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Abstract Acquisition duration of correlated spectroscopy in vivo can be longer due to a large number of t1 increments along the indirect (F1) dimension. Limited number of t1 increments on the other hand leads to poor spectral resolution along F1. Covariance transformation (CT) instead of Fourier transform along t1 is an alternative way of increasing the resolution of the 2D COSY spectrum. Prospectively undersampled five-dimensional echo-planar correlated spectroscopic imaging (EP-COSI) data from ten malignant patients and ten healthy women were acquired and reconstructed using compressed sensing. The COSY spectrum at each voxel location was then generated using FFT, CT and a variant of CT called Inner Product (IP). Metabolite and lipid ratios were computed with respect to water from unsuppressed one-dimensional spectrum. The effects of t1-ridging artifacts commonly seen with FFT were not observed with CT/IP. Statistically significant differences were observed in the fat cross peaks measured with CT/IP/FFT. Spectral resolution was increased ~ 8.5 times (~ 19.53 Hz in FFT, ~ 2.32 Hz in CT/IP) without affecting the spectral width along F1 was possible with CT/IP. CT and IP enabled substantially increased F1 resolution effectively with significant gain in scan time and reliable measure of unsaturation index as a biomarker for malignant breast cancer.

Background: Artificial intelligence (AI) platforms, such as ChatGPT, have become increasingly popular outlets for the consumption and distribution of health care–related advice. Because of a lack of regulation and oversight, the reliability of health care–related responses has become a topic of controversy in the medical community. To date, no study has explored the quality of AI-derived information as it relates to common foot and ankle pathologies. This study aims to assess the quality and educational benefit of ChatGPT responses to common foot and ankle–related questions. Methods: ChatGPT was asked a series of 5 questions, including “What is the optimal treatment for ankle arthritis?” “How should I decide on ankle arthroplasty versus ankle arthrodesis?” “Do I need surgery for Jones fracture?” “How can I prevent Charcot arthropathy?” and “Do I need to see a doctor for my ankle sprain?” Five responses (1 per each question) were included after applying the exclusion criteria. The content was graded using DISCERN (a well-validated informational analysis tool) and AIRM (a self-designed tool for exercise evaluation). Results: Health care professionals graded the ChatGPT-generated responses as bottom tier 4.5% of the time, middle tier 27.3% of the time, and top tier 68.2% of the time. Conclusion: Although ChatGPT and other related AI platforms have become a popular means for medical information distribution, the educational value of the AI-generated responses related to foot and ankle pathologies was variable. With 4.5% of responses receiving a bottom-tier rating, 27.3% of responses receiving a middle-tier rating, and 68.2% of responses receiving a top-tier rating, health care professionals should be aware of the high viewership of variable-quality content easily accessible on ChatGPT. Level of Evidence: Level III, cross sectional study.

Routinely, adolescent idiopathic scoliosis (AIS) curves that progress beyond 40° in skeletally immature adolescents require surgery. However, some adolescents with AIS and their parents utterly refuse surgery and insist on wearing a brace. Debate continues regarding the appropriateness of bracing for AIS curves exceeding 40° in patients who have rejected surgical intervention. This systematic review and meta-analysis was conducted to review the literature on the effectiveness of bracing and its predictive factors in larger-magnitude AIS curves ≥40°. This study replicated the search strategy used by the PICOS system for formulating study questions, which include consideration of the patient/population (P), intervention (I), comparison (C), outcome (O), and study design (S). The search was conducted up to January 2022 in the following bibliographic online databases only in the English language: PubMed, Google Scholar, Scopus, and Web of Science. Two assessors reviewed the articles for qualification. Eligible studies were assessed for risk of bias at the study level using the Newcastle-Ottawa Scale. The effect size across the studies was determined using standardized mean differences (Cohen’s d) and 95% confidence intervals for the meta-analysis. Among the eight included moderate quality studies, evidence of potential publication bias (p

We evaluated white matter microstructure integrity in perinatally HIV-infected (PHIV) youths receiving cART compared to age- and gender-matched healthy youths through DTI metrics using voxel-based morphometry (VBM). We investigated 14 perinatally HIV-infected patients (age 17.9 ± 2.5 years) on cART and 17 healthy youths (HC) (age 18.0 ± 3.0 years) using a 3T MRI scanner. Four DTI-derived metrics were fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD). Statistical analysis was done with voxel-based analysis of covariance (ANCOVA), with age and gender as covariates. Region-of-interest secondary analyses in statistically significant regions were also performed. Regional increases in FA in the PHIV youths were found in left middle frontal gyrus, right precuneus, right lingual gyrus, and left supramarginal gyrus. Increased MD was found in the right precentral gyrus while decreased MD was found in the white matter of the right superior parietal lobule and right inferior temporal gyrus/fusiform gyrus. Regions of increased/decreased RD overlapped with regions of increased/decreased MD. Both increased and decreased AD were found in three to four regions respectively. The regional FA, MD, RD, and AD values were consistent with the voxel-based analysis findings. The findings are mostly consistent with previous literature, but increased FA has not been previously reported for perinatally HIV-infected youths. Potentially early and prolonged therapy in our population may have contributed to this new finding. Both toxicity of antiretroviral therapy and indolent infection must be considered as causative factors in the DTI metric changes that we have observed.

IntroductionFailed back surgery syndrome (FBSS) is defined as back pain which either persists after attempted surgical intervention or originates after a spine surgery. There is a high risk of perioperative morbidity and a high likelihood of extensive revision surgery in geriatric patients with FBSS or post-laminectomy foraminal stenosis.MethodsThere is a need for less invasive methodologies for the treatment of FBSS, such as patient-tailored exercise training, with attention to the cost and special needs of the geriatric patients with FBSS. This commentary will provide some background regarding teleexercise (utilizing an internet-based platform for the provision of exercise-related care) for FBSS and will propose three exercises which are easy to administer over online-based platforms and can be the subject of future investigation.ResultsGiven the documented benefits of regular rehabilitative exercises for patients with FBSS, the high cost of face-to-face services, and the need for infection mitigation in the wake of the COVID-19 Pandemic, teleexercise may be a practical and cost-beneficial method of exercise delivery, especially for geriatric patients with limitations in mobility and access to care. It should be noted that, prescription of these exercises should be done after face-to-face evaluation by the physician and careful evaluation for any “red flag” symptoms.ConclusionIn this commentary, we will suggest three practical exercise training methodologies and discuss the benefits of teleexercise for geriatric patients with FBSS. Future research should aim to assess the efficacy of these exercises, especially when administered through telehealth platforms.

IntroductionObstructive sleep apnea (OSA) patients show hippocampal-related autonomic and neurological symptoms, including impaired memory and depression, which differ by sex, and are mediated in distinct hippocampal subfields. Determining sites and extent of hippocampal sub-regional injury in OSA could reveal localized structural damage linked with OSA symptoms.MethodsHigh-resolution T1-weighted images were collected from 66 newly-diagnosed, untreated OSA (mean age ± SD: 46.3 ± 8.8 years; mean AHI ± SD: 34.1 ± 21.5 events/h;50 male) and 59 healthy age-matched control (46.8 ± 9.0 years;38 male) participants. We added age-matched controls with T1-weighted scans from two datasets (IXI, OASIS-MRI), for 979 controls total (426 male/46.5 ± 9.9 years). We segmented the hippocampus and analyzed surface structure with "FSL FIRST" software, scaling volumes for brain size, and evaluated group differences with ANCOVA (covariates: total-intracranial-volume, sex; P

It has been shown that density-weighted (DW) k-space sampling with spiral and conventional phase encoding trajectories reduces spatial side lobes in magnetic resonance spectroscopic imaging (MRSI). In this study, we propose a new concentric ring trajectory (CRT) for DW-MRSI that samples k-space with a density that is proportional to a spatial, isotropic Hanning window. The properties of two different DW-CRTs were compared against a radially equidistant (RE) CRT and an echo-planar spectroscopic imaging (EPSI) trajectory in simulations, phantoms and in vivo experiments. These experiments, conducted at 7 T with a fixed nominal voxel size and matched acquisition times, revealed that the two DW-CRT designs improved the shape of the spatial response function by suppressing side lobes, also resulting in improved signal-to-noise ratio (SNR). High-quality spectra were acquired for all trajectories from a specific region of interest in the motor cortex with an in-plane resolution of 7.5 × 7.5 mm2 in 8 min 3 s. Due to hardware limitations, high-spatial-resolution spectra with an in-plane resolution of 5 × 5 mm2 and an acquisition time of 12 min 48 s were acquired only for the RE and one of the DW-CRT trajectories and not for EPSI. For all phantom and in vivo experiments, DW-CRTs resulted in the highest SNR. The achieved in vivo spectral quality of the DW-CRT method allowed for reliable metabolic mapping of eight metabolites including N-acetylaspartylglutamate, γ-aminobutyric acid and glutathione with Cramér-Rao lower bounds below 50%, using an LCModel analysis. Finally, high-quality metabolic mapping of a whole brain slice using DW-CRT was achieved with a high in-plane resolution of 5 × 5 mm2 in a healthy subject. These findings demonstrate that our DW-CRT MRSI technique can perform robustly on MRI systems and within a clinically feasible acquisition time.

The main objective of this work was to evaluate the application of individual and ensemble machine learning models to classify malignant and benign breast masses using features from two-dimensional (2D) correlated spectroscopy spectra extracted from five-dimensional echo-planar correlated spectroscopic imaging (5D EP-COSI) and diffusion-weighted imaging (DWI). Twenty-four different metabolite and lipid ratios with respect to diagonal fat peaks (1.4 ppm, 5.4 ppm) from 2D spectra, and water and fat peaks (4.7 ppm, 1.4 ppm) from one-dimensional non-water-suppressed (NWS) spectra were used as the features. Additionally, water fraction, fat fraction and water-to-fat ratios from NWS spectra and apparent diffusion coefficients (ADC) from DWI were included. The nine most important features were identified using recursive feature elimination, sequential forward selection and correlation analysis. XGBoost (AUC: 93.0%, Accuracy: 85.7%, F1-score: 88.9%, Precision: 88.2%, Sensitivity: 90.4%, Specificity: 84.6%) and GradientBoost (AUC: 94.3%, Accuracy: 89.3%, F1-score: 90.7%, Precision: 87.9%, Sensitivity: 94.2%, Specificity: 83.4%) were the best-performing models. Conventional biomarkers like choline, myo-Inositol, and glycine were statistically significant predictors. Key features contributing to the classification were ADC, 2D diagonal peaks at 0.9 ppm, 2.1 ppm, 3.5 ppm, and 5.4 ppm, cross peaks between 1.4 and 0.9 ppm, 4.3 and 4.1 ppm, 2.3 and 1.6 ppm, and the triglyceryl–fat cross peak. The results highlight the contribution of the 2D spectral peaks to the model, and they demonstrate the potential of 5D EP-COSI for early breast cancer detection.

Despite recent advances in treatment, hepatitis C remains a significant public health problem. The hepatitis C virus (HCV) is known to infiltrate the brain, yet findings from studies on associated neurocognitive and neuropathological changes are mixed. Furthermore, it remains unclear if HCV eradication improves HCV-associated neurological compromise. This study examined the longitudinal relationship between neurocognitive and neurophysiologic markers among healthy HCV- controls and HCV+ adults following successful HCV eradication. We hypothesized that neurocognitive outcomes following treatment would be related to both improved cognition and white matter integrity. Participants included 57 HCV+ participants who successfully cleared the virus at the end of treatment (sustained virologic responders [SVRs]) and 22 HCV- controls. Participants underwent neuropsychological testing and, for a nested subset of participants, neuroimaging (diffusion tensor imaging) at baseline and 12 weeks following completion of HCV therapy. Contrary to expectation, group-level longitudinal analyses did not reveal significant improvement in neurocognitive performance in the SVRs compared to the control group. However, a subgroup of SVRs demonstrated a significant improvement in cognition relative to controls, which was related to improved white matter integrity. Indeed, neuroimaging data revealed beneficial effects associated with clearing the virus, particularly in the posterior corona radiata and the superior longitudinal fasciculus. Findings suggest that a subgroup of HCV+ patients experienced improvements in cognitive functioning following eradication of HCV, which appears related to positive changes in white matter integrity. Future research should examine whether any additional improvements in neurocognition and white matter integrity among SVRs occur with longer follow-up periods.

Two-dimensional localized correlated spectroscopy (2D L-COSY) offers greater spectral dispersion than conventional one-dimensional (1D) MRS techniques, yet long acquisition times and limited post-processing support have slowed its clinical adoption. Improving acquisition efficiency and developing versatile post-processing techniques can bolster the clinical viability of 2D MRS. The purpose of this study was to implement a non-uniformly weighted sampling (NUWS) scheme for faster acquisition of 2D-MRS. A NUWS 2D L-COSY sequence was developed for 7T whole-body MRI. A phantom containing metabolites commonly observed in the brain at physiological concentrations was scanned ten times with both the NUWS scheme of 12:48 duration and a 17:04 constant eight-average sequence using a 32-channel head coil. 2D L-COSY spectra were also acquired from the occipital lobe of four healthy volunteers using both the proposed NUWS and the conventional uniformly-averaged L-COSY sequence. The NUWS 2D L-COSY sequence facilitated 25% shorter acquisition time while maintaining comparable SNR in humans (+0.3%) and phantom studies (+6.0%) compared to uniform averaging. NUWS schemes successfully demonstrated improved efficiency of L-COSY, by facilitating a reduction in scan time without affecting signal quality.

Abstract Nuclear magnetic resonance spectroscopy (MRS) allows for the determination of atomic structures and concentrations of different chemicals in a biochemical sample of interest. MRS is used in vivo clinically to aid in the diagnosis of several pathologies that affect metabolic pathways in the body. Typically, this experiment produces a one dimensional (1D) 1H spectrum containing several peaks that are well associated with biochemicals, or metabolites. However, since many of these peaks overlap, distinguishing chemicals with similar atomic structures becomes much more challenging. One technique capable of overcoming this issue is the localized correlated spectroscopy (L-COSY) experiment, which acquires a second spectral dimension and spreads overlapping signal across this second dimension. Unfortunately, the acquisition of a two dimensional (2D) spectroscopy experiment is extremely time consuming. Furthermore, quantitation of a 2D spectrum is more complex. Recently, artificial intelligence has emerged in the field of medicine as a powerful force capable of diagnosing disease, aiding in treatment, and even predicting treatment outcome. In this study, we utilize deep learning to: (1) accelerate the L-COSY experiment and (2) quantify L-COSY spectra. All training and testing samples were produced using simulated metabolite spectra for chemicals found in the human body. We demonstrate that our deep learning model greatly outperforms compressed sensing based reconstruction of L-COSY spectra at higher acceleration factors. Specifically, at four-fold acceleration, our method has less than 5% normalized mean squared error, whereas compressed sensing yields 20% normalized mean squared error. We also show that at low SNR (25% noise compared to maximum signal), our deep learning model has less than 8% normalized mean squared error for quantitation of L-COSY spectra. These pilot simulation results appear promising and may help improve the efficiency and accuracy of L-COSY experiments in the future.

Switzerland and the UK recently signed the Berne Financial Services Agreement on Mutual Recognition of Financial Services. This agreement addresses the mutual recognition of the equivalence of the respective legal and supervisoryframeworks in selected areas of the financial sector, with the goal of strengthening competitiveness, promoting cooperation and enabling and facilitating cross-border market access of the two countries. [ABSTRACT FROM AUTHOR]

Osteoarthritis (OA) pain management options are currently limited. Fasinumab, an anti‐nerve growth factor monoclonal antibody, has been investigated in healthy volunteers and patients with OA‐related pain, among other conditions. Data from 12 Phase I‐III clinical trials of 92 healthy volunteers and 7430 patients with OA were used to develop a population pharmacokinetic model to characterize fasinumab concentration‐time profiles and assess the covariates' effect on fasinumab pharmacokinetic parameters. Participants received single or repeated fasinumab doses intravenously (IV)/subcutaneously (SC), based on body weight (0.03‐1 mg/kg IV or 0.1‐0.3 mg/kg SC)/fixed dose (9‐12 mg IV or 1‐12 mg SC). Fasinumab concentration‐time data following IV and SC administration in healthy volunteers and patients with OA‐related pain were adequately described by a 2‐compartment model. Bioavailability increased with higher doses; estimated at 55.1% with 1 mg SC dose, increasing in a greater‐than‐proportional manner above this. Body weight had the largest predicted impact on fasinumab steady‐state exposures, participants at the 5th and 95th percentiles had a 43%‐45% higher/22%‐23% lower exposure versus reference, respectively. Other covariates had small but clinically irrelevant impacts. [ABSTRACT FROM AUTHOR]

Abstract In perinatally HIV-infected (PHIV) children, neurodevelopment occurs in the presence of HIV-infection, and even with combination antiretroviral therapy (cART) the brain can be a reservoir for latent HIV. Consequently, patients often demonstrate long-term cognitive deficits and developmental delay, which may be reflected in altered functional brain activity. Our objective was to examine brain function in PHIV on cART by quantifying the amplitude of low frequency fluctuations (ALFF) and regional homogeneity (ReHo). Further, we studied ALFF and ReHo changes with neuropsychological performance and measures of immune health including CD4 count and viral loads in the HIV-infected youths. We found higher ALFF and ReHo in cerebral white matter in the medial orbital lobe for PHIV (N = 11, age mean ± sd = 22.5 ± 2.9 years) compared to controls (N = 16, age = 22.5 ± 3.0 years), with age and gender as co-variates. Bilateral cerebral white matter showed increased spontaneous regional activity in PHIV compared to healthy controls. No brain regions showed lower ALFF or ReHo in PHIV compared to controls. Higher log10 viral load was associated with higher ALFF and ReHo in PHIV in bilateral cerebral white matter and right cerebral white matter respectively after masking the outcomes intrinsic to the brain regions that showed significantly higher ALFF and ReHo in the PHIV compared to the control. Reductions in social cognition and abstract thinking in PHIV were correlated with higher ALFF at the left cerebral white matter in the left medial orbital gyrus and higher ReHo at the right cerebral white matter in the PHIV patients. Although neuroinflammation and associated neuro repair were not directly measured, the findings support their potential role in PHIV impacting neurodevelopment and cognition.

PurposeTo evaluate the utility of two-dimensional (2D) Localized Correlated Spectroscopy (L-COSY) in metabolic profiling of the human brain at 7 Tesla (T).Materials and methodsThe 2D L-COSY sequence was implemented at 7 T and its reliability was assessed by test-retest studies of a metabolite phantom and a healthy volunteer. L-COSY data were acquired from the occipital lobe of healthy subjects (n = 6; all male; age, 30-72 years) to assess intersubject variability. Additionally, two subjects underwent scans from the parieto-occipital region, basal ganglia, frontal lobe or dorsolateral prefrontal cortex to test the versatility of L-COSY in studying differing anatomy. Integral/volume measurements of L-COSY spectra were used to estimate normalized metabolite-to-creatine concentrations.ResultsPhantom test-retest studies revealed coefficients of variation (CVs) of 3-20% for most metabolites. Human 2D L-COSY spectra permitted detection of several metabolite resonances from multiple locations and inter-subject variation studies demonstrated CVs of 4-26%. Cross-peaks from gamma-aminobutyric acid (GABA), isoleucine (Ile), lysine (Lys) and Ethanolamine (Eth) were quantified, which are not readily resolvable with conventional one-dimensional (1D) MR spectroscopy.Conclusion2D L-COSY at 7 T demonstrated improved sensitivity in detecting additional metabolites with reliability comparable to established techniques at lower fields, which may aid in the metabolic assessment of diseased states.

Sustainable development aims at minimising waste and reducing exploitation of natural resources and energy, so that needs of the future generations are taken care of. Circular Economy (CE) is a new drift towards sustainability that aims at minimising waste and promoting material reuse, thereby creating a regenerative system. The construction industry is responsible for the extraction of raw materials and the generation of waste in large quantities, thereby making it an opportune sector for transition to a circular economy. On account of the complex nature of the built environment comprising various phases and associated actors, a proper framework or indexing for the circular economy is missing at present. This study aims to develop an integrated model of CE in the built environment which considers various construction stages and applicable strategies. An index for measuring the circularity potential in construction materials is also proposed, based on attributes developed from literature review and analysis of questionnaire survey. Simple Additive Weighting Method (SAWM), an elementary multi-criteria decision-making method is used for developing the index. It is anticipated that Circular Economy Potential Index (CEPI) would support decision-making in the initial stage of construction projects and help to compare the circularity of materials.

Achieving bone fracture union after trauma represents a major challenge for the orthopedic surgeon. Fracture non-healing has a multifactorial etiology and there are many risk factors for non-fusion. Environmental factors such as wound contamination, infection, and open fractures can contribute to non-healing, as can patient specific factors such as poor vascular status and improper immunologic response to fracture. Nitric oxide (NO) is a small, neutral, hydrophobic, highly reactive free radical that can diffuse across local cell membranes and exert paracrine functions in the vascular wall. This molecule plays a role in many biologic pathways, and participates in wound healing through decontamination, mediating inflammation, angiogenesis, and tissue remodeling. Additionally, NO is thought to play a role in fighting wound infection by mitigating growth of both Gram negative and Gram positive pathogens. Herein, we discuss recent developments in NO delivery mechanisms and potential implications for patients with bone fractures. NO donors are functional groups that store and release NO, independent of the enzymatic actions of NOS. Donor molecules include organic nitrates/nitrites, metal-NO complexes, and low molecular weight NO donors such as NONOates. Numerous advancements have also been made in developing mechanisms for localized nanomaterial delivery of nitric oxide to bone. NO-releasing aerogels, sol- gel derived nanomaterials, dendrimers, NO-releasing micelles, and core cross linked star (CCS) polymers are all discussed as potential avenues of NO delivery to bone. As a further target for improved fracture healing, 3d bone scaffolds have been developed to include potential for nanoparticulated NO release. These advancements are discussed in detail, and their potential therapeutic advantages are explored. This review aims to provide valuable insight for translational researchers who wish to improve the armamentarium of the feature trauma surgeon through use of NO mediated augmentation of bone healing.

A reliable and practical renal-lipid quantification and imaging method is needed. Here, the feasibility of an accelerated MRSI method to map renal fat fractions (FF) at 3T and its repeatability were investigated. A 2D density-weighted concentric-ring-trajectory MRSI was used for accelerating the acquisition of 48 × 48 voxels (each of 0.25 mL spatial resolution) without respiratory navigation implementations. The data were collected over 512 complex-FID timepoints with a 1250 Hz spectral bandwidth. The MRSI sequence was designed with a metabolite-cycling technique for lipid–water separation. The in vivo repeatability performance of the sequence was assessed by conducting a test–reposition–retest study within healthy subjects. The coefficient of variation (CV) in the estimated FF from the test–retest measurements showed a high degree of repeatability of MRSI-FF (CV = 4.3 ± 2.5%). Additionally, the matching level of the spectral signature within the same anatomical region was also investigated, and their intrasubject repeatability was also high, with a small standard deviation (8.1 ± 6.4%). The MRSI acquisition duration was ~3 min only. The proposed MRSI technique can be a reliable technique to quantify and map renal metabolites within a clinically acceptable scan time at 3T that supports the future application of this technique for the non-invasive characterization of heterogeneous renal diseases and tumors.

Construction safety is a matter of great concern for practitioners and researchers worldwide. Even after risk assessments have been conducted and adequate controls have been implemented, workers are still subject to safety hazards in construction work environments. The need for personal protective equipment (PPE) is important in this context. Automatic and real-time detection of the non-compliance of workers in using PPE is an important concern. Developments in the field of computer vision and data analytics, especially using deep learning algorithms have the potential to address this challenge in construction. This study developed a framework to sense in real-time, the safety compliance of construction workers with respect to PPE, which is intended to be integrated into the safety workflow of an organization. The study makes use of the Convolutional Neural Networks model, which was developed by applying transfer learning to a base version of the YOLOv3 deep learning network. Taking into account the presence of hardhat and safety jackets, the model predicts compliance in four categories such as NOT SAFE, SAFE, NoHardHat, and NoJacket. A data set of 2,509 images was collected from video recordings from several construction sites and this web-based collection was used to train the model. The model reported an F1 score of 0.96 with an average precision and recall rate at 96% on the test data set. Once a non “SAFE” category is detected by the model, an alarm and a time-stamped report are also incorporated to enable a real-time integration and adoption on the construction sites. Overall, the study provides evidence on the feasibility and utility of computer vision-based techniques in automating the safety-related compliance processes at construction sites.

The animal model of infection following ventral hernia repair (VHR) has previously been utilized in exploring treatments and innovative therapies, such as implantation of biologic mesh imbedded with various anti-bacterial properties. The rat model has been utilized most commonly, but prior work has failed to recreate an adequately clinically representative model of infection following VHR. Additionally, there is lack of standardization of mesh infection severity across existing literature. Therefore, the aim of this paper is to describe the creation of a clinically representative VHR infection model utilizing an index procedure where a hernia defect is created followed by a VHR using biologic mesh and subsequent infectious agent inoculation. Additionally, we describe the development of a standardization index to quantify severity of mesh infection: the Mesh Infection Severity Index (MISI). • Our protocol involves two procedures, an index procedure where a hernia model is created, and a subsequent procedure where an infectious inoculant is introduced. • We describe the MISI, a standardization tool we hope will allow for ease of cross-institutional data assessment. • In summary, our protocol not only serves as a more clinically representative animal model, but also includes a novel metric to standardize mesh infection severity.