Effects of canagliflozin on kidney oxygenation evaluated using blood oxygenation level-dependent MRI in patients with type 2 diabetes.

Background: Recent clinical studies suggest protective effects of SGLT2 inhibitors on kidney disease outcome. Chronic hypoxia has a critical role in kidney disease development, thus we speculated that canagliflozin, an SGLT2 inhibitor, can improve kidney oxygenation. Methods: A single-arm study was conducted to investigate the effects of canagliflozin on T2* value, which reflects oxygenation level, in patients with type 2 diabetes (T2D) using repeated blood oxygenation level-dependent MRI (BOLD MRI) examinations. Changes in cortical T2* from before (Day 0) to after single-dose treatment (Day 1) and after five consecutive treatments (Day 5) were evaluated using 12-layer concentric objects (TLCO) and region of interest (ROI) methods. Results: In the full analysis set (n=14 patients), the TLCO method showed no change of T2* with canagliflozin treatment, whereas the ROI method found that cortical T2* was significantly increased on Day 1 but not on Day 5. Sensitivity analysis using TLCO in 13 well-measured patients showed that canagliflozin significantly increased T2* on Day 1 with no change on Day 5, whereas a significant improvement in cortical T2* following canagliflozin treatment was found on both Day 1 and 5 using ROI. Conclusions: Short-term canagliflozin treatment may improve cortical oxygenation and lead to better kidney outcomes in patients with T2D.

Two cases of intrathoracic herniation of the gastric tube from the retrosternal route requiring reoperation after oesophagectomy.

Intrathoracic herniation of the gastric tube pulled up through the retrosternal route after oesophagectomy is relatively rare and usually can be managed by conservative treatment.We present two patients who needed reoperation for intrathoracic herniation of gastric tube after minimally invasive oesophagectomy for thoracic oesophageal cancer. Postoperatively, both patients showed herniation and acute twist of the gastric tube. Due to the twist of the gastric tube, one patient had ischaemic change of the proximal tip of the gastric tube, and the other patient showed delayed gastric emptying, both of which led to surgical repairs. In this case report, we discuss why the herniation of gastric tube from the retrosternal route occurs, how to decide to do reoperation and how to prevent this complication.

Proteomic insights into extracellular vesicles in ALS for therapeutic potential of Ropinirole and biomarker discovery.

BACKGROUND: Extracellular vesicles (EVs) hold the potential for elucidating the pathogenesis of amyotrophic lateral sclerosis (ALS) and serve as biomarkers. Notably, the comparative and longitudinal alterations in the protein profiles of EVs in serum (sEVs) and cerebrospinal fluid (CSF; cEVs) of sporadic ALS (SALS) patients remain uncharted. Ropinirole hydrochloride (ROPI; dopamine D2 receptor [D2R] agonist), a new anti-ALS drug candidate identified through induced pluripotent stem cell (iPSC)-based drug discovery, has been suggested to inhibit ALS disease progression in the Ropinirole Hydrochloride Remedy for Amyotrophic Lateral Sclerosis (ROPALS) trial, but its mechanism of action is not well understood. Therefore, we tried to reveal longitudinal changes with disease progression and the effects of ROPI on protein profiles of EVs. METHODS: We collected serum and CSF at fixed intervals from ten controls and from 20 SALS patients participating in the ROPALS trial. Comprehensive proteomic analysis of EVs, extracted from these samples, was conducted using liquid chromatography/mass spectrometer (LC/MS). Furthermore, we generated iPSC-derived astrocytes (iPasts) and performed RNA sequencing on astrocytes with or without ROPI treatment. RESULTS: The findings revealed notable disparities yet high congruity in sEVs and cEVs protein profiles concerning disease status, time and ROPI administration. In SALS, both sEVs and cEVs presented elevated levels of inflammation-related proteins but reduced levels associated with unfolded protein response (UPR). These results mirrored the longitudinal changes after disease onset and correlated with the revised ALS Functional Rating Scale (ALSFRS-R) at sampling time, suggesting a link to the onset and progression of SALS. ROPI appeared to counteract these changes, attenuating inflammation-related protein levels and boosting those tied to UPR in SALS, proposing an anti-ALS impact on EV protein profiles. Reverse translational research using iPasts indicated that these changes may partly reflect the DRD2-dependent neuroinflammatory inhibitory effects of ROPI. We have also identified biomarkers that predict diagnosis and disease progression by machine learning-driven biomarker search. CONCLUSIONS: Despite the limited sample size, this study pioneers in reporting time-series proteomic alterations in serum and CSF EVs from SALS patients, offering comprehensive insights into SALS pathogenesis, ROPI-induced changes, and potential prognostic and diagnostic biomarkers.

Three-dimensional convolutional neural network-based classification of chronic kidney disease severity using kidney MRI.

A three-dimensional convolutional neural network model was developed to classify the severity of chronic kidney disease (CKD) using magnetic resonance imaging (MRI) Dixon-based T1-weighted in-phase (IP)/opposed-phase (OP)/water-only (WO) imaging. Seventy-three patients with severe renal dysfunction (estimated glomerular filtration rate [eGFR] < 30 mL/min/1.73 m2, CKD stage G4-5); 172 with moderate renal dysfunction (30 ≤ eGFR < 60 mL/min/1.73 m2, CKD stage G3a/b); and 76 with mild renal dysfunction (eGFR ≥ 60 mL/min/1.73 m2, CKD stage G1-2) participated in this study. The model was applied to the right, left, and both kidneys, as well as to each imaging method (T1-weighted IP/OP/WO images). The best performance was obtained when using bilateral kidneys and IP images, with an accuracy of 0.862 ± 0.036. The overall accuracy was better for the bilateral kidney models than for the unilateral kidney models. Our deep learning approach using kidney MRI can be applied to classify patients with CKD based on the severity of kidney disease.

Super-resolution deep learning reconstruction approach for enhanced visualization in lumbar spine MR bone imaging.

OBJECTIVES: This study aims to assess the effectiveness of super-resolution deep-learning-based reconstruction (SR-DLR), which leverages k-space data, on the image quality of lumbar spine magnetic resonance (MR) bone imaging using a 3D multi-echo in-phase sequence. MATERIALS AND METHODS: In this retrospective study, 29 patients who underwent lumbar spine MRI, including an MR bone imaging sequence between January and April 2023, were analyzed. Images were reconstructed with and without SR-DLR (Matrix sizes: 960 × 960 and 320 × 320, respectively). The signal-to-noise ratio (SNR) of the vertebral body and spinal canal and the contrast and contrast-to-noise ratio (CNR) between the vertebral body and spinal canal were quantitatively evaluated. Furthermore, the slope at half-peak points of the profile curve drawn across the posterior border of the vertebral body was calculated. Two radiologists independently assessed image noise, contrast, artifacts, sharpness, and overall image quality of both image types using a 4-point scale. Interobserver agreement was evaluated using weighted kappa coefficients, and quantitative and qualitative scores were compared via the Wilcoxon signed-rank test. RESULTS: SNRs of the vertebral body and spinal canal were notably improved in images with SR-DLR (p < 0.001). Contrast and CNR were significantly enhanced with SR-DLR compared to those without SR-DLR (p = 0.023 and p = 0.022, respectively). The slope of the profile curve at half-peak points across the posterior border of the vertebral body and spinal canal was markedly higher with SR-DLR (p < 0.001). Qualitative scores (noise: p < 0.001, contrast: p < 0.001, artifact p = 0.042, sharpness: p < 0.001, overall image quality: p < 0.001) were superior in images with SR-DLR compared to those without. Kappa analysis indicated moderate to good agreement (noise: κ = 0.56, contrast: κ = 0.51, artifact: κ = 0.46, sharpness: κ = 0.76, overall image quality: κ = 0.44). CONCLUSION: SR-DLR, which is based on k-space data, has the potential to enhance the image quality of lumbar spine MR bone imaging utilizing a 3D gradient echo in-phase sequence. CLINICAL RELEVANCE STATEMENT: The application of SR-DLR can lead to improvements in lumbar spine MR bone imaging quality.

A Novel Approach to Detecting Contrast Extravasation in Computed Tomography: Evaluating the Injection Pressure-to-Injection Rate Ratio.

OBJECTIVE: The purpose of this study was to evaluate the usefulness of the injection pressure-to-injection rate (IPIR) ratio for the early detection of contrast extravasation at the venipuncture site during contrast-enhanced computed tomography. METHODS: We retrospectively enrolled 57,528 patients who underwent contrast-enhanced computed tomography examinations in a single hospital. The power injector recorded the contrast injection pressure at 0.25-second intervals. We constructed logistic regression models using the IPIR ratio as the independent variable and extravasation occurrence as the dependent variable (IPIR ratio models) at 1, 2, 3, 4, 5, and 6 seconds after the start of contrast administration. Univariate logistic regression models in which injection pressure is used as an independent variable (injection pressure models) were also constructed as a reference baseline. The performance of the models was evaluated with the area under the receiver operating characteristic curves. RESULTS: Of the 57,528 cases, 46,022 were assigned to the training group and 11,506 were assigned to the test group, which included 112 extravasation cases (0.24%) in the training group and 28 (0.24%) in the test group. The area under the receiver operating characteristic curves for the IPIR ratio models and injection pressure models were 0.555 versus 0.563 at t = 1 (P = 0.270), 0.712 versus 0.678 at t = 2 (P = 0.305), 0.758 versus 0.693 at t = 3 (P = 0.032), 0.776 versus 0.688 at t = 4 (P = 0.005), 0.810 versus 0.699 at t = 5 (P = 0.002), and 0.811 versus 0.706 at t = 6 (P = 0.002). CONCLUSIONS: The IPIR ratio models perform better in detecting contrast extravasation at 3 to 6 seconds after the start of contrast administration than injection pressure models.

An experimental verification of fungal overgrowth in temporary houses at the site of the Great East Japan Earthquake.

Fungal contamination in the indoor air of prefabricated temporary houses at the site of the Great East Japan Earthquake revealed extremely high levels compared to those found in conventional residences. We experimentally investigated fungal growth levels on different interior materials to support fungal overgrowth in prefabricated temporary houses. Three species each of allergenic fungi and invasive fungi observed in temporary housing were selected for inoculation tests with various interior materials. The experiments with fungal inoculation were conducted in conformance with standards for industrial products described in the Japanese" JIS Z 2911:2018 Methods of test for fungus resistance" with small modifications. After incubation, visual and stereomicroscopic assessments were performed to determine fungal growth levels. The viability of the fungi varied according to the interior material type. Our findings demonstrate the importance of antifungal measures in indoor environments and the need for additional research on the growth levels of fungal species on various interior materials.

High-throughput screening of small-molecules libraries identified antibacterials against clinically relevant multidrug-resistant A. baumannii and K. pneumoniae.

BACKGROUND: The current pipeline for new antibiotics fails to fully address the significant threat posed by drug-resistant Gram-negative bacteria that have been identified by the World Health Organization (WHO) as a global health priority. New antibacterials acting through novel mechanisms of action are urgently needed. We aimed to identify new chemical entities (NCEs) with activity against Klebsiella pneumoniae and Acinetobacter baumannii that could be developed into a new treatment for drug-resistant infections. METHODS: We developed a high-throughput phenotypic screen and selection cascade for generation of hit compounds active against multidrug-resistant (MDR) strains of K. pneumoniae and A. baumannii. We screened compound libraries selected from the proprietary collections of three pharmaceutical companies that had exited antibacterial drug discovery but continued to accumulate new compounds to their collection. Compounds from two out of three libraries were selected using "eNTRy rules" criteria associated with increased likelihood of intracellular accumulation in Escherichia coli. FINDINGS: We identified 72 compounds with confirmed activity against K. pneumoniae and/or drug-resistant A. baumannii. Two new chemical series with activity against XDR A. baumannii were identified meeting our criteria of potency (EC50 ≤50 µM) and absence of cytotoxicity (HepG2 CC50 ≥100 µM and red blood cell lysis HC50 ≥100 µM). The activity of close analogues of the two chemical series was also determined against A. baumannii clinical isolates. INTERPRETATION: This work provides proof of principle for the screening strategy developed to identify NCEs with antibacterial activity against multidrug-resistant critical priority pathogens such as K. pneumoniae and A. baumannii. The screening and hit selection cascade established here provide an excellent foundation for further screening of new compound libraries to identify high quality starting points for new antibacterial lead generation projects. FUNDING: BMBF and GARDP.

Development of progressive multifocal leukoencephalopathy after cord blood transplantation in a patient with refractory angioimmunoblastic T-cell lymphoma.

A patient undergoing cord blood transplantation for refractory angioimmunoblastic T-cell lymphoma was subsequently managed with long-term immunosuppressants for chronic graft-versus-host disease (GVHD). On day 591 post-transplant, she exhibited disorientation and cognitive dysfunction. Magnetic resonance imaging (MRI) of the brain revealed two hyperintense foci in the white matter, suggestive of progressive multifocal leukoencephalopathy (PML). However, we did not include PML in the differential diagnosis at that time. Unfortunately, she developed progressive cognitive impairment, and repeated brain MRIs showed a progression in lesion size. She was still taking immunosuppressants to control her GVHD, therefore we suspected PML. The diagnosis of PML was confirmed through the detection of a John Cunningham (JC) virus in the cerebrospinal fluid on day 640 post-transplant. This report highlights the critical need to consider PML in differential diagnoses for post-allogeneic transplant patients, especially those who exhibit progressive neurological symptoms while on prolonged immunosuppressant therapy.

Imeglimin improves systemic metabolism by targeting brown adipose tissue and gut microbiota in obese model mice.

Imeglimin is a recently developed anti-diabetic drug that could concurrently promote insulin secretion and insulin sensitivity, while its mechanisms of action are not fully understood. Here we show that imeglimin administration could protect mice from high fat diet-induced weight gain with enhanced energy expenditure and attenuated whitening of brown adipose tissue. Imeglimin administration led to significant alteration of gut microbiota, which included an increase of Akkermansia genus, with attenuation of obesity-associated gut pathologies. Ablation of microbiota by antibiotic treatment partially abrogated the insulin sensitizing effects of imeglimin, while not affecting its actions on body weight gain or brown adipose tissue. Collectively, our results characterize imeglimin as a potential agent promoting energy expenditure and gut integrity, providing new insights into its mechanisms of action.

Pentafluorosulfanyl-substituted biaryl derivatives as MATE-type transporter inhibitors targeting drug-resistant bacteria.

Multidrug and toxin extrusion (MATE) inhibitors improve the antimicrobial susceptibility of drug-resistant bacteria by preventing the efflux of administered antibiotics. In this study, we optimized the chemical structure of a previously identified bacterial-selective MATE inhibitor 1 (EC50 > 30 µM) to improve its activity further. Compound 1 was divided into three fragments (aromatic part, linker part, and guanidine part), and each part was individually optimized. Compound 31 (EC50 = 1.8 µM), a novel pentafluorosulfanyl-containing molecule synthesized following optimized parts, showed antimicrobial activity against MATE-expressing strains at concentrations lower than conventional inhibitor 1 when co-administrated with norfloxacin. Furthermore, 31 was not cytotoxic at effective concentrations. This suggests that compound 31 can be a promising candidate for combating bacterial infections, particularly those resistant to conventional antibiotics by MATE expression.

The Feasibility of Using a Deep Learning-Based Model to Determine Cardiac Computed Tomographic Contrast Dose.

PURPOSE: This study aimed to predict contrast effects in cardiac computed tomography (CT) from CT localizer radiographs using a deep learning (DL) model and to compare the prediction performance of the DL model with that of conventional models based on patients' physical size. METHODS: This retrospective study included 473 (256 men and 217 women) cardiac CT scans between May 2014 and August 2017. We developed and evaluated DL models that predict milligrams of iodine per enhancement of the aorta from CT localizer radiographs. To assess the model performance, we calculated and compared Pearson correlation coefficient ( r ) between the actual iodine dose that was necessary to obtain a contrast effect of 1 HU (iodine dose per contrast effect [IDCE]) and IDCE predicted by DL, body weight, lean body weight, and body surface area of patients. RESULTS: The model was tested on 52 cases for the male group (mean [SD] age, 63.7 ± 11.4) and 44 cases for the female group (mean [SD] age, 69.8 ± 11.6). Correlation coefficients between the actual and predicted IDCE were 0.607 for the male group and 0.412 for the female group, which were higher than the correlation coefficients between the actual IDCE and body weight (0.539 for male, 0.290 for female), lean body weight (0.563 for male, 0.352 for female), and body surface area (0.587 for male, 0.349 for female). CONCLUSIONS: The performance for predicting contrast effects by analyzing CT localizer radiographs with the DL model was at least comparable with conventional methods using the patient's body size, notwithstanding that no additional measurements other than CT localizer radiographs were required.

Preliminary assessment of automated radiology report generation with generative pre-trained transformers: comparing results to radiologist-generated reports.

PURPOSE: In this preliminary study, we aimed to evaluate the potential of the generative pre-trained transformer (GPT) series for generating radiology reports from concise imaging findings and compare its performance with radiologist-generated reports. METHODS: This retrospective study involved 28 patients who underwent computed tomography (CT) scans and had a diagnosed disease with typical imaging findings. Radiology reports were generated using GPT-2, GPT-3.5, and GPT-4 based on the patient's age, gender, disease site, and imaging findings. We calculated the top-1, top-5 accuracy, and mean average precision (MAP) of differential diagnoses for GPT-2, GPT-3.5, GPT-4, and radiologists. Two board-certified radiologists evaluated the grammar and readability, image findings, impression, differential diagnosis, and overall quality of all reports using a 4-point scale. RESULTS: Top-1 and Top-5 accuracies for the different diagnoses were highest for radiologists, followed by GPT-4, GPT-3.5, and GPT-2, in that order (Top-1: 1.00, 0.54, 0.54, and 0.21, respectively; Top-5: 1.00, 0.96, 0.89, and 0.54, respectively). There were no significant differences in qualitative scores about grammar and readability, image findings, and overall quality between radiologists and GPT-3.5 or GPT-4 (p > 0.05). However, qualitative scores of the GPT series in impression and differential diagnosis scores were significantly lower than those of radiologists (p < 0.05). CONCLUSIONS: Our preliminary study suggests that GPT-3.5 and GPT-4 have the possibility to generate radiology reports with high readability and reasonable image findings from very short keywords; however, concerns persist regarding the accuracy of impressions and differential diagnoses, thereby requiring verification by radiologists.

Donor Lymphocyte Infusion for Relapsed Acute Leukemia or Myelodysplastic Syndrome after Hematopoietic Stem Cell Transplantation: A Single-Institute Retrospective Analysis.

Objective The prognosis of the patients who relapsed after allogeneic hematopoietic stem cell transplantation (allo-HSCT) is poor, and therapeutic options are limited. In the present study, we investigated the efficacy and factors associated with the survival in patients with acute leukemia or myelodysplastic syndrome (MDS) who relapsed following allo-HSCT and were treated with donor lymphocyte infusion (DLI) in real-world practice. Patients Twenty-nine patients with acute myeloid leukemia21, acute lymphoid leukemia4 or MDS4 were enrolled. Eleven patients were diagnosed with hematological relapse, and 18 were diagnosed with molecular or cytogenetic relapse. Results The median injection number and median total number of infused CD3+ T cells were 2 and 5.0×107/kg, respectively. The cumulative incidence of acute graft-versus-host disease (aGVHD) of grade ≥II at 4 months after the initiation of DLI was 31.0%. Extensive chronic graft-versus-host disease (cGVHD) occurred in 3 (10.3%) patients. The overall response rate was 51.7%, including 3 cases of hematological complete remission (CR) and 12 cases of molecular/cytogenetic CR. Cumulative relapse rates at 24 and 60 months following DLI in patients who achieved CR were 21.4% and 30.0%, respectively. The overall survival rates at 1, 2 and 3 years after DLI were 41.4%, 37.9% and 30.3%, respectively. Molecular/cytogenetic relapse, a longer interval from HSCT to relapse, and concomitant chemotherapy with 5-azacytidine (Aza) were significantly associated with a relatively long survival following DLI. Conclusion These results indicated that DLI was beneficial for patients with acute leukemia or MDS who relapsed after allo-HSCT and suggested that DLI in combination with Aza for molecular or cytogenetic relapse might result in favorable outcomes.

Impact of Visually Induced Motion Sickness from VR Depending on Viewing Patterns, View Movement, and Background Motion.

The impact of visually induced motion sickness from virtual reality (VR) because of viewing patterns, view movements, and background global motion was investigated experimentally through classification into four categories.Each of the ten subjects underwent watching four patterns with bio-signal measurements, such as electrocardiogram and respiration, answering a subjective questionnaire.The results of the subjective evaluation of biological effects indicate that the VR viewing pattern has a significantly lower influence on visually induced motion sickness than other patterns, such as viewing with global background motion or sight motion by operating the controller.Clinical Relevance- This study aimed to reduce the effects of visually induced motion sickness in VR viewing, particularly to reduce the burden on the eyes and body.

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.

Gut insulin action protects from hepatocarcinogenesis in diabetic mice comorbid with nonalcoholic steatohepatitis.

Diabetes is known to increase the risk of nonalcoholic steatohepatitis (NASH) and hepatocellular carcinoma (HCC). Here we treat male STAM (STelic Animal Model) mice, which develop diabetes, NASH and HCC associated with dysbiosis upon low-dose streptozotocin and high-fat diet (HFD), with insulin or phlorizin. Although both treatments ameliorate hyperglycemia and NASH, insulin treatment alone lead to suppression of HCC accompanied by improvement of dysbiosis and restoration of antimicrobial peptide production. There are some similarities in changes of microflora from insulin-treated patients comorbid with diabetes and NASH. Insulin treatment, however, fails to suppress HCC in the male STAM mice lacking insulin receptor specifically in intestinal epithelial cells (ieIRKO), which show dysbiosis and impaired gut barrier function. Furthermore, male ieIRKO mice are prone to develop HCC merely on HFD. These data suggest that impaired gut insulin signaling increases the risk of HCC, which can be countered by restoration of insulin action in diabetes.

[Efficacy of Bright Greenish-Yellow Fluorescence Sorting on Mycotoxin-Contaminated Nutmeg Selection and Characterization of Fungal Flora Related to Mycotoxin Production].

Spices have been known to be highly contaminated commodities with mycotoxins. The Codex Alimentarius reports that nutmeg is particularly contaminated with aflatoxins (AFs) and ochratoxin A (OTA). To eliminate contaminated commodities, visual sorting and bright greenish-yellow fluorescence (BGYF) sorting are used as low-cost technologies in production engineering. In Indonesia, nutmeg is mainly sorted by visual sorting and classified into three grades according to the Indonesian national standards, with importers further defining their own brand as imported products. In this study, we evaluate the efficacy of BGYF sorting as a further selection method to reduce AFs and OTA using the importer's own brand. Further, the level of these mycotoxins and the relationship between fungal flora and mycotoxin contamination were examined. These results showed that BGYF sorting effectively reduces AFs as well as OTA. In addition, BGYF-positive groups were infected by Aspergillus sections Flavi, Nigri, and Circumdati.