<strong>Introduction:</strong> To perform a Latin-American multicentric study for the prediction of benign and malignant thyroid nodules using Alpha Score, and to compare it with ACR TIRADS<sup><s...<strong>Introduction:</strong> To perform a Latin-American multicentric study for the prediction of benign and malignant thyroid nodules using Alpha Score, and to compare it with ACR TIRADS<sup><span style="white-space:nowrap;">®</span></sup> and Bethesda<sup><span style="white-space:nowrap;">®</span></sup>. <strong>Materials and Methods:</strong> A prospective multicentric study in 10 radiological hospitals and institutions of Latin America was performed and 818 thyroid nodules were analyzed by ultrasound and classified by using both ACR TIRADS<sup><span style="white-space:nowrap;">®</span></sup> and Alpha Score;fine-needle aspiration biopsy was performed when needed and classified with Bethesda. The relationships between predictors were analyzed by using binary logistic regression, statistical significance was defined by a p-value of 0.05, with an error margin of 4% and 95% confidence intervals. <strong>Results:</strong> Alpha Score 2.0 establishes five types of malignant predictors: microcalcifications, irregular borders, taller-than-wide shape, predominant solid texture and hypoechogenicity;a diameter equal to or greater than 1.5 cm adds an extra point to the final score. Resulting classification divides TNs into 4 categories: benign (1.9%), low suspicion (8.7%), mild suspicion (13.6%) and high suspicion (75.7%) of malignancy probability;sensitivity of 82%, specificity of 74%, the positive predictive value of 94%, the negative predictive value of 51%, the statistical accuracy of 81%, odds ratio of 108.89 and correlation with ACR TIRADS of 0.77 and Bethesda of 0.91.<strong> Conclusions: </strong>Alpha Score 2.0 has superior diagnostic accuracy and performance compared to the previously published Alpha Score and is able to classify a benign TN in a precise, safe and accurate way, avoiding unnecessary FNABs or determining the necessity of FNAB in cases of moderate to high suspicion of malignancy.展开更多
Objective: The aim of this study was to develop a simple predictor model to diagnose malignancy by using ultrasound features of thyroid nodules and the association with cytopathological diagnosis obtained by fine need...Objective: The aim of this study was to develop a simple predictor model to diagnose malignancy by using ultrasound features of thyroid nodules and the association with cytopathological diagnosis obtained by fine needle aspiration. Materials and Methods: The likelihood of malignancy from ultrasound features was assessed in thyroid nodules obtained by fine-needle aspiration biopsy (FNAB) according to cytopathological findings reported using Bethesda System. A score was developed depending on the presence of each ultrasound feature evaluated. Results: 429 nodules were assessed, 103 (24%) were malignant. The following ultrasound features were associated with malignancy, according to the logistic regression analysis and were assigned a score of 0, +1, +2 depending on the presence or absence of each one: hypoechogenicity, solid appearance, irregular margins, microcalcifications, absence of a halo, diameter of ≥10 mm and intranodular vascular flow. The area under the curve of the proposed model was 0.900, demonstrating its predictive capacity. 4 risk categories were stablished based on the score obtained. Malignant nodules scored higher than the benign nodules (7.24 ±1.87 vs. 3.74 ±1.83). Conclusions: The proposed predictive model demonstrated to be useful and easy to apply when stratifying thyroid nodule risk of malignancy using presented US features and applying the proposed risk categories to increase the accuracy at selecting nodules that need to be studied with FNA.展开更多
Exploring hydroclimatic variability and its driving mechanisms during the Holocene is essential for comprehending both historical and prospective responses of water resources to climatic shifts in Arid Central Asia(AC...Exploring hydroclimatic variability and its driving mechanisms during the Holocene is essential for comprehending both historical and prospective responses of water resources to climatic shifts in Arid Central Asia(ACA)region.However,debate persists regarding whether dryland lakes in this region exhibited aridification or humidification during the Holocene.Lopnur serves as the terminal lake of Tarim rivers during the Holocene,which offers an ideal natural laboratory to address the questions.In this study,a high-resolution chronological framework was established through precise radiocarbon dating.Multi-proxy analyses,including geochemical composition,grain size distributions,MS,LOI,and C/N ratios were conducted from a lacustrine profile in the core area of“Great ear”in the southern part of Lopnur catchment.These analyses enabled the reconstruction of hydrological dynamics and facilitated the disentanglement of independent signals linked to climate variability,runoff fluctuations,and lake-level changes.The results demonstrate that the MidHolocene(7800–4000 cal yr B.P.)was characterized by cold and humid conditions,resulting in elevated surface runoff and lake level.The Late Holocene(4000–1300 cal yr B.P.)experienced intensified aridification,characterized by reduced runoff and declining lake level.These evidences suggested a climatic regime of a distinctive alternation between“cold-wet”and“warm-dry”climatic regimes during the Mid-to-Late Holocene.Compared with the previous studies from adjacent regions,we speculate that the hydroclimatic evolution of Lopnur catchment possibly influenced by a complex interplay of large spatial scale forcings,including variations in annual insolation,greenhouse gas concentrations,and ice sheets,as well as the localized controls such as topographic features,vegetation cover,and cloud-radiative feedbacks.Our findings enhance the understanding of past climatic complexity and provide valuable insights for future water resource management strategies in drylands.展开更多
The cloud liquid water content(LWC)of the Tibetan Plateau(TP)is crucial for cloud water conversion.There are very few accurate observations of the LWC on the TP.This makes our estimation of the LWC and precipitation i...The cloud liquid water content(LWC)of the Tibetan Plateau(TP)is crucial for cloud water conversion.There are very few accurate observations of the LWC on the TP.This makes our estimation of the LWC and precipitation inaccurate on the TP.This paper introduces an indirect estimation scheme for the LWC profile obtained using a monochromatic radiative transfer model(MonoRTM)and microwave radiometers(MWRs)on the TP.The LWC estimation method was improved using an optimization of the difference between the simulated and observed brightness temperature(TB)at specific microwave channels that are sensitive to liquid water.The accuracy of the LWC estimation method depends heavily on the value of the cloud-base environment humidity criterion(CBEHC).Our experiment confirmed that the default CBEHC value of 95%is unsuitable for the TP.For the rainfall scenarios,the optimization method suggested the use of CBEHC values of 81%,76%,and 83%for Mangya,Nagqu,and Qamdo stations,respectively.The new CBEHC values produced a 30 K improvement in the TB simulation when compared to that of 95%CBEHC under rainfall conditions.This demonstrates the robustness of the LWC estimation scheme and its significant improvement in LWC estimation on the TP.For no-rainfall scenarios,the original Karstens model remained suitable for Nagqu station.An adjustment of the CBEHC to 94%for Mangya station resulted in a 1 K improvement of its TB simulation.Qamdo station had a 2.5 K improvement when the CBEHC was adjusted to 98%.The relationship between the simulated TB simulation error and the maximum relative humidity of the radiosonde profiles weakened after CBEHC optimization.Thus,the innovative method proposed in this article provides a practical estimation method for LWC in the TP region.This LWC estimation method has a higher potential for rainfall days than no-rainfall days.Under no-rainfall conditions,the accuracy of the proposed LWC estimation method is sensitive to TB errors included in its measurement and simulation.An accurate estimation of LWC for no-rainfall conditions relies more on the equipment and radiation model.展开更多
The advent of artificial intelligence(AI)in recent years has brought about transformative changes across various sectors,including healthcare.In nursing practice,education,and research,AI has the potential to revoluti...The advent of artificial intelligence(AI)in recent years has brought about transformative changes across various sectors,including healthcare.In nursing practice,education,and research,AI has the potential to revolutionize traditional methodologies,enhance learning experiences,and improve patient outcomes.Integrating AI tools and techniques can provide clinicians with smarter clinical solutions and nursing students with more robust and interactive learning environments,while also advancing research capabilities in the field.Despite the promising prospects,the incorporation of AI into nursing practice,education,and research presents several challenges.Firstly,there is a concern about the potential displacement of human roles in nursing due to automation,which may affect the human-centric nature of nursing care.Secondly,there are issues related to the lag in AI competency among nurses.Many current nursing curricula do not include comprehensive AI training,leading to a lack of preparedness in utilizing these technologies effectively.Lastly,the ethical implications of AI in healthcare,such as data privacy,patient consent,and the potential for biased algorithms,need to be meticulously addressed.To harness the full potential of AI in nursing practice,education,and research,several strategic actions including reinvesting in humanistic practice,revising core competencies and curriculum,and developing new ethical guidelines.展开更多
文摘<strong>Introduction:</strong> To perform a Latin-American multicentric study for the prediction of benign and malignant thyroid nodules using Alpha Score, and to compare it with ACR TIRADS<sup><span style="white-space:nowrap;">®</span></sup> and Bethesda<sup><span style="white-space:nowrap;">®</span></sup>. <strong>Materials and Methods:</strong> A prospective multicentric study in 10 radiological hospitals and institutions of Latin America was performed and 818 thyroid nodules were analyzed by ultrasound and classified by using both ACR TIRADS<sup><span style="white-space:nowrap;">®</span></sup> and Alpha Score;fine-needle aspiration biopsy was performed when needed and classified with Bethesda. The relationships between predictors were analyzed by using binary logistic regression, statistical significance was defined by a p-value of 0.05, with an error margin of 4% and 95% confidence intervals. <strong>Results:</strong> Alpha Score 2.0 establishes five types of malignant predictors: microcalcifications, irregular borders, taller-than-wide shape, predominant solid texture and hypoechogenicity;a diameter equal to or greater than 1.5 cm adds an extra point to the final score. Resulting classification divides TNs into 4 categories: benign (1.9%), low suspicion (8.7%), mild suspicion (13.6%) and high suspicion (75.7%) of malignancy probability;sensitivity of 82%, specificity of 74%, the positive predictive value of 94%, the negative predictive value of 51%, the statistical accuracy of 81%, odds ratio of 108.89 and correlation with ACR TIRADS of 0.77 and Bethesda of 0.91.<strong> Conclusions: </strong>Alpha Score 2.0 has superior diagnostic accuracy and performance compared to the previously published Alpha Score and is able to classify a benign TN in a precise, safe and accurate way, avoiding unnecessary FNABs or determining the necessity of FNAB in cases of moderate to high suspicion of malignancy.
文摘Objective: The aim of this study was to develop a simple predictor model to diagnose malignancy by using ultrasound features of thyroid nodules and the association with cytopathological diagnosis obtained by fine needle aspiration. Materials and Methods: The likelihood of malignancy from ultrasound features was assessed in thyroid nodules obtained by fine-needle aspiration biopsy (FNAB) according to cytopathological findings reported using Bethesda System. A score was developed depending on the presence of each ultrasound feature evaluated. Results: 429 nodules were assessed, 103 (24%) were malignant. The following ultrasound features were associated with malignancy, according to the logistic regression analysis and were assigned a score of 0, +1, +2 depending on the presence or absence of each one: hypoechogenicity, solid appearance, irregular margins, microcalcifications, absence of a halo, diameter of ≥10 mm and intranodular vascular flow. The area under the curve of the proposed model was 0.900, demonstrating its predictive capacity. 4 risk categories were stablished based on the score obtained. Malignant nodules scored higher than the benign nodules (7.24 ±1.87 vs. 3.74 ±1.83). Conclusions: The proposed predictive model demonstrated to be useful and easy to apply when stratifying thyroid nodule risk of malignancy using presented US features and applying the proposed risk categories to increase the accuracy at selecting nodules that need to be studied with FNA.
基金supported by the National Natural Science Foundation of China(No.42001084)the Major Science and Technology Projects of Xinjiang Uygur Autonomous Region(Nos.2022A03009-2,2022A03009)the Third Xinjiang Scientific Expedition Program(No.2022xjkk1303)。
文摘Exploring hydroclimatic variability and its driving mechanisms during the Holocene is essential for comprehending both historical and prospective responses of water resources to climatic shifts in Arid Central Asia(ACA)region.However,debate persists regarding whether dryland lakes in this region exhibited aridification or humidification during the Holocene.Lopnur serves as the terminal lake of Tarim rivers during the Holocene,which offers an ideal natural laboratory to address the questions.In this study,a high-resolution chronological framework was established through precise radiocarbon dating.Multi-proxy analyses,including geochemical composition,grain size distributions,MS,LOI,and C/N ratios were conducted from a lacustrine profile in the core area of“Great ear”in the southern part of Lopnur catchment.These analyses enabled the reconstruction of hydrological dynamics and facilitated the disentanglement of independent signals linked to climate variability,runoff fluctuations,and lake-level changes.The results demonstrate that the MidHolocene(7800–4000 cal yr B.P.)was characterized by cold and humid conditions,resulting in elevated surface runoff and lake level.The Late Holocene(4000–1300 cal yr B.P.)experienced intensified aridification,characterized by reduced runoff and declining lake level.These evidences suggested a climatic regime of a distinctive alternation between“cold-wet”and“warm-dry”climatic regimes during the Mid-to-Late Holocene.Compared with the previous studies from adjacent regions,we speculate that the hydroclimatic evolution of Lopnur catchment possibly influenced by a complex interplay of large spatial scale forcings,including variations in annual insolation,greenhouse gas concentrations,and ice sheets,as well as the localized controls such as topographic features,vegetation cover,and cloud-radiative feedbacks.Our findings enhance the understanding of past climatic complexity and provide valuable insights for future water resource management strategies in drylands.
基金supported by the National Natural Science Foundation of China(Grant Nos.41975009 and U2442213).
文摘The cloud liquid water content(LWC)of the Tibetan Plateau(TP)is crucial for cloud water conversion.There are very few accurate observations of the LWC on the TP.This makes our estimation of the LWC and precipitation inaccurate on the TP.This paper introduces an indirect estimation scheme for the LWC profile obtained using a monochromatic radiative transfer model(MonoRTM)and microwave radiometers(MWRs)on the TP.The LWC estimation method was improved using an optimization of the difference between the simulated and observed brightness temperature(TB)at specific microwave channels that are sensitive to liquid water.The accuracy of the LWC estimation method depends heavily on the value of the cloud-base environment humidity criterion(CBEHC).Our experiment confirmed that the default CBEHC value of 95%is unsuitable for the TP.For the rainfall scenarios,the optimization method suggested the use of CBEHC values of 81%,76%,and 83%for Mangya,Nagqu,and Qamdo stations,respectively.The new CBEHC values produced a 30 K improvement in the TB simulation when compared to that of 95%CBEHC under rainfall conditions.This demonstrates the robustness of the LWC estimation scheme and its significant improvement in LWC estimation on the TP.For no-rainfall scenarios,the original Karstens model remained suitable for Nagqu station.An adjustment of the CBEHC to 94%for Mangya station resulted in a 1 K improvement of its TB simulation.Qamdo station had a 2.5 K improvement when the CBEHC was adjusted to 98%.The relationship between the simulated TB simulation error and the maximum relative humidity of the radiosonde profiles weakened after CBEHC optimization.Thus,the innovative method proposed in this article provides a practical estimation method for LWC in the TP region.This LWC estimation method has a higher potential for rainfall days than no-rainfall days.Under no-rainfall conditions,the accuracy of the proposed LWC estimation method is sensitive to TB errors included in its measurement and simulation.An accurate estimation of LWC for no-rainfall conditions relies more on the equipment and radiation model.
文摘The advent of artificial intelligence(AI)in recent years has brought about transformative changes across various sectors,including healthcare.In nursing practice,education,and research,AI has the potential to revolutionize traditional methodologies,enhance learning experiences,and improve patient outcomes.Integrating AI tools and techniques can provide clinicians with smarter clinical solutions and nursing students with more robust and interactive learning environments,while also advancing research capabilities in the field.Despite the promising prospects,the incorporation of AI into nursing practice,education,and research presents several challenges.Firstly,there is a concern about the potential displacement of human roles in nursing due to automation,which may affect the human-centric nature of nursing care.Secondly,there are issues related to the lag in AI competency among nurses.Many current nursing curricula do not include comprehensive AI training,leading to a lack of preparedness in utilizing these technologies effectively.Lastly,the ethical implications of AI in healthcare,such as data privacy,patient consent,and the potential for biased algorithms,need to be meticulously addressed.To harness the full potential of AI in nursing practice,education,and research,several strategic actions including reinvesting in humanistic practice,revising core competencies and curriculum,and developing new ethical guidelines.
