The Long-Term Survival of LVAD Patients-A TriNetX Database Analysis.

Background: Donor shortage limits the utilization of heart transplantation, making it available for only a fraction of the patients on the transplant waiting list. Therefore, continuous-flow left ventricular assist devices (CF-LVADs) have evolved as a standard of care for end-stage heart failure. It is imperative therefore to investigate long-term survival in this population. Methods: This study assesses the impact of demographics, infections, comorbidities, types of cardiomyopathies, arrhythmias, and end-organ dysfunction on the long-term survival of LVAD recipients. The TriNetX database comprises de-identified patient information across healthcare organizations. The log-rank test assessed post-implant survival effects, while Cox regression was used in the univariate analysis to obtain the Hazard Ratio (HR). All analyses were conducted using the Python programming language and the lifelines library. Results: This study identified CMV, hepatitis A exposure, atrial fibrillation, paroxysmal ventricular tachycardia, ischemic cardiomyopathy, renal dysfunction, diabetes, COPD, mitral valve disease, and essential hypertension as risk factors that impact long-term survival. Interestingly, hypokalemia seems to have a protective effect and gender does not affect survival significantly. Conclusions: This is the first report of a detailed long-term survival assessment of the LVAD population using a decoded database.

Mental Health and Substance Use Disorders in Transplant Waitlist, VAD, and Heart Transplant Patients: A TriNetX Database Analysis.

Background/Objectives: Mental health and substance use disorders (MHDs and SUDs) affect cardiac allograft and VAD recipients and impact their quality of life and compliance. Limited research currently exists on MHDs and SUDs in this population. Methods: This study compares the incidence of MHDs and SUDs in the transplant list, VAD, and post-transplant patients with that in heart failure patients. Study cohorts were derived from the TriNetX database using ICD-10 codes. Differences in incidence were examined using the log-rank test. Adults with MHDs and SUDs before the window of time were excluded. All comparisons were made between propensity-matched cohorts. Statistical significance was set at p < 0.05. Results: Transplant waitlist patients showed a significant increase in the incidence of anxiety, depression, panic, adjustment, mood, alcohol use, and eating disorders. Post-transplant patients showed a significant increase in depression and opioid use. VAD patients showed a significant increase in depression and a decrease in panic disorder and anxiety. These results allow for further investigations on prevention and coping strategies. Conclusions: The deterioration of mental health can significantly impact medication compliance, survival, and quality of life. Opioid use for pain management in the early postoperative period should be further investigated to assess its impact on long-term substance use and addiction.

ABDS: a bioinformatics tool suite for analyzing biologically diverse samples.

Bioinformatics software tools are essential to identify informative molecular features that define different phenotypic sample groups. Among the most fundamental and interrelated tasks are missing value imputation, signature gene detection, and differential pattern visualization. However, many commonly used analytics tools can be problematic when handling biologically diverse samples if either informative missingness possess high missing rates with mixed missing mechanisms, or multiple sample groups are compared and visualized in parallel. We developed the ABDS tool suite specifically for analyzing biologically diverse samples. Collectively, a mechanism-integrated group-wise pre-imputation scheme is proposed to retain informative missingness associated with signature genes, a cosine-based one-sample test is extended to detect group-silenced signature genes, and a unified heatmap is designed to display multiple sample groups. We describe the methodological principles and demonstrate the effectiveness of three analytics tools under targeted scenarios, supported by comparative evaluations and biomedical showcases. As an open-source R package, ABDS tool suite complements rather than replaces existing tools and will allow biologists to more accurately detect interpretable molecular signals among phenotypically diverse sample groups.

Propensity matched post-transplant survival in durable CF-axial pump BTT patients with and without diabetes: A UNOS database analysis.

Diabetes and post-transplant survival have been linked. However, the impact on post-transplant survival of patients supported on Continuous Flow (CF) axial left ventricular assist devices (LVAD) as a bridge to transplant (BTT) with diabetes has not been widely studied. This study attempts to assess the impact of diabetes type II (DM type II) as a comorbidity influencing survival patterns in the post-cardiac transplant population supported on LVADs and to test if the presence of a pre- transplant durable LVAD acts as an independent risk factor in long-term post-transplant survival. The UNOS database population from 2004 to 2015 was used to construct the cohorts. A total of 21,032 were transplanted during this period. The transplant data were further queried to extract CF-axial flow pumps BTT (HMII-BTT) patients and patients who did not have VAD support before the transplant. A total of 4224 transplant recipients had HMII at the time of transplant, and 13,131 did not have VAD support. Propensity analysis was performed, and 4107 recipients of similar patient characteristics to those in the BTT group were selected for comparison. The patients with a VAD had significantly reduced survival at 2 years post-transplant (p = 0.00514) but this trend did not persist at 5 years (p = 0.0617) and 10 years post-transplant (p = 0.183). Patients with diabetes and a VAD significantly decreased survival at 2 years (p = 0.00204), 5 years (p = 0.00029), and 10 years (p = 0.00193). The presence of a durable LVAD is not an independent risk factor for long-term survival. Diabetes has a longstanding effect on the posttransplant survival of BTT patients.

Artificial Intelligence Approaches for Predicting the Risks of Durable Mechanical Circulatory Support Therapy and Cardiac Transplantation.

Background: The use of AI-driven technologies in probing big data to generate better risk prediction models has been an ongoing and expanding area of investigation. The AI-driven models may perform better as compared to linear models; however, more investigations are needed in this area to refine their predictability and applicability to the field of durable MCS and cardiac transplantation. Methods: A literature review was carried out using Google Scholar/PubMed from 2000 to 2023. Results: This review defines the knowledge gaps and describes different AI-driven approaches that may be used to further our understanding. Conclusions: The limitations of current models are due to missing data, data imbalances, and the uneven distribution of variables in the datasets from which the models are derived. There is an urgent need for predictive models that can integrate a large number of clinical variables from multicenter data to account for the variability in patient characteristics that influence patient selection, outcomes, and survival for both durable MCS and HT; this may be fulfilled by AI-driven risk prediction models.

Machine learning models for diagnosis and risk prediction in eating disorders, depression, and alcohol use disorder.

This study uses machine learning models to uncover diagnostic and risk prediction markers for eating disorders (EDs), major depressive disorder (MDD), and alcohol use disorder (AUD). Utilizing case-control samples (ages 18-25 years) and a longitudinal population-based sample (n=1,851), the models, incorporating diverse data domains, achieved high accuracy in classifying EDs, MDD, and AUD from healthy controls. The area under the receiver operating characteristic curves (AUC-ROC [95% CI]) reached 0.92 [0.86-0.97] for AN and 0.91 [0.85-0.96] for BN, without relying on body mass index as a predictor. The classification accuracies for MDD (0.91 [0.88-0.94]) and AUD (0.80 [0.74-0.85]) were also high. Each data domain emerged as accurate classifiers individually, with personality distinguishing AN, BN, and their controls with AUC-ROCs ranging from 0.77 to 0.89. The models demonstrated high transdiagnostic potential, as those trained for EDs were also accurate in classifying AUD and MDD from healthy controls, and vice versa (AUC-ROCs, 0.75-0.93). Shared predictors, such as neuroticism, hopelessness, and symptoms of attention-deficit/hyperactivity disorder, were identified as reliable classifiers. For risk prediction in the longitudinal population sample, the models exhibited moderate performance (AUC-ROCs, 0.64-0.71), highlighting the potential of combining multi-domain data for precise diagnostic and risk prediction applications in psychiatry.

CAM3.0: determining cell type composition and expression from bulk tissues with fully unsupervised deconvolution.

MOTIVATION: Complex tissues are dynamic ecosystems consisting of molecularly distinct yet interacting cell types. Computational deconvolution aims to dissect bulk tissue data into cell type compositions and cell-specific expressions. With few exceptions, most existing deconvolution tools exploit supervised approaches requiring various types of references that may be unreliable or even unavailable for specific tissue microenvironments. RESULTS: We previously developed a fully unsupervised deconvolution method-Convex Analysis of Mixtures (CAM), that enables estimation of cell type composition and expression from bulk tissues. We now introduce CAM3.0 tool that improves this framework with three new and highly efficient algorithms, namely, radius-fixed clustering to identify reliable markers, linear programming to detect an initial scatter simplex, and a smart floating search for the optimum latent variable model. The comparative experimental results obtained from both realistic simulations and case studies show that the CAM3.0 tool can help biologists more accurately identify known or novel cell markers, determine cell proportions, and estimate cell-specific expressions, complementing the existing tools particularly when study- or datatype-specific references are unreliable or unavailable. AVAILABILITY AND IMPLEMENTATION: The open-source R Scripts of CAM3.0 is freely available at https://github.com/ChiungTingWu/CAM3/(https://github.com/Bioconductor/Contributions/issues/3205). A user's guide and a vignette are provided.

ABDS: tool suite for analyzing biologically diverse samples.

Motivation: Analytics tools are essential to identify informative molecular features about different phenotypic groups. Among the most fundamental tasks are missing value imputation, signature gene detection, and expression pattern visualization. However, most commonly used analytics tools may be problematic for characterizing biologically diverse samples when either signature genes possess uneven missing rates across different groups yet involving complex missing mechanisms, or multiple biological groups are simultaneously compared and visualized. Results: We develop ABDS tool suite tailored specifically to analyzing biologically diverse samples. Mechanism-integrated group-wise imputation is developed to recruit signature genes involving informative missingness, cosine-based one-sample test is extended to detect enumerated signature genes, and unified heatmap is designed to comparably display complex expression patterns. We discuss the methodological principles and demonstrate the conceptual advantages of the three software tools. We also showcase the biomedical applications of these individual tools. Implemented in open-source R scripts, ABDS tool suite complements rather than replaces the existing tools and will allow biologists to more accurately detect interpretable molecular signals among diverse phenotypic samples. Availability and implementation: The R Scripts of ABDS tool suite is freely available at https://github.com/niccolodpdu/ABDS.

Preventive Effect of Local Lidocaine Administration on the Formation of Traumatic Neuroma.

BACKGROUND: Traumatic neuroma is a common sequela of peripheral nerve injury or amputation, which often leads to severe neuropathic pain. The present study investigated the effect of local lidocaine administration on preventing the formation of traumatic neuroma. METHODS: Forty-eight male Sprague-Dawley rats were randomly assigned to two groups. The lidocaine group underwent sciatic nerve transection, followed by an injection of lidocaine (0.5%) around the proximal of a severed sciatic nerve under ultrasound-guidance 2-7 days after neurectomy. In the control group, rats received an injection of saline following neurectomy. The autotomy score, mechanical allodynia, thermal hyperalgesia, histological assessment, expression of neuroma, and pain-related markers were detected. RESULTS: Lidocaine treatment reduced the autotomy score and attenuated mechanical allodynia and thermal hyperalgesia. The mRNA expression of α-SMA, NGF, TNF-α, and IL-1ß all significantly decreased in the lidocaine group in comparison to those in the saline control group. The histological results showed nerve fibers, demyelination, and collagen hyperplasia in the proximal nerve stump in the saline control group, which were significantly inhibited in the lidocaine group. CONCLUSIONS: The present study demonstrated that local lidocaine administration could inhibit the formation of painful neuroma due to traumatic nerve injury.

Prediction of the Efficacy of Lumbar Sympathetic Block in Patients with Lower Extremity Complex Regional Pain Syndrome Type 1 Based on the Sympathetic Skin Response.

INTRODUCTION: Complex regional pain syndrome type 1 (CRPS-1) is prevalent after trauma, with intractable pain being the most prominent clinical symptom. The impact of sympathetic block on CRPS is unclear. The goal of this study was to explore the characteristics that predict successful symptom relief with lumbar sympathetic block (LSB) in patients with lower extremity CRPS-1. METHODS: The study was designed as a prospective cohort study. Ninety-eight patients diagnosed with lower extremity CRPS-1 between March 2021 and March 2022 were enrolled as participants. All of the patients received two LSB treatments within a month. Sympthetic skin response (SSR) and numeric rating scale (NRS) were recorded before and after LSB treatment. The procedure was judged as a clinically positive response if the patients a 50% or greater reduction in NRS scores. Patients were divided into positive response and negative response groups after LSB treatment: LSB (+) and LSB (-), and the different characteristics and examination findings of the two groups of patients were compared. Furthermore, a multivariable logistic regression model was utilized to evaluate the predictors of successful symptom relief following LSB treatment. RESULTS: A total of 43.9% (43/98) of patients experienced successful symptom relief, while 56.1% (55/98) had unsuccessful symptom relief. After LSB treatment of all subjects, the overall NRS score decreased, the SSR amplitude increased, and the SSR latency shortened in the affected extremity (P < 0.05). There was a significant difference in the change in SSR amplitude between the LSB (-) and LSB (+) groups (P = 0.000). A 12-month disease duration had an OR (odds ratio) of 4.477 (P = 0.009), and a 510-µV baseline SSR amplitude of the affected extremity had an OR of 7.508 (P = 0.000) in the multivariable analysis that included these explanatory variables. CONCLUSIONS: Patients with lower extremity CRPS-1 can experience significant pain relief after LSB treatment. The predictors of successful symptom relief after LSB treatment were a baseline SSR amplitude of the affected extremity < 510 µV and a disease duration < 12 months. TRIAL REGISTRATION: The study was registered in the Chinese Clinical Trial Registry (ID: ChiCTR2000037755, date of registration: September 4, 2020).

Skin Cancer Risk Prediction in Heart Transplant Recipients.

OBJECTIVES: This study was undertaken to derive a risk prediction model for skin cancer utilizing the United Network for Organ Sharing database population. MATERIALS AND METHODS: Of the 24734 adults (>18 years old) heart transplant recipients (2000-2015) in the United Network for Organ Sharing database, 2625 recipients developed skin cancer. Univariate and multivariate Cox regression analyses were performed; P values, hazard ratios, and confidence intervals were derived. The model was tested using receiver operating characteristics curves and area under the curves. MATLAB software (MathWorks) was used for analyses. RESULTS: Multivariate analysis showed that White patients had a hazard ratio of 31.7 compared with Black patients (P < .001). Male patients had a hazard ratio of 2.52 (P < .001) compared with female patients. Malignancy at listing showed a hazard ratio of 1.77 (P < .001). Thymoglobulin had a hazard ratio of 1.19 (P = .005) compared with other induction agents. The receiver operating characteristic curves generated for 5 years, 8 years, and 10 years after transplant showed area under the curve values of 0.78, 0.77, and 0.76, respectively, in the training set and 0.75, 0.75, and 0.74, respectively, in the validation set. CONCLUSIONS: Male sex, White ethnicity, older age, malignancy at the time of listing or at time of transplant, and thymoglobulin induction are major risk factors for skin cancers after transplant. This risk prediction model has a C statistic of 0.75. To our knowledge,this is the firsttime such a model has been generated for skin cancers in this population.

Mechanosensitive Ion Channel TMEM63A Gangs Up with Local Macrophages to Modulate Chronic Post-amputation Pain.

Post-amputation pain causes great suffering to amputees, but still no effective drugs are available due to its elusive mechanisms. Our previous clinical studies found that surgical removal or radiofrequency treatment of the neuroma at the axotomized nerve stump effectively relieves the phantom pain afflicting patients after amputation. This indicated an essential role of the residual nerve stump in the formation of chronic post-amputation pain (CPAP). However, the molecular mechanism by which the residual nerve stump or neuroma is involved and regulates CPAP is still a mystery. In this study, we found that nociceptors expressed the mechanosensitive ion channel TMEM63A and macrophages infiltrated into the dorsal root ganglion (DRG) neurons worked synergistically to promote CPAP. Histology and qRT-PCR showed that TMEM63A was mainly expressed in mechanical pain-producing non-peptidergic nociceptors in the DRG, and the expression of TMEM63A increased significantly both in the neuroma from amputated patients and the DRG in a mouse model of tibial nerve transfer (TNT). Behavioral tests showed that the mechanical, heat, and cold sensitivity were not affected in the Tmem63a-/- mice in the naïve state, suggesting the basal pain was not affected. In the inflammatory and post-amputation state, the mechanical allodynia but not the heat hyperalgesia or cold allodynia was significantly decreased in Tmem63a-/- mice. Further study showed that there was severe neuronal injury and macrophage infiltration in the DRG, tibial nerve, residual stump, and the neuroma-like structure of the TNT mouse model, Consistent with this, expression of the pro-inflammatory cytokines TNF-α, IL-6, and IL-1ß all increased dramatically in the DRG. Interestingly, the deletion of Tmem63a significantly reduced the macrophage infiltration in the DRG but not in the tibial nerve stump. Furthermore, the ablation of macrophages significantly reduced both the expression of Tmem63a and the mechanical allodynia in the TNT mouse model, indicating an interaction between nociceptors and macrophages, and that these two factors gang up together to regulate the formation of CPAP. This provides a new insight into the mechanisms underlying CPAP and potential drug targets its treatment.

Musical therapy attenuates neuroma pain by modifying leptin expression.

BACKGROUND: Accumulating evidence reveals that music therapy appears to help patients with pain. However, there is a limited understanding of the underlying mechanisms. Several studies indicate that leptin level has a crucial relationship with acute and chronic pain. Herein, we evaluated the effects of music stimulation and the potential roles of adipokines (leptin) in pain behaviors. METHODS: We used a tibial neuroma transposition (TNT) rat model to mimic neuroma pain. Adult male Sprague-Dawley rats were randomly assigned to one of the three groups (n = 6):group 1 (GC), TNT with white noise; group 2(GM), TNT with music; and group 3(GH), TNT. White noise and music stimulation was given once a day following surgery until the end of the study (42nd day). Pain behavioral tests were carried out before surgery and on the 3rd, 10th, 14th, 21st, 28th, 35th, and 42nd days after surgery. At the end of the observation period, we analyzed the histological samples of blood, spinal cord, and prefrontal cortex to investigate the role of leptin in pain behaviors modulated by white noise and sound stimulation. RESULT: Music therapy might improve the pain of TNT rats. Music stimulation ameliorated paw withdrawal thermal latency (PWTL) from the 3rd day after the surgery while the mechanical pain was improved 21 days after the operation.Music stimulation also increased leptin expression in the spinal cord, prefrontal cortex.White noise had no obvious effect. CONCLUSION: Music therapy might improve the pain of TNT rats. Besides, music stimulation ameliorated TNT-induced pain behaviors and affected leptin expression.

Cosbin: cosine score-based iterative normalization of biologically diverse samples.

Motivation: Data normalization is essential to ensure accurate inference and comparability of gene expression measures across samples or conditions. Ideally, gene expression data should be rescaled based on consistently expressed reference genes. However, to normalize biologically diverse samples, the most commonly used reference genes exhibit striking expression variability and size-factor or distribution-based normalization methods can be problematic when the amount of asymmetry in differential expression is significant. Results: We report an efficient and accurate data-driven method-Cosine score-based iterative normalization (Cosbin)-to normalize biologically diverse samples. Based on the Cosine scores of cross-condition expression patterns, the Cosbin pipeline iteratively eliminates asymmetric differentially expressed genes, identifies consistently expressed genes, and calculates sample-wise normalization factors. We demonstrate the superior performance and enhanced utility of Cosbin compared with six representative peer methods using both simulation and real multi-omics expression datasets. Implemented in open-source R scripts and specifically designed to address normalization bias due to significant asymmetry in differential expression across multiple conditions, the Cosbin tool complements rather than replaces the existing methods and will allow biologists to more accurately detect true molecular signals among diverse phenotypic groups. Availability and implementation: The R scripts of Cosbin pipeline are freely available at https://github.com/MinjieSh/Cosbin. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Risk Prediction Model for Survival of Wait-List Patients on Axial CF-LVAD: A UNOS Database Analysis.

BACKGROUND: Continuous flow left ventricular assist devices (CF-LVADs) have become a viable option for patients with end-stage heart failure as a bridge to transplantation or the destination therapy. METHODS: Adult patients listed for heart transplantation (2010-2015) with an axial CF-LVAD on the wait list were obtained from the UNOS database. The multivariate Cox regression model was used to predict the probability of survival after listing. Patients were divided into derivation (80%) and validation (20%) groups. Receiver operating characteristics curves and area under curves were used to define the strength of the model. RESULTS: Risk factors on multivariate analyses were diabetes type I (hazard ratio [HR], 2.5; P = .018), presence of inotropes (HR, 1.6; P = .005), creatinine at listing (HR, 1.2; P < .001). No significant differences were observed between the derivation and validation groups for any of the variables. The area under the curve at 3, 6, and 12 months on the wait list was 0.69, 0.65, 0.63, respectively in the training set and 0.71, 0.65, 0.60, respectively in the validation set. Survival analyses showed that patients implanted with Heartmate II before listing had a better survival than those who were implanted after being on the wait list (HR, 0.78; P = .048). CONCLUSION: To our knowledge, this was the first time a risk prediction model was generated for wait-list survival of Heartmate II patients. A significant difference in survival was noted between patients who received their Heartmate II before being put on a wait list vs those who were implanted while on the list.

The Tourniquet Ischemia Test Effectively Predicts the Efficacy of Lumbar Sympathetic Block in Patients with Lower Extremity Complex Regional Pain Syndrome Type 1.

Background: Neuropathic pain is the most common clinical sign of complex regional pain syndrome (CRPS). Currently, lumbar sympathetic block (LSB) is commonly utilized in lower extremity CRPS that has failed to respond to medication therapy and physical therapy, but its effectiveness is unknown. The tourniquet ischemia test (IT) can distinguish between two types of CRPS: IT-positive CRPS and IT-negative CRPS. Objective: The aim of the study was to investigate whether LSB improves pain scores in patients with lower extremity CRPS-1 and to screen factors to predict its efficacy. Study Design: Prospective clinical observational study. Setting: Pain management center. Subjects: Forty-three patients diagnosed with lower extremity CRPS-1 using the Budapest criteria were included as participants. Methods: Forty-three CRPS-1 patients were treated with LSB therapy, and all of them underwent a tourniquet ischemia test (IT) before undergoing LSB therapy. LSB therapy was performed using a combination of ultrasonography and fluoroscopy. Then, numeric rating scale (NRS) scores and the symptom relief rates of patients were evaluated at 1, 4, and 12 weeks. Finally, peripheral blood inflammatory cytokine samples were collected before and after the LSB treatment. Results: At 4 weeks after the treatment, the total effective symptom relief rate of LSB on CRPS-1 was 25.6% (11/43), with 52.6% (10/19) of IT(+) patients and 4.2% (1/24) of IT(-) patients. There was a significant difference between the IT(-) and IT(+) groups (P = 0.001). The multivariate binary logistic regression analysis revealed that the response to the tourniquet IT was the only significant independent predictor of sympathetic block success (p = 0.007). Conclusion: Tourniquet IT is a simple, safe and effective test to distinguish patients with lower extremity CRPS-1. The response to the tourniquet IT is a reliable predictor of LSB effectiveness in lower extremity CRPS-1 patients.

Ultrasound-Guided Extraforaminal Thoracic Nerve Root Block Through the Midpoint of the Inferior Articular Process and the Parietal Pleura: A Clinical Application of Thoracic Paravertebral Nerve Block.

PURPOSE: Thoracic nerve root (TNR) block is performed primarily under computed tomography or X-ray fluoroscopy but is associated with radiation exposure. Ultrasound requires no radiation and distinguishes vessels, nerves, pleura, and other tissues. Few reports of ultrasound-guided TNR (US-TNR) block have been described, and the puncture end point has not been clearly defined. Herein, we evaluated the feasibility of US-TNR block using the midpoint of the inferior articular process (IAP) and parietal pleura (PP) as the puncture end point. PATIENTS AND METHODS: A prospective series of 10 patients with Herpes Zoster-associated pain underwent US-TNR-guided block performed using an in-plane technique with the midpoint of thoracic IAP and PP as the puncture end points of ultrasonography. The US-TNR block procedure was performed with ultrasound as the primary imaging tool followed by fluoroscopic confirmation. RESULTS: In all patients, the needle tips were visible at the lateral margin of the pedicle in the anteroposterior view and at the extraforaminal zone in the lateral view. The TNR and dorsal root ganglion (DRG) were delineated in all 10 patients. Furthermore, 2 mL of radiopaque agent could delineate the epidural space in 8 patients and the thoracic paravertebral (TPV) space in the other 2 patients. All patients developed numbness along the corresponding dermatome 30 min after injection of local anesthetics. The numeric rating scale (NRS) score at baseline, and at two- and four-week follow-ups were 6.50 ± 1.35, 3.50 ± 0.85 (vs NRS at baseline, P < 0.01), and 4.00 ± 0.82 (vs NRS at baseline, P < 0.01), respectively. CONCLUSION: This study demonstrated the feasibility of US-TNR block using the in-plane technique with the midpoint of thoracic IAP and PP as the puncture end point to effectively block the TNR and DRG. This technique is an accurate clinical application of TPV nerve block and provides a potential therapeutic option for the treatment of neuropathic pain.

Deep Neural Network-Based Survival Analysis for Skin Cancer Prediction in Heart Transplant Recipients.

Heart-transplant recipients are at high risk of developing skin cancer, while Squamous Cell Carcinoma (SCC) and Basal Cell Carcinoma (BCC) are commonly detected. This paper utilized the database from the United Network for Organ Sharing (UNOS) to study the incidence rate of SCC and BCC among heart transplant recipients. Cox proportional hazards model and two deep neural network-based models were studied, and their performance were compared. In addition, Lasso regression, Chi-square test, and Wilcoxon signed-rank test were applied to identify key risk factors. The neural network-based survival models showed better accuracy compared to the standard Cox regression model, which indicates the advantage of deep learning approaches in survival analysis and risk prediction for post-transplant skin cancer.This study investigates the performance of deep learning (DL) models in clinical applications for predicting the risk of skin cancer in heart transplant recipients. The DL models outperform the standard models in assessing the incidence rate of skin cancer across different time spans.

Control of a Mechanical Blood Pump based on a Trade-off between Aortic Valve Dynamics and Cardiac Outputs.

Left ventricular assist device (LVAD) is a therapeutic option for advanced heart failure (HF) patients. This mechanical device assists a failing heart to circulate blood in the human body by adjusting its pump speed according to cardiac output. However, to use an LVAD for bridge-to-recovery, other criteria (e.g., aortic valve function) should be also considered to reduce complications of the LVAD implantation. In this work, we present an optimization-based control approach to meet the circulatory demand of blood, while maintaining the aortic valve to open and close repeatedly in a cardiac cycle. To validate the performance of the control method, several case studies were investigated, which incorporate different levels of HF severity and physical activity. The results show that the optimization-based control algorithm can quantify the trade-off between the aortic valve function and the blood flow, which will meet clinicians' long quest to improve the myocardial functions for the use of an LVAD as bridge-to-recovery.Clinical Relevance-The efficacy of the control algorithm was validated with computer experiments, showing its potential as a bridge to recovery or as a long-term treatment plan for HF.