Evidence of the protective role of Carvacrol in a retinal degeneration animal model.

Neurodegenerative pathologies affecting the posterior segment of the eye, are characterized by being devastating and responsible for the majority of visual dysfunctions worldwide. These diseases are primarily degenerative, progressing chronically, and can inflict gradual harm to the optic nerve, retinal ganglion cells (RGC), photoreceptors, and other retinal cells. This retinal damage leads to a progressive loss of vision, marking these conditions as a significant health concern worldwide. The intravitreal administration of the phytochemical Carvacrol (CAR) is expected to demonstrate a neuroprotective and antiapoptotic effect on retinal cells, with a specific focus on RGC. This effect will be observed in a retinal degeneration model (RDM) in rabbits induced by cytotoxic and oxidative agents, namely glutamate (GLUT) and L-buthionine-S, R-sulfoximine (BSO). An in vivo study was conducted using New Zealand rabbits in which retinal damage was created to evaluate the effectiveness of CAR. The effectiveness of CAR on the functionality of retinal neuronal cells in RDM was evaluated using pupillary light reflection (PLR). Furthermore, the phytotherapeutic's influence on cell viability was determined through flow cytometry analysis. Finally, the neuroprotective and antiapoptotic capabilities of CAR were specifically scrutinized in RGC through histological studies, quantifying cell survival, and employing immunohistochemical assays to detect the apoptotic index (%) using the TUNEL technique. Our results demonstrated that CAR promoted the recovery of the pupillary contraction profile over time, maintaining the functionality of retinal cells as healthy controls. Additionally, it showed increased cell viability under oxidative and cytotoxic conditions given by GLUT-BSO agents. Finally, we found that CAR protects the survival of RGC and decreases the percentage of apoptotic cells when compared to RDM. CAR demonstrated to have positive effects on the functionality of photoreceptive nerve cells by restoring pupillary contraction. Likewise, it was shown to have neuroprotective and antiapoptotic effects when evaluated in a general and specific way on retinal nerve cells.

Feasibility of complex exercise therapy with Standing Ovation and peripheral neuromodulation for gait rehabilitation after incomplete spinal cord injury-a case report.

BACKGROUND: Spinal cord injuries (SCIs) are a global concern, annually affecting hundreds of thousands of individuals. Among these cases, incomplete SCIs, allowing some muscle activity below the injury, pose unique challenges. This case study focuses on a 55-year-old male with a moderate incomplete SCI (AIS-D). CASE PRESENTATION: After initial treatments and pharmaceutical antispastic therapy, a novel intervention was introduced featuring the Standing Ovation gait exercise system (Standing Ovation GmbH, Hallwang, Austria). This individually tailored system, equipped with a rail system and seat-lifting unit, provided a secure environment for balance training. Over 14 training sessions spanning from October 13, 2021, to March 23, 2022, improvements in functional walking were observed. DISCUSSION AND CONCLUSION: Locomotor improvement in SCI rehabilitation is important; the potential of task-specific gait exercises with the Standing Ovation system in incomplete spinal cord injury seems to be a promising approach. Although promising, these findings call for further systematic studies with larger patient cohorts to strengthen their reliability. Ongoing research endeavors are essential to fully understand the benefits and limitations of this intervention in spinal cord injury rehabilitation.

Dermoscopic Evaluation of Actinic Changes in the Lips of Indigenous Children Living at High Altitude in Ecuador: A Descriptive Study.

BACKGROUND High altitude increases sunlight exposure, resulting in actinic keratosis, which predisposes people to skin cancer. The dermoscopy procedure evaluates keratotic and pigmented skin changes. This study aimed to describe the clinical and dermoscopic actinic changes in the lips of 25 indigenous children living at high altitude in Ecuador. MATERIAL AND METHODS An observational study was conducted in a public school in the Andes region of Ecuador (August-November 2019). Twenty-five children, males and females, age 5-15 years were assessed by complete physical examination, digital dermoscopic photographs, and punch biopsies. Descriptive statistics and Fisher's exact test were used to summarize and analyze the data. RESULTS We included 17 (68%) boys and 8 (32%) girls with a mean age of 9.8±2.0 years. Clinical lips findings reported desquamation [52% Upper Lip (UL); 40% Lower Lip (LL)], fissuring (8% UL; 8% LL), scabs (8% UL; 8% LL), and discoloration (40% UL; 20% LL). Dermoscopic features included a white-yellow lip color (24% UL; p=0.02). The main morphologic pattern of blood vessels was monomorphic (88% UL; p<0.001), polymorphous (60% LL; p<0.001), dotted pattern (64% UL; 28% LL; p=0.02), and linear-irregular (32% UL; 72% LL; p=0.01). Girls had radiating white structures on UL (p=0.025), while boys presented white structureless areas (UL 63.6%; LL 77.8%; p=0.032). No differences in dermoscopic findings were observed according to Fitzpatrick scale score (FSS). Punch biopsies showed no indications of actinic cheilitis. CONCLUSIONS Dermoscopic features in indigenous children living in high altitudes were related to actinic damage, but histopathological findings were negative.

Expert-augmented machine learning.

Machine learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption is limited by the level of trust afforded by given models. Human vs. machine performance is commonly compared empirically to decide whether a certain task should be performed by a computer or an expert. In reality, the optimal learning strategy may involve combining the complementary strengths of humans and machines. Here, we present expert-augmented machine learning (EAML), an automated method that guides the extraction of expert knowledge and its integration into machine-learned models. We used a large dataset of intensive-care patient data to derive 126 decision rules that predict hospital mortality. Using an online platform, we asked 15 clinicians to assess the relative risk of the subpopulation defined by each rule compared to the total sample. We compared the clinician-assessed risk to the empirical risk and found that, while clinicians agreed with the data in most cases, there were notable exceptions where they overestimated or underestimated the true risk. Studying the rules with greatest disagreement, we identified problems with the training data, including one miscoded variable and one hidden confounder. Filtering the rules based on the extent of disagreement between clinician-assessed risk and empirical risk, we improved performance on out-of-sample data and were able to train with less data. EAML provides a platform for automated creation of problem-specific priors, which help build robust and dependable machine-learning models in critical applications.

The Molecular Role of Polyamines in Age-Related Diseases: An Update.

Polyamines (Pas) are short molecules that exhibit two or three amine groups that are positively charged at a physiological pH. These small molecules are present in high concentrations in a wide variety of organisms and tissues, suggesting that they play an important role in cellular physiology. Polyamines include spermine, spermidine, and putrescine, which play important roles in age-related diseases that have not been completely elucidated. Aging is a natural process, defined as the time-related deterioration of the physiological functions; it is considered a risk factor for degenerative diseases such as cardiovascular, neurodegenerative, and musculoskeletal diseases; arthritis; and even cancer. In this review, we provide a new perspective on the participation of Pas in the cellular and molecular processes related to age-related diseases, focusing our attention on important degenerative diseases such as Alzheimerߣs disease, Parkinsonߣs disease, osteoarthritis, sarcopenia, and osteoporosis. This new perspective leads us to propose that Pas function as novel biomarkers for age-related diseases, with the main purpose of achieving new molecular alternatives for healthier aging.

[Complicated hematomediastinum in a 76-year-old patient after performing an endosonographically guided transbronchial cryobiopsy (EBUS-TBCB) with suspected lymphoma]. / Komplikatives Hämatomediastinum einer 76-jährigen Patientin nach Durchführung einer endosonografisch gesteuerten transbronchialen Kryobiopsie (EBUS-TBKB) bei Verdacht einer Lymphomerkrankung.

We present the case of a patient with severe complications from mediastinal bleeding after endosonographically guided transbronchial cryobiopsy (EBUS-TBKB) with suspected advanced lymphoma. The EBUS-TBKB is a new effective examination method in interventional pneumology for the diagnosis of diseases with mediastinal lymph node enlargement and intrathoracic tumors, with which large tissue cylinders in the mediastinum can be obtained. Due to the high diagnostic value of endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) for the clarification of lymphadenopathy, the examination should not be carried out as a routine application. Indications for a primary EBUS-TBKB arise when there is a suspicion of intrathoracic malignant lymphomas or other rare tumors in which extensive unfragmented tissue material is required for diagnosis. A rare complication that has not yet been described in the literature is a hematomediastinum, so that a careful risk assessment of possible bleeding complications should be carried out before intervention and the more invasive mediastinoscopy can be a safer examination method.

Digital surveillance in Latin American diseases outbreaks: information extraction from a novel Spanish corpus.

BACKGROUND: In order to detect threats to public health and to be well-prepared for endemic and pandemic illness outbreaks, countries usually rely on event-based surveillance (EBS) and indicator-based surveillance systems. Event-based surveillance systems are key components of early warning systems and focus on fast capturing of data to detect threat signals through channels other than traditional surveillance. In this study, we develop Natural Language Processing tools that can be used within EBS systems. In particular, we focus on information extraction techniques that enable digital surveillance to monitor Internet data and social media. RESULTS: We created an annotated Spanish corpus from ProMED-mail health reports regarding disease outbreaks in Latin America. The corpus has been used to train algorithms for two information extraction tasks: named entity recognition and relation extraction. The algorithms, based on deep learning and rules, have been applied to recognize diseases, hosts, and geographical locations where a disease is occurring, among other entities and relations. In addition, an in-depth analysis of micro-average F1 metrics shows the suitability of our approaches for both tasks. CONCLUSIONS: The annotated corpus and algorithms presented could leverage the development of automated tools for extracting information from news and health reports written in Spanish. Moreover, this framework could be useful within EBS systems to support the early detection of Latin American disease outbreaks.

Changes in Perioperative Platelet Lymphocyte Ratio Predict Survival in Oesophago-Gastric Adenocarcinoma.

BACKGROUND: Platelet to lymphocyte ratio (PLR) is associated with survival in oesophageal cancer. We explored whether PLR changes during different stages of treatment correlate with survival outcomes. METHODS: A retrospective single-centre study was performed. Consecutive patients who received neoadjuvant chemotherapy followed by surgery for oesophageal adenocarcinoma were identified. Changes in PLR were calculated during two time periods: the first spanning the neoadjuvant period (T1); the second the perioperative period (T2). Differences in PLR were calculated for clinicopathological variables during both T1 and T2 and for variables with regards to their association with median overall survival (OS). Variables found to be significant on univariate analysis were included in a multivariate Cox regression model. Using ROC analysis, optimal cut-offs for PLR changes were identified and plotted on a Kaplan-Meir curve. RESULTS: Of the 370 patients identified, 110 (29.7%) were included in the analysis. During T1 a positive correlation was noted between higher positive lymph node ratio and PLR change. During T2, PLR change was positively higher in patients who suffered major postoperative complications. Median survival for the cohort as a whole was 42.3 months and 5-year OS was 57.3%. Survival at 5 years was associated with lower PLR changes during T2. On univaraite analysis, median OS was significantly less for patients with a tumour size > 3 cm, poor differentiation and change in PLR ≥ 43.4 during T2. The latter two variables remained significant on multivariate analysis. Using the same PLR threshold, the survival curve comparing changes in PLR during T2 remained statistically significant. CONCLUSION: Perioperative PLR changes are highly prognostic of survival outcomes in patients treated for oesophageal adenocarcinoma.

An LRP6 mutation (Arg360His) associated with low bone mineral density but not cardiovascular events in a Caucasian family.

We present a family with a rare mutation of the LRP6 gene and for the first time provide evidence for its association with low bone mineral density. INTRODUCTION: The Wnt pathway plays a critical role in bone homeostasis. Pathogenic variants of the Wnt co-receptor LRP6 have been associated with abnormal skeletal phenotypes or increased risk of cardiovascular events. PATIENT AND METHODS: Here we report an index premenopausal patient and her family carrying a rare missense LRP6 pathogenic variant (rs141212743; 0.0002 frequency among Europeans). This variant has been previously associated with metabolic syndrome and atherosclerosis, in the presence of normal bone mineral density. However, the LRP6 variant was associated with low bone mineral density in this family, without evidence for association with serum lipid levels or cardiovascular events. CONCLUSION: Thus, this novel association shows that LRP6 pathogenic variants may be involved in some cases of early-onset osteoporosis, but the predominant effect, either skeletal or cardiovascular, may vary depending on the genetic background or other acquired factors.

Orthogonal array composite designs for drug combination experiments with applications for tuberculosis.

The aim of this article is to provide an overview of the orthogonal array composite design (OACD) methodology, illustrate the various advantages, and provide a real-world application. An OACD combines a two-level factorial design with a three-level orthogonal array and it can be used as an alternative to existing composite designs for building response surface models. We compare the D$$ D $$ -efficiencies of OACDs relative to the commonly used central composite design (CCD) when there are a few missing observations and demonstrate that OACDs are more robust to missing observations for two scenarios. The first scenario assumes one missing observation either from one factorial point or one additional point. The second scenario assumes two missing observations either from two factorial points or from two additional points, or from one factorial point and one additional point. Furthermore, we compare OACDs and CCDs in terms of I$$ I $$ -optimality for precise predictions. Lastly, a real-world application of an OACD for a tuberculosis drug combination study is provided.

Benchmarking Molecular Feature Attribution Methods with Activity Cliffs.

Feature attribution techniques are popular choices within the explainable artificial intelligence toolbox, as they can help elucidate which parts of the provided inputs used by an underlying supervised-learning method are considered relevant for a specific prediction. In the context of molecular design, these approaches typically involve the coloring of molecular graphs, whose presentation to medicinal chemists can be useful for making a decision of which compounds to synthesize or prioritize. The consistency of the highlighted moieties alongside expert background knowledge is expected to contribute to the understanding of machine-learning models in drug design. Quantitative evaluation of such coloring approaches, however, has so far been limited to substructure identification tasks. We here present an approach that is based on maximum common substructure algorithms applied to experimentally-determined activity cliffs. Using the proposed benchmark, we found that molecule coloring approaches in conjunction with classical machine-learning models tend to outperform more modern, graph-neural-network alternatives. The provided benchmark data are fully open sourced, which we hope will facilitate the testing of newly developed molecular feature attribution techniques.

PlayMolecule Glimpse: Understanding Protein-Ligand Property Predictions with Interpretable Neural Networks.

Deep learning has been successfully applied to structure-based protein-ligand affinity prediction, yet the black box nature of these models raises some questions. In a previous study, we presented KDEEP, a convolutional neural network that predicted the binding affinity of a given protein-ligand complex while reaching state-of-the-art performance. However, it was unclear what this model was learning. In this work, we present a new application to visualize the contribution of each input atom to the prediction made by the convolutional neural network, aiding in the interpretability of such predictions. The results suggest that KDEEP is able to learn meaningful chemistry signals from the data, but it has also exposed the inaccuracies of the current model, serving as a guideline for further optimization of our prediction tools.

Transferable Classical Force Field for Pure and Mixed Metal Halide Perovskites Parameterized from First-Principles.

Many key features in photovoltaic perovskites occur in relatively long time scales and involve mixed compositions. This requires realistic but also numerically simple models. In this work we present a transferable classical force field to describe the mixed hybrid perovskite MAxFA1-xPb(BryI1-y)3 for variable composition (∀x, y ∈ [0, 1]). The model includes Lennard-Jones and Buckingham potentials to describe the interactions between the atoms of the inorganic lattice and the organic molecule, and the AMBER model to describe intramolecular atomic interactions. Most of the parameters of the force field have been obtained by means of a genetic algorithm previously developed to parametrize the CsPb(BrxI1-x)3 perovskite (Balestra et al. J. Mater. Chem. A. 2020, DOI: 10.1039/d0ta03200j). The algorithm finds the best parameter set that simultaneously fits the DFT energies obtained for several crystalline structures with moderate degrees of distortion with respect to the equilibrium configuration. The resulting model reproduces correctly the XRD patterns, the expansion of the lattice upon I/Br substitution, and the thermal expansion coefficients. We use the model to run classical molecular dynamics simulations with up to 8600 atoms and simulation times of up to 40 ns. From the simulations we have extracted the ion diffusion coefficient of the pure and mixed perovskites, presenting for the first time these values obtained by a fully dynamical method using a transferable model fitted to first-principles calculations. The values here reported can be considered as the theoretical upper limit, that is, without grain boundaries or other defects, for ion migration dynamics induced by halide vacancies in photovoltaic perovskite devices under operational conditions.

Δ-Quantum machine-learning for medicinal chemistry.

Many molecular design tasks benefit from fast and accurate calculations of quantum-mechanical (QM) properties. However, the computational cost of QM methods applied to drug-like molecules currently renders large-scale applications of quantum chemistry challenging. Aiming to mitigate this problem, we developed DelFTa, an open-source toolbox for the prediction of electronic properties of drug-like molecules at the density functional (DFT) level of theory, using Δ-machine-learning. Δ-Learning corrects the prediction error (Δ) of a fast but inaccurate property calculation. DelFTa employs state-of-the-art three-dimensional message-passing neural networks trained on a large dataset of QM properties. It provides access to a wide array of quantum observables on the molecular, atomic and bond levels by predicting approximations to DFT values from a low-cost semiempirical baseline. Δ-Learning outperformed its direct-learning counterpart for most of the considered QM endpoints. The results suggest that predictions for non-covalent intra- and intermolecular interactions can be extrapolated to larger biomolecular systems. The software is fully open-sourced and features documented command-line and Python APIs.

Gastroschisis: A Successful, Prospectively Evaluated Treatment Model in a Middle-Income Country.

BACKGROUND: This research adopted a care protocol from high-income countries in a level II/III hospital in a middle-income country to decrease morbidity and mortality associated with gastroschisis. METHODS: We established a multidisciplinary protocol to treat patients with gastroschisis prospectively from November 2012 to November 2018. This included prenatal diagnosis, presence of a neonatologist and pediatric surgeon at birth, and either performing primary closure on the patients with an Apgar score of 8/9, mild serositis, and no breathing difficulty or placing a preformed silo, when unable to fulfill these criteria, under sedation and analgesia (no intubation) in the operating room or at the patients' bedside. The subsequent management took place in the neonatal intensive care unit. The data were analyzed through the Mann-Whitney and Student's t-distribution for the two independent samples; the categorical variables were analyzed through a chi-square distribution or Fisher's exact test. RESULTS: In total, 55 patients were included in the study: 33 patients (60%) were managed with a preformed silo, whereas 22 patients (40%) underwent primary closure. Prenatal diagnosis (P = 0.02), birth at the main hospital (P = 0.02), and the presence of a pediatric surgeon at birth (P = 0.04) were associated with successful primary closure. The primary closure group had fewer fasting days (P < 0.001) and a shorter neonatal intensive care unit length of stay (P = 0.025). The survival rate was 92.7% (51 patients). CONCLUSION: The treatment model modified to fit the means of our hospital proved successful.

Building more accurate decision trees with the additive tree.

The expansion of machine learning to high-stakes application domains such as medicine, finance, and criminal justice, where making informed decisions requires clear understanding of the model, has increased the interest in interpretable machine learning. The widely used Classification and Regression Trees (CART) have played a major role in health sciences, due to their simple and intuitive explanation of predictions. Ensemble methods like gradient boosting can improve the accuracy of decision trees, but at the expense of the interpretability of the generated model. Additive models, such as those produced by gradient boosting, and full interaction models, such as CART, have been investigated largely in isolation. We show that these models exist along a spectrum, revealing previously unseen connections between these approaches. This paper introduces a rigorous formalization for the additive tree, an empirically validated learning technique for creating a single decision tree, and shows that this method can produce models equivalent to CART or gradient boosted stumps at the extremes by varying a single parameter. Although the additive tree is designed primarily to provide both the model interpretability and predictive performance needed for high-stakes applications like medicine, it also can produce decision trees represented by hybrid models between CART and boosted stumps that can outperform either of these approaches.

HIF2α Upregulates the Migration Factor ODZ1 under Hypoxia in Glioblastoma Stem Cells.

Background: Glioblastoma (GBM) remains a major clinical challenge due to its invasive capacity, resistance to treatment, and recurrence. We have previously shown that ODZ1 contributes to glioblastoma invasion and that ODZ1 mRNA levels can be upregulated by epigenetic mechanisms in response to hypoxia. Herein, we have further studied the transcriptional regulation of ODZ1 in GBM stem cells (GSCs) under hypoxic conditions and analyzed whether HIF2α has any role in this regulation. Methods: We performed the experiments in three primary GSC cell lines established from tumor specimens. GSCs were cultured under hypoxia, treated with HIF regulators (DMOG, chetomin), or transfected with specific siRNAs, and the expression levels of ODZ1 and HIF2α were analyzed. In addition, the response of the ODZ1 promoter cloned into a luciferase reporter plasmid to the activation of HIF was also studied. Results: The upregulation of both mRNA and protein levels of HIF2α under hypoxia conditions correlated with the expression of ODZ1 mRNA. Moreover, the knockdown of HIF2α by siRNAs downregulated the expression of ODZ1. We found, in the ODZ1 promoter, a HIF consensus binding site (GCGTG) 1358 bp from the transcription start site (TSS) and a HIF-like site (CCGTG) 826 bp from the TSS. Luciferase assays revealed that the stabilization of HIF by DMOG resulted in the increased activity of the ODZ1 promoter. Conclusions: Our data indicate that the HIF2α-mediated upregulation of ODZ1 helps strengthen the transcriptional control of this migration factor under hypoxia in glioblastoma stem cells. The discovery of this novel transcriptional pathway identifies new targets to develop strategies that may avoid GBM tumor invasion and recurrence.

Orbital compartment syndrome due to periorbital angioedema.

A 68-year-old female presented with an episode of unilateral orbital compartment syndrome due to periorbital angioedema. The patient made a consultation at the general Emergency Room with sudden left periorbital edema and serious diminished ipsilateral visual acuity, with examination detecting orbital compartment syndrome secondary to a probable allergic angioedema after ingestion of ibuprofen. She received treatment with intravenous and oral corticosteroids, achieving a rapid improvement in the condition and clinical follow-up was carried out, with evaluation of the peripapillary retinal nerve fiber layer thickness and computed perimetry. Periorbital angioedema due to ibuprofen can be a cause of orbital compartment syndrome whose diagnosis and treatment must be carried out urgently to prevent permanent visual impairment.

[The Importance of Electromagnetic Navigation Bronchoscopy in the Clarification of Pulmonary Nodules]. / Stellenwert der elektromagnetischen Navigationsbronchoskopie bei der Abklärung pulmonaler Rundherde.

Electromagnetic navigation bronchoscopy (ENB) is a very helpful examination for clarifying pulmonary nodules (PDs). The advantages are the low complication rate, avoiding the dangers of punctures in several areas of the lungs during an examination under general anesthesia and no radiation exposure. If malignancy is confirmed, PDs are curatively treatable as they are often early-stage cancers and, in case the disease is benign, it is treatable as other infectious diseases. With experience and routine use, the ENB procedure can contribute to the successful diagnosis of peripheral lesions (at least 1.5 cm in size) in about 75 % of cases. Due to the increase in CT chest examinations and the aging population, clarification of patients with PD is becoming increasingly important in lung clinics. The ENB should therefore develop into a routine examination procedure in interventional pulmonology in addition to the now indispensable endobronchial ultrasound examination. Unfortunately, the ENB is not yet mapped in the DRG system in a cost-relevant manner.

ERAS 2.0: Continued Refinement of an Established Enhanced Recovery Protocol for Esophagectomy.

BACKGROUND: We have previously demonstrated that implementing an enhanced recovery protocol (ERP) improved outcomes after esophagectomy. We sought to examine if, after a decade of an established ERP, further improvements in postoperative outcomes could be made after continually optimizing and revising the pathway. METHODS: Patients undergoing esophagectomy for cancer from January 2019 to January 2020 were compared with our early-experience group within the initial ERP (June 2010-May 2011) and pre-ERP traditional care (June 2009-May 2010). The original ERP was initiated on June 2010 and underwent several revisions from 2014 to 2018, incorporating the following, amongst other elements: shorten the planned length of stay from 7 to 6 days, elimination of nasogastric tubes, use of soft closed-suction chest drains, and increased application of minimally invasive esophagectomy (MIE). Thirty-day outcomes (complications, length of stay, readmission) were compared for patients undergoing esophagectomy during the initial and most recent ERPs. RESULTS: Overall, 175 patients were identified; 47 underwent esophagectomy before ERP implementation (traditional care), 59 patients underwent esophagectomy after implementation of the original ERP, and 69 patients underwent esophagectomy after the most recent ERP (ERP 2.0). The groups were similar with respect to age, sex, and diagnosis. There were three times more MIEs in the ERP 2.0 group with a shorter median length of stay (7 [6-9] vs. 8 [7-17] vs. 10 [9-17]; p < 0.001) without impacting postoperative morbidity or readmission rate. CONCLUSION: Continued evaluation of institutional outcomes after esophagectomy should be performed to identify target areas for optimization and revision of established enhanced recovery protocols. ERPs are dynamic processes that can be further refined to yield greater improvements in outcomes.