Design, construction, and in vivo augmentation of a complex gut microbiome.

Efforts to model the human gut microbiome in mice have led to important insights into the mechanisms of host-microbe interactions. However, the model communities studied to date have been defined or complex, but not both, limiting their utility. Here, we construct and characterize in vitro a defined community of 104 bacterial species composed of the most common taxa from the human gut microbiota (hCom1). We then used an iterative experimental process to fill open niches: germ-free mice were colonized with hCom1 and then challenged with a human fecal sample. We identified new species that engrafted following fecal challenge and added them to hCom1, yielding hCom2. In gnotobiotic mice, hCom2 exhibited increased stability to fecal challenge and robust colonization resistance against pathogenic Escherichia coli. Mice colonized by either hCom2 or a human fecal community are phenotypically similar, suggesting that this consortium will enable a mechanistic interrogation of species and genes on microbiome-associated phenotypes.

Predicting Increased Incidence of Common Antibiotic-Resistant and Antibiotic-Associated Pathogens Using Ensemble Species Distribution Modeling.

The Centers for Disease Control estimates antibiotic-associated pathogens result in 2.8 million infections and 38 000 deaths annually in the United States. This study applies species distribution modeling to elucidate the impact of environmental determinants of human infectious disease in an era of rapid global change. We modeled methicillin-resistant Staphylococcus aureus and Clostridioides difficile using 31 publicly accessible bioclimatic, health care, and sociodemographic variables. Ensemble models were created from 8 unique statistical and machine learning algorithms. Using International Classification of Diseases, 10th edition codes, we identified 305 528 diagnoses of methicillin-resistant S. aureus and 203 001 diagnoses of C. difficile presence. Three environmental factors-average maximum temperature, specific humidity, and agricultural land density-emerged as major predictors of increased methicillin-resistant S. aureus and C. difficile presence; variables representing health care availability were less important. Species distribution modeling may be a powerful tool for identifying areas at increased risk for disease presence and have important implications for disease surveillance systems.

Practical guide to building machine learning-based clinical prediction models using imbalanced datasets.

Clinical prediction models often aim to predict rare, high-risk events, but building such models requires robust understanding of imbalance datasets and their unique study design considerations. This practical guide highlights foundational prediction model principles for surgeon-data scientists and readers who encounter clinical prediction models, from feature engineering and algorithm selection strategies to model evaluation and design techniques specific to imbalanced datasets. We walk through a clinical example using readable code to highlight important considerations and common pitfalls in developing machine learning-based prediction models. We hope this practical guide facilitates developing and critically appraising robust clinical prediction models for the surgical community.

Practical Computer Vision Application to Compute Total Body Surface Area Burn: Reappraising a Fundamental Burn Injury Formula in the Modern Era.

Importance: Critical burn management decisions rely on accurate percent total body surface area (%TBSA) burn estimation. Existing %TBSA burn estimation models (eg, Lund-Browder chart and rule of nines) were derived from a linear formula and a limited number of individuals a century ago and do not reflect the range of body habitus of the modern population. Objective: To develop a practical %TBSA burn estimation tool that accounts for exact burn injury pattern, sex, and body habitus. Design, Setting, and Participants: This population-based cohort study evaluated the efficacy of a computer vision algorithm application in processing an adult laser body scan data set. High-resolution surface anthropometry laser body scans of 3047 North American and European adults aged 18 to 65 years from the Civilian American and European Surface Anthropometry Resource data set (1998-2001) were included. Of these, 1517 participants (49.8%) were male. Race and ethnicity data were not available for analysis. Analyses were conducted in 2020. Main Outcomes and Measures: The contributory %TBSA for 18 body regions in each individual. Mobile application for real-time %TBSA burn computation based on sex, habitus, and exact burn injury pattern. Results: Of the 3047 individuals aged 18 to 65 years for whom body scans were available, 1517 (49.8%) were male. Wide individual variability was found in the extent to which major body regions contributed to %TBSA, especially in the torso and legs. Anterior torso %TBSA increased with increasing body habitus (mean [SD], 15.1 [0.9] to 19.1 [2.0] for male individuals; 15.1 [0.8] to 18.0 [1.7] for female individuals). This increase was attributable to increase in abdomen %TBSA (mean [SD], 5.3 [0.7] to 8.7 [1.8]) among male individuals and increase in abdomen (mean [SD], 4.6 [0.6] to 6.8 [1.7]) and pelvis (mean [SD], 1.5 [0.2] to 2.9 [0.9]) %TBSAs among female individuals. For most body regions, Lund-Browder chart and rule of nines estimates fell outside the population's measured interquartile ranges. The mobile application tested in this study, Burn Area, facilitated accurate %TBSA burn computation based on exact burn injury pattern for 10 sex and body habitus-specific models. Conclusions and Relevance: Computer vision algorithm application to a large laser body scan data set may provide a practical tool that facilitates accurate %TBSA burn computation in the modern era.

Influence of Socioeconomic and Environmental Determinants of Health on Human Infection and Colonization with Antibiotic-Resistant and Antibiotic-Associated Pathogens: A Scoping Review.

Background: Antibiotic-resistant and antibiotic-associated pathogens are commonly encountered by surgeons. Pathogens such as methicillin-resistant Staphylococcus aureus (MRSA), Clostridioides difficile infection (CDI), and carbapenem-resistant Enterobacteriaceae (CRE) result in considerable human morbidity, mortality, and excess healthcare expenditure. Human colonization or infection can result from exposure to these pathogens across a range of domains both inside and outside of the built healthcare environment, exposure that may be influenced by socioeconomic and environmental determinants of health, the importance of which has not been investigated fully. Methods: We performed a scoping review of published literature describing potential socioeconomic and environmental variables that may increase the likelihood of human infection or colonization with common antibiotic-resistant or antibiotic-associated pathogens, using MRSA, CDI, and CRE as examples. Results: We identified 7,916 articles meeting initial search criteria. Of these, 101 provided supportive evidence of socioeconomic and environmental determinants of human infection or colonization and were included in the scoping review after abstract and full-text screening. Sixty-seven evaluated MRSA, nine evaluated CRE, and 29 evaluated CDI. Twenty-nine articles evaluated exposure to livestock or companion animals; 28, exposure to antibiotics; 20, impact of socioeconomic factors, education level, or race; 14, the influence of temperature, humidity, or season; 13, the effect of travel or human population migration; 11, exposure to built healthcare environments; and eight assessed impact of population density or urbanization. Conclusions: Although articles outlining socioeconomic and environmental drivers of antibiotic-resistant and antibiotic-associated infection are still disconcertedly few, evidence of such associations are overwhelming for MRSA and CDI and supportive for CRE. Additional research is needed to investigate the role and importance of different potential socioeconomic and environmental drivers of antibiotic-resistant and antibiotic-associated infections and colonization in humans.

Scalable deep learning algorithm to compute percent pulmonary contusion among patients with rib fractures.

BACKGROUND: Pulmonary contusion exists along a spectrum of severity, yet is commonly binarily classified as present or absent. We aimed to develop a deep learning algorithm to automate percent pulmonary contusion computation and exemplify how transfer learning could facilitate large-scale validation. We hypothesized that our deep learning algorithm could automate percent pulmonary contusion computation and that greater percent contusion would be associated with higher odds of adverse inpatient outcomes among patients with rib fractures. METHODS: We evaluated admission-day chest computed tomography scans of adults 18 years or older admitted to our institution with multiple rib fractures and pulmonary contusions (2010-2020). We adapted a pretrained convolutional neural network that segments three-dimensional lung volumes and segmented contused lung parenchyma, pulmonary blood vessels, and computed percent pulmonary contusion. Exploratory analysis evaluated associations between percent pulmonary contusion (quartiles) and odds of mechanical ventilation, mortality, and prolonged hospital length of stay using multivariable logistic regression. Sensitivity analysis included pulmonary blood vessel volumes during percent contusion computation. RESULTS: A total of 332 patients met inclusion criteria (median, 5 rib fractures), among whom 28% underwent mechanical ventilation and 6% died. The study population's median (interquartile range) percent pulmonary contusion was 4% (2%-8%). Compared to the lowest quartile of percent pulmonary contusion, each increasing quartile was associated with higher adjusted odds of undergoing mechanical ventilation (odds ratio [OR], 1.5; 95% confidence interval [95% CI], 1.1-2.1) and prolonged hospitalization (OR, 1.6; 95% CI, 1.1-2.2), but not with mortality (OR, 1.1; 95% CI, 0.6-2.0). Findings were similar on sensitivity analysis. CONCLUSION: We developed a scalable deep learning algorithm to automate percent pulmonary contusion calculating using chest computed tomography scans of adults admitted with rib fractures. Open code sharing and collaborative research are needed to validate our algorithm and exploratory analysis at a large scale. Transfer learning can help harness the full potential of big data and high-performing algorithms to bring precision medicine to the bedside. LEVEL OF EVIDENCE: Prognostic and epidemiological, Level III.

Evidence-based surgery for laparoscopic cholecystectomy.

Background: Laparoscopic cholecystectomy is frequently performed for acute cholecystitis and symptomatic cholelithiasis. Considerable variation in the execution of key steps of the operation remains. We conducted a systematic review of evidence regarding best practices for critical intraoperative steps for laparoscopic cholecystectomy. Methods: We identified 5 main intraoperative decision points in laparoscopic cholecystectomy: (1) number and position of laparoscopic ports; (2) identification of cystic artery and duct; (3) division of cystic artery and duct; (4) indications for subtotal cholecystectomy; and (5) retrieval of the gallbladder. PubMed, EMBASE, and Web of Science were queried for relevant studies. Randomized controlled trials and systematic reviews were included for analysis, and evidence quality was assessed using the Grading of Recommendations, Assessment, Development, and Evaluation framework. Results: Fifty-two articles were included. Although all port configurations were comparable from a safety standpoint, fewer ports sometimes resulted in improved cosmesis or decreased pain but longer operative times. The critical view of safety should be obtained for identification of the cystic duct and artery but may be obtained through fundus-first dissection and augmented with cholangiography or ultrasound. Insufficient evidence exists to compare harmonic-shear, clipless ligation against clip ligation of the cystic duct and artery. Stump closure during subtotal cholecystectomy may reduce rates of bile leak and reoperation. Use of retrieval bag for gallbladder extraction results in minimal benefit. Most studies were underpowered to detect differences in incidence of rare complications. Conclusion: Key operative steps of laparoscopic cholecystectomy should be informed by both compiled data and surgeon preference/patient considerations.

Efficacy of intercostal cryoneurolysis as an analgesic adjunct for chest wall pain after surgery or trauma: systematic review.

BACKGROUND: There is a critical need for non-narcotic analgesic adjuncts in the treatment of thoracic pain. We evaluated the efficacy of intercostal cryoneurolysis as an analgesic adjunct for chest wall pain, specifically addressing the applicability of intercostal cryoneurolysis for pain control after chest wall trauma. METHODS: A systematic review was performed through searches of PubMed, EMBASE, and the Cochrane Library. We included studies involving patients of all ages that evaluated the efficacy of intercostal cryoneurolysis as a pain adjunct for chest wall pathology. Quantitative and qualitative synthesis was performed. RESULTS: Twenty-three studies including 570 patients undergoing cryoneurolysis met eligibility criteria for quantitative analysis. Five subgroups of patients treated with intercostal cryoneurolysis were identified: pectus excavatum (nine studies); thoracotomy (eight studies); post-thoracotomy pain syndrome (three studies); malignant chest wall pain (two studies); and traumatic rib fractures (one study). There is overall low-quality evidence supporting intercostal cryoneurolysis as an analgesic adjunct for chest wall pain. A majority of studies demonstrated decreased inpatient narcotic use with intercostal cryoneurolysis compared with conventional pain modalities. Intercostal cryoneurolysis may also lead to decreased hospital length of stay. The procedure did not definitively increase operative time, and risk of complications was low. CONCLUSIONS: Given the favorable risk-to-benefit profile, both percutaneous and thoracoscopic intercostal cryoneurolysis may serve as a worthwhile analgesic adjunct in trauma patients with rib fractures who have failed conventional medical management. However, further prospective studies are needed to improve quality of evidence. LEVEL OF EVIDENCE: Level IV systematic reviews and meta-analyses.

A new approach to biomining: Bioengineering surfaces for metal recovery from aqueous solutions.

Electronics waste production has been fueled by economic growth and the demand for faster, more efficient consumer electronics. The glass and metals in end-of-life electronics components can be reused or recycled; however, conventional extraction methods rely on energy-intensive processes that are inefficient when applied to recycling e-waste that contains mixed materials and small amounts of metals. To make e-waste recycling economically viable and competitive with obtaining raw materials, recovery methods that lower the cost of metal reclamation and minimize environmental impact need to be developed. Microbial surface adsorption can aid in metal recovery with lower costs and energy requirements than traditional metal-extraction approaches. We introduce a novel method for metal recovery by utilizing metal-binding peptides to functionalize fungal mycelia and enhance metal recovery from aqueous solutions such as those found in bioremediation or biomining processes. Using copper-binding as a proof-of-concept, we compared binding parameters between natural motifs and those derived in silico, and found comparable binding affinity and specificity for Cu. We then combined metal-binding peptides with chitin-binding domains to functionalize a mycelium-based filter to enhance metal recovery from a Cu-rich solution. This finding suggests that engineered peptides could be used to functionalize biological surfaces to recover metals of economic interest and allow for metal recovery from metal-rich effluent with a low environmental footprint, at ambient temperatures, and under circumneutral pH.