Integrated convolution and self-attention for improving peptide toxicity prediction.

MOTIVATION: Peptides are promising agents for the treatment of a variety of diseases due to their specificity and efficacy. However, the development of peptide-based drugs is often hindered by the potential toxicity of peptides, which poses a significant barrier to their clinical application. Traditional experimental methods for evaluating peptide toxicity are time-consuming and costly, making the development process inefficient. Therefore, there is an urgent need for computational tools specifically designed to predict peptide toxicity accurately and rapidly, facilitating the identification of safe peptide candidates for drug development. RESULTS: We provide here a novel computational approach, CAPTP, which leverages the power of convolutional and self-attention to enhance the prediction of peptide toxicity from amino acid sequences. CAPTP demonstrates outstanding performance, achieving a Matthews correlation coefficient of approximately 0.82 in both cross-validation settings and on independent test datasets. This performance surpasses that of existing state-of-the-art peptide toxicity predictors. Importantly, CAPTP maintains its robustness and generalizability even when dealing with data imbalances. Further analysis by CAPTP reveals that certain sequential patterns, particularly in the head and central regions of peptides, are crucial in determining their toxicity. This insight can significantly inform and guide the design of safer peptide drugs. AVAILABILITY AND IMPLEMENTATION: The source code for CAPTP is freely available at https://github.com/jiaoshihu/CAPTP.

CodLncScape Provides a Self-Enriching Framework for the Systematic Collection and Exploration of Coding LncRNAs.

Recent studies have revealed that numerous lncRNAs can translate proteins under specific conditions, performing diverse biological functions, thus termed coding lncRNAs. Their comprehensive landscape, however, remains elusive due to this field's preliminary and dispersed nature. This study introduces codLncScape, a framework for coding lncRNA exploration consisting of codLncDB, codLncFlow, codLncWeb, and codLncNLP. Specifically, it contains a manually compiled knowledge base, codLncDB, encompassing 353 coding lncRNA entries validated by experiments. Building upon codLncDB, codLncFlow investigates the expression characteristics of these lncRNAs and their diagnostic potential in the pan-cancer context, alongside their association with spermatogenesis. Furthermore, codLncWeb emerges as a platform for storing, browsing, and accessing knowledge concerning coding lncRNAs within various programming environments. Finally, codLncNLP serves as a knowledge-mining tool to enhance the timely content inclusion and updates within codLncDB. In summary, this study offers a well-functioning, content-rich ecosystem for coding lncRNA research, aiming to accelerate systematic studies in this field.

Multi-omics clustering for cancer subtyping based on latent subspace learning.

The increased availability of high-throughput technologies has enabled biomedical researchers to learn about disease etiology across multiple omics layers, which shows promise for improving cancer subtype identification. Many computational methods have been developed to perform clustering on multi-omics data, however, only a few of them are applicable for partial multi-omics in which some samples lack data in some types of omics. In this study, we propose a novel multi-omics clustering method based on latent sub-space learning (MCLS), which can deal with the missing multi-omics for clustering. We utilize the data with complete omics to construct a latent subspace using PCA-based feature extraction and singular value decomposition (SVD). The data with incomplete multi-omics are then projected to the latent subspace, and spectral clustering is performed to find the clusters. The proposed MCLS method is evaluated on seven different cancer datasets on three levels of omics in both full and partial cases compared to several state-of-the-art methods. The experimental results show that the proposed MCLS method is more efficient and effective than the compared methods for cancer subtype identification in multi-omics data analysis, which provides important references to a comprehensive understanding of cancer and biological mechanisms. AVAILABILITY: The proposed method can be freely accessible at https://github.com/ShangCS/MCLS.

Interactive gene identification for cancer subtyping based on multi-omics clustering.

Recent advances in multi-omics databases offer the opportunity to explore complex systems of cancers across hierarchical biological levels. Some methods have been proposed to identify the genes that play a vital role in disease development by integrating multi-omics. However, the existing methods identify the related genes separately, neglecting the gene interactions that are related to the multigenic disease. In this study, we develop a learning framework to identify the interactive genes based on multi-omics data including gene expression. Firstly, we integrate different omics based on their similarities and apply spectral clustering for cancer subtype identification. Then, a gene co-expression network is construct for each cancer subtype. Finally, we detect the interactive genes in the co-expression network by learning the dense subgraphs based on the L1 prosperities of eigenvectors in the modularity matrix. We apply the proposed learning framework on a multi-omics cancer dataset to identify the interactive genes for each cancer subtype. The detected genes are examined by DAVID and KEGG tools for systematic gene ontology enrichment analysis. The analysis results show that the detected genes have relationships to cancer development and the genes in different cancer subtypes are related to different biological processes and pathways, which are expected to yield important references for understanding tumor heterogeneity and improving patient survival.

Missing Value Imputation With Low-Rank Matrix Completion in Single-Cell RNA-Seq Data by Considering Cell Heterogeneity.

Single-cell RNA-sequencing (scRNA-seq) technologies enable the measurements of gene expressions in individual cells, which is helpful for exploring cancer heterogeneity and precision medicine. However, various technical noises lead to false zero values (missing gene expression values) in scRNA-seq data, termed as dropout events. These zero values complicate the analysis of cell patterns, which affects the high-precision analysis of intra-tumor heterogeneity. Recovering missing gene expression values is still a major obstacle in the scRNA-seq data analysis. In this study, taking the cell heterogeneity into consideration, we develop a novel method, called single cell Gauss-Newton Gene expression Imputation (scGNGI), to impute the scRNA-seq expression matrices by using a low-rank matrix completion. The obtained experimental results on the simulated datasets and real scRNA-seq datasets show that scGNGI can more effectively impute the missing values for scRNA-seq gene expression and improve the down-stream analysis compared to other state-of-the-art methods. Moreover, we show that the proposed method can better preserve gene expression variability among cells. Overall, this study helps explore the complex biological system and precision medicine in scRNA-seq data.

Extracellular vesicles derived from Wharton's Jelly mesenchymal stem cells inhibit the tumor environment via the miR-125b/HIF1α signaling pathway.

Triple negative breast cancer (TNBC) is associated with worse outcomes and results in high mortality; therefore, great efforts are required to find effective treatment. In the present study, we suggested a novel strategy to treat TNBC using mesenchymal stem cell (MSC)-derived extracellular vesicles (EV) to transform the behaviors and cellular communication of TNBC cells (BCC) with other non-cancer cells related to tumorigenesis and metastasis. Our data showed that, BCC after being internalized with EV derived from Wharton's Jelly MSC (WJ-EV) showed the impaired proliferation, stemness properties, tumorigenesis and metastasis under hypoxic conditions. Moreover, these inhibitory effects may be involved in the transfer of miRNA-125b from WJ-EV to BCC, which downregulated the expression of HIF1α and target genes related to proliferation, epithelial-mesenchymal transition, and angiogenesis. Of note, WJ-EV-internalized BCC (wBCC) showed transformed behaviors that attenuated the in vivo development and metastatic ability of TNBC, the angiogenic abilities of endothelial cells and endothelial progenitor cells and the generation of cancer-associated fibroblasts from MSC. Furthermore, wBCC generated a new EV with modified functions that contributed to the inhibitory effects on tumorigenesis and metastasis of TNBC. Taken together, our findings suggested that WJ-EV treatment is a promising therapy that results in the generation of wBCC to interrupt the cellular crosstalk in the tumor environment and inhibit the tumor progression in TNBC.

Pharmacological intervention of cholesterol sulfate-mediated T cell exclusion promotes antitumor immunity.

Effective cancer immunotherapy requires physical contact of T cells with cancer cells. However, tumors often constitute special microenvironments that exclude T cells and resist immunotherapy. Cholesterol sulfate (CS) is a product of sulfotransferase SULT2B1b and acts as an endogenous inhibitor of DOCK2, a Rac activator essential for migration and activation of lymphocytes. We have recently shown that cancer-derived CS prevents tumor infiltration by effector T cells. Therefore, SULT2B1b may be a therapeutic target to dampen CS-mediated immune evasion. Here, we identified 3ß-hydroxy-5-cholenoic acid (3ß-OH-5-Chln) as a cell-active inhibitor of SULT2B1b. 3ß-OH-5-Chln inhibited the cholesterol sulfotransferase activity of SULT2B1b in vitro and suppressed CS production from cancer cells expressing SULT2B1b. In vivo administration of 3ß-OH-5-Chln locally reduced CS level in murine CS-producing tumors and increased infiltration of CD8+ T cells. When combined with immune checkpoint blockade or antigen-specific T cell transfer, 3ß-OH-5-Chln suppressed the growth of CS-producing tumors. These results demonstrate that pharmacological inhibition of SULT2B1b can promote antitumor immunity through suppressing CS-mediated T cell exclusion.

Sequential reinforcement active feature learning for gene signature identification in renal cell carcinoma.

Renal cell carcinoma (RCC) is one of the deadliest cancers and mainly consists of three subtypes: kidney clear cell carcinoma (KIRC), kidney papillary cell carcinoma (KIRP), and kidney chromophobe (KICH). Gene signature identification plays an important role in the precise classification of RCC subtypes and personalized treatment. However, most of the existing gene selection methods focus on statically selecting the same informative genes for each subtype, and fail to consider the heterogeneity of patients which causes pattern differences in each subtype. In this work, to explore different informative gene subsets for each subtype, we propose a novel gene selection method, named sequential reinforcement active feature learning (SRAFL), which dynamically acquire the different genes in each sample to identify the different gene signatures for each subtype. The proposed SRAFL method combines the cancer subtype classifier with the reinforcement learning (RL) agent, which sequentially select the active genes in each sample from three mixed RCC subtypes in a cost-sensitive manner. Moreover, the module-based gene filtering is run before gene selection to filter the redundant genes. We mainly evaluate the proposed SRAFL method based on mRNA and long non-coding RNA (lncRNA) expression profiles of RCC datasets from The Cancer Genome Atlas (TCGA). The experimental results demonstrate that the proposed method can automatically identify different gene signatures for different subtypes to accurately classify RCC subtypes. More importantly, we here for the first time show the proposed SRAFL method can consider the heterogeneity of samples to select different gene signatures for different RCC subtypes, which shows more potential for the precision-based RCC care in the future.

ToxIBTL: prediction of peptide toxicity based on information bottleneck and transfer learning.

MOTIVATION: Recently, peptides have emerged as a promising class of pharmaceuticals for various diseases treatment poised between traditional small molecule drugs and therapeutic proteins. However, one of the key bottlenecks preventing them from therapeutic peptides is their toxicity toward human cells, and few available algorithms for predicting toxicity are specially designed for short-length peptides. RESULTS: We present ToxIBTL, a novel deep learning framework by utilizing the information bottleneck principle and transfer learning to predict the toxicity of peptides as well as proteins. Specifically, we use evolutionary information and physicochemical properties of peptide sequences and integrate the information bottleneck principle into a feature representation learning scheme, by which relevant information is retained and the redundant information is minimized in the obtained features. Moreover, transfer learning is introduced to transfer the common knowledge contained in proteins to peptides, which aims to improve the feature representation capability. Extensive experimental results demonstrate that ToxIBTL not only achieves a higher prediction performance than state-of-the-art methods on the peptide dataset, but also has a competitive performance on the protein dataset. Furthermore, a user-friendly online web server is established as the implementation of the proposed ToxIBTL. AVAILABILITY AND IMPLEMENTATION: The proposed ToxIBTL and data can be freely accessible at http://server.wei-group.net/ToxIBTL. Our source code is available at https://github.com/WLYLab/ToxIBTL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Cancer-derived cholesterol sulfate is a key mediator to prevent tumor infiltration by effector T cells.

Effective tumor immunotherapy requires physical contact of T cells with cancer cells. However, tumors often constitute a specialized microenvironment that excludes T cells from the vicinity of cancer cells, and its underlying mechanisms are still poorly understood. DOCK2 is a Rac activator critical for migration and activation of lymphocytes. We herein show that cancer-derived cholesterol sulfate (CS), a lipid product of the sulfotransferase SULT2B1b, acts as a DOCK2 inhibitor and prevents tumor infiltration by effector T cells. Using clinical samples, we found that CS was abundantly produced in certain types of human cancers such as colon cancers. Functionally, CS-producing cancer cells exhibited resistance to cancer-specific T-cell transfer and immune checkpoint blockade. Although SULT2B1b is known to sulfate oxysterols and inactivate their tumor-promoting activity, the expression levels of cholesterol hydroxylases, which mediate oxysterol production, are low in SULT2B1b-expressing cancers. Therefore, SULT2B1b inhibition could be a therapeutic strategy to disrupt tumor immune evasion in oxysterol-non-producing cancers. Thus, our findings define a previously unknown mechanism for tumor immune evasion and provide a novel insight into the development of effective immunotherapies.

DOCK8 deficiency causes a skewing to type 2 immunity in the gut with expansion of group 2 innate lymphoid cells.

Dedicator of cytokinesis 8 (DOCK8) is a guanine nucleotide exchange factor (GEF) for Cdc42. In humans, homozygous or compound heterozygous deletions in DOCK8 cause a combined immunodeficiency characterized by various allergic diseases including food allergies. Although group 2 innate lymphoid cells (ILC2s) contribute to the development of allergic inflammation by producing interleukin (IL)-5 and IL-13, the role of ILC2s in DOCK8 deficiency has not been fully explored. With the use of cytometry by time-of-flight (CyTOF), we performed high-dimensional phenotyping of intestinal immune cells and found that DOCK8-deficient (Dock8-/-) mice exhibited expansion of ILC2s and other leukocytes associated with type 2 immunity in the small intestine. Moreover, IL-5- and IL-13-producing cells markedly increased in Dock8-/- mice, and the majority of them were lineage-negative cells, most likely ILC2s. Intestinal ILC2s expanded when DOCK8 expression was selectively deleted in hematopoietic cells. Importantly, intestinal ILC2 expansion was also observed in Dock8VAGR mice having mutations in the catalytic center of DOCK8, thereby failing to activate Cdc42. Our findings indicate that DOCK8 is a negative regulator of intestinal ILC2s to inhibit their expansion via Cdc42 activation, and that deletion of DOCK8 causes a skewing to type 2 immunity in the gut.

ATSE: a peptide toxicity predictor by exploiting structural and evolutionary information based on graph neural network and attention mechanism.

MOTIVATION: Peptides have recently emerged as promising therapeutic agents against various diseases. For both research and safety regulation purposes, it is of high importance to develop computational methods to accurately predict the potential toxicity of peptides within the vast number of candidate peptides. RESULTS: In this study, we proposed ATSE, a peptide toxicity predictor by exploiting structural and evolutionary information based on graph neural networks and attention mechanism. More specifically, it consists of four modules: (i) a sequence processing module for converting peptide sequences to molecular graphs and evolutionary profiles, (ii) a feature extraction module designed to learn discriminative features from graph structural information and evolutionary information, (iii) an attention module employed to optimize the features and (iv) an output module determining a peptide as toxic or non-toxic, using optimized features from the attention module. CONCLUSION: Comparative studies demonstrate that the proposed ATSE significantly outperforms all other competing methods. We found that structural information is complementary to the evolutionary information, effectively improving the predictive performance. Importantly, the data-driven features learned by ATSE can be interpreted and visualized, providing additional information for further analysis. Moreover, we present a user-friendly online computational platform that implements the proposed ATSE, which is now available at http://server.malab.cn/ATSE. We expect that it can be a powerful and useful tool for researchers of interest.

Detecting Interactive Gene Groups for Single-Cell RNA-Seq Data Based on Co-Expression Network Analysis and Subgraph Learning.

High-throughput sequencing technologies have enabled the generation of single-cell RNA-seq (scRNA-seq) data, which explore both genetic heterogeneity and phenotypic variation between cells. Some methods have been proposed to detect the related genes causing cell-to-cell variability for understanding tumor heterogeneity. However, most existing methods detect the related genes separately, without considering gene interactions. In this paper, we proposed a novel learning framework to detect the interactive gene groups for scRNA-seq data based on co-expression network analysis and subgraph learning. We first utilized spectral clustering to identify the subpopulations of cells. For each cell subpopulation, the differentially expressed genes were then selected to construct a gene co-expression network. Finally, the interactive gene groups were detected by learning the dense subgraphs embedded in the gene co-expression networks. We applied the proposed learning framework on a real cancer scRNA-seq dataset to detect interactive gene groups of different cancer subtypes. Systematic gene ontology enrichment analysis was performed to examine the detected genes groups by summarizing the key biological processes and pathways. Our analysis shows that different subtypes exhibit distinct gene co-expression networks and interactive gene groups with different functional enrichment. The interactive genes are expected to yield important references for understanding tumor heterogeneity.

Ensemble Feature Learning to Identify Risk Factors for Predicting Secondary Cancer.

Background: In recent years, the development and diagnosis of secondary cancer have become the primary concern of cancer survivors. A number of studies have been developing strategies to extract knowledge from the clinical data, aiming to identify important risk factors that can be used to prevent the recurrence of diseases. However, these studies do not focus on secondary cancer. Secondary cancer is lack of the strategies for clinical treatment as well as risk factor identification to prevent the occurrence. Methods: We propose an effective ensemble feature learning method to identify the risk factors for predicting secondary cancer by considering class imbalance and patient heterogeneity. We first divide the patients into some heterogeneous groups based on spectral clustering. In each group, we apply the oversampling method to balance the number of samples in each class and use them as training data for ensemble feature learning. The purpose of ensemble feature learning is to identify the risk factors and construct a diagnosis model for each group. The importance of risk factors is measured based on the properties of patients in each group separately. We predict secondary cancer by assigning the patient to a corresponding group and based on the diagnosis model in this corresponding group. Results: Analysis of the results shows that the decision tree obtains the best results for predicting secondary cancer in the three classifiers. The best results of the decision tree are 0.72 in terms of AUC when dividing the patients into 15 groups, 0.38 in terms of F1 score when dividing the patients into 20 groups. In terms of AUC, decision tree achieves 67.4% improvement compared to using all 20 predictor variables and 28.6% improvement compared to no group division. In terms of F1 score, decision tree achieves 216.7% improvement compared to using all 20 predictor variables and 80.9% improvement compared to no group division. Different groups provide different ranking results for the predictor variables. Conclusion: The accuracies of predicting secondary cancer using k-nearest neighbor, decision tree, support vector machine indeed increased after using the selected important risk factors as predictors. Group division on patients to predict secondary cancer on the separated models can further improve the prediction accuracies. The information discovered in the experiments can provide important references to the personality and clinical symptom representations on all phases of guide interventions, with the complexities of multiple symptoms associated with secondary cancer in all phases of the recurrent trajectory.

Cholesterol sulfate is a DOCK2 inhibitor that mediates tissue-specific immune evasion in the eye.

Although immune responses are essential to protect the body from infection, they can also harm tissues. Certain tissues and organs, including the eye, constitute specialized microenvironments that locally inhibit immune reactivity. Dedicator of cytokinesis protein 2 (DOCK2) is a Rac-specific guanine nucleotide exchange factor (GEF) that is predominantly found in hematopoietic cells. DOCK2 plays a key role in immune surveillance because it is essential for the activation and migration of leukocytes. DOCK2 mutations cause severe immunodeficiency in humans. We found that DOCK2-mediated Rac activation and leukocyte migration were effectively inhibited by cholesterol sulfate (CS), but not by cholesterol or other sulfated steroids. CS bound to the catalytic domain of DOCK2 and suppressed its GEF activity. Mass spectrometric quantification revealed that CS was most abundantly produced in the Harderian gland, which provides the lipids that form the oily layer of the tear film. Sulfation of cholesterol is mediated by the sulfotransferases SULT2B1b and, to a lesser extent, SULT2B1a, which are produced from the same gene through alternative splicing. By genetically inactivating Sult2b1, we showed that the lack of CS in mice augmented ultraviolet- and antigen-induced ocular surface inflammation, which was suppressed by administration of eye drops containing CS. Thus, CS is a naturally occurring DOCK2 inhibitor and contributes to the generation of the immunosuppressive microenvironment in the eye.

Clinical relevance of active straight leg raising, standing up, and walking after total knee arthroplasty in a cross-sectional study.

BACKGROUND: Active straight leg raising (ASLR) is used to assess restoration of the quadriceps muscle immediately after total knee arthroplasty (TKA). This study aimed to (1) compare the times required to accomplish ASLR, standing up, and walking after TKA, and (2) evaluate the correlation between the time required to accomplish ASLR and perioperative patient-related factors. METHODS: This cross-sectional study included 271 patients (335 primary TKAs performed using the conventional medial parapatellar approach). Postoperative times required until each activity was accomplished were confirmed. Various factors that might impact ASLR, including prosthetic design, were also evaluated. RESULTS: Post-TKA, it took 1.5 ± 0.5 days to accomplish ASLR, 1.3 ± 0.6 days to accomplish standing up, and 1.4 ± 0.7 days to accomplish walking. There were no significant correlations between any factor and ASLR. Strong correlations were found between the times required to accomplish standing up and walking (p < 0.0001, r = 0.804). There were no significant correlations between the times required to accomplish ASLR and standing up/walking. A longer time was necessary for ASLR accomplishment than for standing up (p < 0.001) and walking (p < 0.001). Standing up was accomplished earlier than walking (p = 0.008). CONCLUSIONS: There was no delay in post-TKA ASLR accomplishment compared with previous reports. No factors affecting ASLR during the perioperative period suggested that ASLR was controlled by factors other than knee joint-related factors. ASLR was not correlated with standing up/walking; hence, the clinical significance of ASLR immediately after TKA for early ambulation is unclear. LEVEL OF EVIDENCE: Prognostic study, Level II.

Quadriceps strength impairment in the mid- to long-term follow-up period after total knee arthroplasty.

PURPOSE: Quadriceps strength impairment after total knee arthroplasty (TKA) continues to be a concern. However, most studies of quadriceps strength have short-term follow-up periods. Whether quadriceps strength impairment occurs in the long-term follow-up period after TKA remains unclear. The purpose of this study was to compare the quadriceps strength between posterior cruciate ligament-retaining (CR) and substituting (PS) design mobile-bearing TKA (1) in the same patients after an average of 10 years and (2) between TKA patients and age-matched controls. METHODS: A prospective, quasi-randomized design was used. Thirty-four patients (68 knees) who underwent bilateral TKA (CR on one side and PS on the other) were followed for a minimum of 5 years, and 35 age-matched controls (70 knees) were evaluated. A handheld dynamometer was used to measure quadriceps isometric strength. For each patient, the maximum value of three trials was used. The ratio of muscle strength to body weight (MS/BW ratio; N/kg) was used to evaluate outcomes. RESULTS: The median MS/BW ratio was 3.3 (range 1.4-10.5) for CR 3.4 (range 0.9-9.3) for PS, and 4.6 (range 0.4-8.8) for controls. The MS/BW ratio did not differ between prosthesis designs, but was significantly smaller in both CR (p = 0.020) and PS (p = 0.024) than in controls. CONCLUSIONS: Posterior cruciate ligament-retaining TKA does not confer a substantial advantage an average of 10 years postoperatively. In addition, quadriceps strength, as measured using a hand-held dynamometer, was significantly lower in both TKA patient groups than in age-matched controls. Clinically, the results of this study indicate that quadriceps-strengthening exercises should be continued in the long term after TKA. LEVEL OF EVIDENCE: II.

Comparison of long-term clinical outcomes after bilateral mobile-bearing total knee arthroplasties using PCL-retaining and PCL-substituting implants in the same patients.

PURPOSE: Whether the posterior cruciate ligament (PCL) should be retained or substituted in total knee arthroplasty (TKA) remains an issue of concern. The purpose of this study was to perform within-patient comparisons of mid- and long-term clinical outcomes after mobile-bearing TKA using PCL-retaining (PCLR) and PCL-substituting (PCLS) implant designs. METHODS: Clinical outcomes were assessed in thirty-eight patients (76 knees) who underwent bilateral scheduled staged TKA with a PCLR design on one side and a PCLS design on the other. Median follow-up periods were 118 months (range 60-211) and 114 months (62-198) in knees with PCLR and PCLS implants, respectively. The preoperative diagnosis for all patients was osteoarthritis. The postoperative clinical results of mobile-bearing TKAs using PCLR and PCLS implant designs were evaluated. RESULTS: The postoperative Hospital for Special Surgery and the new Knee Society Knee Scoring System scores revealed no differences between PCLR and PCLS implant designs. Postoperative flexion and extension also did not differ between designs. Postoperative median femorotibial alignment was 4° for PCLR and 5° for PCLS implants, respectively; this difference was not significant. Six of the knees with PCLR and three of the knees with PCLS implants had radiolucent lines around the tibial prostheses; these were less than 1 mm and nonprogressive. CONCLUSIONS: Clinically good results were obtained at approximately 10 years after mobile-bearing TKA using both PCLR and PCLS implant designs bilaterally in the same patients. These results provide conclusive evidence that equivalent clinical results can be obtained with either implant design. LEVEL OF EVIDENCE: Therapeutic study, Level II.

Anteroposterior translation and range of motion after total knee arthroplasty using posterior cruciate ligament-retaining versus posterior cruciate ligament-substituting prostheses.

PURPOSE: It is still controversial whether anteroposterior (AP) translation magnitude after total knee arthroplasty (TKA) affects clinical outcomes, particularly range of motion (ROM). This study examined the following two questions: (1) are AP translations at the mid- and long-term follow-up different for knees within the same patient treated with posterior cruciate ligament-retaining (PCLR) versus posterior cruciate ligament-substituting (PCLS) mobile-bearing TKA prosthesis designs? (2) Is the ROM at the mid- and long-term follow-up for knees treated with PCLR and PCLS designs correlated with the AP translation? METHODS: Thirty-seven patients undergoing sequential bilateral TKA for osteoarthritis were prospectively enrolled. Patients received a PCLR implant in one knee and a PCLS implant in the other and were followed-up for an average 9.8 ± 3.2 years. The AP translations at 30° and 75° of knee flexion and the ROM of both knees were assessed. RESULTS: The implant design (p < 0.001), but not flexion angle (n.s.), had a significant effect on AP translation. AP translation values were larger in PCLR knees than in PCLS knees at both flexion angles (p < 0.0001). The ROM at the final follow-up in the two implant designs was similar (both 115°, n.s.). There was a weak correlation between ROM and AP translation at 30° in the PCLR knees (r = 0.397, p = 0.015), but no correlation at 75° or in the PCLS knees. CONCLUSIONS: Differently constrained prosthesis designs resulted in significantly different AP translational values within the same patient. This indicates that achieving good clinical outcomes and ROM after TKA may not be strongly influenced by the specifics of each patient's anatomical characteristics, but instead by knee constrainment. Clinically, this means that surgeons should familiarize themselves with the AP translation of the implant being used, as this may be the most important factor for optimizing outcomes after mobile-bearing TKA. Level of evidence II, prospective, comparative study.

Chemical Assignment of Structural Isomers of Sulfur-Containing Metabolites in Garlic by Liquid Chromatography-Fourier Transform Ion Cyclotron Resonance-Mass Spectrometry.

BACKGROUND: The chemical assignment of metabolites is crucial to understanding the relation between food composition and biological activity. OBJECTIVE: This study was designed to detect and chemically assign sulfur-containing metabolites by using LC-Fourier transform ion cyclotron resonance-mass spectrometry (FTICR-MS) in Allium plants. METHODS: Ultrahigh resolution (>250,000 full width at half-maximum) and mass accuracy (<1 mDa) by FTICR-MS allowed us to distinguish ions containing sulfur isotopes ((32)S and (34)S). RESULTS: Putative 69 S-containing monoisotopic ions (S-ions) were extracted from the metabolome data of onion (Allium cepa), green onion (Allium fistulosum), and garlic (Allium sativum) on the basis of theoretical mass differences between (32)S-ions and their (34)S-substituted counterparts and on the natural abundance of (34)S. Eight S-ions were chemically assigned by using the reference data according to the guidelines of the Metabolomics Standards Initiative. Three ions detected in garlic were assigned as derived from the isomers γ-glutamyl-S-1-propenylcysteine and γ-glutamyl-S-2-propenylcysteine and as S-2-propenylmercaptoglutathione on the basis of differences in key product ions identified in reference tandem MS spectra. CONCLUSION: The ability to discriminate between such geometric isomers will be extremely useful for the chemical assignment of unknown metabolites in MS-based metabolomics.