Navigation-assisted anchor insertion in shoulder arthroscopy: a validity study

Background: This study aimed to compare conventional and navigation-assisted arthroscopic rotator cuff repair in terms of anchor screw insertion. Methods: The surgical performance of five operators while using the conventional and proposed navigation-assisted systems in a phantom surgical model and cadaveric shoulders were compared. The participating operators were divided into two groups, the expert group (n = 3) and the novice group (n = 2). In the phantom model, the experimental tasks included anchor insertion in the rotator cuff footprint and sutures retrieval. A motion analysis camera system was used to track the surgeons’ hand movements. The surgical performance metric included the total path length, number of movements, and surgical duration. In cadaveric experiments, the repeatability and reproducibility of the anchor insertion angle were compared among the three experts, and the feasibility of the navigation-assisted anchor insertion was validated. Results: No significant differences in the total path length, number of movements, and time taken were found between the conventional and proposed systems in the phantom model. In cadaveric experiments, however, the clustering of the anchor insertion angle indicated that the proposed system enabled both novice and expert operators to reproducibly insert the anchor with an angle close to the predetermined target angle, resulting in an angle error of < 2° (P = 0.0002). Conclusion: The proposed navigation-assisted system improved the surgical performance from a novice level to an expert level. All the experts achieved high repeatability and reproducibility for anchor insertion. The navigation-assisted system may help surgeons, including those who are inexperienced, easily familiarize themselves to of suture anchors insertion in the right direction by providing better guidance for anchor orientation. Level of evidence: A retrospective study (level 2). © 2020, The Author(s).1

New Era of Air Quality Monitoring from Space: Geostationary Environment Monitoring Spectrometer (GEMS)

GEMS will monitor air quality over Asia at unprecedented spatial and temporal resolution from GEO for the first time, providing column measurements of aerosol, ozone and their precursors (nitrogen dioxide, sulfur dioxide and formaldehyde). Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled for launch in late 2019 - early 2020 to monitor Air Quality (AQ) at an unprecedented spatial and temporal resolution from a Geostationary Earth Orbit (GEO) for the first time. With the development of UV-visible spectrometers at sub-nm spectral resolution and sophisticated retrieval algorithms, estimates of the column amounts of atmospheric pollutants (O3, NO2, SO2, HCHO, CHOCHO and aerosols) can be obtained. To date, all the UV-visible satellite missions monitoring air quality have been in Low Earth orbit (LEO), allowing one to two observations per day. With UV-visible instruments on GEO platforms, the diurnal variations of these pollutants can now be determined. Details of the GEMS mission are presented, including instrumentation, scientific algorithms, predicted performance, and applications for air quality forecasts through data assimilation. GEMS will be onboard the GEO-KOMPSAT-2 satellite series, which also hosts the Advanced Meteorological Imager (AMI) and Geostationary Ocean Color Imager (GOCI)-2. These three instruments will provide synergistic science products to better understand air quality, meteorology, the long-range transport of air pollutants, emission source distributions, and chemical processes. Faster sampling rates at higher spatial resolution will increase the probability of finding cloud-free pixels, leading to more observations of aerosols and trace gases than is possible from LEO. GEMS will be joined by NASA's TEMPO and ESA's Sentinel-4 to form a GEO AQ satellite constellation in early 2020s, coordinated by the Committee on Earth Observation Satellites (CEOS)

Loss of SP-A in the Lung Exacerbates Pulmonary Fibrosis

Idiopathic pulmonary fibrosis (IPF) is a devastating and common chronic lung disease that is pathologically characterized by the destruction of lung architecture and the accumulation of extracellular matrix in the lung. Previous studies have shown an association between lung surfactant protein (SP) and the pathogenesis of IPF, as demonstrated by mutations and the altered expression of SP in patients with IPF. However, the role of SP in the development of lung fibrosis is poorly understood. In this study, the role of surfactant protein A (SP-A) was explored in experimental lung fibrosis induced with a low or high dose of bleomycin (BLM) and CRISPR/Cas9-mediated genetic deletion of SP-A. Our results showed that lung SP-A deficiency in mice promoted the development of fibrotic damage and exacerbated inflammatory responses to the BLM challenge. In vitro experiments with murine lung epithelial LA-4 cells demonstrated that in response to transforming growth factor-β1 (TGF-β1), LA-4 cells had a decreased protein expression of SP-A. Furthermore, exogenous SP administration to LA-4 cells inhibited the TGF-β1-induced upregulation of fibrotic markers. Overall, these findings suggest a novel antifibrotic mechanism of SP-A in the development of lung fibrosis, which indicates the therapeutic potential of the lung SP-A in preventing the development of IPF

The impact of artificial intelligence on the reading times of radiologists for chest radiographs

Abstract Whether the utilization of artificial intelligence (AI) during the interpretation of chest radiographs (CXRs) would affect the radiologists’ workload is of particular interest. Therefore, this prospective observational study aimed to observe how AI affected the reading times of radiologists in the daily interpretation of CXRs. Radiologists who agreed to have the reading times of their CXR interpretations collected from September to December 2021 were recruited. Reading time was defined as the duration in seconds from opening CXRs to transcribing the image by the same radiologist. As commercial AI software was integrated for all CXRs, the radiologists could refer to AI results for 2 months (AI-aided period). During the other 2 months, the radiologists were automatically blinded to the AI results (AI-unaided period). A total of 11 radiologists participated, and 18,680 CXRs were included. Total reading times were significantly shortened with AI use, compared to no use (13.3 s vs. 14.8 s, p < 0.001). When there was no abnormality detected by AI, reading times were shorter with AI use (mean 10.8 s vs. 13.1 s, p < 0.001). However, if any abnormality was detected by AI, reading times did not differ according to AI use (mean 18.6 s vs. 18.4 s, p = 0.452). Reading times increased as abnormality scores increased, and a more significant increase was observed with AI use (coefficient 0.09 vs. 0.06, p < 0.001). Therefore, the reading times of CXRs among radiologists were influenced by the availability of AI. Overall reading times shortened when radiologists referred to AI; however, abnormalities detected by AI could lengthen reading times

Optimization of a chest computed tomography protocol for detecting pure ground glass opacity nodules: A feasibility study with a computer-assisted detection system and a lung cancer screening phantom.

OBJECTIVE:This study aimed to optimize computed tomography (CT) parameters for detecting ground glass opacity nodules (GGNs) using a computer-assisted detection (CAD) system and a lung cancer screening phantom. METHODS:A lung cancer screening phantom containing 15 artificial GGNs (-630 Hounsfield unit [HU], 2-10 mm) in the left lung was examined with a CT scanner. Three tube voltages of 80, 100, and 120 kVp were used in combination with five tube currents of 25, 50, 100, 200, and 400 mA; additionally, three slice thicknesses of 0.625, 1.25, and 2.5 mm and four reconstruction algorithms of adaptive statistical iterative reconstruction (ASIR-V) of 30, 60, and 90% were used. For each protocol, accuracy of the CAD system was evaluated for nine target GGNs of 6, 8, or 10 mm in size. The cut-off size was set to 5 mm to minimize false positives. RESULTS:Among the 180 combinations of tube voltage, tube current, slice thickness, and reconstruction algorithms, combination of 80 kVp, 200 mA, and 1.25-mm slice thickness with an ASIR-V of 90% had the best performance in the detection of GGNs with six true positives and no false positives. Other combinations had fewer than five true positives. In particular, any combinations with a 0.625-mm slice thickness had 0 true positive and at least one false positive result. CONCLUSION:Low-voltage chest CT with a thin slice thickness and a high iterative reconstruction algorithm improve the detection rate of GGNs with a CAD system in a phantom model, and may have potential in lung cancer screening

Feasibility of Coronary Artery Calcium Scoring on Dual-Energy Chest Computed Tomography: A Prospective Comparison with Electrocardiogram-Gated Calcium Score Computed Tomography

Rationale and Objectives: This study aimed to evaluate the feasibility of assessment using the coronary artery calcium score (CACS) in dual-energy chest computed tomography (CT). Materials and Methods: We prospectively enrolled 30 patients (19 male, 11 female; mean age, 63.73 ± 9.40 years) who clinically required contrast-enhanced chest CT. The patients underwent electrocardiogram-gated cardiac calcium-scoring CT with a slice thickness of 2.5 mm followed by a sequentially non-gated contrast-enhanced dual-energy chest CT using 140/80 fast kVp switching technology with slice thicknesses of 1.25 mm and 2.5 mm. Virtual unenhanced (VUE) images were then reconstructed from the dual-energy CT using the material suppressed iodine (MSI) technique. Results: The mean heart rates were 63.33 ± 12.01 beats per minute. The mean CACS on the coronary calcium-scoring CT was 361.1 ± 435.5, and CACSs of the VUE images were 76.8 ± 128.6 (2.5 mm slice) and 108.7 ± 165.1 (1.25 mm slice). The correlation coefficients of CACS between the coronary calcium-scoring CT with the VUE 2.5 mm and 1.25 mm images were 0.888 and 0.904, respectively. The inter-observer agreements for the calcium score measurement between the calcium-scoring CT, VUE 2.5 mm, and VUE 1.25 mm were 1.000, 0.999, and 1.000, respectively. Conclusions: In conclusion, assessment of CACS using dual-energy chest CT might be feasible when using MSI virtual unenhanced dual-energy chest CT images with a slice thickness of 1.25 mm

The Current Status of Medical Decision-Making for Dying Patients in a Medical Intensive Care Unit: A Single-Center Study

BACKGROUND: Many terminally ill patients die while receiving life-sustaining treatment. Recently, the discussion of life-sustaining treatment in intensive care units (ICUs) has increased. This study is aimed to evaluate the current status of medical decision-making for dying patients. METHODS: The medical records of patients who had died in the medical ICU from March 2011 to February 2012 were reviewed retrospectively. RESULTS: Eighty-nine patients were enrolled. Their mean age was 65.8 +/- 13.3 years and 73.0% were male. The most common diagnosis was acute respiratory failure, and the most common comorbidity was hemato-oncologic malignancy. Withdrawing or withholding life-sustaining treatment including do-not-resuscitate (DNR) orders was discussed for 64 (71.9%) patients. In almost all cases, the discussion involved a physician and the patient&apos;s family. No patient wrote advance directives themselves before ICU admission. Of the patients for whom withdrawing or withholding life-sustaining treatment was discussed, the decisions were recorded in formal consent documents in 36 (56.3%) cases, while 28 (43.7%) cases involved verbal consent. In patients granting verbal consent, death within one day of the consent was more common than in those with formal document consent (85.7% vs. 61.1%, p &amp;lt; 0.05). The most common demand was a DNR order. Patients died 2.7 +/- 1.0 days after the decision for removal of life-sustaining treatment. CONCLUSIONS: The decision-making for life-sustaining treatment of dying patients in the ICU very often involves conflict. There is a general need to heighten our sensitivity on the objective decision-making based on patient autonomy

Clinical utility of mono-exponential model diffusion weighted imaging using two b-values compared to the bi- or stretched exponential model for the diagnosis of biliary atresia in infant liver MRI.

PurposeTo investigate the clinical utility of mono-exponential model diffusion weighted imaging (DWI) using two b-values compared to the bi- or stretched exponential model to differentiate biliary atresia (BA) from non-BA in pediatric liver magnetic resonance imaging (MRI).MethodsPatients who underwent liver MRI with DWI for suspected BA from November 2017 to September 2018 were retrospectively included and divided into BA and non-BA groups. Laboratory results including γ-glutamyl transferase (γGT) were compared between the two groups using the Mann-Whitney U test and Fisher's exact test. The hepatic apparent diffusion coefficient (ADC) 10 using ten b-values and ADC 2 using two b-values were obtained from the mono-exponential model. The slow diffusion coefficient (D), fast diffusion coefficient (D*), and perfusion fraction (f) were obtained from the bi-exponential model. The distributed diffusion coefficient (DDC) and heterogeneity index (α) were measured from the stretched exponential model. Parameters were compared between the two groups using a linear mixed model and diagnostic performance was assessed using the area under the curve (AUC) analysis.ResultsFor 12 patients in the BA and five patients in the non-BA group, the ADC 10 (median 0.985 ×10-3 mm2/s vs. 1.332 ×10-3 mm2/s, p = 0.008), ADC 2 (median 0.987 ×10-3 mm2/s vs. 1.335 ×10-3 mm2/s, p = 0.017), D* (median 33.2 ×10-3 mm2/s vs. 55.3 ×10-3 mm2/s, p = 0.021), f (median 13.4%, vs. 22.1%, p = 0.009), and DDC (median 0.889 ×10-3 mm2/s vs. 1.323 ×10-3 mm2/s, p = 0.009) values were lower and the γGT (median 368.0 IU/L vs. 93.5 IU/L, p = 0.02) and α (median 0.699 vs. 0.556, p = 0.023) values were higher in the BA group. The AUC values for γGT (AUC 0.867 95% confidence interval [CI] 0.616-0.984), ADC 10 (AUC 0.963, 95% CI 0.834-0.998), ADC 2 (AUC 0.925, 95% CI 0.781-0.987), f (AUC 0.850, 95% CI 0.686-0.949), and DDC (AUC 0.925, 95% CI 0.781-0.987) were not significantly different, except for the D* and α values.ConclusionPatients with BA had lower ADC 10, ADC 2, D*, f, and DDC values and higher γGT and α values than those in the non-BA group. The diagnostic performance of ADC 2 using only two b-values showed excellent diagnostic performance and was not significantly different from that of γGT, ADC 10, f, and DDC for diagnosing BA