Cubic GeTe thermoelectrics have been historically focused on, while this work utilizes a slight symmetry-breaking strategy to converge the split valence bands, to reduce the lattice thermal conductivity and therefore realize a record thermoelectric performance, all enabled in GeTe in a rhombohedral structure. This not only promotes GeTe alloys as excellent materials for thermoelectric power generation below 800 K, but also expands low-symmetry materials as efficient thermoelectrics.
The development of efficient thermal energy management devices such as thermoelectrics and barrier coatings often relies on compounds having low lattice thermal conductivity (κl). Here, we present the computational discovery of a large family of 628 thermodynamically stable quaternary chalcogenides, AMM′Q3 (A = alkali/alkaline earth/post-transition metals; M/M′ = transition metals, lanthanides; Q = chalcogens) using high-throughput density functional theory (DFT) calculations. We validate the presence of low κl in these materials by calculating κl of several predicted stable compounds using the Peierls–Boltzmann transport equation. Our analysis reveals that the low κl originates from the presence of either a strong lattice anharmonicity that enhances the phonon-scatterings or rattler cations that lead to multiple scattering channels in their crystal structures. Our thermoelectric calculations indicate that some of the predicted semiconductors may possess high energy conversion efficiency with their figure-of-merits exceeding 1 near 600 K. Our predictions suggest experimental research opportunities in the synthesis and characterization of these stable, low κl compounds.
Background Diabetes mellitus is a prevalent metabolic disease characterized by chronic hyperglycemia. The avalanche of healthcare data is accelerating precision and personalized medicine. Artificial intelligence and algorithm-based approaches are becoming more and more vital to support clinical decision-making. These methods are able to augment health care providers by taking away some of their routine work and enabling them to focus on critical issues. However, few studies have used predictive modeling to uncover associations between comorbidities in ICU patients and diabetes. This study aimed to use Unified Medical Language System (UMLS) resources, involving machine learning and natural language processing (NLP) approaches to predict the risk of mortality. Methods We conducted a secondary analysis of Medical Information Mart for Intensive Care III (MIMIC-III) data. Different machine learning modeling and NLP approaches were applied. Domain knowledge in health care is built on the dictionaries created by experts who defined the clinical terminologies such as medications or clinical symptoms. This knowledge is valuable to identify information from text notes that assert a certain disease. Knowledge-guided models can automatically extract knowledge from clinical notes or biomedical literature that contains conceptual entities and relationships among these various concepts. Mortality classification was based on the combination of knowledge-guided features and rules. UMLS entity embedding and convolutional neural network (CNN) with word embeddings were applied. Concept Unique Identifiers (CUIs) with entity embeddings were utilized to build clinical text representations. Results The best configuration of the employed machine learning models yielded a competitive AUC of 0.97. Machine learning models along with NLP of clinical notes are promising to assist health care providers to predict the risk of mortality of critically ill patients. Conclusion UMLS resources and clinical notes are powerful and important tools to predict mortality in diabetic patients in the critical care setting. The knowledge-guided CNN model is effective (AUC = 0.97) for learning hidden features.
ObjectiveTo observe and evaluate the effect of a single intravenous injection of low-dose esketamine on post-operative pain and post-partum depression (PPD) in cesarean delivery patients.MethodsA total of 210 patients undergoing elective cesarean delivery under combined spinal-epidural anesthesia were divided into an esketamine group (Group S, n = 105) and a normal saline group (Group L, n = 105) by a random number table. At 5 min after childbirth, patients in the S group were given 0.25 mg/kg esketamine, whereas patients in the L group received an equal volume of saline. The primary outcomes included post-operative pain control according to the Numerical Rating Scale (NRS) and the incidence of PPD according to the Edinburgh Post-partum Depression Scale (EPDS). The secondary outcomes included analgesia-related adverse events and Ramsay sedation scores.ResultsThis clinical study was a prospective, randomized, double-blind trial. A total of 210 patients were enrolled in this study. The NRS pain (cough pain) score was lower in the S group than in the L group at 24 h after surgery (P = 0.016), and there was no significant difference in resting pain and mobilization pain at 4, 8, and 48 h after surgery or resting pain at 24 h after surgery between the two groups. There was no significant difference in the prevalence of PPD between the two groups on the day before delivery, or at the first week, the second week, or the fourth week after childbirth. At 5 min after dosing, the incidence of hallucinations (P < 0.001) and dizziness (P < 0.001) was higher in the S group than in the L group. At 15 min after dosing, the incidence of dizziness (P < 0.001) and nausea (P = 0.011) was higher in the S group than in the L group. The incidence of dizziness (P < 0.001) was higher in the S group than in the L group when leaving the operating room. The Ramsay scores in Group S were lower than in Group L at 5 min (p < 0.001), 15 min (p < 0.001) after dosing and at the time of leaving the operating room (p < 0.001).ConclusionIn this study, a single intravenous injection of 0.25 mg/kg esketamine did not reduce the incidence of depression at 1, 2, or 4 w post-partum but improved pain during exercise at 24 h post-operatively under the conditions of this clinical trial.Clinical trial registration[www.chictr.org.cn], identifier [ChiCTR2100054332].
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