Analysis of 3D pathology samples using weakly supervised AI.

Human tissue, which is inherently three-dimensional (3D), is traditionally examined through standard-of-care histopathology as limited two-dimensional (2D) cross-sections that can insufficiently represent the tissue due to sampling bias. To holistically characterize histomorphology, 3D imaging modalities have been developed, but clinical translation is hampered by complex manual evaluation and lack of computational platforms to distill clinical insights from large, high-resolution datasets. We present TriPath, a deep-learning platform for processing tissue volumes and efficiently predicting clinical outcomes based on 3D morphological features. Recurrence risk-stratification models were trained on prostate cancer specimens imaged with open-top light-sheet microscopy or microcomputed tomography. By comprehensively capturing 3D morphologies, 3D volume-based prognostication achieves superior performance to traditional 2D slice-based approaches, including clinical/histopathological baselines from six certified genitourinary pathologists. Incorporating greater tissue volume improves prognostic performance and mitigates risk prediction variability from sampling bias, further emphasizing the value of capturing larger extents of heterogeneous morphology.

Coccydynia: A Review of Anatomy, Causes, Diagnosis, and Treatment.

¼ Coccydynia is a painful condition affecting many patients at the terminal caudal end of the spine.¼ An understanding of coccyx anatomy and variations of morphology is necessary for proper diagnosis. A multifactorial etiology for pain may be responsible for this clinical entity.¼ Several treatment options exist. Successful outcomes for patients depend on individual patient characteristics and the etiology of pain.

Impact of bone density and integrated screw configuration on standalone anterior lumbar interbody construct strength.

Background: In anterior lumbar interbody fusion (ALIF), the use of integrated screws is attractive to surgeons because of the ease of implantation and no additional profile. However, the number and length of screws necessary for safe and stable implantation in various bone densities is not yet fully understood. The current study aims to determine how important both length and number of screws are for stability of ALIFs. Methods: Three bone models with densities of 10, 15, and 20 pounds per cubic foot (PCF) were chosen as surrogates. These were instrumented using the Z-Link lumbar interbody system with either 2, 3, or 4 integrated 4.5 × 20 mm screws or 4.5 × 25 mm screws (Zavation, LLC, Flowood, MS). The bone surrogates were tested with loading conditions resulting in spine extension to measure construct stiffness and peak force. Results: The failure load of the construct was influenced by the length of screws (p=.01) and density of the bone surrogate (p<.01). There was no difference in failure load between using 2 screws and 3 screws (p=.32) or when using four 20 mm screws versus three 25 mm screws (p=.295). Conclusion: In our study, both bone density and length of screws significantly affected the construct's load to failure. In certain cases where a greater number of screws are unable to be implanted, the same stability can potentially be conferred with use of longer screws. Future clinical studies should be performed to test these biomechanical results.

A high-quality genome of the convergent lady beetle, Hippodamia convergens.

Here, we describe a high-quality genome assembly and annotation of the convergent lady beetle, Hippodamia convergens (Coleoptera: Coccinellidae). The highest quality unmasked genome comprises 619 megabases (Mb) of chromosomal DNA, organized into 899 contigs, with a contig N50 score of 89 Mbps. The genome was assessed to be 96% complete (BUSCO). Reconstruction of a whole-genome phylogeny resolved H. convergens as sister to the Harlequin lady beetle, Harmonia axyridis, and nested within a clade of several known agricultural pests.

Recurrent central odontogenic fibroma in a patient with nevoid basal cell carcinoma syndrome: case report and in vitro analysis.

OBJECTIVE: Central odontogenic fibromas (COF) are rare, benign tumors derived from dental mesenchymal tissue that may occur in the maxilla or mandible. This report describes primary and recurrent COF in the mandible of a patient with nevoid basal cell carcinoma syndrome (NBCCS). STUDY DESIGN: A 36-year-old African American male presented with a COF and its recurrence 17 months later. Tissue pieces were obtained from both occurrences with IRB-approved signed consent. Collected tissue pieces were dissected; one portion was formalin-fixed and paraffin-embedded, and the other was cultured for the isolation of cell populations from the primary (COdF-1) and recurrent (COdF-1a) tumors. Quantification real-time polymerase chain reaction (qRT-PCR), immunohistochemistry, and DNA sequencing were used for gene and protein analysis of the primary tumor and cell populations. RESULTS: Histopathologic analysis of the tumor showed sparse odontogenic epithelial cords in fibrous connective tissue, and qRT-PCR analysis of tumor and cell populations (COdF-1 and COdF-1a) detected VIM, CK14, CD34, CD99 and ALPL mRNA expression. Protein expression was confirmed by immunohistochemistry. CD34 expression in primary tissues was higher than in tumor cells due to tumor vascularization. DNA sequencing indicated the patient had PTCH1 mutations. CONCLUSIONS: Histopathology, mRNA, and protein expression indicate the rare occurrence of COF in a patient with mutated PTCH1 gene and NBCCS.

Integrated Proteomics Characterization of NLRP3 Inflammasome Inhibitor MCC950 in Monocytic Cell Line Confirms Direct MCC950 Engagement with Endogenous NLRP3.

Inhibition of the NLRP3 inflammasome is a promising strategy for the development of new treatments for inflammatory diseases. MCC950 is a potent and selective small-molecule inhibitor of the NLRP3 pathway and has been validated in numerous species and disease models. Although the capacity of MCC950 to block NLRP3 signaling is well-established, it is still critical to identify the mechanism of action and molecular targets of MCC950 to inform and derisk drug development. Quantitative proteomics performed in disease-relevant systems provides a powerful method to study both direct and indirect pharmacological responses to small molecules to elucidate the mechanism of action and confirm target engagement. A comprehensive target deconvolution campaign requires the use of complementary chemical biology techniques. Here we applied two orthogonal chemical biology techniques: compressed Cellular Thermal Shift Assay (CETSA) and photoaffinity labeling chemoproteomics, performed under biologically relevant conditions with LPS-primed THP-1 cells, thereby deconvoluting, for the first time, the molecular targets of MCC950 using chemical biology techniques. In-cell chemoproteomics with inlysate CETSA confirmed the suspected mechanism as the disruption of inflammasome formation via NLRP3. Further cCETSA (c indicates compressed) in live cells mapped the stabilization of NLRP3 inflammasome pathway proteins, highlighting modulation of the targeted pathway. This is the first evidence of direct MCC950 engagement with endogenous NLRP3 in a human macrophage cellular system using discovery proteomics chemical biology techniques, providing critical information for inflammasome studies.

A visual-language foundation model for computational pathology.

The accelerated adoption of digital pathology and advances in deep learning have enabled the development of robust models for various pathology tasks across a diverse array of diseases and patient cohorts. However, model training is often difficult due to label scarcity in the medical domain, and a model's usage is limited by the specific task and disease for which it is trained. Additionally, most models in histopathology leverage only image data, a stark contrast to how humans teach each other and reason about histopathologic entities. We introduce CONtrastive learning from Captions for Histopathology (CONCH), a visual-language foundation model developed using diverse sources of histopathology images, biomedical text and, notably, over 1.17 million image-caption pairs through task-agnostic pretraining. Evaluated on a suite of 14 diverse benchmarks, CONCH can be transferred to a wide range of downstream tasks involving histopathology images and/or text, achieving state-of-the-art performance on histology image classification, segmentation, captioning, and text-to-image and image-to-text retrieval. CONCH represents a substantial leap over concurrent visual-language pretrained systems for histopathology, with the potential to directly facilitate a wide array of machine learning-based workflows requiring minimal or no further supervised fine-tuning.

Towards a general-purpose foundation model for computational pathology.

Quantitative evaluation of tissue images is crucial for computational pathology (CPath) tasks, requiring the objective characterization of histopathological entities from whole-slide images (WSIs). The high resolution of WSIs and the variability of morphological features present significant challenges, complicating the large-scale annotation of data for high-performance applications. To address this challenge, current efforts have proposed the use of pretrained image encoders through transfer learning from natural image datasets or self-supervised learning on publicly available histopathology datasets, but have not been extensively developed and evaluated across diverse tissue types at scale. We introduce UNI, a general-purpose self-supervised model for pathology, pretrained using more than 100 million images from over 100,000 diagnostic H&E-stained WSIs (>77 TB of data) across 20 major tissue types. The model was evaluated on 34 representative CPath tasks of varying diagnostic difficulty. In addition to outperforming previous state-of-the-art models, we demonstrate new modeling capabilities in CPath such as resolution-agnostic tissue classification, slide classification using few-shot class prototypes, and disease subtyping generalization in classifying up to 108 cancer types in the OncoTree classification system. UNI advances unsupervised representation learning at scale in CPath in terms of both pretraining data and downstream evaluation, enabling data-efficient artificial intelligence models that can generalize and transfer to a wide range of diagnostically challenging tasks and clinical workflows in anatomic pathology.

Discovery, Optimization, and Biological Evaluation of Arylpyridones as Cbl-b Inhibitors.

Casitas B-lymphoma proto-oncogene-b (Cbl-b), a member of the Cbl family of RING finger E3 ubiquitin ligases, has been demonstrated to play a central role in regulating effector T-cell function. Multiple studies using gene-targeting approaches have provided direct evidence that Cbl-b negatively regulates T, B, and NK cell activation via a ubiquitin-mediated protein modulation. Thus, inhibition of Cbl-b ligase activity can lead to immune activation and has therapeutic potential in immuno-oncology. Herein, we describe the discovery and optimization of an arylpyridone series as Cbl-b inhibitors by structure-based drug discovery to afford compound 31. This compound binds to Cbl-b with an IC50 value of 30 nM and induces IL-2 production in T-cells with an EC50 value of 230 nM. Compound 31 also shows robust intracellular target engagement demonstrated through inhibition of Cbl-b autoubiquitination, inhibition of ubiquitin transfer to ZAP70, and the cellular modulation of phosphorylation of a downstream signal within the TCR axis.

Toward 3D printed microfluidic artificial lungs for respiratory support.

Microfluidic artificial lungs (µALs) are a new class of membrane oxygenators. Compared to traditional hollow-fiber oxygenators, µALs closely mimic the alveolar microenvironment due to their size-scale and promise improved gas exchange efficiency, hemocompatibility, biomimetic blood flow networks, and physiologically relevant blood vessel pressures and shear stresses. Clinical translation of µALs has been stalled by restrictive microfabrication techniques that limit potential artificial lung geometries, overall device size, and throughput. To address these limitations, a high-resolution Asiga MAX X27 UV digital light processing (DLP) 3D printer and custom photopolymerizable polydimethylsiloxane (PDMS) resin were used to rapidly manufacture small-scale µALs via vat photopolymerization (VPP). Devices were designed in SOLIDWORKS with 500 blood channels and 252 gas channels, where gas and blood flow channels were oriented orthogonally and separated by membranes on the top and bottom, permitting two-sided gas exchange. Successful devices were post-processed to remove uncured resin from microchannels and assembled with external tubing in preparation for gas exchange performance testing with ovine whole blood. 3D printed channel dimensions were 172 µm-tall × 320 µm-wide, with 62 µm-thick membranes and 124 µm-wide support columns. Measured outlet blood oxygen saturation (SO2) agreed with theoretical models and rated flow of the device was 1 mL min-1. Blood side pressure drop was 1.58 mmHg at rated flow. This work presents the highest density of 3D printed microchannels in a single device, one of the highest CO2 transfer efficiencies of any artificial lung to date, and a promising approach to translate µALs one step closer to the clinic.

Risk factors for surgical site infection after surgical treatment of closed distal radial fractures.

We assessed operatively treated closed distal radial fractures to identify independent risk factors for surgical site infection after treatment. A retrospective review was carried out of 531 operatively treated closed distal radial fractures over a 5-year period. Multiple logistic regression was performed with infection as the dependent variable, using a stepwise regression procedure to select variables to construct the final model. In total, 19 (3.6%) fractures were complicated by postoperative surgical site infection. Uncontrolled diabetes with HbA1c >7, the presence of external fixation or external Kirschner wires, and tobacco use were significant independent predictors of infection. Age and time in the operating room were also statistically significant predictors but deemed to be not clinically meaningful.Level of evidence: IV.

The MT1 receptor as the target of ramelteon neuroprotection in ischemic stroke.

Stroke is the leading cause of death and disability worldwide. Novel and effective therapies for ischemic stroke are urgently needed. Here, we report that melatonin receptor 1A (MT1) agonist ramelteon is a neuroprotective drug candidate as demonstrated by comprehensive experimental models of ischemic stroke, including a middle cerebral artery occlusion (MCAO) mouse model of cerebral ischemia in vivo, organotypic hippocampal slice cultures ex vivo, and cultured neurons in vitro; the neuroprotective effects of ramelteon are diminished in MT1-knockout (KO) mice and MT1-KO cultured neurons. For the first time, we report that the MT1 receptor is significantly depleted in the brain of MCAO mice, and ramelteon treatment significantly recovers the brain MT1 losses in MCAO mice, which is further explained by the Connectivity Map L1000 bioinformatic analysis that shows gene-expression signatures of MCAO mice are negatively connected to melatonin receptor agonist like Ramelteon. We demonstrate that ramelteon improves the cerebral blood flow signals in ischemic stroke that is potentially mediated, at least, partly by mechanisms of activating endothelial nitric oxide synthase. Our results also show that the neuroprotection of ramelteon counteracts reactive oxygen species-induced oxidative stress and activates the nuclear factor erythroid 2-related factor 2/heme oxygenase-1 pathway. Ramelteon inhibits the mitochondrial and autophagic death pathways in MCAO mice and cultured neurons, consistent with gene set enrichment analysis from a bioinformatics perspective angle. Our data suggest that Ramelteon is a potential neuroprotective drug candidate, and MT1 is the neuroprotective target for ischemic stroke, which provides new insights into stroke therapy. MT1-KO mice and cultured neurons may provide animal and cellular models of accelerated ischemic damage and neuronal cell death.

Functional Organization of Glycolytic Metabolon on Mitochondria.

Glucose, the primary cellular energy source, is metabolized through glycolysis initiated by the rate-limiting enzyme Hexokinase (HK). In energy-demanding tissues like the brain, HK1 is the dominant isoform, primarily localized on mitochondria, crucial for efficient glycolysis-oxidative phosphorylation coupling and optimal energy generation. This study unveils a unique mechanism regulating HK1 activity, glycolysis, and the dynamics of mitochondrial coupling, mediated by the metabolic sensor enzyme O-GlcNAc transferase (OGT). OGT catalyzes reversible O-GlcNAcylation, a post-translational modification, influenced by glucose flux. Elevated OGT activity induces dynamic O-GlcNAcylation of HK1's regulatory domain, subsequently promoting the assembly of the glycolytic metabolon on the outer mitochondrial membrane. This modification enhances HK1's mitochondrial association, orchestrating glycolytic and mitochondrial ATP production. Mutations in HK1's O-GlcNAcylation site reduce ATP generation, affecting synaptic functions in neurons. The study uncovers a novel pathway that bridges neuronal metabolism and mitochondrial function via OGT and the formation of the glycolytic metabolon, offering new prospects for tackling metabolic and neurological disorders.

A General-Purpose Self-Supervised Model for Computational Pathology.

Tissue phenotyping is a fundamental computational pathology (CPath) task in learning objective characterizations of histopathologic biomarkers in anatomic pathology. However, whole-slide imaging (WSI) poses a complex computer vision problem in which the large-scale image resolutions of WSIs and the enormous diversity of morphological phenotypes preclude large-scale data annotation. Current efforts have proposed using pretrained image encoders with either transfer learning from natural image datasets or self-supervised pretraining on publicly-available histopathology datasets, but have not been extensively developed and evaluated across diverse tissue types at scale. We introduce UNI, a general-purpose self-supervised model for pathology, pretrained using over 100 million tissue patches from over 100,000 diagnostic haematoxylin and eosin-stained WSIs across 20 major tissue types, and evaluated on 33 representative CPath clinical tasks in CPath of varying diagnostic difficulties. In addition to outperforming previous state-of-the-art models, we demonstrate new modeling capabilities in CPath such as resolution-agnostic tissue classification, slide classification using few-shot class prototypes, and disease subtyping generalization in classifying up to 108 cancer types in the OncoTree code classification system. UNI advances unsupervised representation learning at scale in CPath in terms of both pretraining data and downstream evaluation, enabling data-efficient AI models that can generalize and transfer to a gamut of diagnostically-challenging tasks and clinical workflows in anatomic pathology.

Comparison of ChatGPT-3.5, ChatGPT-4, and Orthopaedic Resident Performance on Orthopaedic Assessment Examinations.

INTRODUCTION: Artificial intelligence (AI) programs have the ability to answer complex queries including medical profession examination questions. The purpose of this study was to compare the performance of orthopaedic residents (ortho residents) against Chat Generative Pretrained Transformer (ChatGPT)-3.5 and GPT-4 on orthopaedic assessment examinations. A secondary objective was to perform a subgroup analysis comparing the performance of each group on questions that included image interpretation versus text-only questions. METHODS: The ResStudy orthopaedic examination question bank was used as the primary source of questions. One hundred eighty questions and answer choices from nine different orthopaedic subspecialties were directly input into ChatGPT-3.5 and then GPT-4. ChatGPT did not have consistently available image interpretation, so no images were directly provided to either AI format. Answers were recorded as correct versus incorrect by the chatbot, and resident performance was recorded based on user data provided by ResStudy. RESULTS: Overall, ChatGPT-3.5, GPT-4, and ortho residents scored 29.4%, 47.2%, and 74.2%, respectively. There was a difference among the three groups in testing success, with ortho residents scoring higher than ChatGPT-3.5 and GPT-4 ( P < 0.001 and P < 0.001). GPT-4 scored higher than ChatGPT-3.5 ( P = 0.002). A subgroup analysis was performed by dividing questions into question stems without images and question stems with images. ChatGPT-3.5 was more correct (37.8% vs. 22.4%, respectively, OR = 2.1, P = 0.033) and ChatGPT-4 was also more correct (61.0% vs. 35.7%, OR = 2.8, P < 0.001), when comparing text-only questions versus questions with images. Residents were 72.6% versus 75.5% correct with text-only questions versus questions with images, with no significant difference ( P = 0.302). CONCLUSION: Orthopaedic residents were able to answer more questions accurately than ChatGPT-3.5 and GPT-4 on orthopaedic assessment examinations. GPT-4 is superior to ChatGPT-3.5 for answering orthopaedic resident assessment examination questions. Both ChatGPT-3.5 and GPT-4 performed better on text-only questions than questions with images. It is unlikely that GPT-4 or ChatGPT-3.5 would pass the American Board of Orthopaedic Surgery written examination.

Weakly Supervised AI for Efficient Analysis of 3D Pathology Samples.

Human tissue consists of complex structures that display a diversity of morphologies, forming a tissue microenvironment that is, by nature, three-dimensional (3D). However, the current standard-of-care involves slicing 3D tissue specimens into two-dimensional (2D) sections and selecting a few for microscopic evaluation1,2, with concomitant risks of sampling bias and misdiagnosis3-6. To this end, there have been intense efforts to capture 3D tissue morphology and transition to 3D pathology, with the development of multiple high-resolution 3D imaging modalities7-18. However, these tools have had little translation to clinical practice as manual evaluation of such large data by pathologists is impractical and there is a lack of computational platforms that can efficiently process the 3D images and provide patient-level clinical insights. Here we present Modality-Agnostic Multiple instance learning for volumetric Block Analysis (MAMBA), a deep-learning-based platform for processing 3D tissue images from diverse imaging modalities and predicting patient outcomes. Archived prostate cancer specimens were imaged with open-top light-sheet microscopy12-14 or microcomputed tomography15,16 and the resulting 3D datasets were used to train risk-stratification networks based on 5-year biochemical recurrence outcomes via MAMBA. With the 3D block-based approach, MAMBA achieves an area under the receiver operating characteristic curve (AUC) of 0.86 and 0.74, superior to 2D traditional single-slice-based prognostication (AUC of 0.79 and 0.57), suggesting superior prognostication with 3D morphological features. Further analyses reveal that the incorporation of greater tissue volume improves prognostic performance and mitigates risk prediction variability from sampling bias, suggesting that there is value in capturing larger extents of spatially heterogeneous 3D morphology. With the rapid growth and adoption of 3D spatial biology and pathology techniques by researchers and clinicians, MAMBA provides a general and efficient framework for 3D weakly supervised learning for clinical decision support and can help to reveal novel 3D morphological biomarkers for prognosis and therapeutic response.

Longitudinal Assessment of Modern Spine Surgery Training: 10-Year Follow-up of a Nationwide Survey of Residency and Spine Fellowship Program Directors.

Spine surgeons complete training through residency in orthopaedic surgery (ORTH) or neurosurgery (NSGY). A survey was conducted in 2013 to evaluate spine surgery training. Over the past decade, advances in surgical techniques and the changing dynamics in fellowship training may have affected training and program director (PD) perceptions may have shifted. Methods: This study is a cross-sectional survey distributed to all PDs of ORTH and NSGY residencies and spine fellowships in the United States. Participants were queried regarding characteristics of their program, ideal characteristics of residency training, and opinions regarding the current training environment. χ2 tests were used to compare answers over the years. Results: In total, 241 PDs completed the survey. From 2013 to 2023, NSGY increased the proportion of residents with >300 spine cases (86%-100%) while ORTH remained with >90% of residents with < 225 cases (p < 0.05). A greater number of NSGY PDs encouraged spine fellowship even for community spine surgery practice (0% in 2013 vs. 14% in 2023, p < 0.05), which continued to be significantly different from ORTH PDs (∼88% agreed, p > 0.05). 100% of NSGY PDs remained confident in their residents performing spine surgery, whereas ORTH confidence significantly decreased from 43% in 2013 to 25% in 2023 (p < 0.05). For spinal deformity, orthopaedic PDs (92%), NSGY PDs (96%), and fellowship directors (95%), all agreed that a spine fellowship should be pursued (p = 0.99). In both 2013 and 2023, approximately 44% were satisfied with the spine training model in the United States. In 2013, 24% of all PDs believed we should have a dedicated spine residency, which increased to 39% in 2023 (fellowship: 57%, ORTH: 38%, NSGY: 21%) (p < 0.05). Conclusion: Spine surgery training continues to evolve, yet ORTH and neurological surgery training remains significantly different in case volumes and educational strengths. In both 2013 and 2023, less than 50% of PDs were satisfied with the current spine surgery training model, and a growing minority believe that spine surgery should have its own residency training pathway. Level of Evidence: IV.

Doubly Constrained Waveform Optimization for Integrated Sensing and Communications.

This paper investigates threshold-constrained joint waveform optimization for an integrated sensing and communication (ISAC) system. Unlike existing studies, we employ mutual information (MI) and sum rate (SR) as sensing and communication metrics, respectively, and optimize the waveform under constraints to both metrics simultaneously. This provides significant flexibility in meeting system performance. We formulate three different optimization problems that constrain the radar performance only, the communication performance only, and the ISAC performance, respectively. New techniques are developed to solve the original problems, which are NP-hard and cannot be directly solved by conventional semi-definite programming (SDP) techniques. Novel gradient descent methods are developed to solve the first two problems. For the third non-convex optimization problem, we transform it into a convex problem and solve it via convex toolboxes. We also disclose the connections between three optimizations using numerical results. Finally, simulation results are provided and validate the proposed optimization solutions.

Complications of Lateral Decubitus Positioning During Orthopaedic Surgery.

¼ Lateral decubitus positioning is a nonanatomical position used for multiple orthopaedic procedures to obtain adequate surgical exposure.¼ Unique ophthalmologic, musculoskeletal, neurovascular, and hemodynamic complications may arise inadvertently from positioning.¼ Orthopaedic surgeons should be aware of the possible complications that may manifest from placing patients in the lateral decubitus position to adequately prevent and to properly manage them.

Psychological impact of the COVID-19 pandemic on breast cancer patients.

PURPOSE: The COVID-19 pandemic uniquely impacted patients with breast cancer as mastectomies were allowed to proceed, yet breast reconstruction surgeries were halted. The purpose of this study was to examine the effect of the COVID-19 pandemic on the rates of breast reconstruction and patients' well-being. METHODS: A chart review included all patients who underwent mastectomy from December 2019 to September 2021. Patients were contacted by a member of the research team and asked to participate in a COVID-19-specific survey and to complete the Hospital Anxiety and Depression Scale (HADS). Patients were then grouped into "surge" or "nonsurge" groups based on the date of mastectomy. RESULTS: Two hundred and fifty-nine patients were included in this study. During the study period, 42% (n = 111) of the patients underwent breast reconstruction. The "surge" group included 106 patients whereas the "nonsurge" group included 153 patients. Fewer patients began breast reconstruction during the surge period compared with the nonsurge period (34.0% vs. 49.0%, p = 0.017). Eighty-six patients participated in the COVID-19 survey. Forty-one percent (n = 35) of the patients felt that their care was disrupted because of COVID-19. Eighty-three patients completed the HADS survey. Overall, 16.8% and 15.7% of the respondents fell into the moderate to severe ranges for both anxiety and depression scales, respectively. CONCLUSIONS: Patients with breast cancer have faced increased difficulties with access to breast reconstruction throughout the COVID-19 pandemic. Our institution demonstrated decreased rates of breast reconstruction and an increase in anxiety and depression. The positive benefits of breast reconstruction cannot be overlooked when determining resource allocation in the future.