety between harmonic scalpel and conventional resection in thyroid surgery. A total of 1620 patients with thyroid tumor were enrolled, including 802 patients in harmonic scalpel group and 818 patients in conventional resection group. Compared with conventional resection group, the harmonic scalpel group showed shorter time of surgery, the weighted mean difference (WMD) and their 95% confidence interval (95%CI) was -21.06[-25.65, -16.47], Z = 8.99, P < 0.00001; less intra-operative blood loss, WMD and 95%CI was -14.36[-20.67, -8.06], Z = 4.46, P < 0.00001; less post-operative drain output (WMD and 95%CI was -7.47[-11.35, -3.58], Z = 3.77, P = 0.0002); less hospitalization charges (WMD and 95%CI was -117.97[-131.65, -104.29], Z = 16.90, P < 0.00001). The incidence of postoperative transient recurrent laryngeal nerve dysfunction and transient hypocalcemia were similar in both groups. CONCLUSION: Using the harmonic scalpel in thyroid surgery was as safe as that of the conventional technique with the advantage of shorter time of surgery, less intraoperative blood loss and less postoperative drain output.

[Biological characteristics and management of familial papillary thyroid carcinoma].

OBJECTIVE: To analyze the clinical biological characteristics and investigate the managements of familial papillary thyroid carcinoma (FPTC). METHODS: Clinical data of 36 patients with PTC from 15 families were retrospectively analyzed compared with 95 control cases taken randomly from the patients with sporadic PTC diagnosed and treated in Tianjin Cancer Hospital between January 2010 and August 2011. RESULTS: Of the 36 patients with FPTC, 15 (41.7%) were ≥45 years old, 12 (33.3%) had bilateral carcinoma, 20 (55.6%) were multifocality, 27 (75.0%) had neck lymph node metastases, 17 (47.2%) coexisted thyroid benign tumors. Of the 95 patients with SPTC, 60 (63.2%) were ≥45 years old, 12(12.6%)had bilateral carcinomas, 21 (22.1%) were multifocality, 51 (53.7%) had neck lymph node metastases, and 26(27.4%)coexisted thyroid benign tumors. Of the 36 patients with FPTC, 22 (61.1%) underwent total thyroidectomy and 14 (38.9%) with unilateral thyroidectomy plus isthmusectomy, 3 (8.3%) received unilateral or bilateral lateral neck dissection and central compartment neck dissection (CND), 7 (19.4%) received unilateral or bilateral posterolateral neck dissection and CND, 6 (16.6%) received posterolateral neck dissection and bilateral CND, and 20 (55.6%) received unilateral or bilateral CND. CONCLUSIONS: Age at disease presentation of FPTC was younger than that of SPTC. FPTC has higher rates of multifocality and bilateral carcinoma coexisting with thyroid benign tumor than those of SPTC. It necessary to take family history in detail and to evaluate diseases before operation.