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Title: Strategies and Discoveries Leading to the Synthesis of Trichoaurantianolide Natural Products
Several years ago, a small family of diterpenoid natural products attracted our attention as novel targets for synthesis studies. Initially, four compounds were independently characterized by the research teams of Vidari1 and Steglich.2 Trichoaurantianolides AeD (1e4 of Fig. 9.1) were isolated from fruiting bodies of the mushrooms Tricholoma aurantium and Tricholoma fracticum in 1995. Subsequent efforts of Stermer and coworkers3 described the isolation of the closely related lepistal (5) and lepistol (6) of Fig. 9.2 as the corresponding C8 deoxygenated compounds of this family. In addition, the corresponding acetate of trichoaurantianolide B was discovered and named as 6-O-aetyl- trichoaurantin (7).2 Structure assignments were based upon extensive nuclear magnetic resonance (NMR) studies, and the features of relative stereo- chemistry were confirmed by an X-ray crystallographic analysis of trichoaurantianolide B (2).1b,2 These original investigators described the trichoaurantianolides as examples of a new class of diterpenes named as neodolastanes that signified a structural relationship to the tricyclic metabo- lites of marine origins known as dolastanes as represented by dolatriol (8)4 and the clavularane 95 of Fig. 9.2. Neodolastanes were defined as substances in which the bridgehead methyl substituent appears in a vicinal relationship with respect to the isopropyl group as exemplified in 4,5-deoxyneodolabelline (10) of Fig. 9.2, a related class of marine natural products.6 Steglich and coworkers2 also indicated an assignment of absolute stereo- chemistry for 2 that was based on Hamilton’s applications of linear-hypothesis testing of crystallographic data. This seldom-used technique was in agreement with the proposed absolute configuration of 2 that was advanced by Vidari, based on an assessment of the observed Cotton effects in CD spectroscopy. In 2003, Ohta and coworkers7 reported the discovery of related neodolastanes tricholomalides A, B, and C (structures 11, 12, and 13 of Fig. 9.3) from Tricholoma sp. They concluded that the tricholomalides possessed the opposite absolute configuration claimed for the trichoaurantianolides. This conclusion was based upon the independent analysis of their circular dichroism studies. By application of the octant rule for substituent effects on cisoid a,b- unsaturated ketones,8 Ohta and coworkers suggested a revision of the prior assignment of absolute configuration for the trichoaurantianolides. This asser- tion was advanced in spite of the consistently positive specific rotations recorded in different solvents for trichoaurantianolides A, B, and C1,2 versus the negative values of tricholomalides A (11) and B (12) (compare values in Figs. 9.1 and 9.3). Note that tricholomalide C (13) only differs from trichoaurantianolide B (2) as a C-8 diastereomeric alcohol, presented in the antipodal series. The specific rotation of 13 was of little value since it was recorded as [a]0 (c 0.01, MeOH).7 In 2006, Danishefsky described a pathway for the total synthesis of racemic tricholomalides A and B, and this effort led to a revision of the relative C-2 stereochemistry (Fig. 9.3; revised structures 14 and 15).9 It seemed rather unusual that genetically similar fungi would produce closely related metabolites as enantiomers, but certainly this is not unprecedented. As a starting point, this issue lacked clarity, and we concluded that our synthesis plans must unambig- uously address the issues of absolute configuration. The chemistry of dolabellane and dolastane diterpenes has been reviewed.10 The proposed pathway for biosynthesis of the trichoaurantianolides and related compounds (Fig. 9.4) follows an established sequence from geranyl- geranyl pyrophosphate (16), which undergoes p-cation cyclization to initially form the eleven-membered ring of 17. The event is followed by a second cyclization to form the dolabellane cation 18, and this [9.3.0]cyclotetradecane skeleton is central to several families of natural products. Direct capture or elimination from 18 leads to the 3,7-dolabelladiene 19, which presents the most common pattern of unsaturation within this class. Compounds within this group are traditionally numbered beginning with C-1 as the bridgehead carbon bearing the methyl group rather than following the connectivity presented in ger- anylgeranyl 16. The cation 18 also undergoes a 1,2-hydrogen migration and elimination, which leads to a transannular cyclization yielding the 5e7e6 tri- cyclic dolastane 20. The secodolastanes, represented by 21, are a small collec- tion of marine natural products, which arise from oxidative cleavage of C10eC14 in the parent tricycle 20. In analogous fashion, the neodolabellane structure 22 is produced from 18 by stereospecific backbone migrations that result in the vicinal placement of the bridgehead methyl and isopropyl substituents. Transannular cyclizations, stemming from 22, yield the class of neodolastane diterpenes (23). Trichoaurantianolides and the related lepistal A (5) are the result of oxidations and cleavage of the C-ring (C4eC5) of 23, which leads to the features of an unusual butyrolactone system. The guanacastepenes, such as 24,11 and heptemerones, such as 25,12 are primary examples of the 5e7e6 neodolastane family, and these metabolites have also been isolated from fungi sources. A characteristic structural feature is the vicinal, syn-relationship of the bridgehead methyl and isopropyl sub- stituents as compared with the 1,3-trans relationship found in dolastanes (Fig. 9.2, structures 8 and 9). Guanacastepenes have proven to be attractive targets for synthesis studies.11,13 However, these fungal metabolites exhibit the antipodal, absolute stereochemistry as compared with neodolastanes from marine origins, such as sphaerostanol (26) (Fig. 9.5).14  more » « less
Award ID(s):
1665356
NSF-PAR ID:
10320996
Author(s) / Creator(s):
Editor(s):
Harmata, Michael
Date Published:
Journal Name:
Strategies and tactics in organic synthesis
Volume:
15
ISSN:
1874-6004
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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Spreadsheet: annual precip_drainage Description: Precipitation measured from nearby Kellogg Biological Station (KBS) Long Term Ecological Research (LTER) Weather station, over 2009-2016 study period. Data shown in Figure 1; original data source for precipitation (https://lter.kbs.msu.edu/datatables/7). Drainage estimated from SALUS crop model. Note that drainage is percolation out of the root zone (0-125 cm). Annual precipitation and drainage values shown here are calculated for growing and non-growing crop periods. Variate    Description year    year of the observation crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” precip_G    precipitation during growing period (milliMeter) precip_NG    precipitation during non-growing period (milliMeter) drainage_G    drainage during growing period (milliMeter) drainage_NG    drainage during non-growing period (milliMeter)      2. Spreadsheet: biomass_corn, perennial grasses Description: Maximum aboveground biomass measurements from corn, switchgrass, miscanthus, native grass and restored prairie plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2015. Data shown in Figure 2.   Variate    Description year    year of the observation date    day of the observation (mm/dd/yyyy) crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” replicate    each crop has four replicated plots, R1, R2, R3 and R4 station    stations (S1, S2 and S3) of samplings within the plot. For more details, refer to link (https://data.sustainability.glbrc.org/protocols/156) species    plant species that are rooted within the quadrat during the time of maximum biomass harvest. See protocol for more information, refer to link (http://lter.kbs.msu.edu/datatables/36) For maize biomass, grain and whole biomass reported in the paper (weed biomass or surface litter are excluded). Surface litter biomass not included in any crops; weed biomass not included in switchgrass and miscanthus, but included in grass mixture and prairie. fraction    Fraction of biomass biomass_plot    biomass per plot on dry-weight basis (Grams_Per_SquareMeter) biomass_ha    biomass (megaGrams_Per_Hectare) by multiplying column biomass per plot with 0.01 3. Spreadsheet: biomass_poplar Description: Maximum aboveground biomass measurements from poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2015. Data shown in Figure 2. Note that poplar biomass was estimated from crop growth curves until the poplar was harvested in the winter of 2013-14. Variate    Description year    year of the observation method    methods of poplar biomass sampling date    day of the observation (mm/dd/yyyy) replicate    each crop has four replicated plots, R1, R2, R3 and R4 diameter_at_ground    poplar diameter (milliMeter) at the ground diameter_at_15cm    poplar diameter (milliMeter) at 15 cm height biomass_tree    biomass per plot (Grams_Per_Tree) biomass_ha    biomass (megaGrams_Per_Hectare) by multiplying biomass per tree with 0.01 4. Spreadsheet: annual N leaching_vol-wtd conc Description: Annual leaching rate (kiloGrams_N_Per_Hectare) and volume-weighted mean N concentrations (milliGrams_N_Per_Liter) of nitrate (no3) and dissolved organic nitrogen (don) in the leachate samples collected from corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2016. Data for nitrogen leached and volume-wtd mean N concentration shown in Figure 3a and Figure 3b, respectively. Note that ammonium (nh4) concentration were much lower and often undetectable (<0.07 milliGrams_N_Per_Liter). Also note that in 2009 and 2010 crop-years, data from some replicates are missing.    Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” crop-year    year of the observation replicate    each crop has four replicated plots, R1, R2, R3 and R4 no3 leached    annual leaching rates of nitrate (kiloGrams_N_Per_Hectare) don leached    annual leaching rates of don (kiloGrams_N_Per_Hectare) vol-wtd no3 conc.    Volume-weighted mean no3 concentration (milliGrams_N_Per_Liter) vol-wtd don conc.    Volume-weighted mean don concentration (milliGrams_N_Per_Liter) 5. Spreadsheet: summary_N leached Description: Summary of total amount and forms of N leached (kiloGrams_N_Per_Hectare) and the percent of applied N lost to leaching over the seven years for corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2016. Data for nitrogen amount leached shown in Figure 4a and percent of applied N lost shown in Figure 4b. Note the fraction of unleached N includes in harvest, accumulation in root biomass, soil organic matter or gaseous N emissions were not measured in the study. Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” no3 leached    annual leaching rates of nitrate (kiloGrams_N_Per_Hectare) don leached    annual leaching rates of don (kiloGrams_N_Per_Hectare) N unleached    N unleached (kiloGrams_N_Per_Hectare) in other sources are not studied % of N applied N lost to leaching    % of N applied N lost to leaching 6. Spreadsheet: annual DOC leachin_vol-wtd conc Description: Annual leaching rate (kiloGrams_Per_Hectare) and volume-weighted mean N concentrations (milliGrams_Per_Liter) of dissolved organic carbon (DOC) in the leachate samples collected from corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2016. Data for DOC leached and volume-wtd mean DOC concentration shown in Figure 5a and Figure 5b, respectively. Note that in 2009 and 2010 crop-years, water samples were not available for DOC measurements.     Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” crop-year    year of the observation replicate    each crop has four replicated plots, R1, R2, R3 and R4 doc leached    annual leaching rates of nitrate (kiloGrams_Per_Hectare) vol-wtd doc conc.    volume-weighted mean doc concentration (milliGrams_Per_Liter) 7. Spreadsheet: growing season length Description: Growing season length (days) of corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in the Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2015. Date shown in Figure S2. Note that growing season is from the date of planting or emergence to the date of harvest (or leaf senescence in case of poplar).   Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” year    year of the observation growing season length    growing season length (days) 8. Spreadsheet: correlation_nh4 VS no3 Description: Correlation of ammonium (nh4+) and nitrate (no3-) concentrations (milliGrams_N_Per_Liter) in the leachate samples from corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2013-2015. Data shown in Figure S3. Note that nh4+ concentration in the leachates was very low compared to no3- and don concentration and often undetectable in three crop-years (2013-2015) when measurements are available. 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Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” year    year of the observation don    don concentration (milliGrams_N_Per_Liter) no3     no3 concentration (milliGrams_N_Per_Liter) doc    doc concentration (milliGrams_Per_Liter) 
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The online system implements Phase 1 by taking advantage of the Linux piping mechanism, multithreading techniques, and multi-core processors. To convert Phase 1 into an online system, we divide the system into five major modules: signal preprocessor, feature extractor, event decoder, postprocessor, and visualizer. The system reads 0.1-second frames from each EEG channel and sends them to the feature extractor and the visualizer. The feature extractor generates LFCC features in real time from the streaming EEG signal. Next, the system computes seizure and background probabilities using a channel-based LSTM model and applies a postprocessor to aggregate the detected events across channels. The system then displays the EEG signal and the decisions simultaneously using a visualization module. The online system uses C++, Python, TensorFlow, and PyQtGraph in its implementation. The online system accepts streamed EEG data sampled at 250 Hz as input. The system begins processing the EEG signal by applying a TCP montage [8]. Depending on the type of the montage, the EEG signal can have either 22 or 20 channels. To enable the online operation, we send 0.1-second (25 samples) length frames from each channel of the streamed EEG signal to the feature extractor and the visualizer. Feature extraction is performed sequentially on each channel. The signal preprocessor writes the sample frames into two streams to facilitate these modules. In the first stream, the feature extractor receives the signals using stdin. In parallel, as a second stream, the visualizer shares a user-defined file with the signal preprocessor. This user-defined file holds raw signal information as a buffer for the visualizer. The signal preprocessor writes into the file while the visualizer reads from it. Reading and writing into the same file poses a challenge. The visualizer can start reading while the signal preprocessor is writing into it. To resolve this issue, we utilize a file locking mechanism in the signal preprocessor and visualizer. Each of the processes temporarily locks the file, performs its operation, releases the lock, and tries to obtain the lock after a waiting period. The file locking mechanism ensures that only one process can access the file by prohibiting other processes from reading or writing while one process is modifying the file [9]. The feature extractor uses circular buffers to save 0.3 seconds or 75 samples from each channel for extracting 0.2-second or 50-sample long center-aligned windows. The module generates 8 absolute LFCC features where the zeroth cepstral coefficient is replaced by a temporal domain energy term. For extracting the rest of the features, three pipelines are used. The differential energy feature is calculated in a 0.9-second absolute feature window with a frame size of 0.1 seconds. The difference between the maximum and minimum temporal energy terms is calculated in this range. Then, the first derivative or the delta features are calculated using another 0.9-second window. Finally, the second derivative or delta-delta features are calculated using a 0.3-second window [6]. The differential energy for the delta-delta features is not included. In total, we extract 26 features from the raw sample windows which add 1.1 seconds of delay to the system. We used the Temple University Hospital Seizure Database (TUSZ) v1.2.1 for developing the online system [10]. The statistics for this dataset are shown in Table 1. A channel-based LSTM model was trained using the features derived from the train set using the online feature extractor module. A window-based normalization technique was applied to those features. In the offline model, we scale features by normalizing using the maximum absolute value of a channel [11] before applying a sliding window approach. Since the online system has access to a limited amount of data, we normalize based on the observed window. The model uses the feature vectors with a frame size of 1 second and a window size of 7 seconds. We evaluated the model using the offline P1 postprocessor to determine the efficacy of the delayed features and the window-based normalization technique. As shown by the results of experiments 1 and 4 in Table 2, these changes give us a comparable performance to the offline model. The online event decoder module utilizes this trained model for computing probabilities for the seizure and background classes. These posteriors are then postprocessed to remove spurious detections. The online postprocessor receives and saves 8 seconds of class posteriors in a buffer for further processing. It applies multiple heuristic filters (e.g., probability threshold) to make an overall decision by combining events across the channels. These filters evaluate the average confidence, the duration of a seizure, and the channels where the seizures were observed. The postprocessor delivers the label and confidence to the visualizer. The visualizer starts to display the signal as soon as it gets access to the signal file, as shown in Figure 1 using the “Signal File” and “Visualizer” blocks. Once the visualizer receives the label and confidence for the latest epoch from the postprocessor, it overlays the decision and color codes that epoch. The visualizer uses red for seizure with the label SEIZ and green for the background class with the label BCKG. Once the streaming finishes, the system saves three files: a signal file in which the sample frames are saved in the order they were streamed, a time segmented event (TSE) file with the overall decisions and confidences, and a hypotheses (HYP) file that saves the label and confidence for each epoch. The user can plot the signal and decisions using the signal and HYP files with only the visualizer by enabling appropriate options. For comparing the performance of different stages of development, we used the test set of TUSZ v1.2.1 database. It contains 1015 EEG records of varying duration. The any-overlap performance [12] of the overall system shown in Figure 2 is 40.29% sensitivity with 5.77 FAs per 24 hours. For comparison, the previous state-of-the-art model developed on this database performed at 30.71% sensitivity with 6.77 FAs per 24 hours [3]. The individual performances of the deep learning phases are as follows: Phase 1’s (P1) performance is 39.46% sensitivity and 11.62 FAs per 24 hours, and Phase 2 detects seizures with 41.16% sensitivity and 11.69 FAs per 24 hours. We trained an LSTM model with the delayed features and the window-based normalization technique for developing the online system. Using the offline decoder and postprocessor, the model performed at 36.23% sensitivity with 9.52 FAs per 24 hours. The trained model was then evaluated with the online modules. The current performance of the overall online system is 45.80% sensitivity with 28.14 FAs per 24 hours. Table 2 summarizes the performances of these systems. The performance of the online system deviates from the offline P1 model because the online postprocessor fails to combine the events as the seizure probability fluctuates during an event. The modules in the online system add a total of 11.1 seconds of delay for processing each second of the data, as shown in Figure 3. In practice, we also count the time for loading the model and starting the visualizer block. When we consider these facts, the system consumes 15 seconds to display the first hypothesis. The system detects seizure onsets with an average latency of 15 seconds. Implementing an automatic seizure detection model in real time is not trivial. We used a variety of techniques such as the file locking mechanism, multithreading, circular buffers, real-time event decoding, and signal-decision plotting to realize the system. A video demonstrating the system is available at: https://www.isip.piconepress.com/projects/nsf_pfi_tt/resources/videos/realtime_eeg_analysis/v2.5.1/video_2.5.1.mp4. The final conference submission will include a more detailed analysis of the online performance of each module. ACKNOWLEDGMENTS Research reported in this publication was most recently supported by the National Science Foundation Partnership for Innovation award number IIP-1827565 and the Pennsylvania Commonwealth Universal Research Enhancement Program (PA CURE). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the official views of any of these organizations. REFERENCES [1] A. Craik, Y. He, and J. L. Contreras-Vidal, “Deep learning for electroencephalogram (EEG) classification tasks: a review,” J. Neural Eng., vol. 16, no. 3, p. 031001, 2019. https://doi.org/10.1088/1741-2552/ab0ab5. [2] A. C. Bridi, T. Q. Louro, and R. C. L. Da Silva, “Clinical Alarms in intensive care: implications of alarm fatigue for the safety of patients,” Rev. Lat. Am. Enfermagem, vol. 22, no. 6, p. 1034, 2014. https://doi.org/10.1590/0104-1169.3488.2513. [3] M. Golmohammadi, V. Shah, I. Obeid, and J. Picone, “Deep Learning Approaches for Automatic Seizure Detection from Scalp Electroencephalograms,” in Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications, 1st ed., I. Obeid, I. Selesnick, and J. Picone, Eds. New York, New York, USA: Springer, 2020, pp. 233–274. https://doi.org/10.1007/978-3-030-36844-9_8. [4] “CFM Olympic Brainz Monitor.” [Online]. Available: https://newborncare.natus.com/products-services/newborn-care-products/newborn-brain-injury/cfm-olympic-brainz-monitor. [Accessed: 17-Jul-2020]. [5] M. L. Scheuer, S. B. Wilson, A. Antony, G. Ghearing, A. Urban, and A. I. Bagic, “Seizure Detection: Interreader Agreement and Detection Algorithm Assessments Using a Large Dataset,” J. Clin. Neurophysiol., 2020. https://doi.org/10.1097/WNP.0000000000000709. [6] A. Harati, M. Golmohammadi, S. Lopez, I. Obeid, and J. Picone, “Improved EEG Event Classification Using Differential Energy,” in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium, 2015, pp. 1–4. https://doi.org/10.1109/SPMB.2015.7405421. [7] V. Shah, C. Campbell, I. Obeid, and J. Picone, “Improved Spatio-Temporal Modeling in Automated Seizure Detection using Channel-Dependent Posteriors,” Neurocomputing, 2021. [8] W. Tatum, A. Husain, S. Benbadis, and P. Kaplan, Handbook of EEG Interpretation. New York City, New York, USA: Demos Medical Publishing, 2007. [9] D. P. Bovet and C. Marco, Understanding the Linux Kernel, 3rd ed. O’Reilly Media, Inc., 2005. https://www.oreilly.com/library/view/understanding-the-linux/0596005652/. [10] V. Shah et al., “The Temple University Hospital Seizure Detection Corpus,” Front. Neuroinform., vol. 12, pp. 1–6, 2018. https://doi.org/10.3389/fninf.2018.00083. [11] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011. https://dl.acm.org/doi/10.5555/1953048.2078195. [12] J. Gotman, D. Flanagan, J. Zhang, and B. Rosenblatt, “Automatic seizure detection in the newborn: Methods and initial evaluation,” Electroencephalogr. Clin. Neurophysiol., vol. 103, no. 3, pp. 356–362, 1997. https://doi.org/10.1016/S0013-4694(97)00003-9. 
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  5. INTRODUCTION: Quadriceps tendon autografts have experienced a rapid rise in popularity for anterior cruciate ligament (ACL) reconstruction due to advantages in graft sizing and potential improvement in biomechanics. While there is a growing body of literature on use of quadriceps tendon grafts, deeper investigation into the biomechanical properties of stitch techniques in this construct has been limited. The purpose of this study was to evaluate the performance of a novel suture needle against different conventional suture needles by comparing the biomechanical properties of two commonly used stitch methods, a whip stitch, and a locking stitch in quadriceps tendon. It was hypothesized that the new device would be capable of creating both whip stitches and locking stitches that are biomechanically equivalent to similar stitch techniques performed with conventional needle products. METHODS: This was a controlled biomechanical study. A total of 24 matched pair cadaveric knees were dissected and a total of 48 quadriceps tendons were harvested and tested. All tendon grafts were standardized to the same size. Samples were then randomized into the following groups, keeping the matched pairs together: (Group 1, n=16) consisted of Company W’s novel two-part suture needle design, (Group 2, n=16) consisted of Company A suture, and (Group 3, n=16) consisted of Company B suture. For each group, the matched pairs were categorized into subgroups to be instrumented with either a whip stitch or a locking stitch. Two fellowship-trained surgeons performed all stitching, where they each instrumented 8 tendon grafts per group. For instrumentation, the grafts were clamped to a preparation stand in accordance with the manufacturer’s recommendations for passing each suture needle. A skin marker was used to identify and mark five evenly spaced points, 0.5 cm apart, as a guide to create a 5-stitch series. For Group 1, the whip stitch as well as the locking whip stitch were performed with a novel 2-part needle. For Group 2, the whip stitch was performed with loop suture needle and the locking stitch was krackow with a curved needle. Similarly, for Group 3, the whip stitch was performed with loop suture needle and the locking stitch was krackow with a curved needle (Figure 1). Cyclical testing was performed using a servohydraulic testing machine (MTS Bionix) equipped with a 5kN load cell. A standardized length of tendon, 7 cm, was coupled to the MTS actuator by passing it through a cryoclamp cooled by dry ice to a temperature of -5°C (Figure 2). All testing samples were then pre-conditioned to normalize viscoelastic effects and testing variability through application of cyclical loading to 25-100 N for three cycles. The samples were then held at 89 N for 15 minutes. Thereafter, the samples were loaded to 50-200 N for 500 cycles at 1 Hz. If samples survived, they were ramped to failure at 20 mm/min. Displacement and force data was collected throughout testing. Metrics of interest were total elongation (mm), stiffness (N/mm), ultimate failure load (N) and failure mode. Data are presented as averages plus/minus standard deviation. A one-way analysis of variance (ANOVA) with a Tukey pairwise comparison post hoc analysis was used to evaluate differences between the various stitching methods. Statistical significance was set at P = .05. RESULTS SECTION: For the whip stitch methods, the total elongation was found to be equivalent across all methods (W: 36 ± 10 mm; A: 32 ± 18 mm; B: 33 ± 8 mm). The stiffness of Company A (103 ± 11 N/mm) method was significantly larger than Company W (64 ± 8 N/mm; p=.001), whereas stiffness of whip stitch by Company W was equivalent to Company B (80 ± 32 N/mm). The ultimate failure load was equivalent across all whip stitch methods (W: 379 ± 31 mm; A: 412 ± 103 mm; B: 438 ± 63 mm). For the locking stitch method, the total elongation (W: 26 ± 10 mm; A: 14 ± 2 mm; B: 29 ± 5 mm), stiffness (W: 75 ± 11 N/mm; A: 104 ± 23 N/mm; B: 79 ± 10 N/mm) and ultimate load (W: 343 ± 22 N; A: 369 ± 30 N; B: 438 ± 63 N) were found to be equivalent across all methods. The failure mode for all groups is in Table 1. The common mode of failure across study groups and stitch configuration was suture breakage. However, the whip stitch from Company A and Company B had varied failure modes. DISCUSSION: Products from the three manufacturers were found to produce biomechanically equivalent whip stitches and locking stitches with respect to elongation and ultimate failure load. The only significant difference observed was that the whip stitch created with Company A’s product had a higher stiffness than Company W’s product, which could have been due to differences in the suture material. In this cadaveric quadriceps tendon model, it was shown that when using Company W’s novel two-part suture needle, users were capable of creating whip stitches and locking stitches that achieved equivalent biomechanical performance compared to similar stitch techniques performed with conventional needle products. A failure mode limited solely to suture breakage for methods completed with Company W’s needle product suggest a reliable suture construct with limited tissue damage. SIGNIFICANCE/CLINICAL RELEVANCE: Having a suture needle device with the versatility to easily perform different stitching constructs may provide surgeons an advantage needed to improve clinical outcomes. The data presented illustrates a strong new suture technique that has equivalent performance when compared to conventional needle devices and has promising applications in graft preparation for ligament and tendon reconstruction. 
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