Delayed hepatic rupture post ultrasound-guided percutaneous liver biopsy: A case report.

RATIONALE: Hemorrhage, one of complications after liver biopsy, is often identified immediately after the procedure while delayed liver rupture is relatively rare. PATIENT CONCERNS: A 45-year-old woman was diagnosed with undetermined liver cirrhosis and abnormal liver function. To determine the etiology and severity of liver cirrhosis, ultrasound-guided liver biopsy was arranged. The patients did not complain any pain during the procedure. Ultrasound examination on postoperative day1 (POD 1) and MRI on POD 3 showed no evidence of hematoma and ascites. On POD 7, however, the patient was taken to the hospital with a sudden onset of pain in the right upper quadrant of the abdomen. DIAGNOSES: Contrast-enhanced computed tomography revealed liver rupture of right inferior segment of the liver with subcapsular hematoma. INTERVENTIONS: Patient was treated with infusion of 2-unit red blood cell suspension, fluid and hemostatics. OUTCOMES: The vital signs of the patient were stabilized after the therapy. The follow-up ultrasound 1 month later showed a shrunken subcapsular hematoma measuring 4.2 × 2.1 cm at the right lobe. LESSONS: Whenever a liver biopsy procedure is performed, the care should be taken to avoid puncturing those areas that may have liver incisure. Moreover, the patient need to rest for several days and to avoid heavy activities, which is one of the major risk factors for post-procedure bleeding

D2^2: Decentralized Training over Decentralized Data

While training a machine learning model using multiple workers, each of which collects data from their own data sources, it would be most useful when the data collected from different workers can be {\em unique} and {\em different}. Ironically, recent analysis of decentralized parallel stochastic gradient descent (D-PSGD) relies on the assumption that the data hosted on different workers are {\em not too different}. In this paper, we ask the question: {\em Can we design a decentralized parallel stochastic gradient descent algorithm that is less sensitive to the data variance across workers?} In this paper, we present D$^2$, a novel decentralized parallel stochastic gradient descent algorithm designed for large data variance \xr{among workers} (imprecisely, "decentralized" data). The core of D$^2$ is a variance blackuction extension of the standard D-PSGD algorithm, which improves the convergence rate from $O\left({\sigma \over \sqrt{nT}} + {(n\zeta^2)^{\frac{1}{3}} \over T^{2/3}}\right)$ to $O\left({\sigma \over \sqrt{nT}}\right)$ where $\zeta^{2}$ denotes the variance among data on different workers. As a result, D$^2$ is robust to data variance among workers. We empirically evaluated D$^2$ on image classification tasks where each worker has access to only the data of a limited set of labels, and find that D$^2$ significantly outperforms D-PSGD

Universal impurity-induced bound state in topological superfluids

We predict a universal mid-gap bound state in topological superfluids, induced by either non-magnetic or magnetic impurities in the strong scattering limit. This universal state is similar to the lowest-energy Caroli-de Gennes-Martricon bound state in a vortex core, but is bound to localized impurities. We argue that the observation of such a universal bound state can be a clear signature for identifying topological superfluids. We theoretically examine our argument for a spin-orbit coupled ultracold atomic Fermi gas trapped in a two-dimensional harmonic potential, by performing extensive self-consistent calculations within the mean-field Bogoliubov-de Gennes theory. A realistic scenario for observing universal bound state in ultracold $^{40}$K atoms is proposed.Comment: 5 pages + 4 figures; published in PRL; see the relevant study in 1D: Phys. Rev. A 87, 013622 (2013); see also the accompanying Physics Synopsis: http://physics.aps.org/synopsis-for/10.1103/PhysRevLett.110.02040

Diagnostic Accuracy of CEUS LI-RADS for the Characterization of Liver Nodules 20 mm or Smaller in Patients at Risk for Hepatocellular Carcinoma.

Background: American College of Radiology contrast agent–enhanced US Liver Imaging Reporting and Data System (CEUS LI-RADS) was developed to improve the accuracy of hepatocellular carcinoma (HCC) diagnosis at contrast agent2enhanced US. However, to the knowledge of the authors, the diagnostic accuracy of the system in characterization of liver nodules 20 mm or smaller has not been fully evaluated. Purpose: To evaluate the diagnostic accuracy of CEUS LI-RADS in diagnosing HCC in liver nodules 20 mm or smaller in patients at risk for HCC. Materials and Methods: Between January 2015 and February 2018, consecutive patients at risk for HCC presenting with untreated liver nodules 20 mm or less were enrolled in this retrospective double-reader study. Each nodule was categorized according to the CEUS LI-RADS and World Federation for Ultrasound in Medicine and Biology (WFUMB)–European Federation of Societies for Ultrasound in Medicine and Biology (EFSUMB) criteria. Diagnostic performance of CEUS LI-RADS and WFUMB-EFSUMB characterization was evaluated by using tissue histologic analysis, multiphase contrast-enhanced CT and MRI, and imaging follow-up as reference standard and compared by using McNemar test. Results: The study included 175 nodules (mean diameter, 16.1 mm 6 3.4) in 172 patients (mean age, 51.8 years 6 10.6; 136 men). The sensitivity of CEUS LR-5 versus WFUMB-EFSUMB criteria in diagnosing HCC was 73.3% (95% confidence inter-val [CI]: 63.8%, 81.5%) versus 88.6% (95% CI: 80.9%, 94%), respectively (P, .001). The specificity of CEUS LR-5 versus WFUMB-EFSUMB criteria was 97.1% (95% CI: 90.1%, 99.7%) versus 87.1% (95% CI: 77%, 94%), respectively (P = .02). No malignant lesions were found in CEUS LR-1 and LR-2 categories. Only two nodules (of 41; 5%, both HCC) were malignant in CEUS LR-3 category. The incidences of HCC in CEUS LR-4, LR-5, and LR-M were 48% (11 of 23), 98% (77 of 79), and 75% (15 of 20), respectively. Two of 175 (1.1%) histologic analysis2confirmed intrahepatic cholangiocarcinomas were categorized as CEUS LR-M by CEUS LI-RADS and misdiagnosed as HCC by WFUMB-EFSUMB criteria. Conclusion: The contrast-enhanced US Liver Imaging Reporting and Data System (CEUS LI-RADS) algorithm was an effective tool for characterization of small (≤20 mm) liver nodules in patients at risk for hepatocellular carcinoma (HCC). Compared with World Federation for Ultrasound in Medicine and Biology2European Federation of Societies for Ultrasound in Medicine and Biology criteria, CEUS LR-5 demonstrated higher specificity for diagnosing small HCCs with lower sensitivity