| 1. |
- Zhang, Huai, et al.
(författare)
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A global survey on the use of the international classification of diseases codes for metabolic dysfunction-associated fatty liver disease.
- 2024
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Ingår i: Hepatology international. - 1936-0541.
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Tidskriftsartikel (refereegranskat)abstract
- With the implementation of the 11th edition of the International Classification of Diseases (ICD-11) and the publication of the metabolic dysfunction-associated fatty liver disease (MAFLD) nomenclature in 2020, it is important to establish consensus for the coding of MAFLD in ICD-11. This will inform subsequent revisions of ICD-11.Using the Qualtrics XM and WJX platforms, questionnaires were sent online to MAFLD-ICD-11 coding collaborators, authors of papers, and relevant association members.A total of 890 international experts in various fields from 61 countries responded to the survey. We also achieved full coverage of provincial-level administrative regions in China. 77.1% of respondents agreed that MAFLD should be represented in ICD-11 by updating NAFLD, with no significant regional differences (77.3% in Asia and 76.6% in non-Asia, p=0.819). Over 80% of respondents agreed or somewhat agreed with the need to assign specific codes for progressive stages of MAFLD (i.e. steatohepatitis) (92.2%), MAFLD combined with comorbidities (84.1%), or MAFLD subtypes (i.e., lean, overweight/obese, and diabetic) (86.1%).This global survey by a collaborative panel of clinical, coding, health management and policy experts, indicates agreement that MAFLD should be coded in ICD-11. The data serves as a foundation for corresponding adjustments in the ICD-11 revision.
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| 2. |
- Yu, Wenjin, et al.
(författare)
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Deep Learning-Based Classification of Cancer Cell in Leptomeningeal Metastasis on Cytomorphologic Features of Cerebrospinal Fluid
- 2022
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Ingår i: Frontiers in Oncology. - : Frontiers Media SA. - 2234-943X. ; 12, s. 1-11
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Tidskriftsartikel (refereegranskat)abstract
- Background: It is a critical challenge to diagnose leptomeningeal metastasis (LM), given its technical difficulty and the lack of typical symptoms. The existing gold standard of diagnosing LM is to use positive cerebrospinal fluid (CSF) cytology, which consumes significantly more time to classify cells under a microscope.Objective: This study aims to establish a deep learning model to classify cancer cells in CSF, thus facilitating doctors to achieve an accurate and fast diagnosis of LM in an early stage.Method: The cerebrospinal fluid laboratory of Xijing Hospital provides 53,255 cells from 90 LM patients in the research. We used two deep convolutional neural networks (CNN) models to classify cells in the CSF. A five-way cell classification model (CNN1) consists of lymphocytes, monocytes, neutrophils, erythrocytes, and cancer cells. A four-way cancer cell classification model (CNN2) consists of lung cancer cells, gastric cancer cells, breast cancer cells, and pancreatic cancer cells. Here, the CNN models were constructed by Resnet-inception-V2. We evaluated the performance of the proposed models on two external datasets and compared them with the results from 42 doctors of various levels of experience in the human-machine tests. Furthermore, we develop a computer-aided diagnosis (CAD) software to generate cytology diagnosis reports in the research rapidly.Results: With respect to the validation set, the mean average precision (mAP) of CNN1 is over 95% and that of CNN2 is close to 80%. Hence, the proposed deep learning model effectively classifies cells in CSF to facilitate the screening of cancer cells. In the human-machine tests, the accuracy of CNN1 is similar to the results from experts, with higher accuracy than doctors in other levels. Moreover, the overall accuracy of CNN2 is 10% higher than that of experts, with a time consumption of only one-third of that consumed by an expert. Using the CAD software saves 90% working time of cytologists.Conclusion: A deep learning method has been developed to assist the LM diagnosis with high accuracy and low time consumption effectively. Thanks to labeled data and step-by-step training, our proposed method can successfully classify cancer cells in the CSF to assist LM diagnosis early. In addition, this unique research can predict cancer’s primary source of LM, which relies on cytomorphologic features without immunohistochemistry. Our results show that deep learning can be widely used in medical images to classify cerebrospinal fluid cells. For complex cancer classification tasks, the accuracy of the proposed method is significantly higher than that of specialist doctors, and its performance is better than that of junior doctors and interns. The application of CNNs and CAD software may ultimately aid in expediting the diagnosis and overcoming the shortage of experienced cytologists, thereby facilitating earlier treatment and improving the prognosis of LM.
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| 3. |
- Pitcher, Grant C., et al.
(författare)
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System controls of coastal and open ocean oxygen depletion
- 2021
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Ingår i: Progress in Oceanography. - : Elsevier BV. - 0079-6611. ; 197
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Forskningsöversikt (refereegranskat)abstract
- The epoch of the Anthropocene, a period during which human activity has been the dominant influence on climate and the environment, has witnessed a decline in oxygen concentrations and an expansion of oxygen-depleted environments in both coastal and open ocean systems since the middle of the 20th century. This paper provides a review of system-specific drivers of low oxygen in a range of case studies representing marine systems in the open ocean, on continental shelves, in enclosed seas and in the coastal environment. Identification of similar and contrasting responses within and across system types and corresponding oxygen regimes is shown to be informative both in understanding and isolating key controlling processes and provides a sound basis for predicting change under anticipated future conditions. Case studies were selected to achieve a balance in system diversity and global coverage. Each case study describes system attributes, including the present-day oxygen environment and known trends in oxygen concentrations over time. Central to each case study is the identification of the physical and biogeochemical processes that determine oxygen concentrations through the tradeoff between ventilation and respiration. Spatial distributions of oxygen and time series of oxygen data provide the opportunity to identify trends in oxygen availability and have allowed various drivers of low oxygen to be distinguished through correlative and causative relationships. Deoxygenation results from a complex interplay of hydrographic and biogeochemical processes and the superposition of these processes, some additive and others subtractive, makes attribution to any particular driver challenging. System-specific models are therefore required to achieve a quantitative understanding of these processes and of the feedbacks between processes at varying scales.
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| 4. |
- Shi, Zhi Sheng, et al.
(författare)
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Enrichment of Ni–Mo–V via pyrometallurgical reduction from spent hydrogenation catalysts and the multi-reaction mechanism
- 2023
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Ingår i: Rare Metals. - 1001-0521 .- 1867-7185. ; 42:8, s. 2700-2712
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Tidskriftsartikel (refereegranskat)abstract
- Spent hydrogenation catalysts are important secondary resources due to richness in the valuable metals of Ni, Mo and V. Recovery of valuable metals from spent catalysts has high economic value and environmental benefits since they are hazardous wastes as well. Traditional recycling processes including hydrometallurgical leaching and soda roasting-leaching have disadvantages such as generating large amounts of wastewater, long process, and low recovery efficiency of valuable metals. Thus, this paper proposed synergistic enrichment of Ni, Mo and V via pyrometallurgical reduction at 1400–1500 °C. The melting temperature and viscosity of slag were reduced through slag designing by software FactSage 7.1. The phase diagram of Al2O3-CaO-SiO2-Na2O-B2O3 was drawn, and low-temperature region (≤ 1300 °C) was selected as target slag composition. Ni, Mo, and V can be collaborative captured and recovered through the mutual solubility at molten state. Increasing the melting temperature and the amount of CaO, Na2O and C were conducive to improving the metals recovery rates. The kilogram-scale experiments were carried out, and the recovery efficiencies of Ni, Mo and V were 98.3%, 95.3% and 97.9% under optimized conditions: at 1500 °C, with the basicity of 1.0, 13.1 wt% SiO2, 7.0 wt% B2O3, 7.7 wt% Na2O and 20.0 wt% C. The distribution behavior of valuable metals was clarified by investigating the melting process of slag and the reduction in valuable metals. Ni was preferentially reduced and acted as a capturing agent, which captured other metals to form NiMoV alloys. Graphical abstract: [Figure not available: see fulltext.]
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| 5. |
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The Seventeenth Data Release of the Sloan Digital Sky Surveys : Complete Release of MaNGA, MaStar, and APOGEE-2 Data
- 2022
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Ingår i: Astrophysical Journal Supplement Series. - : Institute of Physics (IOP). - 0067-0049 .- 1538-4365. ; 259:2
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Tidskriftsartikel (refereegranskat)abstract
- This paper documents the seventeenth data release (DR17) from the Sloan Digital Sky Surveys; the fifth and final release from the fourth phase (SDSS-IV). DR17 contains the complete release of the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, which reached its goal of surveying over 10,000 nearby galaxies. The complete release of the MaNGA Stellar Library accompanies this data, providing observations of almost 30,000 stars through the MaNGA instrument during bright time. DR17 also contains the complete release of the Apache Point Observatory Galactic Evolution Experiment 2 survey that publicly releases infrared spectra of over 650,000 stars. The main sample from the Extended Baryon Oscillation Spectroscopic Survey (eBOSS), as well as the subsurvey Time Domain Spectroscopic Survey data were fully released in DR16. New single-fiber optical spectroscopy released in DR17 is from the SPectroscipic IDentification of ERosita Survey subsurvey and the eBOSS-RM program. Along with the primary data sets, DR17 includes 25 new or updated value-added catalogs. This paper concludes the release of SDSS-IV survey data. SDSS continues into its fifth phase with observations already underway for the Milky Way Mapper, Local Volume Mapper, and Black Hole Mapper surveys.
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