Citations

Abstract Goals/Purpose The imperative for precision in aesthetic surgery necessitates a robust framework for evaluating the impact of facial interventions on perceived age. Our study introduces a cutting-edge AI model aimed at discerning an individual's perceived age from facial characteristics. This tool is designed to augment the assessment of various plastic surgery procedures, facilitating the tailoring of interventions to each patient's unique facial aging pattern. Methods/Technique We harnessed a deep convolutional neural network (DCNN), pre-trained on the extensive ImageNet dataset, and further refined using 523,051 pre-annotated facial images from the IMBD-WIKI database, normalized as per the Mathias et al. face detection paradigm. Faces were processed into a 299x299 pixel matrix, maintaining a 40% margin around the face for uniformity. The Xception architecture was employed for its advanced feature extraction capabilities. The model was refined and tested against a diverse set of 100 patient faces from the Mayo Clinic's database, categorized by demographic and procedural data. The AI model employed regression analysis and softmax probability for precise age estimation. Results/Complications The AI model exhibited a remarkable accuracy rate of 92.5% in age estimation for pre procedural patients, with a standard deviation of 3.2 years. It significantly outperformed traditional methods in identifying fine-grained age-related features. The AI model discerned an average perceived age reduction of 3.5 years across all patients post-procedure, with a notable variance among different types of surgeries. Certain procedures, such as rhytidectomy and blepharoplasty, showed a more pronounced age-reduction effect. Conclusion The AI model presents an accurate and objective method for quantifying perceived age, serving as a significant benchmark in facial aesthetic evaluation. By illustrating measurable age reduction following various procedures, with some surgeries yielding more substantial changes in perceived age, the model stands as a testament to the effectiveness of plastic surgery interventions. The precision of our model in predicting age pre- and post-procedure underscores its potential to assist surgeons in custom-tailoring surgeries to individual aging patterns. This innovation is poised to refine the decision-making process in aesthetic surgery, ensuring treatments are aligned with the desired outcomes for rejuvenation and patient-specific needs, ultimately advancing the frontier of personalized plastic surgery.

Abstract This is a single Institute, prospective cohort study. We collected twenty- two postmenopausal women with pelvic organ prolapse planning to undergo vaginal hysterectomy with transvaginal pelvic reconstructive surgery, with or without a concomitant anti-incontinence procedure. Vaginal swabs and urine samples were longitudinally collected at five time points: preoperative consult visit (T1), day of surgery prior to surgical scrub (T2), immediately postoperative (T3), day of hospital discharge (T4), and at the postoperative exam visit (T5). Women experiencing urinary tract infection symptoms provided a sample set prior to antibiotic administration (T6). Microbiome analysis on vaginal and urinary specimens at each time point. Region V3-V5 of the 16S ribosomal RNA gene was amplified and sequenced. Sample DNA was analyzed with visit T1, T2, T5 and T6. Six (27.3%) participants developed postoperative urinary tract infection whose vaginal sample at first clinical visit (T1) revealed beta-diversity analysis with significant differences in microbiome structure and composition. Women diagnosed with a postoperative urinary tract infection had a vaginal microbiome characterized by low abundance of Lactobacillus and high prevalence of Prevotella and Gardnerella species. In our cohort, preoperative vaginal swabs can predict who will develop a urinary tract infection following transvaginal surgery for pelvic organ prolapse.

Time series biomechanical data inform our understanding of normal gait mechanics and pathomechanics. This study examines the utility of different quantitative methods to distinguish vertical ground reaction forces (VGRFs) from experimentally distinct gait strategies. The goals of this study are to compare measures of VGRF data—using the shape factor method and a Fourier series-based analysis—to (1) describe how these methods reflect and distinguish gait patterns and (2) determine which Fourier series coefficients discriminate normal walking, with a relatively stiff-legged gait, from compliant walking, using deep knee flexion and limited vertical oscillation. This study includes a reanalysis of previously presented VGRF data. We applied the shape factor method and fit 3- to 8-term Fourier series to zero-padded VGRF data. We compared VGRF renderings using Euclidean L 2 distances and correlations stratified by gait strategy. Euclidean L 2 distances improved with additional harmonics, with limited improvement after the seventh term. Euclidean L 2 distances were greater in shape factor versus Fourier series renderings. In the 8 harmonic model, amplitudes of 9 Fourier coefficients—which contribute to VGRF features including peak and local minimum amplitudes and limb loading rates—were different between normal and compliant walking. The results suggest that Fourier series-based methods distinguish between gait strategies.

AbstractBackgroundUnited Network for Organ Sharing (UNOS) allocation criteria changed in 2018 to accommodate the increased prevalence of patients on a ventricular assist device as a bridge to heart transplant and prioritize sicker people in anticipation of a heart graft. We aimed to assess the impact of patient age in the new allocation policy on mortality following heart transplantation. Secondary outcomes included the effect of age ≥70 on post‐transplant events, including stroke, dialysis, pacemaker, and rejection requiring treatment.MethodsThe UNOS Registry was queried to identify patients who underwent heart transplants alone in the US between 2000 and 2021. Patients were divided into groups according to their age (over 70 and under 70 years old).ResultsPatients aged over 70 were more likely to require dialysis during follow‐up, but less likely to experience rejection requiring treatment, compared with patients aged <70. Age ≥70 in the new allocation system was a significant predictor of 1‐year mortality (adjusted HR: 1.41; 95% CI: 1.05–1.91; p = .024), but its effect on 5‐year mortality was not significant after adjusting for potential confounders (adjusted HR: 1.27; 95% CI:.97–1.66; p = .077). Undergoing transplantation under the new allocation policy vs the old allocation policy was not a significant predictor of mortality in patients over 70 years old.ConclusionsAge ≥70 is a significant predictor of 1‐year mortality following heart transplantation, but not at 5 and 10 years; however, the new allocation does not seem to have changed the outcomes for this group of patients.

Background and Aims: Alcohol-associated hepatitis (AH) poses significant short-term mortality. Existing prognostic models lack precision for 90-day mortality. Utilizing artificial intelligence in a global cohort, we sought to derive and validate an enhanced prognostic model. Approach and Results: The Global AlcHep initiative, a retrospective study across 23 centers in 12 countries, enrolled patients with AH per National Institute for Alcohol Abuse and Alcoholism criteria. Centers were partitioned into derivation (11 centers, 860 patients) and validation cohorts (12 centers, 859 patients). Focusing on 30 and 90-day postadmission mortality, 3 artificial intelligence algorithms (Random Forest, Gradient Boosting Machines, and eXtreme Gradient Boosting) informed an ensemble model, subsequently refined through Bayesian updating, integrating the derivation cohort’s average 90-day mortality with each center’s approximate mortality rate to produce posttest probabilities. The ALCoholic Hepatitis Artificial INtelligence Ensemble score integrated age, gender, cirrhosis, and 9 laboratory values, with center-specific mortality rates. Mortality was 18.7% (30 d) and 27.9% (90 d) in the derivation cohort versus 21.7% and 32.5% in the validation cohort. Validation cohort 30 and 90-day AUCs were 0.811 (0.779–0.844) and 0.799 (0.769–0.830), significantly surpassing legacy models like Maddrey’s Discriminant Function, Model for End-Stage Liver Disease variations, age-serum bilirubin-international normalized ratio-serum Creatinine score, Glasgow, and modified Glasgow Scores (p < 0.001). ALCoholic Hepatitis Artificial INtelligence Ensemble score also showcased superior calibration against MELD and its variants. Steroid use improved 30-day survival for those with an ALCoholic Hepatitis Artificial INtelligence Ensemble score > 0.20 in both derivation and validation cohorts. Conclusions: Harnessing artificial intelligence within a global consortium, we pioneered a scoring system excelling over traditional models for 30 and 90-day AH mortality predictions. Beneficial for clinical trials, steroid therapy, and transplant indications, it’s accessible at: https://aihepatology.shinyapps.io/ALCHAIN/.

AbstractBackgroundStandard flow cytometry protocols for CD34+ cell enumeration designed for fresh samples are not appropriate for cryopreserved products. Special protocols have been developed to remove the cryoprotectant by quickly washing a freshly thawed sample. Exposing cells to a large volume of hypotonic solution and subsequent washing process was hypothesized to cause lab‐induced cell death. Moreover, standard gating strategies must be altered to avoid reporting falsely high viabilities.Study Design and MethodsWe developed a novel method whereby thawed samples were diluted step‐wise to 1:2 by 3 additions of 1/3 sample volume using 1% Human Albumin in Dextran 40 (10% Low Molecular Weight Dextran in 0.9% NaCl) separated by 5 min between each addition. An additional 1:10 dilution was required to obtain a desired cell concentration for flow cytometry testing resulting in a 1:20 dilution.ResultsTwenty samples were tested simultaneously in a method comparison; the new method demonstrated significant increases in mean cell viabilities for white blood cells, hematopoietic progenitor cells, and T cells as well as reduced standard deviations for each parameter.DiscussionSlow, step‐wise dilutions of freshly thawed samples of cryopreserved apheresis products to 1:20 yielded higher and more precise viability measurements compared to quickly washing samples to remove DMSO.

Background: In 2014, the Affordable Care Act Medicaid Expansion (ME) increased Medicaid eligibility for adults with an income level up to 138% of the federal poverty level. In this study, we examined the impact of ME on mortality and amputation in patients with peripheral artery disease (PAD). Methods: The 100% MedPAR and Part-B Carrier files from 2011 to 2018 were queried to identify all fee-for-service Medicare beneficiaries with PAD using International Classification of Diseases codes. Our primary exposure was whether a state had adopted the ME on January 1, 2014. Our primary outcomes were the change in all-cause 1-year mortality and leg amputation. We used a state-level difference-in-differences (DID) analysis to compare the rates of the primary outcomes among patients who were in states (including the District of Columbia) who adopted ME ( n = 25) versus those who were in states that did not ( n = 26). We performed a subanalysis stratifying by sex, race, region, and dual-eligibility status. Results: Over the 8-year period, we studied 37,743,929 patients. The average unadjusted 1-year mortality decreased from 2011 to 2018 in both non-ME (9.5% to 8.7%, p < 0.001) and ME (9.1% to 8.3%, p < 0.001) states. The average unadjusted 1-year amputation rate did not improve in either the non-ME (0.86% to 0.87%, p = 0.17) or ME (0.69% to 0.69%, p = 0.65) states. Across the entire cohort, the DID model revealed that ME did not lead to a significant change in mortality ( p = 0.15) or amputation ( p = 0.34). Conclusion: Medicaid Expansion was not associated with reduced mortality or leg amputation in Medicare beneficiaries with PAD.