Intraspecific divergence in a coastal plant, Euphorbia jolkinii, at a major biogeographic boundary in East Asia.

PREMISE: Quaternary climatic fluctuations and long-distance seed dispersal across the sea are critical factors affecting the distribution of coastal plants, but the spatiotemporal nature of population expansion and distribution change of East Asian coastal plants during this period are rarely examined. To explore this process, we investigated the genome-wide phylogenetic patterns of Euphorbia jolkinii Boiss. (Euphorbiaceae), which grows widely on littoral areas of Japan, Korea, and Taiwan. METHODS: We used plastome sequences and genome-wide single nucleotide polymorphisms in samples across the species range to reveal phylogeographic patterns and spatiotemporal distributional changes. We conducted ecological niche modeling for the present and the last glacial maximum (LGM). RESULTS: Genetic differentiation was observed between the northern and southern populations of E. jolkinii, separated by the major biogeographic boundary, the Tokara Gap. These two groups of populations differentiated during the glacial period and subsequently intermingled in the intermorainic areas of the central Ryukyu Islands after the LGM. Ecological niche models suggested that the potential range of E. jolkinii was restricted to southern Kyushu; however, it was widespread in the southern Ryukyu Islands and Taiwan during the LGM. CONCLUSIONS: This study provides evidence of genetic differentiation among coastal plant populations separated by the prominent biogeographical boundary. Although coastal plants are typically expected to maintain population connectivity through sea-drifted seed dispersal, our findings suggest that genetic differences may arise because of a combination of limited gene flow and changes in climate during the glacial period.

Usual Interstitial Pneumonia: Associations With Complications After Percutaneous Transthoracic Needle Lung Biopsy.

BACKGROUND. Changes in lung parenchyma elasticity in usual interstitial pneumonia (UIP) may increase the risk for complications after percutaneous transthoracic needle biopsy (PTNB) of the lung. OBJECTIVE. The purpose of this article was to investigate the association of UIP findings on CT with complications after PTNB, including pneumothorax, pneumothorax requiring chest tube insertion, and hemoptysis. METHODS. This retrospective single-center study included 4187 patients (mean age, 63.8 ± 11.9 [SD] years; 2513 men, 1674 women) who underwent PTNB between January 2010 and December 2015. Patients were categorized into a UIP group and non-UIP group by review of preprocedural CT. In the UIP group, procedural CT images were reviewed to assess for traversal of UIP findings by needle. Multivariable logistic regression analyses were performed to identify associations between the UIP group and needle traversal with postbiopsy complications, controlling for a range of patient, lesion, and procedural characteristics. RESULTS. The UIP and non-UIP groups included 148 and 4039 patients, respectively; in the UIP group, traversal of UIP findings by needle was observed in 53 patients and not observed in 95 patients. The UIP group, in comparison with the non-UIP group, had a higher frequency of pneumothorax (35.1% vs 17.9%, p < .001) and pneumothorax requiring chest tube placement (6.1% vs 1.5%, p = .001) and lower frequency of hemoptysis (2.0% vs 6.1%, p = .03). In multivariable analyses, the UIP group with traversal of UIP findings by needle, relative to the non-UIP group, showed independent associations with pneumothorax (OR, 5.25; 95% CI, 2.94-9.37; p < .001) and pneumothorax requiring chest tube placement (OR, 9.55; 95% CI, 3.74-24.38; p < .001). The UIP group without traversal of UIP findings by needle, relative to the non-UIP group, was not independently associated with pneumothorax (OR, 1.18; 95% CI, 0.71-1.97; p = .51) or pneumothorax requiring chest tube placement (OR, 1.08; 95% CI, 0.25-4.72; p = .92). The UIP group, with or without traversal of UIP findings by needle, was not independently associated with hemoptysis. No patient experienced air embolism or procedure-related death. CONCLUSION. Needle traversal of UIP findings is a risk factor for pneumothorax and pneumothorax requiring chest tube placement after PTNB. CLINICAL IMPACT. When performing PTNB in patients with UIP, radiologists should plan a needle trajectory that does not traverse UIP findings, when possible.

Genetic and demographic signatures accompanying the evolution of the selfing syndrome in Daphne kiusiana, an evergreen shrub.

BACKGROUND AND AIMS: The evolution of mating systems from outcrossing to self-fertilization is a common transition in flowering plants. This shift is often associated with the 'selfing syndrome', which is characterized by less visible flowers with functional changes to control outcrossing. In most cases, the evolutionary history and demographic dynamics underlying the evolution of the selfing syndrome remain poorly understood. METHODS: Here, we characterize differences in the demographic genetic consequences and associated floral-specific traits between two distinct geographical groups of a wild shrub, Daphne kiusiana, endemic to East Asia; plants in the eastern region (southeastern Korea and Kyushu, Japan) exhibit smaller and fewer flowers compared to those of plants in the western region (southwestern Korea). Genetic analyses were conducted using nuclear microsatellites and chloroplast DNA (multiplexed phylogenetic marker sequencing) datasets. KEY RESULTS: A high selfing rate with significantly increased homozygosity characterized the eastern lineage, associated with lower levels of visibility and herkogamy in the floral traits. The two lineages harboured independent phylogeographical histories. In contrast to the western lineage, the eastern lineage showed a gradual reduction in the effective population size with no signs of a severe bottleneck despite its extreme range contraction during the last glacial period. CONCLUSIONS: Our results suggest that the selfing-associated morphological changes in D. kiusiana are of relatively old origin (at least 100 000 years ago) and were driven by directional selection for efficient self-pollination. We provide evidence that the evolution of the selfing syndrome in D. kiusiana is not strongly associated with a severe population bottleneck.

75% radiation dose reduction using deep learning reconstruction on low-dose chest CT.

OBJECTIVE: Few studies have explored the clinical feasibility of using deep-learning reconstruction to reduce the radiation dose of CT. We aimed to compare the image quality and lung nodule detectability between chest CT using a quarter of the low dose (QLD) reconstructed with vendor-agnostic deep-learning image reconstruction (DLIR) and conventional low-dose (LD) CT reconstructed with iterative reconstruction (IR). MATERIALS AND METHODS: We retrospectively collected 100 patients (median age, 61 years [IQR, 53-70 years]) who received LDCT using a dual-source scanner, where total radiation was split into a 1:3 ratio. QLD CT was generated using a quarter dose and reconstructed with DLIR (QLD-DLIR), while LDCT images were generated using a full dose and reconstructed with IR (LD-IR). Three thoracic radiologists reviewed subjective noise, spatial resolution, and overall image quality, and image noise was measured in five areas. The radiologists were also asked to detect all Lung-RADS category 3 or 4 nodules, and their performance was evaluated using area under the jackknife free-response receiver operating characteristic curve (AUFROC). RESULTS: The median effective dose was 0.16 (IQR, 0.14-0.18) mSv for QLD CT and 0.65 (IQR, 0.57-0.71) mSv for LDCT. The radiologists' evaluations showed no significant differences in subjective noise (QLD-DLIR vs. LD-IR, lung-window setting; 3.23 ± 0.19 vs. 3.27 ± 0.22; P = .11), spatial resolution (3.14 ± 0.28 vs. 3.16 ± 0.27; P = .12), and overall image quality (3.14 ± 0.21 vs. 3.17 ± 0.17; P = .15). QLD-DLIR demonstrated lower measured noise than LD-IR in most areas (P < .001 for all). No significant difference was found between QLD-DLIR and LD-IR for the sensitivity (76.4% vs. 72.2%; P = .35) or the AUFROCs (0.77 vs. 0.78; P = .68) in detecting Lung-RADS category 3 or 4 nodules. Under a noninferiority limit of -0.1, QLD-DLIR showed noninferior detection performance (95% CI for AUFROC difference, -0.04 to 0.06). CONCLUSION: QLD-DLIR images showed comparable image quality and noninferior nodule detectability relative to LD-IR images.

Deep Learning for Detecting Pneumothorax on Chest Radiographs after Needle Biopsy: Clinical Implementation.

Background Accurate detection of pneumothorax on chest radiographs, the most common complication of percutaneous transthoracic needle biopsies (PTNBs), is not always easy in practice. A computer-aided detection (CAD) system may help detect pneumothorax. Purpose To investigate whether a deep learning-based CAD system can improve detection performance for pneumothorax on chest radiographs after PTNB in clinical practice. Materials and Methods A CAD system for post-PTNB pneumothorax detection on chest radiographs was implemented in an institution in February 2020. This retrospective cohort study consecutively included chest radiographs interpreted with CAD assistance (CAD-applied group; February 2020 to November 2020) and those interpreted before implementation (non-CAD group; January 2018 to January 2020). The reference standard was defined by consensus reading by two radiologists. The diagnostic accuracy for pneumothorax was compared between the two groups using generalized estimating equations. Matching was performed according to whether the radiograph reader and PTNB operator were the same using the greedy method. Results A total of 676 radiographs from 655 patients (mean age: 67 years ± 11; 390 men) in the CAD-applied group and 676 radiographs from 664 patients (mean age: 66 years ± 12; 400 men) in the non-CAD group were included. The incidence of pneumothorax was 18.2% (123 of 676 radiographs) in the CAD-applied group and 22.5% (152 of 676 radiographs) in the non-CAD group (P = .05). The CAD-applied group showed higher sensitivity (85.4% vs 67.1%), negative predictive value (96.8% vs 91.3%), and accuracy (96.8% vs 92.3%) than the non-CAD group (all P < .001). The sensitivity for a small amount of pneumothorax improved in the CAD-applied group (pneumothorax of <10%: 74.5% vs 51.4%, P = .009; pneumothorax of 10%-15%: 92.7% vs 70.2%, P = .008). Among patients with pneumothorax, 34 of 655 (5.0%) in the non-CAD group and 16 of 664 (2.4%) in the CAD-applied group (P = .009) required subsequent drainage catheter insertion. Conclusion A deep learning-based computer-aided detection system improved the detection performance for pneumothorax on chest radiographs after lung biopsy. © RSNA, 2022 See also the editorial by Schiebler and Hartung in this issue.

Value of a deep learning-based algorithm for detecting Lung-RADS category 4 nodules on chest radiographs in a health checkup population: estimation of the sample size for a randomized controlled trial.

OBJECTIVE: To explore the value of a deep learning-based algorithm in detecting Lung CT Screening Reporting and Data System category 4 nodules on chest radiographs from an asymptomatic health checkup population. METHODS: Data from an annual retrospective cohort of individuals who underwent chest radiographs for health checkup purposes and chest CT scanning within 3 months were collected. Among 3073 individuals, 118 with category 4 nodules on CT were selected. A reader performance test was performed using those 118 radiographs and randomly selected 51 individuals without any nodules. Four radiologists independently evaluated the radiographs without and with the results of the algorithm; and sensitivities/specificities were compared. The sample size needed to confirm the difference in detection rates was calculated, i.e., the number of true-positive radiographs divided by the total number of radiographs. RESULTS: The sensitivity of the radiologists substantially increased aided by the algorithm (38.8% [183/472] to 45.1% [213/472]; p < .001) without significant change in specificity (94.1% [192/204] vs. 92.2% [188/204]; p = .22). Pooled radiologists detected more nodules with the algorithm (32.0% [156/488] vs. 38.9% [190/488]; p < .001), without alteration of false-positive rates (0.09 [62/676], both). Pooled detection rates for the annual cohort were 1.49% (183/12,292) and 1.73% (213/12,292) without and with the algorithm, respectively. A sample size of 41,776 in each arm would be required to demonstrate significant detection rate difference with < 5% type I error and > 80% power. CONCLUSION: Although readers substantially increased sensitivity in detecting nodules on chest radiographs from a health checkup population aided by the algorithm, detection rate difference was only 0.24%, requiring a sample size >80,000 for a randomized controlled trial. KEY POINTS: • Aided by a deep learning algorithm, pooled radiologists improved their sensitivity in detecting Lung-RADS category 4 nodules on chest radiographs from a health checkup population (38.8% [183/472] to 45.1% [213/472]; p < .001), without increasing false-positive rate. • The prevalence of the Lung-RADS category 4 nodules was 3.8% (118/3073) on the population, resulting in only 0.24% increase of the detection rate for the radiologists with assistance of the algorithm. • To confirm the significant detection rate increase by a randomized controlled trial, a sample size of 84,000 would be required.

Artificial intelligence system for identification of false-negative interpretations in chest radiographs.

OBJECTIVES: To investigate the efficacy of an artificial intelligence (AI) system for the identification of false negatives in chest radiographs that were interpreted as normal by radiologists. METHODS: We consecutively collected chest radiographs that were read as normal during 1 month (March 2020) in a single institution. A commercialized AI system was retrospectively applied to these radiographs. Radiographs with abnormal AI results were then re-interpreted by the radiologist who initially read the radiograph ("AI as the advisor" scenario). The reference standards for the true presence of relevant abnormalities in radiographs were defined by majority voting of three thoracic radiologists. The efficacy of the AI system was evaluated by detection yield (proportion of true-positive identification among the entire examination) and false-referral rate (FRR, proportion of false-positive identification among all examinations). Decision curve analyses were performed to evaluate the net benefits of applying the AI system. RESULTS: A total of 4208 radiographs from 3778 patients (M:F = 1542:2236; median age, 56 years) were included. The AI system identified initially overlooked relevant abnormalities with a detection yield and an FRR of 2.4% and 14.0%, respectively. In the "AI as the advisor" scenario, radiologists detected initially overlooked relevant abnormalities with a detection yield and FRR of 1.2% and 0.97%, respectively. In a decision curve analysis, AI as an advisor scenario exhibited a positive net benefit when the cost-to-benefit ratio was below 1:0.8. CONCLUSION: An AI system could identify relevant abnormalities overlooked by radiologists and could enable radiologists to correct their false-negative interpretations by providing feedback to radiologists. KEY POINTS: • In consecutive chest radiographs with normal interpretations, an artificial intelligence system could identify relevant abnormalities that were initially overlooked by radiologists. • The artificial intelligence system could enable radiologists to correct their initial false-negative interpretations by providing feedback to radiologists when overlooked abnormalities were present.

Image quality of ultralow-dose chest CT using deep learning techniques: potential superiority of vendor-agnostic post-processing over vendor-specific techniques.

OBJECTIVE: To compare the image quality between the vendor-agnostic and vendor-specific algorithms on ultralow-dose chest CT. METHODS: Vendor-agnostic deep learning post-processing model (DLM), vendor-specific deep learning image reconstruction (DLIR, high level), and adaptive statistical iterative reconstruction (ASiR, 70%) algorithms were employed. One hundred consecutive ultralow-dose noncontrast CT scans (CTDIvol; mean, 0.33 ± 0.056 mGy) were reconstructed with five algorithms: DLM-stnd (standard kernel), DLM-shrp (sharp kernel), DLIR, ASiR-stnd, and ASiR-shrp. Three thoracic radiologists blinded to the reconstruction algorithms reviewed five sets of 100 images and assessed subjective noise, spatial resolution, distortion artifact, and overall image quality. They selected the most preferred algorithm among five image sets for each case. Image noise and signal-to-noise ratio were measured. Edge-rise-distance was measured at a pulmonary vessel, i.e., the distance between two points where attenuation was 10% and 90% of maximal intravascular intensity. The skewness of attenuation was calculated in homogeneous areas. RESULTS: DLM-stnd, followed by DLIR, showed the best subjective noise on both lung and mediastinal windows, while DLIR yielded the least measured noise (ps < .0001). Compared to DLM-stnd, DLIR showed inferior subjective spatial resolution on lung window and higher edge-rise-distance (ps < .0001). Additionally, DLIR showed the most frequent distortion artifacts and deviated skewness (ps < .0001). DLM-stnd scored the best overall image quality, followed by DLM-shrp and DLIR (mean score 3.89 ± 0.19, 3.68 ± 0.24, and 3.53 ± 0.33; ps < .001). Two among three readers preferred DLM-stnd on both windows. CONCLUSION: Although DLIR provided the best quantitative noise profile, DLM-stnd showed the best overall image quality with fewer artifacts and was preferred by two among three readers. KEY POINTS: • A vendor-agnostic deep learning post-processing algorithm applied to ultralow-dose chest CT exhibited the best image quality compared to vendor-specific deep learning algorithm and ASiR techniques. • Two out of three readers preferred a vendor-agnostic deep learning post-processing algorithm in comparison to vendor-specific deep learning algorithm and ASiR techniques. • A vendor-specific deep learning reconstruction algorithm yielded the least image noise, but showed significantly more frequent specific distortion artifacts and increased skewness of attenuation compared to a vendor-agnostic algorithm.

Prediction of visceral pleural invasion in lung cancer on CT: deep learning model achieves a radiologist-level performance with adaptive sensitivity and specificity to clinical needs.

OBJECTIVES: To develop and validate a preoperative CT-based deep learning model for the prediction of visceral pleural invasion (VPI) in early-stage lung cancer. METHODS: In this retrospective study, dataset 1 (for training, tuning, and internal validation) included 676 patients with clinical stage IA lung adenocarcinomas resected between 2009 and 2015. Dataset 2 (for temporal validation) included 141 patients with clinical stage I adenocarcinomas resected between 2017 and 2018. A CT-based deep learning model was developed for the prediction of VPI and validated in terms of discrimination and calibration. An observer performance study and a multivariable regression analysis were performed. RESULTS: The area under the receiver operating characteristic curve (AUC) of the model was 0.75 (95% CI, 0.67-0.84), which was comparable to those of board-certified radiologists (AUC, 0.73-0.79; all p > 0.05). The model had a higher standardized partial AUC for a specificity range of 90 to 100% than the radiologists (all p < 0.05). The high sensitivity cutoff (0.245) yielded a sensitivity of 93.8% and a specificity of 31.2%, and the high specificity cutoff (0.448) resulted in a sensitivity of 47.9% and a specificity of 86.0%. Two of the three radiologists provided highly sensitive (93.8% and 97.9%) but not specific (48.4% and 40.9%) diagnoses. The model showed good calibration (p > 0.05), and its output was an independent predictor for VPI (adjusted odds ratio, 1.07; 95% CI, 1.03-1.11; p < 0.001). CONCLUSIONS: The deep learning model demonstrated a radiologist-level performance. The model could achieve either highly sensitive or highly specific diagnoses depending on clinical needs. KEY POINTS: • The preoperative CT-based deep learning model demonstrated an expert-level diagnostic performance for the presence of visceral pleural invasion in early-stage lung cancer. • Radiologists had a tendency toward highly sensitive, but not specific diagnoses for the visceral pleural invasion.

Quantitative Assessment of Chest CT Patterns in COVID-19 and Bacterial Pneumonia Patients: a Deep Learning Perspective.

BACKGROUND: It is difficult to distinguish subtle differences shown in computed tomography (CT) images of coronavirus disease 2019 (COVID-19) and bacterial pneumonia patients, which often leads to an inaccurate diagnosis. It is desirable to design and evaluate interpretable feature extraction techniques to describe the patient's condition. METHODS: This is a retrospective cohort study of 170 confirmed patients with COVID-19 or bacterial pneumonia acquired at Yeungnam University Hospital in Daegu, Korea. The Lung and lesion regions were segmented to crop the lesion into 2D patches to train a classifier model that could differentiate between COVID-19 and bacterial pneumonia. The K-means algorithm was used to cluster deep features extracted by the trained model into 20 groups. Each lesion patch cluster was described by a characteristic imaging term for comparison. For each CT image containing multiple lesions, a histogram of lesion types was constructed using the cluster information. Finally, a Support Vector Machine classifier was trained with the histogram and radiomics features to distinguish diseases and severity. RESULTS: The 20 clusters constructed from 170 patients were reviewed based on common radiographic appearance types. Two clusters showed typical findings of COVID-19, with two other clusters showing typical findings related to bacterial pneumonia. Notably, there is one cluster that showed bilateral diffuse ground-glass opacities (GGOs) in the central and peripheral lungs and was considered to be a key factor for severity classification. The proposed method achieved an accuracy of 91.2% for classifying COVID-19 and bacterial pneumonia patients with 95% reported for severity classification. The CT quantitative parameters represented by the values of cluster 8 were correlated with existing laboratory data and clinical parameters. CONCLUSION: Deep chest CT analysis with constructed lesion clusters revealed well-known COVID-19 CT manifestations comparable to manual CT analysis. The constructed histogram features improved accuracy for both diseases and severity classification, and showed correlations with laboratory data and clinical parameters. The constructed histogram features can provide guidance for improved analysis and treatment of COVID-19.

Accelerated premature stress-induced senescence of young annulus fibrosus cells of rats by high glucose-induced oxidative stress.

PURPOSES: Diabetes mellitus (DM) is thought to be an important aetiological factor in intervertebral disc degeneration. A glucose-mediated increase of oxidative stress is a major causative factor in development of diseases associated with DM. The aim of this study was to investigate the effect of high glucose on mitochondrial damage, oxidative stress and senescence of young annulus fibrosus (AF) cells. METHODS: AF cells were isolated from four-week-old young rats, cultured, and placed in either 10 % FBS (normal control) or 10 % FBS plus two different high glucose concentrations (0.1 M and 0.2 M) (experimental conditions) for one and three days. We identified and quantified the mitochondrial damage and reactive oxygen species (ROS) (oxidative stress). We also identified and quantified the occurrence of senescence and telomerase activity. Finally, the expressions of proteins were determined related to replicative senescence (p53-p21-pRB) and stress-induced senescence (p16-pRB). RESULTS: Two high glucoses enhanced the mitochondrial damage in young rat AF cells, which resulted in an excessive generation of ROS in a dose- and time-dependent manner for one and three days compared to normal control. Two high glucose concentrations increased the occurrence of senescence of young AF cells in a dose- and time-dependent manner. Telomerase activity declined in a dose- and time-dependent manner. Both high glucose treatments increased the expressions of p16 and pRB proteins in young rat AF cells for one and three days. However, compared to normal control, the expressions of p53 and p21 proteins were decreased in young rat AF cells treated with both high glucoses for one and three days. CONCLUSIONS: The present study demonstrated that high glucose-induced oxidative stress accelerates premature stress-induced senescence in young rat AF cells in a dose- and time-dependent manner rather than replicative senescence. These results suggest that prevention of excessive generation of oxidative stress by strict blood glucose control could be important to prevent or to delay premature intervertebral disc degeneration in young patients with DM.

Slowly Growing Pulmonary Glandular Papilloma with Air Bronchogram: A Case Report.

Pulmonary glandular papilloma is a rare benign neoplasm that has not been studied extensively. This neoplasm presents as a solid nodule, consolidation, or mass, with or without atelectasis, and assessing the correlation between these findings and the risk of malignancy is challenging. A 60-year-old woman presented a solitary pulmonary nodule on screening chest radiography and chest computed tomography (CT). During the subsequent 2-year follow-up, CT showed a progressive increase in nodule size and an air bronchogram, suggesting malignancy. The patient underwent a right upper lobectomy, and the final diagnosis was glandular papilloma. Teaching point: Pulmonary glandular papilloma with growth and an air bronchogram.

Primary Ewing sarcoma of the kidney mimicking cystic papillary renal cell carcinoma in an older patient: A case report.

BACKGROUND: Ewing's sarcoma (ES) is a neuroectodermal tumor that typically occurs in the bones and soft tissues of children and young adults. Primary renal ES is rare; only a few cases and a small case series have been documented, and only four cases involved primary renal ES in older people (> 65 years old). CASE SUMMARY: Herein, we describe the radiological and pathological features of primary renal ES in an older person. A 76-year-old man complained of poor oral intake and was found to have a large cystic renal mass with indistinct margins on computed tomography. Ultrasound-guided biopsy revealed that the tumor contained small round blue cells. The patient underwent a right radical nephrectomy. The tumor cells showed diffuse membranous CD99, and nuclear friend leukemia integration 1 transcription factor and NK2 Homeobox 2. Fluorescence in situ hybridization revealed EWSR1 translocation. Postoperatively, 18F-fluorodeoxyglucose positron emission tomography revealed no evidence of metastasis. The patient was diagnosed with primary renal ES. Six months following the surgery, local recurrence and distant metastasis were observed. Primary renal ES is rare and often lethal in older individuals. The specific imaging findings are unknown, and treatment protocols have not been standardized. CONCLUSION: This case report describes the radiological and pathological features of primary renal ES in an older person.

Pioneering predictions of AKI and AKIN severity in burn patients: a comprehensive CBC approach.

This study aims to evaluate the utility of complete blood count (CBC) markers, in conjunction with the acute kidney injury network (AKIN) criteria, for the early detection, severity assessment, and prediction of mortality outcomes of acute kidney injury (AKI) in burn patients. The research seeks to fill existing gaps in knowledge and validate the cost-effectiveness of using CBC as a routine diagnostic tool for better management of AKI. The study was conducted at Hangang Sacred Heart Hospital. We performed a large-scale retrospective analysis of 2758 adult patients admitted to the burn intensive care unit over a 12-year period. Among these patients, AKI occurred in 1554 patients (56.3%). Based on the AKIN stage classification, 794 patients (28.8%) were categorized as AKIN 1, 494 patients (17.9%) as AKIN 2, and 266 patients (9.6%) as AKIN 3. We defined several ratio markers, including the Neutrophil-to-lymphocyte ratio (NLR), Platelet-to-lymphocyte ratio (PLR), Monocyte-to-lymphocyte ratio (MLR), systemic immune-inflammation index (SII), and various mean platelet volume (MPV) ratios. Our statistical analyses, conducted using the R programming language, revealed significant correlations between these markers and AKI severity. The AUC values for neutrophil count and WBC count were 0.790 and 0.793, respectively, followed by immature granulocyte count with an AUC of 0.727. For red blood cell (RBC)-related parameters, the AUC values for hematocrit (Hct), hemoglobin (Hb), and RBC count were 0.725, 0.713, and 0.713, respectively. Among the platelet-related parameters, only platelet distribution width (PDW) had an AUC of 0.677. Among the ratio markers, the NLR had the highest AUC at 0.772, followed by MPVNR and SII with AUC values of 0.700 and 0.680, respectively. The findings underscore the potential of CBC as an economical, routine test for AKI, thereby paving the way for enhanced patient outcomes. Our study suggests the utility of routine CBC tests, specifically WBC count and PLR, for predicting AKI and platelet, MPV, and NLR for mortality assessment in burn patients. These findings underscore the potential of easily accessible CBC tests in enhancing AKI management. However, further multicenter studies are needed for validation.

Big data insights into the diagnostic values of CBC parameters for sepsis and septic shock in burn patients: a retrospective study.

Sepsis and septic shock are prevalent and life-threatening complications in burn patients. Despite their severity, existing diagnostic methods are limited. This study aims to evaluate the efficacy of Complete Blood Count (CBC) and CBC ratio markers in diagnosing sepsis and septic shock, and in predicting mortality among burn patients. A cohort of 2757 burn patients was examined to ascertain the correlation between various CBC parameters, their ratios, and the incidence of sepsis and related mortality. Key markers analyzed included Red Cell Distribution Width (RDW), Mean Platelet Volume (MPV), Neutrophil-to-Lymphocyte Ratio (NLR), Platelet-to-Lymphocyte Ratio (PLR), and Mean Platelet Volume-to-Platelet Ratio (MPVPR). Our findings indicate that 65.5% of the patients developed sepsis, and 24.3% succumbed to their conditions. The CBC parameters RDW, MPV, NLR, MPVPR, and MPV-to-Lymphocyte Ratio (MPVLR) were significantly associated with sepsis and mortality. These markers showed considerable temporal variation and yielded an Area Under the Curve (AUC) of over 0.65 in an unadjusted Generalized Estimating Equations (GEE) model. This study underscores the potential of RDW, MPV, NLR, MPVPR, and MPVLR as vital prognostic tools for diagnosing sepsis, septic shock, and predicting mortality in burn patients. Although based on a single-center dataset, our results contribute to the enhancement of sepsis management by facilitating earlier, more precise diagnosis and treatment strategies. Further multi-center research is necessary to confirm these findings and broaden their applicability, establishing a solid base for future explorations in this crucial field.

Endobronchial Ultrasound Using Guide Sheath-Guided Transbronchial Lung Biopsy in Ground-Glass Opacity Pulmonary Lesions without Fluoroscopic Guidance.

Diagnosing ground-glass opacity (GGO) pulmonary lesions poses challenges. This study evaluates the utility of radial probe endobronchial ultrasound-guided transbronchial lung biopsy (RP-EBUS-TBLB) in diagnosing GGO pulmonary lesions. A total of 1651 RP-EBUS procedures were performed during the study period. This study analyzed 115 GGO lesions. The EBUS visualization yield was 80.1%. Of 115 lesions, 69 (60%) were successfully diagnosed. The average size of diagnosed lesions was significantly larger than that of undiagnosed lesions (21.9 ± 7.3 vs. 17.1 ± 6.6 mm, p < 0.001). Diagnostic yield varied by lesion size: 50.0% for lesions <20 mm, 65.1% for 20-30 mm lesions, and 85.7% for lesions >30 mm. The mixed blizzard sign on EBUS appeared in 60.6% of mixed GGO lesions, with no cases in pure GGO lesions. Multivariable analyses showed that lesion size (odds ratio [OR], 1.10; 95% confidence interval [CI], 1.00-1.16; p < 0.001) and mixed blizzard sign on EBUS (OR, 20.92; CI, 7.50-58.31; p < 0.001) were significantly associated with diagnostic success. Pneumothorax and hemoptysis occurred in 1.7% and 2.6% of patients, respectively. RP-EBUS-TBLB without fluoroscopic guidance is a viable diagnostic approach for GGO pulmonary lesions with acceptable complications.

Central venous catheter tip colonization and associated bloodstream infection in patients with severe burns under routine catheter changing.

BACKGROUND: Although routine changing of central venous catheters (CVCs) is commonly performed in patients with severe burns, information on pathogen colonization of the CVC tip and associated bloodstream infections (BSIs) is limited in those patients. METHODS: The medical records of 214 patients with severe burns who underwent routine CVC changing at 7-day intervals and their results of 686 pairs of CVC tips and concurrent blood cultures were retrospectively reviewed to evaluate the CVC colonization rate and associated BSI pathogens. RESULTS: Of the 686 CVCs, 137 (20.0%) were colonized by pathogens, and 81 (59.1%) of them had BSIs caused by the same pathogen. Nonflame burn (P = .002), total body surface area burn ≥30% (P = .004), femoral catheterization (P = .001), CVC changing during pre-existing BSI (P < .001), and renal replacement therapy (P = .017) were associated with catheter-related BSI in the multivariate analysis. Most BSIs were caused by Gram-negative bacteria (most commonly Acinetobacter baumannii, Klebsiella pneumoniae, and Pseudomonas aeruginosa). CONCLUSIONS: The CVC colonization rate in patients with severe burns and routine CVC changing was not high. Lengthening the CVC duration might be attempted in patients at a lower risk of catheter-related BSI although further prospective studies are necessary.

Sixty-four-fold data reduction of chest radiographs using a super-resolution convolutional neural network.

OBJECTIVES: To develop and validate a super-resolution (SR) algorithm generating clinically feasible chest radiographs from 64-fold reduced data. METHODS: An SR convolutional neural network was trained to produce original-resolution images (output) from 64-fold reduced images (input) using 128 × 128 patches (n = 127 030). For validation, 112 radiographs-including those with pneumothorax (n = 17), nodules (n = 20), consolidations (n = 18), and ground-glass opacity (GGO; n = 16)-were collected. Three image sets were prepared: the original images and those reconstructed using SR and conventional linear interpolation (LI) using 64-fold reduced data. The mean-squared error (MSE) was calculated to measure similarity between the reconstructed and original images, and image noise was quantified. Three thoracic radiologists evaluated the quality of each image and decided whether any abnormalities were present. RESULTS: The SR-images were more similar to the original images than the LI-reconstructed images (MSE: 9269 ± 1015 vs. 9429 ± 1057; P = .02). The SR-images showed lower measured noise and scored better noise level by three radiologists than both original and LI-reconstructed images (Ps < .01). The radiologists' pooled sensitivity with the SR-reconstructed images was not significantly different compared with the original images for detecting pneumothorax (SR vs. original, 90.2% [46/51] vs. 96.1% [49/51]; P = .19), nodule (90.0% [54/60] vs. 85.0% [51/60]; P = .26), consolidation (100% [54/54] vs. 96.3% [52/54]; P = .50), and GGO (91.7% [44/48] vs. 95.8% [46/48]; P = .69). CONCLUSIONS: SR-reconstructed chest radiographs using 64-fold reduced data showed a lower noise level than the original images, with equivalent sensitivity for detecting major abnormalities. ADVANCES IN KNOWLEDGE: This is the first study applying super-resolution in data reduction of chest radiographs.

Survivorship of implanted bone marrow-derived mesenchymal stem cells in acute rotator cuff tear.

BACKGROUND: This study examined whether a mesenchymal stem cells (MSCs)-seeded 3-dimensional construct into a tendon defect would promote cellular differentiation and matrix healing. MATERIALS AND METHODS: Bone marrow was harvested from the iliac crests of 2 male New Zealand White rabbits. The MSCs were cultured, and an open-cell polylactic acid (OPLA) scaffold was encapsulated with these cells. The injury model was a 5-mm × 5-mm-sized full-thickness window defect in the central part of each rotator cuff tendon. The defects on the right side were grafted with the autologous MSCs-seeded OPLA scaffold implant and a biodegradable suture. The same procedure was done on the left side, except a cell-free OPLA scaffold was used. Three rabbits were used as controls, without treatment of the tendon defect. Samples were harvested at 2, 4, and 6 weeks for analysis, which included evaluation of gross morphology, fluorescent analysis, histologic assessment, and immunohistochemistry studies. RESULTS: The expression of immunohistochemical stainings for collagen I was higher in the scaffold with MSCs than in the scaffold without MSCs. The expression of collagen II, however, was not different between the scaffolds with and without MSCs. CONCLUSIONS: Even though this is a short-term study, we demonstrated that many MSCs in the scaffold survived after implantation in an acute rabbit rotator cuff defect. Furthermore, the generation of type I collagen increased more in the scaffold with MSCs than it did in the scaffold without MSCs. MSCs are thought to promote tendon healing by producing type I collagen when they are applied at the tendon defect.