Identifying pediatric heart murmurs and distinguishing innocent from pathologic using deep learning.

OBJECTIVE: To develop a deep learning algorithm to perform multi-class classification of normal pediatric heart sounds, innocent murmurs, and pathologic murmurs. METHODS: We prospectively enrolled children under age 18 being evaluated by the Division of Pediatric Cardiology. Parents provided consent for a deidentified recording of their child's heart sounds with a digital stethoscope. Innocent murmurs were validated by a pediatric cardiologist and pathologic murmurs were validated by echocardiogram. To augment our collection of normal heart sounds, we utilized a public database of pediatric heart sound recordings (Oliveira, 2022). We propose two novel approaches for this audio classification task. We train a vision transformer on either Markov transition field or Gramian angular field image representations of the frequency spectrum. We benchmark our results against a ResNet-50 CNN trained on spectrogram images. RESULTS: Our final dataset consisted of 366 normal heart sounds, 175 innocent murmurs, and 216 pathologic murmurs. Innocent murmurs collected include Still's murmur, venous hum, and flow murmurs. Pathologic murmurs included ventricular septal defect, tetralogy of Fallot, aortic regurgitation, aortic stenosis, pulmonary stenosis, mitral regurgitation and stenosis, and tricuspid regurgitation. We find that the Vision Transformer consistently outperforms the ResNet-50 on all three image representations, and that the Gramian angular field is the superior image representation for pediatric heart sounds. We calculated a one-vs-rest multi-class ROC curve for each of the three classes. Our best model achieves an area under the curve (AUC) value of 0.92 ± 0.05, 0.83 ± 0.04, and 0.88 ± 0.04 for identifying normal heart sounds, innocent murmurs, and pathologic murmurs, respectively. CONCLUSION: We present two novel methods for pediatric heart sound classification, which outperforms the current standard of using a convolutional neural network trained on spectrogram images. To our knowledge, we are the first to demonstrate multi-class classification of pediatric murmurs. Multiclass output affords a more explainable and interpretable model, which can facilitate further model improvement in the downstream model development cycle and enhance clinician trust and therefore adoption.

Using a Machine Learning Approach to Predict the Need for Elective Revision and Unplanned Surgery after Implant-based Breast Reconstruction.

Two-stage implant-based reconstruction after mastectomy may require secondary revision procedures to treat complications, correct defects, and improve aesthetic outcomes. Patients should be counseled on the possibility of additional procedures during the initial visit, but the likelihood of requiring another procedure is dependent on many patient- and surgeon-specific factors. This study aims to identify patient-specific factors and surgical techniques associated with higher rates of secondary procedures and offer a machine learning model to compute individualized assessments for preoperative counseling. A training set of 209 patients (406 breasts) who underwent two-stage alloplastic reconstruction was created, with 45.57% of breasts (185 of 406) requiring revisional or unplanned surgery. On multivariate analysis, hypertension, no tobacco use, and textured expander use corresponded to lower odds of additional surgery. In contrast, higher initial tissue expander volume, vertical radial incision, and larger nipple-inframammary fold distance conferred higher odds of additional surgery. The neural network model trained on clinically significant variables achieved the highest collective performance metrics, with ROC AUC of 0.74, sensitivity of 84.2, specificity of 63.6, and accuracy of 62.1. The proposed machine learning model trained on a single surgeon's data offers a precise and reliable tool to assess an individual patient's risk of secondary procedures. Machine learning models enable physicians to tailor surgical planning and empower patients to make informed decisions aligned with their lifestyle and preferences. The utilization of this technology is especially applicable to plastic surgery, where outcomes are subject to a variety of patient-specific factors and surgeon practices, including threshold to perform secondary procedures.

Robust platform for inline Raman monitoring and control of perfusion cell culture.

Perfusion cell culture has been gaining increasing popularity for biologics manufacturing due to benefits such as smaller footprint, increased productivity, consistent product quality and manufacturing flexibility, cost savings, and so forth. Process Analytics Technologies tools are highly desirable for effective monitoring and control of long-running perfusion processes. Raman has been widely investigated for monitoring and control of traditional fed batch cell culture process. However, implementation of Raman for perfusion cell culture has been very limited mainly due to challenges with high-cell density and long running times during perfusion which cause extremely high fluorescence interference to Raman spectra and consequently it is exceedingly difficult to develop robust chemometrics models. In this work, a platform based on Raman measurement of permeate has been proposed for effective analysis of perfusion process. It has been demonstrated that this platform can effectively circumvent the fluorescence interference issue while providing rich and timely information about perfusion dynamics to enable efficient process monitoring and robust bioreactor feed control. With the highly consistent spectral data from cell-free sample matrix, development of chemometrics models can be greatly facilitated. Based on this platform, Raman models have been developed for good measurement of several analytes including glucose, lactate, glutamine, glutamate, and permeate titer. Performance of Raman models developed this way has been systematically evaluated and the models have shown good robustness against changes in perfusion scale and variations in permeate flowrate; thus models developed from small lab scale can be directly transferred for implementation in much larger scale of perfusion. With demonstrated robustness, this platform provides a reliable approach for automated glucose feed control in perfusion bioreactors. Glucose model developed from small lab scale has been successfully implemented for automated continuous glucose feed control of perfusion cell culture at much larger scale.

Distilling Knowledge From an Ensemble of Vision Transformers for Improved Classification of Breast Ultrasound.

RATIONALE AND OBJECTIVES: To develop a deep learning model for the automated classification of breast ultrasound images as benign or malignant. More specifically, the application of vision transformers, ensemble learning, and knowledge distillation is explored for breast ultrasound classification. MATERIALS AND METHODS: Single view, B-mode ultrasound images were curated from the publicly available Breast Ultrasound Image (BUSI) dataset, which has categorical ground truth labels (benign vs malignant) assigned by radiologists and malignant cases confirmed by biopsy. The performance of vision transformers (ViT) is compared to convolutional neural networks (CNN), followed by a comparison between supervised, self-supervised, and randomly initialized ViT. Subsequently, the ensemble of 10 independently trained ViT, where the ensemble model is the unweighted average of the output of each individual model is compared to the performance of each ViT alone. Finally, we train a single ViT to emulate the ensembled ViT using knowledge distillation. RESULTS: On this dataset that was trained using five-fold cross validation, ViT outperforms CNN, while self-supervised ViT outperform supervised and randomly initialized ViT. The ensemble model achieves an area under the receiver operating characteristics curve (AuROC) and area under the precision recall curve (AuPRC) of 0.977 and 0.965 on the test set, outperforming the average AuROC and AuPRC of the independently trained ViTs (0.958 ± 0.05 and 0.931 ± 0.016). The distilled ViT achieves an AuROC and AuPRC of 0.972 and 0.960. CONCLUSION: Both transfer learning and ensemble learning can each offer increased performance independently and can be sequentially combined to collectively improve the performance of the final model. Furthermore, a single vision transformer can be trained to match the performance of an ensemble of a set of vision transformers using knowledge distillation.

Three-dimensional thermophotonic super-resolution imaging by spatiotemporal diffusion reversal method.

Spatial super-resolution in thermophotonic imaging was achieved using a combination of spatial second-derivative forming, spatial gradient adaptive filtering, and Richardson-Lucy deconvolution in conjunction with the construction of an experimental point spread function. When implemented through enhanced truncation-correlation photothermal coherence tomography (eTC-PCT), it was possible to restore blurred infrared thermophotonic images to their prediffusion optical resolution state. This modality was tested in various biological applications and proved to be capable of imaging fine axial cracks in human teeth, well-patterned anatomical subsurface structures of a mouse brain, and neovascularization in a mouse thigh due to the rapid proliferation of cancer cells. This modality was found to be immune to optical scattering and could reveal the true spatial extent of biological features at subsurface depths that conventional thermal imaging cannot reach because of limitations imposed by the physics of spreading diffusion.

Machine-Learning Prediction of Capsular Contraction after Two-Stage Breast Reconstruction.

Background: Two-stage breast reconstruction is a common technique used to restore preoperative appearance in patients undergoing mastectomy. However, capsular contracture may develop and lead to implant failure and significant morbidity. The objective of this study is to build a machine-learning model that can determine the risk of developing contracture formation after two-stage breast reconstruction. Methods: A total of 209 women (406 samples) were included in the study cohort. Patient characteristics that were readily accessible at the preoperative visit and details pertaining to the surgical approach were used as input data for the machine-learning model. Supervised learning models were assessed using 5-fold cross validation. A neural network model is also evaluated using a 0.8/0.1/0.1 train/validate/test split. Results: Among the subjects, 144 (35.47%) developed capsular contracture. Older age, smaller nipple-inframammary fold distance, retropectoral implant placement, synthetic mesh usage, and postoperative radiation increased the odds of capsular contracture (p < 0.05). The neural network achieved the best performance metrics among the models tested, with a test accuracy of 0.82 and area under receiver operative curve of 0.79. Conclusion: To our knowledge, this is the first study that uses a neural network to predict the development of capsular contraction after two-stage implant-based reconstruction. At the preoperative visit, surgeons may counsel high-risk patients on the potential need for further revisions or guide them toward autologous reconstruction.

Deep learning analysis of blood flow sounds to detect arteriovenous fistula stenosis.

For hemodialysis patients, arteriovenous fistula (AVF) patency determines whether adequate hemofiltration can be achieved, and directly influences clinical outcomes. Here, we report the development and performance of a deep learning model for automated AVF stenosis screening based on the sound of AVF blood flow using supervised learning with data validated by ultrasound. We demonstrate the importance of contextualizing the sound with location metadata as the characteristics of the blood flow sound varies significantly along the AVF. We found the best model to be a vision transformer trained on spectrogram images. Our model can screen for stenosis at a performance level comparable to that of a nephrologist performing a physical exam, but with the advantage of being automated and scalable. In a high-volume, resource-limited clinical setting, automated AVF stenosis screening can help ensure patient safety via early detection of at-risk vascular access, streamline the dialysis workflow, and serve as a patient-facing tool to allow for at-home, self-screening.

On the analysis of data augmentation methods for spectral imaged based heart sound classification using convolutional neural networks.

BACKGROUND: The application of machine learning to cardiac auscultation has the potential to improve the accuracy and efficiency of both routine and point-of-care screenings. The use of convolutional neural networks (CNN) on heart sound spectrograms in particular has defined state-of-the-art performance. However, the relative paucity of patient data remains a significant barrier to creating models that can adapt to a wide range of potential variability. To that end, we examined a CNN model's performance on automated heart sound classification, before and after various forms of data augmentation, and aimed to identify the most optimal augmentation methods for cardiac spectrogram analysis. RESULTS: We built a standard CNN model to classify cardiac sound recordings as either normal or abnormal. The baseline control model achieved a PR AUC of 0.763 ± 0.047. Among the single data augmentation techniques explored, horizontal flipping of the spectrogram image improved the model performance the most, with a PR AUC of 0.819 ± 0.044. Principal component analysis color augmentation (PCA) and perturbations of saturation-value (SV) of the hue-saturation-value (HSV) color scale achieved a PR AUC of 0.779 ± 045 and 0.784 ± 0.037, respectively. Time and frequency masking resulted in a PR AUC of 0.772 ± 0.050. Pitch shifting, time stretching and compressing, noise injection, vertical flipping, and applying random color filters negatively impacted model performance. Concatenating the best performing data augmentation technique (horizontal flip) with PCA and SV perturbations improved model performance. CONCLUSION: Data augmentation can improve classification accuracy by expanding and diversifying the dataset, which protects against overfitting to random variance. However, data augmentation is necessarily domain specific. For example, methods like noise injection have found success in other areas of automated sound classification, but in the context of cardiac sound analysis, noise injection can mimic the presence of murmurs and worsen model performance. Thus, care should be taken to ensure clinically appropriate forms of data augmentation to avoid negatively impacting model performance.

Application of Online NIR Spectroscopy to Enhance Process Understanding and Enable In-process Control Testing of Secondary Drying Process for a Spray-dried Solid Dispersion Intermediate.

Near infrared spectroscopy (NIRS) was utilized to determine the endpoint of secondary drying process (post primary spray drying) of Spray-Dried Intermediates (SDI). In addition, NIR methods have been developed to quantify residual solvents (acetone and water), which are in-process controls (IPCs), and assay on the spray dried intermediate, thereby minimizing the need for off-line sample testing. NIRS calibration models were built with Partial Least Squares (PLS) regression for samples from several statistically designed experiments. Standard errors of prediction (SEP) of 0.1 wt. % for acetone, 0.2 wt. % for water, and 3.0 mg API/g SDI for API potency were obtained from validation of the models. When these methods were transferred to commercial scale on a different analyzer at a different site, additional updates to the NIR models were successfully made to overcome the impact from the differences in instrumentation and scale. Not only could real-time, in-process release be achieved, but also consistency and quality of data could be improved by minimizing or eliminating sample handling issues for off-line sample analysis. These NIR methods for secondary drying might also be used to optimize the drying process cycle time and study the effect of agitation rate, jacket temperature, and drying gas sweep rate on drying cycle time.

Development of an Easily Reproducible Cough Simulator With Droplets and Aerosols for Rapidly Testing Novel Personal Protective Equipment.

INTRODUCTION: The current COVID-19 pandemic has produced numerous innovations in personal protective equipment, barrier devices, and infection mitigation strategies, which have not been validated. During high-risk procedures such as airway manipulation, coughs are common and discrete events that may expose healthcare workers to large amounts of viral particles. A simulated cough under controlled circumstances can rapidly test novel devices and protocols and thus aid in their evaluation and the development of implementation guidelines. Physiologic cough simulators exist but require significant expertise and specialized equipment not available to most clinicians. METHODS: Using components commonly found in healthcare settings, a cough simulator was designed for clinicians to easily assemble and use. Both droplet and aerosol particle generators were incorporated into a bimodal experimental system. High-speed flash photography was used for data collection. RESULTS: Using a gas flow analyzer, video recordings, and high-speed digital photography, the cough and particle simulators were quantitatively and qualitatively compared with known physiologic cough parameters and in vivo Schlieren imaging of human coughs. CONCLUSIONS: Based on our validation studies, this cough and particle simulator model approximates a physiologic, human cough in the context of testing personal protective equipment, barrier devices, and infection prevention measures.

Systematic assessment of process analytical technologies for biologics.

The application of process analytical technology (PAT) for biotherapeutic development and manufacturing has been employed owing to technological, economic, and regulatory advantages across the industry. Typically, chromatographic, spectroscopic, and/or mass spectrometric sensors are integrated into upstream and downstream unit operations in in-line, on-line, or at-line fashion to enable real-time monitoring and control of the process. Despite the widespread utility of PAT technologies at various unit operations of the bioprocess, a holistic business value assessment of PAT has not been well addressed in biologics. Thus, in this study, we evaluated PAT technologies based on predefined criteria for their technological attributes such as enablement of better process understanding, control, and high-throughput capabilities; as well as for business attributes such as simplicity of implementation, lead time, and cost reduction. The study involved an industry-wide survey, where input from subject matter industry experts on various PAT tools were collected, assessed, and ranked. The survey results demonstrated on-line liquid Chromatography (LC), in-line Raman, and gas analysis techniques are of high business value especially at the production bioreactor unit operation of upstream processing. In-line variable path-length UV/VIS measurements (VPE), on-line LC, multiangle light scattering (MALS), and automated sampling are of high business value in Protein A purification and polishing steps of the downstream process. We also provide insights, based on our experience in clinical and commercial manufacturing of biologics, into the development and implementation of some of the PAT tools. The results presented in this study are intended to be helpful for the current practitioners of PAT as well as those new to the field to gauge, prioritize and steer their projects for success.

"The use of bilateral continuous erector spinae plane blocks for postoperative analgesia after right-sided living donor hepatectomy: A feasibility study".

BACKGROUND: Postoperative pain after living donor hepatectomy is significant. Postoperative coagulopathy may limit the use of epidural analgesia, the gold standard for pain control in abdominal surgery. The erector spinae plane block (ESPB) is a novel regional anesthesia technique that has been shown to provide effective analgesia in abdominal surgery. In this study, we examined the effect of continuous ESPB, administered via catheters, on perioperative opioid requirements after right living donor hepatectomies for liver transplantation. METHODS: We performed a retrospective cohort study in patients undergoing right living donor hepatectomy. Twenty-four patients who received preoperative ESPB were compared to 51 historical controls who did not receive regional anesthesia. The primary endpoint was the total amount of oral morphine equivalents (OMEs) required on the day of surgery and postoperative day (POD) 1. RESULTS: Patients in the ESPB group required a lower total amount of OMEs on the day of surgery and POD 1 [141 (107-188) mg] compared the control group [293 (220-380) mg; P < .001]. CONCLUSIONS: The use of continuous ESPB significantly reduced opioid consumption following right living donor hepatectomy.

Machine learning identifies candidates for drug repurposing in Alzheimer's disease.

Clinical trials of novel therapeutics for Alzheimer's Disease (AD) have consumed a large amount of time and resources with largely negative results. Repurposing drugs already approved by the Food and Drug Administration (FDA) for another indication is a more rapid and less expensive option. We present DRIAD (Drug Repurposing In AD), a machine learning framework that quantifies potential associations between the pathology of AD severity (the Braak stage) and molecular mechanisms as encoded in lists of gene names. DRIAD is applied to lists of genes arising from perturbations in differentiated human neural cell cultures by 80 FDA-approved and clinically tested drugs, producing a ranked list of possible repurposing candidates. Top-scoring drugs are inspected for common trends among their targets. We propose that the DRIAD method can be used to nominate drugs that, after additional validation and identification of relevant pharmacodynamic biomarker(s), could be readily evaluated in a clinical trial.

Computer-based video training is effective in teaching basic surgical skills to novices without faculty involvement using a self-directed, sequential and incremental program.

INTRODUCTION: Computer-based video training (CBVT) of surgical skills overcomes limitations of 1:1 instruction. We hypothesized that a self-directed CBVT program could teach novices by dividing basic surgical skills into sequential, easily-mastered steps. METHODS: We developed a 12 video program teaching basic knot tying and suturing skills introduced in discrete, incremental steps. Students were evaluated pre- and post-course with a self-assessment, a written exam and a skill assessment. RESULTS: Students (n = 221) who completed the course demonstrated significant improvement. Their average pre-course product quality score and assessment of technique using standard Global Rating Scale (GRS) were <0.4 for 6 measured skills (scale 0-5) and increased post-course to ≥3.25 except for the skill tying on tension whose GRS = 2.51. Average speed increased for all skills. Students' self-ratings (scale 1-5) increased from an average of 1.4 ± 0.7 pre-elective to 3.9 ± 0.9 post-elective across all skills (P < 0.01). CONCLUSION: Self-directed, incremental and sequential video training is effective teaching basic surgical skills and may be a model to teach other skills or to play a larger role in remote learning.

Barrier Devices, Intubation, and Aerosol Mitigation Strategies: Personal Protective Equipment in the Time of Coronavirus Disease 2019.

BACKGROUND: Numerous barrier devices have recently been developed and rapidly deployed worldwide in an effort to protect health care workers (HCWs) from exposure to coronavirus disease 2019 (COVID-19) during high-risk procedures. However, only a few studies have examined their impact on the dispersion of droplets and aerosols, which are both thought to be significant contributors to the spread of COVID-19. METHODS: Two commonly used barrier devices, an intubation box and a clear plastic intubation sheet, were evaluated using a physiologically accurate cough simulator. Aerosols were modeled using a commercially available fog machine, and droplets were modeled with fluorescein dye. Both particles were propelled by the cough simulator in a simulated intubation environment. Data were captured by high-speed flash photography, and aerosol and droplet dispersion were assessed qualitatively with and without a barrier in place. RESULTS: Droplet contamination after a simulated cough was seemingly contained by both barrier devices. Simulated aerosol escaped the barriers and flowed toward the head of the bed. During barrier removal, simulated aerosol trapped underneath was released and propelled toward the HCW at the head of the bed. Usage of the intubation sheet concentrated droplets onto a smaller area. If no barrier was used, positioning the patient in slight reverse Trendelenburg directed aerosols away from the HCW located at the head of the bed. CONCLUSIONS: Our observations imply that intubation boxes and sheets may reduce HCW exposure to droplets, but they both may merely redirect aerosolized particles, potentially resulting in increased exposure to aerosols in certain circumstances. Aerosols may remain within the barrier device after a cough, and manipulation of the box may release them. Patients should be positioned to facilitate intubation, but slight reverse Trendelenburg may direct infectious aerosols away from the HCW. Novel barrier devices should be used with caution, and further validation studies are necessary.

Intraoperative aerosol box use: does an educational visual aid reduce contamination?

BACKGROUND: The aerosol box was rapidly developed and disseminated to minimize viral exposure during aerosolizing procedures during the COVID-19 pandemic, yet users may not understand how to use and clean the device. This could potentially lead to increased viral exposure to subsequent patients and practitioners. We evaluated intraoperative contamination and aerosol box decontamination and the impact of a preoperative educational visual aid. METHODS: Using a double-blinded randomized design, forty-four anesthesiology trainees and faculty completed a simulated anesthetic case using an aerosol box contaminated with a fluorescent marker; half of the subjects received a visual aid prior to the simulation. Intraoperative contamination was evaluated at 10 standardized locations using an ultraviolet (UV) light. Next, subjects were instructed to clean the aerosol box for use on the next patient. Following cleaning, the box was evaluated for decontamination using an UV light. RESULTS: Median total contamination score was significantly reduced in the experimental group (5.0 vs. 10.0, P < 0.001). The aerosol box was completely cleaned by 36.4% of subjects in the experimental group compared to 4.5% in the control group (P = 0.009). CONCLUSIONS: The use of a visual aid significantly decreased intraoperative contamination and improved box cleaning. Despite these findings, a potentially clinically significant amount of viral exposure may exist. Thorough evaluation of the risks and benefits of the aerosol box should be completed prior to use. If an aerosol box is used, a visual aid should be considered to remind practitioners how to best use and clean the box.

Differential Ventilation Using Flow Control Valves as a Potential Bridge to Full Ventilatory Support during the COVID-19 Crisis.

BACKGROUND: During the COVID-19 pandemic, ventilator sharing was suggested to increase availability of mechanical ventilation. The safety and feasibility of ventilator sharing is unknown. METHODS: A single ventilator in pressure control mode was used with flow control valves to simultaneously ventilate two patients with different lung compliances. The system was first evaluated using high-fidelity human patient simulator mannequins and then tested for 1 h in two pairs of COVID-19 patients with acute respiratory failure. Patients were matched on positive end-expiratory pressure, fractional inspired oxygen tension, and respiratory rate. Tidal volume and peak airway pressure (PMAX) were recorded from each patient using separate independent spirometers and arterial blood gas samples drawn at 0, 30, and 60 min. The authors assessed acid-base status, oxygenation, tidal volume, and PMAX for each patient. Stability was assessed by calculating the coefficient of variation. RESULTS: The valves performed as expected in simulation, providing a stable tidal volume of 400 ml each to two mannequins with compliance ratios varying from 20:20 to 20:90 ml/cm H2O. The system was then tested in two pairs of patients. Pair 1 was a 49-yr-old woman, ideal body weight 46 kg, and a 55-yr-old man, ideal body weight 64 kg, with lung compliance 27 ml/cm H2O versus 35 ml/cm H2O. The coefficient of variation for tidal volume was 0.2 to 1.7%, and for PMAX 0 to 1.1%. Pair 2 was a 32-yr-old man, ideal body weight 62 kg, and a 56-yr-old woman, ideal body weight 46 kg, with lung compliance 12 ml/cm H2O versus 21 ml/cm H2O. The coefficient of variation for tidal volume was 0.4 to 5.6%, and for PMAX 0 to 2.1%. CONCLUSIONS: Differential ventilation using a single ventilator is feasible. Flow control valves enable delivery of stable tidal volume and PMAX similar to those provided by individual ventilators.

Intravenous Fluid Management Practices in Kidney Transplant Patients: A Multicenter Observational Cohort Pilot Study.

BACKGROUND: Limited evidence exists with regard to best practices in fluid management during kidney transplantation, which may directly affect the incidence of DGF. The authors of this study embarked on a collaborative observational multicenter pilot study to evaluate fluid administration practices in different transplant centers, with a focus on the relationship between total administered crystalloid volume and its association with DGF. METHODS: Twenty consecutive kidney transplant patients were included from 9 academic medical centers in the United States. One hundred eighty patients were included in the final cohort and variables were compared between patients with and without DGF. Administered crystalloid volume was the primary variable of interest; however, additional patient and surgical variables were compared between patients with and without DGF. Variation in crystalloid administration was explored between centers by comparing median administered crystalloid volumes per kilogram of body weight. Also, unadjusted and adjusted logistic regression analyses were performed to determine which variables were independently associated with DGF. RESULTS: Multivariable regression modeling demonstrated that cold ischemic time and ephedrine use during surgery were independently associated with DGF. There was no independent association between administered crystalloid volume and DGF. CONCLUSION: In this study of patients having kidney transplantation, we did not find an independent association between administered crystalloid volume and DGF, although there was significant variability in crystalloid administration between centers. Our data suggest that DGF was driven mainly by surgical factors such as cold ischemic time. Ephedrine was also independently associated with DGF, which should be explored in future studies.

ClinVar: improvements to accessing data.

ClinVar is a freely available, public archive of human genetic variants and interpretations of their relationships to diseases and other conditions, maintained at the National Institutes of Health (NIH). Submitted interpretations of variants are aggregated and made available on the ClinVar website (https://www.ncbi.nlm.nih.gov/clinvar/), and as downloadable files via FTP and through programmatic tools such as NCBI's E-utilities. The default view on the ClinVar website, the Variation page, was recently redesigned. The new layout includes several new sections that make it easier to find submitted data as well as summary data such as all diseases and citations reported for the variant. The new design also better represents more complex data such as haplotypes and genotypes, as well as variants that are in ClinVar as part of a haplotype or genotype but have no interpretation for the single variant. ClinVar's variant-centric XML had its production release in April 2019. The ClinVar website and E-utilities both have been updated to support the VCV (variation in ClinVar) accession numbers found in the variant-centric XML file. ClinVar's search engine has been fine-tuned for improved retrieval of search results.

Phlpp1 is associated with human intervertebral disc degeneration and its deficiency promotes healing after needle puncture injury in mice.

Back pain is a leading cause of global disability and is strongly associated with intervertebral disc (IVD) degeneration (IDD). Hallmarks of IDD include progressive cell loss and matrix degradation. The Akt signaling pathway regulates cellularity and matrix production in IVDs and its inactivation is known to contribute to a catabolic shift and increased cell loss via apoptosis. The PH domain leucine-rich repeat protein phosphatase (Phlpp1) directly regulates Akt signaling and therefore may play a role in regulating IDD, yet this has not been investigated. The aim of this study was to investigate if Phlpp1 has a role in Akt dysregulation during IDD. In human IVDs, Phlpp1 expression was positively correlated with IDD and the apoptosis marker cleaved Caspase-3, suggesting a key role of Phlpp1 in the progression of IDD. In mice, 3 days after IVD needle puncture injury, Phlpp1 knockout (KO) promoted Akt phosphorylation and cell proliferation, with less apoptosis. At 2 and 8 months after injury, Phlpp1 deficiency also had protective effects on IVD cellularity, matrix production, and collagen structure as measured with histological and immunohistochemical analyses. Specifically, Phlpp1-deletion resulted in enhanced nucleus pulposus matrix production and more chondrocytic cells at 2 months, and increased IVD height, nucleus pulposus cellularity, and extracellular matrix deposition 8 months after injury. In conclusion, Phlpp1 has a role in limiting cell survival and matrix degradation in IDD and research targeting its suppression could identify a potential therapeutic target for IDD.