Background: The survival of patients with acute pulmonary embolism (PE) is dependent on timely and accurate diagnosis. Recently, artificial intelligence (AI) has emerged as a promising tool to enhance diagnostic performance. This study aimed to evaluate the diagnostic accuracy of a novel AI algorithm developed to detect acute PE on computed tomographic pulmonary angiography (CTPA). Methods: In this single-center observational study, we assessed the diagnostic performance of the AI software using 100 consecutive PE-positive and 100 consecutive PE-negative CTPA cases from patients with suspected PE. The ground truth was established by consensus among three board-certified experts. The AI results and radiology reports were compared against this ground truth. Results: The sensitivity and specificity of the AI were 95.0% (95% confidence interval (CI): 88.7–98.4%) and 99.0% (95% CI: 94.6–100.0%), respectively. Overall, 194 out of 200 cases (95 PE-positive and 99 PE-negative) were correctly identified by the AI, yielding an overall accuracy of 97.0% (95% CI: 93.6–98.9%). Interestingly, the AI software and radiology reports showed discordant patterns of misclassification. Conclusions: This AI software demonstrated excellent diagnostic performance for detecting PE on CTPA. Our research suggest a potential complementary role for AI as an adjunctive diagnostic tool.

Objectives:To evaluate Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) contamination in the rooms of Coronavirus disease 2019 (COVID-19) patients by integrating RNA quantification, viral culture, and post-culture cycle threshold (Ct)-based estimation of infectious titer, and to reassess fomite transmission risk relative to the human infectious dose. Methods:In nine high-ventilation, single-occupancy rooms, adhesive 36 cm2 samplers (stickers for hard surfaces; cloth patches for textiles) were placed on 14 environmental surfaces and on the upper back of night-shift nurses' scrubs for the first 24 hours after admission. N-gene copies were quantified by quantitative reverse transcription PCR (qRT-PCR); infectivity was assessed on VeroE6/TMPRSS2 cells. For culture-positive samples, culture-supernatant Ct values were converted to estimated infectious titers using a published regression. Distance, material, and Severity classification factors were analyzed in prespecified univariable models. Results:Of 134 specimens, 66 (49.3%) were RNA-positive, with a maximum of 8.48 log10 copies per sample, highest values on high-touch surfaces close to patients. Infectious virus was isolated from 4/134 specimens (3.0%). Estimated infectious titers were approximately 5.7 × 102-2.6 × 103 TCID50-eq per sample. Both RNA positivity and viral RNA copy numbers declined significantly with increasing distance from the patient's mouth. Conclusions:In high-ventilation rooms, environmental surface contamination was common and viable virus was occasionally present near patients. Estimated titers on some high-touch surfaces exceeded the reported human infectious dose, implying that fomite transmission may remain plausible under specific conditions. Prioritized cleaning/disinfection of high-touch surfaces near patients, along with consistent hand hygiene, are warranted, alongside ventilation/filtration measures.

BACKGROUND:Super-resolution deep learning reconstruction (SR-DLR) has been developed to reduce image noise and enhance spatial resolution beyond that of normal-resolution deep learning reconstruction (NR-DLR). PURPOSE:To compare the diagnostic performance of CT-derived fractional flow reserve (CT-FFR) against invasive FFR using NR-DLR and SR-DLR. METHODS:In this single-center retrospective study, 129 patients (mean age, 69 years ​±11 [SD]; 94 men) who underwent coronary CT angiography followed by invasive FFR between February 2022 and March 2025 were included. CT-FFR was computed using a mesh-free simulation model. Functionally significant stenosis was defined as FFR ≤0.80. The diagnostic performance of CT-FFR was compared between NR-DLR and SR-DLR using receiver operating characteristic curve analysis. RESULTS:The mean invasive FFR was 0.81 ​± ​0.08, and 70 out of 157 vessels (45 ​%) had FFR ≤0.80. The mean signal-to-noise ratio was higher with SR-DLR than with NR-DLR (33.3 ​± ​6.6 vs. 23.9 ​± ​4.5, p ​< ​0.001). The area under the receiver operating characteristic curve for detecting functionally significant stenosis was higher with SR-DLR (0.85; 95 ​% CI: 0.78, 0.91) than with NR-DLR (0.72; 95 ​% CI: 0.64, 0.81; p ​< ​0.001). Diagnostic accuracy was also higher with SR-DLR (85 ​%; 134 out of 157 vessels; 95 ​% CI: 79, 90) than with NR-DLR (74 ​%; 116 out of 157 vessels; 95 ​% CI: 66, 81; p ​< ​0.001). CONCLUSIONS:Compared with NR-DLR, SR-DLR enhances image quality and improves the diagnostic performance of CT-FFR for identifying functionally significant stenosis.

AIM:This retrospective cohort study aimed to investigate the impact of nutritionist-led dietary guidance on the prognosis of patients with acute myocardial infarction (AMI). METHODS:The cohort encompassed 446 consecutive patients with AMI in a single center who underwent emergency coronary angiography and percutaneous coronary interventions from September 1, 2015 to October 31, 2023. RESULTS:Based on defined caloric intake and macronutrient ratios, 54.7% of the patients received nutritional guidance during hospitalization. During a 39-month median follow-up, this study documented 24 (5.9%) and 6 (1.5%) cases of all-cause and cardiovascular mortality, respectively. Furthermore, among the components of the primary composite endpoint, 15 (3.7%), 17 (4.2%), and 20 (4.9%) cases of nonfatal stroke, nonfatal acute coronary syndrome, and hospitalizations for acute decompensated heart failure, respectively, were noted. Kaplan-Meier curves demonstrated that patients without nutritional guidance showed significantly higher incidence rates of mortality and primary composite endpoints. Considering multiple variables, the multivariate Cox regression model identified nutritional guidance as a significant predictor for long-term mortality but not for primary composite endpoints. Patients receiving nutritional guidance exhibited significantly improved albumin and C-reactive protein levels, with a trend toward better survival rate. CONCLUSION:This study suggests that by addressing dietary factors influencing inflammation and lipid metabolism, structured nutritional guidance can enhance prognosis in patients with AMI. To delineate the dietary components most beneficial for patients with AMI, further research is warranted.

Objective Myocardial energy metabolism is impaired in heart failure (HF), but the in vivo relationship between myocardial triglyceride (MTG) accumulation and fatty acid utilization remains unclear. Proton magnetic resonance spectroscopy ( 1 H-MRS) quantifies MTG, while iodine-123-β-methyl-p-iodophenyl-pentadecanoic acid ( 123 I-BMIPP) scintigraphy assesses myocardial fatty acid uptake and turnover (early heart-to-mediastinum ratio [H/M(e)] and washout rate [WR]). We investigated associations between MTG accumulation and 123 I-BMIPP-derived metabolic parameters in chronic HF patients. Methods Consecutive chronic HF patients (n = 33; mean age 62.8 ± 14.1 years) were prospectively enrolled between September 2020 and January 2024 during a stable phase. All patients underwent 123 I-BMIPP SPECT imaging (acquisition at 20 and 180 minutes post-injection) to calculate H/M(e) and WR, and cine cardiac MRI (1.5T) to measure left ventricular (LV) volumes, ejection fraction, and mass. MTG content was quantified by 1 H-MRS. Results WR did not correlate with any clinical or imaging parameters. In contrast, H/M(e) was significantly correlated with LV volume (r=-0.402, p = 0.020) and mass (r=-0.427, p = 0.013). MTG content was not related to 123 I-BMIPP-derived measures or LV function, but correlated significantly with immunoreactive insulin (IRI; r = 0.641, p = 0.0002) and HOMA-IR (r = 0.589, p = 0.001). No correlations were found among WR, H/M(e), and MTG. Conclusions Comprehensive assessment of myocardial fatty acid metabolism using 123 I-BMIPP scintigraphy and 1 H-MRS in patients with chronic heart failure suggested that WR, H/M(e), and MTG may reflect distinct metabolic stages. These parameters provide independent and complementary information and may contribute to a better understanding of heart failure pathophysiology, as well as to diagnostic support and prognostic assessment.

This study aimed to compare the diagnostic performance of CT-derived fractional flow reserve (CT-FFR) using model-based iterative reconstruction (MBIR) and high-resolution deep learning reconstruction (HR-DLR) images to detect functionally significant stenosis with invasive FFR as the reference standard. This single-center retrospective study included 79 consecutive patients (mean age, 70 ± 11 [SD] years; 57 male) who underwent coronary CT angiography followed by invasive FFR between February 2022 and March 2024. CT-FFR was calculated using a mesh-free simulation. The cutoff for functionally significant stenosis was defined as FFR ≤ 0.80. CT-FFR was compared with MBIR and HR-DLR using receiver operating characteristic curve analysis. The mean invasive FFR value was 0.81 ± 0.09, and 46 of 98 vessels (47

Radiation dose is a major concern in dynamic myocardial CT perfusion scan. The purpose of this study was to investigate the effect of reducing the sampling rate on quantitative and semi-quantitative values. This single-center prospective study included 45 patients with type 2 diabetes mellitus (mean age, 58 ± 10 years [SD]; 30 men). Stress and rest dynamic CT perfusion scans were performed every heartbeat for 25 s. Coronary flow reserve (CFR) was calculated as the ratio of stress to rest myocardial blood flow. The summed difference score (SDS) was evaluated using stress and rest myocardial blood flow. CFR and SDS values were compared using the original dataset (1RR) and datasets with reduced sampling rates of 2 and 3 RR intervals (2RR and 3RR). Simulated effective doses were also compared. The mean CFR using the 1RR dataset was 5.89 ± 2.53, unchanged using the 2RR dataset (5.67 ± 2.42, p = 0.08) and decreased to 5.47 ± 2.45 (p = 0.001) using the 3RR dataset. The median SDS (interquartile range) using the 1RR, 2RR and 3RR datasets were 0 (0, 5.75), 0.5 (0, 7) and 0 (0, 6), respectively, with no difference (p > 0.05). The effective doses simulated using the 2RR and 3RR data were 6.7 ± 1.4 mSv and 5.8 ± 1.3 mSv, respectively, significantly lower than the original dose (9.2 ± 1.8 mSv, p < 0.001). A sampling rate of 2RR might be feasible for both semi-quantitative and quantitative evaluation in dynamic myocardial CT perfusion exams.

Objectives:Acute aortic dissection (AAD) still remains a life-threatening medical emergency. However, only a few data exist on sex-related differences in patients with AAD over the recent years. Materials and Methods:The medical records of 192 consecutive patients admitted to Juntendo University Urayasu Hospital from January 2008 to December 2021 who had been diagnosed with AAD were retrospectively collected. Thereafter, sex-related differences in patient characteristics, type of dissection, maximal diameters of dissection, the onset-to-arrival time, and short-term mortality were determined. Results:A total of 164 patients were ultimately enrolled in this study. Compared to men, women were significantly older, were less obese, and had lower prevalence of metabolic syndrome. A significantly higher proportion of women than men underwent Stanford type A dissections (52% vs. 25%), except among those aged 50 years and younger. Women had significantly larger maximal aortic dissection diameters compared to men (43.3 ± 9.5 vs. 38.9 ± 7.2 mm). Women with type A dissection were older, had lower body mass index and albumin levels compared to men, and demonstrated poorer renal function than women with type B dissection. Conclusion:In the context of Japan's aging society, older, underweight women with chronic kidney disease and malnutrition may represent a high-risk population for Stanford type A aortic dissection.

Persistent COVID-19 is a well recognized issue of concern in patients with hematological malignancies. Such patients are not only at risk of mortality due to the infection itself, but are also at risk of suboptimal malignancy-related outcomes because of delays and terminations of chemotherapy. We report two lymphoma patients with heavily pretreated persistent COVID-19 in which ensitrelvir brought about radical changes in the clinical course leading to rapid remissions. Patient 1 was on ibrutinib treatment for mantle cell lymphoma when he developed COVID-19 pneumonia which was severe and ongoing for 2 months despite therapy with molnupiravir, multiple courses of remdesivir, one course of sotrovimab, tocilizumab, and steroids. Patient 2 was administered R-CHOP therapy for diffuse large B-cell lymphoma when he developed COVID-19 which was ongoing for a month despite treatment with multiple courses of remdesivir and one course of sotrovimab. A 5-day administration of ensitrelvir promptly resolved the persistent COVID-19 accommodated by negative conversions of RT-qPCR tests in both patients within days. Ensitrelvir is a novel COVID-19 therapeutic that accelerates viral clearance through inhibition of the main protease of SARS-CoV-2, 3-chymotrypsin-like protease, which is vital for viral replication. Ensitrelvir is a promising treatment approach for immunocompromised lymphoma patients suffering from persisting and severe COVID-19.

BACKGROUND:On-site computed tomography-derived fractional flow reserve (CT-FFR) is a feasible method for examining lesion-specific ischemia, and plaque analysis of coronary CT angiography (CCTA) is useful for predicting future cardiac events. However, their utility and association on a per-vessel level remain unclear. METHODS:We analyzed vessels showing 50-90 % stenosis on CCTA where planned revascularization was not performed after CCTA within 90 days. Relevant features, including CT-FFR and the plaque burden [necrotic core to the total plaque volume (% necrotic core), and non-calcified plaque (NCP) to vessel volume (% NCP)] using a novel algorithm for analyzing plaque to predict vessel-oriented composite outcomes (VOCO), including cardiac death, non-fatal myocardial infarction, and unplanned vessel-related revascularization, were assessed. RESULTS:In 256 patients (68.7 ± 9.4 years; 73.8 % male) with 354 vessels (10.5 % CT-FFR ≤ 0.80), VOCO occurred in 24 vessels (6.8 %) during a median follow-up of 3.6 years. Multivariable Cox analysis revealed CT-FFR ≤ 0.80 had the pronounced impact on VOCO, and moreover, higher % necrotic core and % NCP were independently associated with VOCO [adjusted hazard ratio 3.43 (95 % confidence interval 1.42-8.29) and 4.05 (1.19-13.71), respectively], especially for vessels with CT-FFR > 0.80. CONCLUSIONS:In vessels without planned revascularization, per-vessel CT-FFR ≤ 0.80 was the notable predictor of future cardiac events. Additionally, necrotic core volume and NCP were identified as independent predictors along with CT-FFR.

Background: It has been reported that zinc deficiency is related to severe inflammatory conditions especially those of respiratory diseases. However, studies that have examined the association between the serum zinc concentration and the severity of coronavirus disease 2019 (COVID-19) are still limited. The aim of this study was to assess that association in Japanese inpatients with COVID-19. Methods: This cross-sectional study, conducted from April 2020 to August 2021, included 467 eligible adult inpatients with COVID-19 whose serum zinc concentration was measured. Serum zinc concentration categories were defined as deficiency (< 60 mu g/dL), marginal deficiency (>= 60 to < 80 mu g/dL), and normal (>= 80 mu g/dL). Multivariate logistic regression was used to assess the association between serum zinc deficiency and severe COVID-19. Serum zinc concentration levels were compared between mild and other severities of COVID-19 by Dunnett's method. The P for trend was estimated using the Jonckheere-Terpstra test. Results: The proportions of subjects with serum zinc deficiency (< 60 mu g/dL) and marginal zinc deficiency (>= 60 to < 80 mu g/dL) were 39.5% and 54.3% in women, and 36.4% and 57.0% in men, respectively. Serum zinc deficiency was significantly associated with severe COVID-19 compared to marginal deficiency and normal (odds ratio = 3.60, 95% confidence interval = 1.60- 8.13, P < 0.01) after adjusting for confounders. An increase in severity of COVID-19 was inversely related to increases in serum zinc concentration levels (P < 0.01 for trend). Each serum zinc concentration of moderate and severe cases was also significantly lower compared with mild cases (P < 0.01). Conclusion: The severity of COVID-19 was significantly related to serum zinc concentration levels. These results suggest the importance of considering the serum zinc concentration when treating patients with COVID-19.

COVID-19 has a range of complications, from no symptoms to severe pneumonia. It can also affect multiple organs including the nervous system. COVID-19 affects the brain, leading to neurological symptoms such as delirium. Delirium, a sudden change in consciousness, can increase the risk of death and prolong the hospital stay. However, research on delirium prediction in patients with COVID-19 is insufficient. This study aimed to identify new risk factors that could predict the onset of delirium in patients with COVID-19 using machine learning (ML) applied to nursing records. This retrospective cohort study used natural language processing and ML to develop a model for classifying the nursing records of patients with delirium. We extracted the features of each word from the model and grouped similar words. To evaluate the usefulness of word groups in predicting the occurrence of delirium in patients with COVID-19, we analyzed the temporal changes in the frequency of occurrence of these word groups before and after the onset of delirium. Moreover, the sensitivity, specificity, and odds ratios were calculated. We identified (1) elimination-related behaviors and conditions and (2) abnormal patient behavior and conditions as risk factors for delirium. Group 1 had the highest sensitivity (0.603), whereas group 2 had the highest specificity and odds ratio (0.938 and 6.903, respectively). These results suggest that these parameters may be useful in predicting delirium in these patients. The risk factors for COVID-19-associated delirium identified in this study were more specific but less sensitive than the ICDSC (Intensive Care Delirium Screening Checklist) and CAM-ICU (Confusion Assessment Method for the Intensive Care Unit). However, they are superior to the ICDSC and CAM-ICU because they can predict delirium without medical staff and at no cost.

Purpose:To compare the objective and subjective image quality and diagnostic performance for coronary stenosis of normal-dose model-based iterative reconstruction and reduced-dose super-resolution deep learning reconstruction in coronary CT angiography. Materials and Methods:This single-center retrospective study included 52 patients (mean age, 68 years ± 10 [SD]; 41 men) who underwent serial coronary CT angiography and subsequent invasive coronary angiography between January and November 2022. The first 25 patients were scanned with a standard dose using model-based iterative reconstruction. The last 27 patients were scanned with a reduced dose using super-resolution deep learning reconstruction. Per-patient objective and subjective image qualities were compared. Diagnostic performance of model-based iterative reconstruction and super-resolution deep learning reconstruction to diagnose significant stenosis on coronary angiography was compared per-vessel using receiver operating characteristics curve analysis. Results:The median tube current of super-resolution deep learning reconstruction was lower than that of model-based iterative reconstruction (median [IQR], 890 mA [680, 900] vs. 900 mA [895, 900], P = 0.03). Image noise of super-resolution deep learning reconstruction was lower than that of model-based iterative reconstruction (14.6 Hounsfield units ± 1.3 vs. 22.7 Hounsfield units ± 4.4, P < .001). Super-resolution deep learning reconstruction improved the overall subjective image quality compared with model-based iterative reconstruction (median [IQR], 4 [3, 4] vs 3 [3, 3], P = .006). No difference in the area under the receiver operating characteristic curve in diagnosing coronary stenosis using super-resolution deep learning reconstruction (0.96; 95% CI, 0.92-0.99) and model-based iterative reconstruction (0.96; 95% CI, 0.92-0.98; P = .98) was observed. Conclusion:Our exploratory analysis suggests that super-resolution deep learning reconstruction could improve image quality with lower tube current settings than model-based iterative reconstruction with similar diagnostic performance to diagnose coronary stenosis in coronary CT angiography.

Whether malnutrition during the early phase of recovery from acute myocardial infarction (AMI) could be a predictor of mortality or morbidity has not been ascertained. We examined 289 AMI patients. All-cause mortality and composite endpoints (all-cause mortality, nonfatal stroke, nonfatal acute coronary syndrome, and hospitalization for acute decompensated heart failure) during the follow-up duration (median 39 months) were evaluated. There were 108 (37.8%) malnourished patients with GNRIs of less than 98 on arrival; however, malnourished patients significantly decreased to 91 (31.4%) during the convalescence period (p < 0.01). The incidence rates of mortality and primary composite endpoints were significantly higher in the malnourished group than in the well-nourished group both on arrival and during the convalescence period (All p < 0.05). Nutrition guidance significantly improved GNRI in a group of patients who were undernourished (94.7 vs. 91.0, p < 0.01). Malnourished patients on admission who received nutritional guidance showed similar all-cause mortality with well-nourished patients, whereas malnourished patients without receiving nutritional guidance demonstrated significantly worse compared to the others (p = 0.03). The assessment of GNRI during the convalescence period is a useful risk predictor for patients with AMI. Nutritional guidance may improve the prognoses of patients with poor nutritional status.

The COVID-19 antibody test was developed to investigate the humoral immune response to SARS-CoV-2 infection. In this study, we examined whether S antibody titers measured using the anti-SARS-CoV-2 IgG II Quant assay (S-IgG), a high-throughput test method, reflects the neutralizing capacity acquired after SARS-CoV-2 infection or vaccination. To assess the antibody dynamics and neutralizing potency, we utilized a total of 457 serum samples from 253 individuals: 325 samples from 128 COVID-19 patients including 136 samples from 29 severe/critical cases (Group S), 155 samples from 71 mild/moderate cases (Group M), and 132 samples from 132 health care workers (HCWs) who have received 2 doses of the BNT162b2 vaccinations. The authentic virus neutralization assay, the surrogate virus neutralizing antibody test (sVNT), and the Anti-N SARS-CoV-2 IgG assay (N-IgG) have been performed along with the S-IgG. The S-IgG correlated well with the neutralizing activity detected by the authentic virus neutralization assay (0.8904. of Spearman's rho value, p < 0.0001) and sVNT (0.9206. of Spearman's rho value, p < 0.0001). However, 4 samples (2.3%) of S-IgG and 8 samples (4.5%) of sVNT were inconsistent with negative results for neutralizing activity of the authentic virus neutralization assay. The kinetics of the SARS-CoV-2 neutralizing antibodies and anti-S IgG in severe cases were faster than the mild cases. All the HCWs elicited anti-S IgG titer after the second vaccination. However, the HCWs with history of COVID-19 or positive N-IgG elicited higher anti-S IgG titers than those who did not have it previously. Furthermore, it is difficult to predict the risk of breakthrough infection from anti-S IgG or sVNT antibody titers in HCWs after the second vaccination. Our data shows that the use of anti-S IgG titers as direct quantitative markers of neutralizing capacity is limited. Thus, antibody tests should be carefully interpreted when used as serological markers for diagnosis, treatment, and prophylaxis of COVID-19.

Abstract Aims To develop an artificial intelligence (AI)-model which enables fully automated accurate quantification of coronary artery calcium (CAC), using deep learning (DL) on electrocardiogram (ECG)-gated non-contrast cardiac computed tomography (gated CCT) images. Methods and results Retrospectively, 560 gated CCT images (including 60 synthetic images) performed at our institution were used to train AI-model, which can automatically divide heart region into five areas belonging to left main (LM), left anterior descending (LAD), circumflex (LCX), right coronary artery (RCA), and another. Total and vessel-specific CAC score (CACS) in each scan were manually evaluated. AI-model was trained with novel Heart-labelling method via DL according to the manual-derived results. Then, another 409 gated CCT images obtained in our institution were used for model validation. The performance of present AI-model was tested using another external cohort of 400 gated CCT images of Stanford Center for Artificial Intelligence of Medical Imaging by comparing with the ground truth. The overall accuracy of the AI-model for total CACS classification was excellent with Cohen’s kappa of k = 0.89 and 0.95 (validation and test, respectively), which surpasses previous research of k = 0.89. Bland-Altman analysis showed little difference in individual total and vessel-specific CACS between AI-derived CACS and ground truth in test cohort (mean difference [95% confidence interval] were 1.5 [−42.6, 45.6], −1.5 [−100.5, 97.5], 6.6 [−60.2, 73.5], 0.96 [−59.2, 61.1], and 7.6 [−134.1, 149.2] for LM, LAD, LCX, RCA, and total CACS, respectively). Conclusion Present Heart-labelling method provides a further improvement in fully automated, total, and vessel-specific CAC quantification on gated CCT.

Background Although lung ultrasound has been reported to be a portable, cost-effective, and accurate method to detect pneumonia, it has not been widely used because of the difficulty in its interpretation. Here, we aimed to investigate the effectiveness of a novel artificial intelligence-based automated pneumonia detection method using point-of-care lung ultrasound (AI-POCUS) for the coronavirus disease 2019 (COVID-19). Methods We enrolled consecutive patients admitted with COVID-19 who underwent computed tomography (CT) in August and September 2021. A 12-zone AI-POCUS was performed by a novice observer using a pocket-size device within 24 h of the CT scan. Fifteen control subjects were also scanned. Additionally, the accuracy of the simplified 8-zone scan excluding the dorsal chest, was assessed. More than three B-lines detected in one lung zone were considered zone-level positive, and the presence of positive AI-POCUS in any lung zone was considered patient-level positive. The sample size calculation was not performed given the retrospective all-comer nature of the study. Results A total of 577 lung zones from 56 subjects (59.4 ± 14.8 years, 23% female) were evaluated using AI-POCUS. The mean number of days from disease onset was 9, and 14% of patients were under mechanical ventilation. The CT-validated pneumonia was seen in 71.4% of patients at total 577 lung zones (53.3%). The 12-zone AI-POCUS for detecting CT-validated pneumonia in the patient-level showed the accuracy of 94.5% (85.1%– 98.1%), sensitivity of 92.3% (79.7%– 97.3%), specificity of 100% (80.6%– 100%), positive predictive value of 95.0% (89.6% - 97.7%), and Kappa of 0.33 (0.27–0.40). When simplified with 8-zone scan, the accuracy, sensitivity, and sensitivity were 83.9% (72.2%– 91.3%), 77.5% (62.5%– 87.7%), and 100% (80.6%– 100%), respectively. The zone-level accuracy, sensitivity, and specificity of AI-POCUS were 65.3% (61.4%– 69.1%), 37.2% (32.0%– 42.7%), and 97.8% (95.2%– 99.0%), respectively. Interpretation AI-POCUS using the novel pocket-size ultrasound system showed excellent agreement with CT-validated COVID-19 pneumonia, even when used by a novice observer.

Summary Triglyceride deposit cardiomyovasculopathy (TGCV) is an intractable disease characterized by massive triglyceride (TG) accumulation in the myocardium and coronary arteries caused by genetic or acquired dysfunction of adipose TG lipase (ATGL). A phase IIa trial has been conducted involving patients with idiopathic TGCV using CNT-01 (tricaprin/trisdecanoin) by the Japan TGCV study group, which showed that CNT-01 improved myocardial lipolysis as demonstrated by iodine-123-beta-methyl iodophenyl-pentadecanoic acid (BMIPP) scintigraphy. We evaluated changes in myocardial TG content using proton magnetic resonance spectroscopy (1H-MRS) before/after CNT-01. This report describes a male patient with hypertension, diabetes, angina pectoris, repeated percutaneous coronary intervention, chest pain, and exertional dyspnea that persisted despite standard medications and nitroglycerin. Idiopathic TGCV was diagnosed based on a remarkably reduced washout rate (WR) for BMIPP scintigraphy, high myocardial TG content on 1H-MRS, and no ATGL mutation. After an 8-week, 1.5 g/day CNT-01 administration, the WR of BMIPP increased from 5.1 to 13.3% and the myocardial TG content decreased from 8.4 to 5.9%, with no adverse effects. CNT-01 corrected myocardial lipolysis and subsequently reduced TG content in idiopathic TGCV as evaluated using 1H-MRS, which may be a useful, noninvasive evaluation of therapeutic efficacy. Learning points Triglyceride deposit cardiomyovasculopathy (TGCV) is an intractable disease characterized by massive triglyceride accumulation in the myocardium and coronary arteries, caused by genetic or acquired dysfunction of adipose triglyceride lipase. Japan TGCV Study Group developed a specific treatment for idiopathic TGCV using CNT-01 (tricaprin/trisdecanoin), a type of medium-chain fatty acid. CNT-01 corrected myocardial lipolysis and reduced TG content in idiopathic TGCV using proton magnetic resonance spectroscopy, which may be a useful noninvasive evaluation of therapeutic efficacy.