An Atlas Characterizing the Shared Genetic Architecture of Inflammatory Bowel Disease with Clinical and Behavioral Traits.

BACKGROUND: Inflammatory bowel disease (IBD) development is a complex, multifactorial process that involves extrinsic and intrinsic factors such as host genetics, the immune system, the gut microbiome, and environmental risks. To help understand the genetic contribution of clinical, behavioral, psychiatric, and diet-related traits, we aim to provide a deep and comprehensive characterization of the shared genetic architecture between IBD and hundreds of potentially related traits. METHODS: Utilizing publicly available summary statistics from a previously published IBD genome-wide association study and hundreds of traits from the United Kingdom BioBank (UKBB), we performed linkage disequilibrium score regression (LDSR) analysis to estimate cross-trait genetic correlations between Crohn's disease (CD), ulcerative colitis (UC), and IBD summary statistics with the UKBB traits of interest. RESULTS: Nominally significant (P < .05) genetic correlations were observed for 181 traits in overall IBD, 239 traits in CD, and 94 traits in UC. We replicate the known association between smoking behavior and CD/UC, namely that current tobacco smoking has a positive genetic correlation with CD (rg = 0.12, P = 4.2 × 10-4), while "ever smoking" has a negative genetic correlation with UC (rg = -0.07, P = .042). Globally, all 3 strata (IBD, CD, and UC) demonstrated increased genetic correlations for psychiatric-related traits related to anxiety and depression. CONCLUSION: The present analysis reveals the shared genetic architecture between multiple traits and IBD, CD, and UC. Understanding the relevance of joint occurrences of IBD with psychiatric diseases may moderate management of these diseases for individuals jointly affected by them.

This study provides an atlas of the genetic correlation between hundreds of United Kingdom Biobank (UKBB) traits with inflammatory bowel disease (IBD), Crohn's disease (CD), and ulcerative colitis (UC). Notable strong correlations are seen between IBD and various psychiatric traits.

Heritable Traits and Lung Cancer Risk: A Two-Sample Mendelian Randomization Study.

INTRODUCTION: Lung cancer is a complex polygenic disorder. Analysis with Mendelian randomization (MR) allows for genetically predicted risks to be estimated between exposures and outcomes. METHODS: We analyzed 345 heritable traits from the United Kingdom Biobank and estimated their associated effects on lung cancer outcomes using two sample MR. In addition to estimating effects with overall lung cancer, adenocarcinoma, small cell lung cancer, and squamous cell lung cancers, we performed conditional effect modeling with multivariate MR (MVMR) and the traits of alcohol use, smoking initiation, average pre-tax income, and educational attainment. RESULTS: Univariate MR provided evidence for increased age at first sexual intercourse (OR, 0.55; P = 6.15 × 10-13), educational attainment (OR, 0.24; P = 1.07 × 10-19), average household income (OR, 0.58; P = 7.85 × 10-05), and alcohol usually taken with meals (OR, 0.19; P = 1.06 × 10-06) associating with decreased odds of overall lung cancer development. In contrast, a lack of additional educational attainment (OR, 8.00; P = 3.48 × 10-12), body mass index (OR, 1.28; P = 9.00 × 10-08), pack years smoking as a proportion of life span (OR, 9.93; P = 7.96 × 10-12), and weekly beer intake (OR, 3.48; P = 4.08 × 10-07) were associated with an increased risk of overall lung cancer development. CONCLUSIONS: Many heritable traits associated with an increased or inverse risk of lung cancer development. Effects vary based on histologic subtype and conditional third trait exposures. IMPACT: We identified several heritable traits and presented their genetically predictable impact on lung cancer development, providing valuable insights for consideration.

A comprehensive analysis of lung cancer highlighting epidemiological factors and psychiatric comorbidities from the All of Us Research Program.

Lung cancer is the leading cause of cancer-related mortality in the United States. Investigating epidemiological and clinical parameters can contribute to an improved understanding of disease development and management. In this cross-sectional, case-control study, we used the All of Us database to compare healthcare access, family history, smoking-related behaviors, and psychiatric comorbidities in light smoking controls, matched smoking controls, and primary and secondary lung cancer patients. We found a decreased odds of primary lung cancer patients versus matched smoking controls reporting inability to afford follow-up or specialist care. Additionally, we found a significantly increased odds of secondary lung cancer patients having comorbid anxiety and insomnia when compared to matched smoking controls. Our study provides a profile of the psychiatric disease burden in lung cancer patients and reports key epidemiological factors in patients with primary and secondary lung cancer. By using two controls, we were able to separate smoking behavior from lung cancer and identify factors that were mediated by heavy smoking alone or by both smoking and lung cancer.

The utility of machine learning for predicting donor discard in abdominal transplantation.

BACKGROUND: Increasing access and better allocation of organs in the field of transplantation is a critical problem in clinical care. Limitations exist in accurately predicting allograft discard. Potential exists for machine learning to provide a balanced assessment of the potential for an organ to be used in a transplantation procedure. METHODS: We accessed and utilized all available deceased donor United Network for Organ Sharing data from 1987 to 2020. With these data, we evaluated the performance of multiple machine learning methods for predicting organ use. The machine learning methods trialed included XGBoost, random forest, Naïve Bayes (NB), logistic regression, and fully connected feedforward neural network classifier methods. The top two methods, XGBoost and random forest, were fully developed using 10-fold cross-validation and Bayesian optimization of hyperparameters. RESULTS: The top performing model at predicting liver organ use was an XGBoost model which achieved an AUC-ROC of .925, an AUC-PR of .868, and an F1 statistic of .756. The top performing model for predicting kidney organ use classification was an XGBoost model which achieved an AUC-ROC of .952, and AUC-PR of .883, and an F1 statistic of .786. CONCLUSIONS: The XGBoost method demonstrated a significant improvement in predicting donor allograft discard for both kidney and livers in solid organ transplantation procedures. Machine learning methods are well suited to be incorporated into the clinical workflow; they can provide robust quantitative predictions and meaningful data insights for clinician consideration and transplantation decision-making.

Risk Stratification by Percent Liver Herniation in Congenital Diaphragmatic Hernia.

INTRODUCTION: Congenital diaphragmatic hernia is associated with pulmonary hypoplasia, pulmonary hypertension, and significant neonatal morbidity. Although intrathoracic liver herniation (LH) >20% is associated with adverse outcomes, the relationship between LH <20% and outcomes is poorly characterized. METHODS: A single-center retrospective cohort study was performed from 2011 to 2020 of 80 fetuses with left-sided congenital diaphragmatic hernia that were delivered and repaired at our institution. Perinatal, perioperative, and postoperative data were collected. We evaluated the association of %LH with outcomes as a stratified ordinal variable (0%-10% LH, 10%-19% LH, and >20% LH) and as a continuous variable. Data were analyzed by analysis of variance with Bonferroni post hoc analysis, chi-square analyses, and univariate logistic regression. RESULTS: Extracorporeal membrane oxygenation (ECMO) (P < 0.001), repair on ECMO (P = 0.002), repair with patch (P < 0.001), length of stay (P = 0.002), inhaled nitric oxide use (P < 0.001), and sildenafil use at discharge (P < 0.001), showed significant differences among LH groups. There were no differences among the groups concerning survival (at discharge, 6 mo, and 1 y) and tracheostomy. On further analysis there was no difference between 10% and 19% LH and ≥20% LH patients concerning ECMO (P = 0.55), repair on ECMO (P = 0.54), repair with patch (P = 1.00), length of stay (P = 1.00), and inhaled nitric oxide use (P = 0.33). Logistic regression analysis displayed a significant association with LH and ECMO, repair on ECMO, repair with patch, inhaled nitric oxide use, and sildenafil use. CONCLUSIONS: Our analysis displays no significant difference in perinatal management between patients with 10%-19% and ≥20% LH. These findings suggest that the historical cutoff of ≥20% LH may not be sufficient alone to guide perinatal counseling and decision-making.

nnU-Net Deep Learning Method for Segmenting Parenchyma and Determining Liver Volume From Computed Tomography Images.

Background: Recipient donor matching in liver transplantation can require precise estimations of liver volume. Currently utilized demographic-based organ volume estimates are imprecise and nonspecific. Manual image organ annotation from medical imaging is effective; however, this process is cumbersome, often taking an undesirable length of time to complete. Additionally, manual organ segmentation and volume measurement incurs additional direct costs to payers for either a clinician or trained technician to complete. Deep learning-based image automatic segmentation tools are well positioned to address this clinical need. Objectives: To build a deep learning model that could accurately estimate liver volumes and create 3D organ renderings from computed tomography (CT) medical images. Methods: We trained a nnU-Net deep learning model to identify liver borders in images of the abdominal cavity. We used 151 publicly available CT scans. For each CT scan, a board-certified radiologist annotated the liver margins (ground truth annotations). We split our image dataset into training, validation, and test sets. We trained our nnU-Net model on these data to identify liver borders in 3D voxels and integrated these to reconstruct a total organ volume estimate. Results: The nnU-Net model accurately identified the border of the liver with a mean overlap accuracy of 97.5% compared with ground truth annotations. Our calculated volume estimates achieved a mean percent error of 1.92% + 1.54% on the test set. Conclusions: Precise volume estimation of livers from CT scans is accurate using a nnU-Net deep learning architecture. Appropriately deployed, a nnU-Net algorithm is accurate and quick, making it suitable for incorporation into the pretransplant clinical decision-making workflow.

Cross-ancestry genome-wide meta-analysis of 61,047 cases and 947,237 controls identifies new susceptibility loci contributing to lung cancer.

To identify new susceptibility loci to lung cancer among diverse populations, we performed cross-ancestry genome-wide association studies in European, East Asian and African populations and discovered five loci that have not been previously reported. We replicated 26 signals and identified 10 new lead associations from previously reported loci. Rare-variant associations tended to be specific to populations, but even common-variant associations influencing smoking behavior, such as those with CHRNA5 and CYP2A6, showed population specificity. Fine-mapping and expression quantitative trait locus colocalization nominated several candidate variants and susceptibility genes such as IRF4 and FUBP1. DNA damage assays of prioritized genes in lung fibroblasts indicated that a subset of these genes, including the pleiotropic gene IRF4, potentially exert effects by promoting endogenous DNA damage.

Association of red blood cell transfusion volume with postoperative complications and mortality in neonatal surgery.

INTRODUCTION: Red blood cell transfusion (RBCT) is commonly administered in neonatal surgical care in the absence of clear clinical indications such as active bleeding or anemia. We hypothesized that higher RBCT volumes are associated with worse postoperative outcomes. METHODS: Neonates within the National Surgical Quality Improvement Program-Pediatric database who underwent inpatient surgery (2012-2016) were stratified by weight-based RBCT volume: <20cc/kg, 20-40cc/kg, and >40cc/kg. Postoperative complications were categorized as wound, systemic infection, central nervous system (CNS), renal, pulmonary, and cardiovascular. Multivariable logistic regression and cubic spline analysis were used to evaluate the association between RBCT volume, postoperative complications, and 30-day mortality. Sensitivity analysis was conducted by performing propensity score matching. RESULTS: Among 9,877 neonates, 1,024 (10%) received RBCTs. Of those who received RBCT, 53% received <20cc/kg, 27% received 20-40cc/kg, and 20% received >40cc/kg. Relative to neonates who were not transfused, RBCT volume was associated with a dose-dependent increase in renal complications, CNS complications, cardiovascular complications, and 30-day mortality. With cubic spline analysis, a lone inflection point for 30-day mortality was identified at a RBCT volume of 30 - 35 cc/kg. After propensity score matching, the dose-dependent relationship was still present for 30-day mortality. CONCLUSION: Total RBCT volume is associated with worse postoperative outcomes in neonates with a significant increase in 30-day mortality at a RBCT volume of 30 - 35 cc/kg. Future prospective studies are needed to better understand the association between large RBCT volumes and poor outcomes after neonatal surgery. LEVEL OF EVIDENCE: Level IV, Retrospective cohort study.

Association of prematurity with complications and failure to rescue in neonatal surgery.

BACKGROUND: The majority of failure to rescue (FTR), or death after a postoperative complication, in pediatric surgery occurs among infants and neonates. The purpose of this study is to evaluate the association between gestational age (GA) and FTR in infants and neonates. METHODS: National cohort study of 46,452 patients < 1 year old within the National Surgical Quality Improvement Program-Pediatric database who underwent inpatient surgery. Patients were categorized as preterm neonates, term neonates, or infants. Neonates were stratified based on GA. Surgical procedures were classified as low- (< 1% mortality) or high-risk (≥ 1%). Multivariable logistic regression and cubic splines were used to evaluate the association between GA and FTR. RESULTS: Preterm neonates had the highest FTR (28%) rates. Among neonates, FTR increased with decreasing GA (≥ 37 weeks, 12%; 33-36 weeks, 15%; 29-32 weeks, 30%; 25-28 weeks 41%; ≤ 24 weeks, 57%). For both low- and high-risk procedures, FTR significantly (trend test, p < 0.01) increased with decreasing GA. When stratifying preterm neonates by GA, all GAs ≤ 28 weeks were associated with significantly higher odds of FTR for low- (OR 2.47, 95% CI [1.38-4.41]) and high-risk (OR 2.27, 95% CI [1.33-3.87]) procedures. A lone inflection point for FTR was identified at 31-32 weeks with cubic spline analysis. CONCLUSIONS: The dose-dependent relationship between decreasing GA and FTR as well as the FTR inflection point noted at GA 31-32 weeks can be used by stakeholders in designing quality improvement initiatives and directing perioperative care. LEVEL OF EVIDENCE: Level IV, Retrospective cohort study.

Industry Payments to Plastic Surgeons: What Has Changed Over the Last 6 Years Following Implementation of the Physician Payments Sunshine Act?

BACKGROUND: The Open Payments Program, as designated by the Physician Payments Sunshine Act, is the single largest repository of industry payments made to licensed physicians within the United States. Though sizeable in its dataset, the database and user interface are limited in their ability to permit expansive data interpretation and summarization. OBJECTIVES: The authors sought to comprehensively compare industry payments made to plastic surgeons with payments made to all surgeons and all physicians to elucidate industry relationships since implementation. METHODS: The Open Payments Database was queried between 2014 and 2019, and inclusion criteria were applied. These data were evaluated in aggregate and for yearly totals, payment type, and geographic distribution. RESULTS: A total 61,000,728 unique payments totaling $11,815,248,549 were identified over the 6-year study period; 9089 plastic surgeons, 121,151 surgeons, and 796,260 total physicians received these payments. Plastic surgeons annually received significantly less payment than all surgeons (P = 0.0005). However, plastic surgeons did not receive significantly more payment than all physicians (P = 0.0840). Cash and cash equivalents proved to be the most common form of payment; stock and stock options were least commonly transferred. Plastic surgeons in Tennessee received the most in payments between 2014 and 2019 (mean $76,420.75). California had the greatest number of plastic surgeons who received payments (1452 surgeons). CONCLUSIONS: Plastic surgeons received more in industry payments than the average of all physicians but received less than all surgeons. The most common payment was cash transactions. Over the past 6 years, geographic trends in industry payments have remained stable.

Artificial intelligence, machine learning, and deep learning for clinical outcome prediction.

AI is a broad concept, grouping initiatives that use a computer to perform tasks that would usually require a human to complete. AI methods are well suited to predict clinical outcomes. In practice, AI methods can be thought of as functions that learn the outcomes accompanying standardized input data to produce accurate outcome predictions when trialed with new data. Current methods for cleaning, creating, accessing, extracting, augmenting, and representing data for training AI clinical prediction models are well defined. The use of AI to predict clinical outcomes is a dynamic and rapidly evolving arena, with new methods and applications emerging. Extraction or accession of electronic health care records and combining these with patient genetic data is an area of present attention, with tremendous potential for future growth. Machine learning approaches, including decision tree methods of Random Forest and XGBoost, and deep learning techniques including deep multi-layer and recurrent neural networks, afford unique capabilities to accurately create predictions from high dimensional, multimodal data. Furthermore, AI methods are increasing our ability to accurately predict clinical outcomes that previously were difficult to model, including time-dependent and multi-class outcomes. Barriers to robust AI-based clinical outcome model deployment include changing AI product development interfaces, the specificity of regulation requirements, and limitations in ensuring model interpretability, generalizability, and adaptability over time.

The shared genetic architecture between epidemiological and behavioral traits with lung cancer.

The complex polygenic nature of lung cancer is not fully characterized. Our study seeks to identify novel phenotypes associated with lung cancer using cross-trait linkage disequilibrium score regression (LDSR). We measured pairwise genetic correlation (rg) and SNP heritability (h2) between 347 traits and lung cancer risk using genome-wide association study summary statistics from the UKBB and OncoArray consortium. Further, we conducted analysis after removing genomic regions previously associated with smoking behaviors to mitigate potential confounding effects. We found significant negative genetic correlations between lung cancer risk and dietary behaviors, fitness metrics, educational attainment, and other psychosocial traits. Alcohol taken with meals (rg = - 0.41, h2 = 0.10, p = 1.33 × 10-16), increased fluid intelligence scores (rg = - 0.25, h2 = 0.22, p = 4.54 × 10-8), and the age at which full time education was completed (rg = - 0.45, h2 = 0.11, p = 1.24 × 10-20) demonstrated negative genetic correlation with lung cancer susceptibility. The body mass index was positively correlated with lung cancer risk (rg = 0.20, h2 = 0.25, p = 2.61 × 10-9). This analysis reveals shared genetic architecture between several traits and lung cancer predisposition. Future work should test for causal relationships and investigate common underlying genetic mechanisms across these genetically correlated traits.

Population simulations of COVID-19 outbreaks provide tools for risk assessment and continuity planning.

OBJECTIVES: We developed COVID-19 Outbreak Simulator (https://ictr.github.io/covid19-outbreak-simulator/) to quantitatively estimate the effectiveness of preventative and interventive measures to prevent and battle COVID-19 outbreaks for specific populations. MATERIALS AND METHODS: Our simulator simulates the entire course of infection and transmission of the virus among individuals in heterogeneous populations, subject to operations and influences, such as quarantine, testing, social distancing, and community infection. It provides command-line and Jupyter notebook interfaces and a plugin system for user-defined operations. RESULTS: The simulator provides quantitative estimates for COVID-19 outbreaks in a variety of scenarios and assists the development of public health policies, risk-reduction operations, and emergency response plans. DISCUSSION: Our simulator is powerful, flexible, and customizable, although successful applications require realistic estimation and robustness analysis of population-specific parameters. CONCLUSION: Risk assessment and continuity planning for COVID-19 outbreaks are crucial for the continued operation of many organizations. Our simulator will be continuously expanded to meet this need.

Reducing COVID-19 quarantine with SARS-CoV-2 testing: a simulation study.

OBJECTIVE: To evaluate the effectiveness of SARS-CoV-2 testing on shortening the duration of quarantines for COVID-19 and to identify the most effective choices of testing schedules. DESIGN: We performed extensive simulations to evaluate the performance of quarantine strategies when one or more SARS-CoV-2 tests were administered during the quarantine. Simulations were based on statistical models for the transmissibility and viral loads of SARS-CoV-2 infections and the sensitivities of available testing methods. Sensitivity analyses were performed to evaluate the impact of perturbations in model assumptions on the outcomes of optimal strategies. RESULTS: We found that SARS-CoV-2 testing can effectively reduce the length of a quarantine without compromising safety. A single reverse transcription-PCR (RT-PCR) test performed before the end of quarantine can reduce quarantine duration to 10 days. Two tests can reduce the duration to 8 days, and three highly sensitive RT-PCR tests can justify a 6-day quarantine. More strategic testing schedules and longer quarantines are needed if tests are administered with less-sensitive RT-PCR tests or antigen tests. Shorter quarantines can be used for applications that tolerate a residual postquarantine transmission risk comparable to a 10-day quarantine. CONCLUSIONS: Testing could substantially reduce the length of isolation, reducing the physical and mental stress caused by lengthy quarantines. With increasing capacity and lowered costs of SARS-CoV-2 tests, test-assisted quarantines could be safer and more cost-effective than 14-day quarantines and warrant more widespread use.

The Shared Genetic Architectures Between Lung Cancer and Multiple Polygenic Phenotypes in Genome-Wide Association Studies.

BACKGROUND: Prior genome-wide association studies have identified numerous lung cancer risk loci and reveal substantial etiologic heterogeneity across histologic subtypes. Analyzing the shared genetic architecture underlying variation in complex traits can elucidate common genetic etiologies across phenotypes. Exploring pairwise genetic correlations between lung cancer and other polygenic traits can reveal the common genetic etiology of correlated phenotypes. METHODS: Using cross-trait linkage disequilibrium score regression, we estimated the pairwise genetic correlation and heritability between lung cancer and multiple traits using publicly available summary statistics. Identified genetic relationships were also examined after excluding genomic regions known to be associated with smoking behaviors, a major risk factor for lung cancer. RESULTS: We observed several traits showing moderate single nucleotide polymorphism-based heritability and significant genetic correlations with lung cancer. We observed highly significant correlations between the genetic architectures of lung cancer and emphysema/chronic bronchitis across all histologic subtypes, as well as among lung cancer occurring among smokers. Our analyses revealed highly significant positive correlations between lung cancer and paternal history of lung cancer. We also observed a strong negative correlation with parental longevity. We observed consistent directions in genetic patterns after excluding genomic regions associated with smoking behaviors. CONCLUSIONS: This study identifies numerous phenotypic traits that share genomic architecture with lung carcinogenesis and are not fully accounted for by known smoking-associated genomic loci. IMPACT: These findings provide new insights into the etiology of lung cancer by identifying traits that are genetically correlated with increased risk of lung cancer.

Optimal test-assisted quarantine strategies for COVID-19.

OBJECTIVE: To evaluate the effectiveness of SARS-CoV-2 testing on shortening the duration of quarantines for COVID-19 and to identify the most effective choices of testing schedules. DESIGN: We performed extensive simulations to evaluate the performance of quarantine strategies when one or more SARS-CoV-2 tests were administered during the quarantine. Simulations were based on statistical models for the transmissibility and viral loads of SARS-CoV-2 infections and the sensitivities of available testing methods. Sensitivity analyses were performed to evaluate the impact of perturbations in model assumptions on the outcomes of optimal strategies. RESULTS: We found that SARS-CoV-2 testing can effectively reduce the length of a quarantine without compromising safety. A single RT-PCR test performed before the end of quarantine can reduce quarantine duration to 10 days. Two tests can reduce the duration to 8 days, and three highly sensitive RT-PCR tests can justify a 6-day quarantine. More strategic testing schedules and longer quarantines are needed if tests are administered with less sensitive RT-PCR tests or antigen tests. Shorter quarantines can be utilized for applications that tolerate a residual post-quarantine transmission risk comparable to a 10-day quarantine. CONCLUSIONS: Testing could substantially reduce the length of isolation, reducing the physical and mental stress caused by lengthy quarantines. With increasing capacity and lowered costs of SARS-CoV-2 tests, test-assisted quarantines could be safer and more cost-effective than 14-day quarantines and warrant more widespread use. RESEARCH IN CONTEXT: What is already known on this topic?: Recommendations for quarantining individuals who could have been infected with COVID-19 are based on limited evidence.Despite recent theoretical and case studies of test-assisted quarantines, there has been no substantive investigation to quantify the safety and efficacy of, nor an exhaustive search for, optimal test-assisted quarantine strategies. WHAT THIS STUDY ADDS: Our simulations indicate that the 14-day quarantine approach is overly conservative and can be safely shortened if testing is performed.Our recommendations include testing schedules that could be immediately adopted and implemented as government and industry policies. ROLE OF THE FUNDING SOURCE: A major technology company asked that we perform simulations to understand the optimal strategy for managing personnel quarantining before forming cohorts of individuals who would work closely together. The funding entity did not influence the scope or output of the study but requested that we include antigen testing as a component of the quarantining process. Patrick Yu and Peter Matos are employees of Corporate Medical Advisors, and International S.O.S employs Julie McCashin. Other funding sources are research grants and did not influence the investigation.

Successful liver transplantation in mitochondrial neurogastrointestinal encephalomyopathy (MNGIE).

Mitochondrial neurogastrointestinal encephalomyopathy (MNGIE) is a fatal disorder characterized by progressive gastrointestinal dysmotility, peripheral neuropathy, leukoencephalopathy, skeletal myopathy, ophthalmoparesis, and ptosis. MNGIE stems from deficient thymidine phosphorylase activity (TP) leading to toxic elevations of plasma thymidine. Hematopoietic stem cell transplant (HSCT) restores TP activity and halts disease progression but has high transplant-related morbidity and mortality. Liver transplant (LT) was reported to restore TP activity in two adult MNGIE patients. We report successful LT in four additional MNGIE patients, including a pediatric patient. Our patients were diagnosed between ages 14 months and 36 years with elevated thymidine levels and biallelic pathogenic variants in TYMP. Two patients presented with progressive gastrointestinal dysmotility, and three demonstrated progressive peripheral neuropathy with two suffering limitations in ambulation. Two patients, including the child, had liver dysfunction and cirrhosis. Following LT, thymidine levels nearly normalized in all four patients and remained low for the duration of follow-up. Disease symptoms stabilized in all patients, with some manifesting improvements, including intestinal function. No patient died, and LT appeared to have a more favorable safety profile than HSCT, especially when liver disease is present. Follow-up studies will need to document the long-term impact of this new approach on disease outcome. Take Home Message: Liver transplantation is effective in stabilizing symptoms and nearly normalizing thymidine levels in patients with mitochondrial neurogastrointestinal encephalomyopathy (MNGIE) and may have an improved safety profile over hematopoietic stem cell transplant.

Artificial Intelligence in Global Health -A Framework and Strategy for Adoption and Sustainability.

Artificial Intelligence (AI) applications in medicine have grown considerably in recent years. AI in the forms of Machine Learning, Natural Language Processing, Expert Systems, Planning and Logistics methods, and Image Processing networks provide great analytical aptitude. While AI methods were first conceptualized for radiology, investigations today are established across all medical specialties. The necessity for proper infrastructure, skilled labor, and access to large, well-organized data sets has kept the majority of medical AI applications in higher-income countries. However, critical technological improvements, such as cloud computing and the near-ubiquity of smartphones, have paved the way for use of medical AI applications in resource-poor areas. Global health initiatives (GHI) have already begun to explore ways to leverage medical AI technologies to detect and mitigate public health inequities. For example, AI tools can help optimize vaccine delivery and community healthcare worker routes, thus enabling limited resources to have a maximal impact. Other promising AI tools have demonstrated an ability to: predict burn healing time from smartphone photos; track regions of socioeconomic disparity combined with environmental trends to predict communicable disease outbreaks; and accurately predict pregnancy complications such as birth asphyxia in low resource settings with limited patient clinical data. In this commentary, we discuss the current state of AI-driven GHI and explore relevant lessons from past technology-centered GHI. Additionally, we propose a conceptual framework to guide the development of sustainable strategies for AI-driven GHI, and we outline areas for future research.

Recurrent Schwannomatosis of the Hand.

Background: Peripheral nerve sheath tumors (PNSTs) are neoplastic soft tissue masses generated from the abnormal proliferation of Schwann cells. Often, these tumors occur in isolation and are known as schwannomas or neurilemmomas. The presence of multiple schwannomas is known as schwannomatosis. The purpose of this article is 2-fold: (1) to review the relevant literature and describe a unique case of this rare condition; and (2) to emphasize salient clinical considerations in the diagnosis and treatment of schwannomatosis. Methods: In this report, we describe the case of a 52-year-old white man who presented with multiple recurrent soft tissue masses of the right hand. On initial presentation, he described pain across his right hand and index finger, which persisted despite numerous prior operations. The index finger had a flexion contracture around the location of the proximal interphalangeal joint, and there were multiple tender masses along the length of the finger and palm. Results: Segmental excision of the affected radial digital nerve was performed. A pulp flap based on contralateral neurovascular bundle resulted in a sensate, pain-free digit. Tissue pathology confirmed the diagnosis of multiple neurilemmomas. Conclusions: We report the success of a radial digital neurectomy in a patient with widespread neurilemmomas, who had previously excluded that painful digit from use. It was through careful consideration of the preoperative differential diagnosis, by valuing the preoperative imaging, and by considering all surgical options with specific attention paid to skin flap design that this good outcome of a fully sensate, pain-free, mobile index finger was achieved.

Evidence for the Use of Acellular Dermal Matrix in Implant-Based Breast Reconstruction.

Acellular dermal matrices (ADMs) are tissue grafts that have been specially processed to remove all cellular components. These machined biological scaffolds have become popular in a variety of surgical settings due to their rapid incorporation into living tissue. As ADMs are highly malleable and cause minimal inflammation, they have come to serve as a useful tool in implant-based breast reconstruction procedures. The major benefits of using an ADM in this setting include superior initial breast contouring, decreased risk of capsular contracture after implant insertion, and consistent sustained positioning of the reconstructed breast. Despite these advantages, these tissue grafts are foreign to the host, and postoperative complications following ADM insertion, including infection and seroma, have been well documented. When considering using ADMs in this setting, it is important to first consider patient-specific factors that could preclude their use, such as low body mass index, small breasts, or a history of radiation exposure to the breast tissue. ADM grafts are also expensive, which may present another barrier to their use. Review of the literature ultimately suggests a continued role for ADMs in implant-based breast reconstruction, and continued research in this field is warranted.