Analyzing drilling noise in rotational atherectomy: Improving safety and effectiveness through visualization and anomaly detection using autoencoder-A preclinical study.

Background and Aims: As the population of aging societies continues to grow, the prevalence of complex coronary artery diseases, including calcification, is expected to increase. Rotational atherectomy (RA) is an essential technique for treating calcified lesions. This study aimed to assess the usefulness of the drilling noise produced during rotablation as a parameter for evaluating the safety and effectiveness of the procedure. Methods: A human body model mimicking calcified stenotic coronary lesions was constructed using plastic resin, and burrs of sizes 1.25 and 1.5 mm were utilized. To identify the noise source during rotablation, we activated the ROTAPRO™ rotablator at a rotational speed of 180,000 rpm, recording the noise near the burr (inside the mock model) and advancer (outside). In addition to regular operation, we simulated two major complications: burr entrapment and guidewire transection. The drilling noise recorded in Waveform Audio File Format files was converted into spectrograms for analysis and an autoencoder analyzed the image data for anomalies. Results: The drilling noise from both inside and outside the mock model was predominantly within the 3000 Hz frequency domain. During standard operation, intermittent noise within this range was observed. However, during simulated complications, there were noticeable changes: a drop to 2000 Hz during burr entrapment and a distinct squealing noise during guidewire transection. The autoencoder effectively reduced the spectrogram data into a two-dimensional representation suitable for anomaly detection in potential clinical applications. Conclusion: By analyzing drilling noise, the evaluation of procedural safety and efficacy during RA can be enhanced.

Effects of Unrestricted Kinematically Aligned Total Knee Arthroplasty with a Modified Soft-Tissue Respecting Technique on the Deformity of Limb Alignment in Japanese Patients.

Background and Objectives: Unrestricted kinematic alignment total knee arthroplasty (KA-TKA) with a soft-tissue respecting technique (STRT) is a soft-tissue-dependent tibial resection entailing the restoration of the original soft-tissue tension using ligamentotaxis after resurfacing the femur, based on the concept of restoring the native or pre-osteoarthritis alignment in each patient. However, there is no consensus on the indications of unrestricted KA-TKA with the STRT. We modified the STRT, followed by an investigation of the effects of surgery on the postoperative hip-knee-ankle angle (HKAA). Materials and Methods: We retrospectively analyzed the clinical background data, including the preoperative and postoperative HKAA, of 87 patients who underwent unrestricted KA-TKA with the modified STRT. Univariate and multivariate analyses were performed to determine the factors affecting the postoperative HKAA. A receiver operating characteristic (ROC) curve was plotted to investigate the change in the cut-off values of preoperative HKAA with respect to the safe zone of the postoperative HKAA. We generated two regression models, the linear regression model and generalized additive model (GAM) using machine learning, to predict the postoperative HKAA. Results: Univariate and multivariate analyses revealed the preoperative HKAA as the factor most relevant to the postoperative HKAA. ROC analysis revealed that the preoperative HKAA exhibited a high predictive utility, with a cut-off value of -10°, when the safe range of postoperative HKAA was set at ±5°. The GAM was the superior machine learning model, indicating a non-linear association between the preoperative and postoperative HKAA. Patients with preoperative HKAAs ranging from -18° to 4° were more likely to fall within the ±5° safe range of the postoperative HKAA. Conclusions: The preoperative HKAA influences the postoperative HKAA in unrestricted KA-TKA with the modified STRT. Machine learning using the GAM may contribute to the selection of patients eligible for the surgical approach.

The utility of automatic segmentation of kidney MRI in chronic kidney disease using a 3D convolutional neural network.

We developed a 3D convolutional neural network (CNN)-based automatic kidney segmentation method for patients with chronic kidney disease (CKD) using MRI Dixon-based T1-weighted in-phase (IP)/opposed-phase (OP)/water-only (WO) images. The dataset comprised 100 participants with renal dysfunction (RD; eGFR < 45 mL/min/1.73 m2) and 70 without (non-RD; eGFR ≥ 45 mL/min/1.73 m2). The model was applied to the right, left, and both kidneys; it was first evaluated on the non-RD group data and subsequently on the combined data of the RD and non-RD groups. For bilateral kidney segmentation of the non-RD group, the best performance was obtained when using IP image, with a Dice score of 0.902 ± 0.034, average surface distance of 1.46 ± 0.75 mm, and a difference of - 27 ± 21 mL between ground-truth and automatically computed volume. Slightly worse results were obtained for the combined data of the RD and non-RD groups and for unilateral kidney segmentation, particularly when segmenting the right kidney from the OP images. Our 3D CNN-assisted automatic segmentation tools can be utilized in future studies on total kidney volume measurements and various image analyses of a large number of patients with CKD.

Generative adversarial network based digital stain conversion for generating RGB EVG stained image from hyperspectral H&E stained image.

Significance: Quantification of elastic fiber in the tissue specimen is an important aspect of diagnosing different diseases. Though hematoxylin and eosin (H&E) staining is a routinely used and less expensive tissue staining technique, elastic and collagen fibers cannot be differentiated using it. So, in conventional pathology, special staining technique, such as Verhoeff's van Gieson (EVG), is applied physically for this purpose. However, the procedure of EVG staining is very expensive and time-consuming. Aim: The goal of our study is to propose a deep-learning-based computerized method for the generation of RGB EVG stained tissue from hyperspectral H&E stained one to save the time and cost of conventional EVG staining procedure. Approach: H&E stained hyperspectral image and EVG stained RGB whole slide image of human pancreatic tissue have been leveraged for this experiment. CycleGAN-based deep learning model has been proposed for digital stain conversion while images from source and target domains are of different modalities (hyperspectral and RGB) with different channel dimensions. A set of three basis functions have been introduced for calculating one of the losses of the proposed method, which retains the relevant features of EVG stained image within the reduced channel dimension of the H&E stained one. Results: The experimental results showed that a set of three basis functions including linear discriminant function and transmittance spectrum of eosin and hematoxylin better retained the essential properties of the elastic fiber to be discriminated from collagen fiber within the reduced dimension of the hyperspectral H&E stained image. Also, only a smaller number of paired training data with our proposed training method contributed significantly to the generation of more realistic EVG stained image with more precise identification of elastic fiber. Conclusions: RGB EVG stained image is generated from hyperspectral H&E stained image for which our model has performed two types of image conversion simultaneously: hyperspectral to RGB and H&E to EVG. The experimental results show that the intentionally designed set of three basis functions contains more relevant information and prove the effectiveness of our proposed method in generating realistic RGB EVG stained image from hyperspectral H&E stained one.

Effects of dimension reduction of hyperspectral images in skin gross pathology.

BACKGROUND: Hyperspectral imaging (HSI) is an emerging modality for the gross pathology of the skin. Spectral signatures of HSI could discriminate malignant from benign tissue. Because of inherent redundancies in HSI and in order to facilitate the use of deep-learning models, dimension reduction is a common preprocessing step. The effects of dimension reduction choice, training scope, and number of retained dimensions have not been evaluated on skin HSI for segmentation tasks. MATERIALS AND METHODS: An in-house dataset of HSI signatures from pigmented skin lesions was prepared and labeled with histology. Eleven different dimension reduction methods were used as preprocessing for tumor margin detection with support vector machines. Cluster-wise principal component analysis (ClusterPCA), a new variant of PCA, was proposed. The scope of application for dimension reduction was also investigated. RESULTS: The components produced by ClusterPCA show good agreement with the expected optical properties of skin chromophores. Random forest importance performed best during classification. However, all methods suffered from low sensitivity and generalization. CONCLUSION: Investigation of more complex reduction and segmentation schemes with emphasis on the nature of HSI and optical properties of the skin is necessary. Insights on dimension reduction for skin tissue could facilitate the development of HSI-based systems for cancer margin detection at gross level.

Miliary tuberculosis-associated hemophagocytic lymphohistiocytosis with a high level of soluble interleukin-2 receptor successfully treated with concomitant recombinant thrombomodulin: A case report.

Hemophagocytic lymphohistiocytosis (HLH) is a fatal disease characterized by a highly inflammatory state due to the abnormal activation of T lymphocytes and macrophages. Miliary tuberculosis (MTB) is a rare cause of HLH and its clinical appearances occasionally resembles that of intravascular lymphoma (IVL). A 76-year-old woman presented with persistent fever and fatigue. Abnormal laboratory findings showing thrombocytopenia (13,000/µL), hypofibrinogenemia (101 mg/dL), hyperferritinemia (2,312 ng/mL), and markedly elevated soluble interleukin-2 receptor (sIL-2R) level (32,200 U/mL), in addition, hemophagocytosis in the bone marrow (BM) smear, were suggestive of IVL-associated HLH. The pathology of the BM biopsy specimen showed granuloma with non-caseous necrosis, and culture tests using sputum, gastric fluid, urine, and peripheral and bone marrow blood revealed the presence of Mycobacterium tuberculosis, leading to the final diagnosis of MTB-associated HLH. Anti-TB medications and corticosteroids were administered, but thrombocytopenia, hypofibrinogenemia, and hyperferritinemia persisted. Concomitant use of recombinant thrombomodulin (rTM) enabled regression of clinical status. In this case, BM biopsy served as the diagnosis of MTB-associated HLH, although IVL-associated HLH is initially suspected by an extremely high level of sIL-2R. Furthermore, this case report informs that using rTM could improve the outcomes of MTB-associated HLH.

Hyperspectral imaging for tumor segmentation on pigmented skin lesions.

Significance: Malignant skin tumors, which include melanoma and nonmelanoma skin cancers, are the most prevalent type of malignant tumor. Gross pathology of pigmented skin lesions (PSL) remains manual, time-consuming, and heavily dependent on the expertise of the medical personnel. Hyperspectral imaging (HSI) can assist in the detection of tumors and evaluate the status of tumor margins by their spectral signatures. Aim: Tumor segmentation of medical HSI data is a research field. The goal of this study is to propose a framework for HSI-based tumor segmentation of PSL. Approach: An HSI dataset of 28 PSL was prepared. Two frameworks for data preprocessing and tumor segmentation were proposed. Models based on machine learning and deep learning were used at the core of each framework. Results: Cross-validation performance showed that pixel-wise processing achieves higher segmentation performance, in terms of the Jaccard coefficient. Simultaneous use of spatio-spectral features produced more comprehensive tumor masks. A three-dimensional Xception-based network achieved performance similar to state-of-the-art networks while allowing for more detailed detection of the tumor border. Conclusions: Good performance was achieved for melanocytic lesions, but margins were difficult to detect in some cases of basal cell carcinoma. The frameworks proposed in this study could be further improved for robustness against different pathologies and detailed delineation of tissue margins to facilitate computer-assisted diagnosis during gross pathology.

The utility of texture analysis of kidney MRI for evaluating renal dysfunction with multiclass classification model.

We evaluated a multiclass classification model to predict estimated glomerular filtration rate (eGFR) groups in chronic kidney disease (CKD) patients using magnetic resonance imaging (MRI) texture analysis (TA). We identified 166 CKD patients who underwent MRI comprising Dixon-based T1-weighted in-phase (IP)/opposed-phase (OP)/water-only (WO) images, apparent diffusion coefficient (ADC) maps, and T2* maps. The patients were divided into severe, moderate, and control groups based on eGFR borderlines of 30 and 60 mL/min/1.73 m2. After extracting 93 texture features (TFs), dimension reduction was performed using inter-observer reproducibility analysis and sequential feature selection (SFS) algorithm. Models were created using linear discriminant analysis (LDA); support vector machine (SVM) with linear, rbf, and sigmoid kernels; decision tree (DT); and random forest (RF) classifiers, with synthetic minority oversampling technique (SMOTE). Models underwent 100-time repeat nested cross-validation. Overall performances of our classification models were modest, and TA based on T1-weighted IP/OP/WO images provided better performance than those based on ADC and T2* maps. The most favorable result was observed in the T1-weighted WO image using RF classifier and the combination model was derived from all T1-weighted images using SVM classifier with rbf kernel. Among the selected TFs, total energy and energy had weak correlations with eGFR.

Hyperspectral and multispectral image processing for gross-level tumor detection in skin lesions: a systematic review.

SIGNIFICANCE: Skin cancer is one of the most prevalent cancers worldwide. In the advent of medical digitization and telepathology, hyper/multispectral imaging (HMSI) allows for noninvasive, nonionizing tissue evaluation at a macroscopic level. AIM: We aim to summarize proposed frameworks and recent trends in HMSI-based classification and segmentation of gross-level skin tissue. APPROACH: A systematic review was performed, targeting HMSI-based systems for the classification and segmentation of skin lesions during gross pathology, including melanoma, pigmented lesions, and bruises. The review adhered to the 2020 Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. For eligible reports published from 2010 to 2020, trends in HMSI acquisition, preprocessing, and analysis were identified. RESULTS: HMSI-based frameworks for skin tissue classification and segmentation vary greatly. Most reports implemented simple image processing or machine learning, due to small training datasets. Methodologies were evaluated on heavily curated datasets, with the majority targeting melanoma detection. The choice of preprocessing scheme influenced the performance of the system. Some form of dimension reduction is commonly applied to avoid redundancies that are inherent in HMSI systems. CONCLUSIONS: To use HMSI for tumor margin detection in practice, the focus of system evaluation should shift toward the explainability and robustness of the decision-making process.

Framework for estimating renal function using magnetic resonance imaging.

Purpose: Nephrologists have empirically predicted renal function from renal morphology. In diagnosing a case of renal dysfunction of unknown course, acute kidney injury and chronic kidney disease are diagnosed from blood tests and an imaging study including magnetic resonance imaging (MRI), and an examination/treatment policy is determined. A framework for the estimation of renal function from water images obtained using the Dixon method is proposed to provide information that helps clinicians reach a diagnosis by accurately estimating renal function on the basis of renal MRI. Approach: The proposed framework consists of four steps. First, the kidney area is extracted by MRI using the Dixon method with a U-net by deep learning. Second, the extracted renal region is registered with the target mask. Third, the kidney features are calculated based on the target mask classification information created by a specialist. Fourth, the estimated glomerular filtration rate (eGFR) representing the renal function is estimated using a regression support vector machine from the calculated features. Results: For the accuracy evaluation, we conducted an experiment to estimate the eGFR when MRI was performed and the eGFR slope, which is the annual rate of decline in eGFR. When the accuracy was evaluated for 165 subjects, the eGFR was estimated to have a root mean square error (RMSE) of 11.99 and a correlation coefficient of 0.83. Moreover, the eGFR slope was estimated to have an RMSE of 4.8 and a correlation coefficient of 0.5. Conclusions: Therefore, the proposed method shows the possibility of estimating the prognosis of renal function based on water images obtained by the Dixon method.

Design of a Hyper-Spectral Imaging System for Gross Pathology of Pigmented Skin Lesions.

Pigmented skin lesions (PSL) are prevalent in Asian populations and their gross pathology remains a manual, tedious task. Hyper-spectral imaging (HSI) is a non-invasive non-ionizing acquisition technique, allowing malignant tissue to be identified by its spectral signature. We set up a hyper-spectral imaging (HSI) system targeting cancer margin detection of PSL. Because classification among PSL is achieved via comparison of spectral signatures, appropriate calibration is necessary to ensure sufficient data quality. We propose a strategy for system building, calibration and pre-processing, under the requirements of fast acquisition and wide field of view. Preliminary results show that the HSI-based system is able to effectively resolve reflectance signatures of ex-vivo tissue.Clinical Relevance-The imaging system proposed in this study can recover reflectance spectra from PSL during gross pathology, providing a wide imaging area.

Comparison of multiparametric magnetic resonance imaging sequences with laboratory parameters for prognosticating renal function in chronic kidney disease.

Magnetic resonance imaging (MRI) is playing an increasingly important role in evaluating chronic kidney disease (CKD). It has the potential to be used not only for evaluation of physiological and pathological states, but also for prediction of disease course. Although different MRI sequences have been employed in renal disease, there are few studies that have compared the different sequences. We compared several multiparametric MRI sequences, and compared their results with the estimated glomerular filtration rate. Principal component analysis showed a similarity between T1 values and tissue perfusion (arterial spin labelling), and between fractional anisotropy (diffusion tensor imaging) and apparent diffusion coefficient values (diffusion-weighted imaging). In multiple regression analysis, only T2* values, derived from the blood oxygenation level-dependent (BOLD) MRI sequence, were associated with estimated glomerular filtration rate slope after adjusting for degree of proteinuria, a classic prognostic factor for CKD. In receiver operating characteristic curve analysis, T2* values were a good predictor of rapid deterioration, regardless of the degree of proteinuria. This suggests further study of the use of BOLD-derived T2* values in the workup of CKD, especially to predict the disease course.

Application of Magnetic Resonance Imaging in the Evaluation of Nutritional Status: A Literature Review with Focus on Dialysis Patients.

Magnetic resonance imaging (MRI) is indispensable in clinical medicine for the morphological and tomographic evaluation of many parenchymal organs. With varied imaging methods, diverse biological information, such as the perfusion volume and measurements of metabolic products, can be obtained. In addition to conventional MRI for morphological assessment, diffusion-weighted MRI/diffusion tensor imaging is used to evaluate white matter structures in the brain; arterial spin labeling is used for cerebral blood flow evaluation; magnetic resonance elastography for fatty liver and cirrhosis evaluation; magnetic resonance spectroscopy for evaluation of metabolites in specific regions of the brain; and blood oxygenation level-dependent imaging for neurological exploration of eating behavior, obesity, and food perception. This range of applications will continue to expand in the future. Nutritional science is a multidisciplinary and all-inclusive field of research; therefore, there are many different applications of MRI. We present a literature review of MRI techniques that can be used to evaluate the nutritional status, particularly in patients on dialysis. We used MEDLINE as the information source, conducted a keyword search in PubMed, and found that, as a nutritional evaluation method, MRI has been used frequently to comprehensively and quantitatively evaluate muscle mass for the determination of body composition.

TAFRO Syndrome With Kidney Involvement: A Case Series of Patients With Kidney Biopsies.

TAFRO (thrombocytopenia, anasarca, fever, reticulin myelofibrosis/renal insufficiency, and organomegaly) syndrome is a systemic inflammatory disease sharing some features with Castleman disease and POEMS (polyneuropathy, organomegaly, endocrinopathy, monoclonal protein, and skin changes) syndrome in relation to abnormal secretions of interleukin 6 and vascular endothelial growth factor. The kidney is a main target organ of TAFRO syndrome but the kidney histopathology associated with TAFRO syndrome is yet to be completely defined. We report 3 TAFRO syndrome cases with different clinical courses in which kidney biopsies were performed. In all 3 cases, kidney biopsies showed similar glomerular lesions of diffuse global swelling of the endothelium and expansion of subendothelial spaces, consistent with severe glomerular endothelial injury. Case 3 showed an additional finding of focal tubulointerstitial injury characterized by marked plasma cell infiltration, which was absent in the other 2 cases. Clinical symptoms in cases 1 and 2, which had lower disease severity scores of TAFRO syndrome, were effectively treated with the administration of corticosteroids or a combination of corticosteroids and cyclosporine A. Case 3, with a higher disease severity score, had an aggressive clinical course that was refractory to corticosteroids and tocilizumab; the patient ultimately died of multiple organ failure. In all 3 cases, kidney biopsy provided indications for the diagnosis process and clinical management of TAFRO syndrome.

Distinct community structures of soil nematodes from three ecologically different sites revealed by high-throughput amplicon sequencing of four 18S ribosomal RNA gene regions.

Quantitative taxonomic compositions of nematode communities help to assess soil environments due to their rich abundance and various feeding habitats. DNA metabarcoding by the 18S ribosomal RNA gene (SSU) regions were preferentially used for analyses of soil nematode communities, but the optimal regions for high-throughput amplicon sequencing have not previously been well investigated. In this work, we performed Illumina-based amplicon sequencing of four SSU regions (regions 1-4) to identify suitable regions for nematode metabarcoding using the taxonomic structures of nematodes from uncultivated field, copse, and cultivated house garden soils. The fewest nematode-derived sequence variants (SVs) were detected in region 3, and the total nematode-derived SVs were comparable in regions 1 and 4. The relative abundances of reads in regions 1 and 4 were consistent in both orders and feeding groups with prior studies, thus suggesting that region 4 is a suitable target for the DNA barcoding of nematode communities. Distinct community structures of nematodes were detected in the taxon, feeding habitat, and life-history strategy of each sample; i.e., Dorylamida- and Rhabditida-derived plant feeders were most abundant in the copse soil, Rhabditida-derived bacteria feeders in the house garden soil, and Mononchida- and Dorylamida-derived omnivores and predators and Rhabditida-derived bacteria feeders in the field soil. Additionally, low- and high-colonizer-persister (cp) groups of nematodes dominated in the house garden and copse soils, respectively, whereas both groups were found in the field soil, suggesting bacteria-rich garden soil, undisturbed and plant-rich copse soil, and a transient status of nematode communities in the field soil. These results were also supported by the maturity indices of the three sampling sites. Finally, the influence of the primer tail sequences was demonstrated to be insignificant on amplification. These findings will be useful for DNA metabarcoding of soil nematode communities by amplicon sequencing.

IsdB antibody-mediated sepsis following S. aureus surgical site infection.

Staphylococcus aureus is prevalent in surgical site infections (SSI) and leads to death in approximately 1% of patients. Phase IIB/III clinical trial results have demonstrated that vaccination against the iron-regulated surface determinant protein B (IsdB) is associated with an increased mortality rate in patients with SSI. Thus, we hypothesized that S. aureus induces nonneutralizing anti-IsdB antibodies, which facilitate bacterial entry into leukocytes to generate "Trojan horse" leukocytes that disseminate the pathogen. Since hemoglobin (Hb) is the primary target of IsdB, and abundant Hb-haptoglobin (Hb-Hp) complexes in bleeding surgical wounds are normally cleared via CD163-mediated endocytosis by macrophages, we investigated this mechanism in vitro and in vivo. Our results demonstrate that active and passive IsdB immunization of mice renders them susceptible to sepsis following SSI. We also found that a multimolecular complex containing S. aureus protein A-anti-IsdB-IsdB-Hb-Hp mediates CD163-dependent bacterial internalization of macrophages in vitro. Moreover, IsdB-immunized CD163-/- mice are resistant to sepsis following S. aureus SSI, as are normal healthy mice given anti-CD163-neutralizing antibodies. These genetic and biologic CD163 deficiencies did not exacerbate local infection. Thus, anti-IsdB antibodies are a risk factor for S. aureus sepsis following SSI, and disruption of the multimolecular complex and/or CD163 blockade may intervene.

Telepathology Support System with Gross Specimen Image Using High Resolution 4K Multispectral Camera.

This study reports on the development of a high-resolution 4K multispectral camera designed to enhance telepathology support systems for remote gross-pathological diagnosis. We experimentally examine and evaluate the camera's effectiveness in three subjects: the reconstruction of precise color images, the emphasis of cancerous tissue areas, and pre-fixed image reproduction from fixed images. The evaluation results of the first and second subjects showed that the camera and supporting methods could be effectively used in gross pathology diagnosis. The images obtained in the third subject received positive evaluations from some pathologists, but others expressed reservations as to its utility.

Destructive arthritis with cutaneous polyarteritis nodosa requiring surgical intervention: a case report and review of the literature.

Arthralgia is a well-known complication of cutaneous polyarteritis nodosa (CPN). However, few cases of destructive arthritis with CPN resulting in chronic pain and disability have been documented. Here we report a case of CPN associated with destructive arthritis of the right foot, accompanied by seronegative arthritis in the right ankle, for which orthopaedic surgery was necessary. X-ray and computed tomography revealed progressive joint destruction in the right talonavicular joint, and magnetic resonance imaging demonstrated severe synovitis of the right talocrural joint. When the talonavicular joint was opened, we observed marked synovitis and a defect in the cartilage that had expanded within the joint. Although mild cartilage degeneration was observed in the talocrural joint, arthroscopic observation revealed no massive defect on the articulated surface. Arthrodesis and arthroscopic synovectomy yielded a good midterm clinical outcome, indicating that surgical intervention was useful for the treatment for destructive arthritis with CPN. Rheumatologists and orthopaedic surgeons should be aware of potential complications such as destructive arthritis when treating patients with CPN.

Effects of empagliflozin versus placebo on cardiac sympathetic activity in acute myocardial infarction patients with type 2 diabetes mellitus: the EMBODY trial.

BACKGROUND: Protection from lethal ventricular arrhythmias leading to sudden cardiac death (SCD) is a crucial challenge after acute myocardial infarction (AMI). Cardiac sympathetic and parasympathetic activity can be noninvasively assessed using heart rate variability (HRV) and heart rate turbulence (HRT). The EMBODY trial was designed to determine whether the Sodium-glucose cotransporter 2 (SGLT2) inhibitor improves cardiac nerve activity. METHODS: This prospective, multicenter, randomized, double-blind, placebo-controlled trial included patients with AMI and type 2 diabetes mellitus (T2DM) in Japan; 105 patients were randomized (1:1) to receive once-daily 10-mg empagliflozin or placebo. The primary endpoints were changes in HRV, e.g., the standard deviation of all 5-min mean normal RR intervals (SDANN) and the low-frequency-to-high-frequency (LF/HF) ratio from baseline to 24 weeks. Secondary endpoints were changes in other sudden cardiac death (SCD) surrogate markers such as HRT. RESULTS: Overall, 96 patients were included (46, empagliflozin group; 50, placebo group). The changes in SDANN were + 11.6 and + 9.1 ms in the empagliflozin (P = 0.02) and placebo groups (P = 0.06), respectively. Change in LF/HF ratio was - 0.57 and - 0.17 in the empagliflozin (P = 0.01) and placebo groups (P = 0.43), respectively. Significant improvement was noted in HRT only in the empagliflozin group (P = 0.01). Whereas intergroup comparison on HRV and HRT showed no significant difference between the empagliflozin and placebo groups. Compared with the placebo group, the empagliflozin group showed significant decreases in body weight, systolic blood pressure, and uric acid. In the empagliflozin group, no adverse events were observed. CONCLUSIONS: This is the first randomized clinical data to evaluate the effect of empagliflozin on cardiac sympathetic and parasympathetic activity in patients with T2DM and AMI. Early SGLT2 inhibitor administration in AMI patients with T2DM might be effective in improving cardiac nerve activity without any adverse events. TRIAL REGISTRATION: The EMBODY trial was registered by the UMIN in November 2017 (ID: 000030158). UMIN000030158; https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000034442 .