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Among patients who are discharged from the Emergency Department (ED), about 3% return within 30 days. Revisits can be related to the nature of the disease, medical errors, and/or inadequate diagnoses and treatment during their initial ED visit. Identification of high-risk patient population can help device new strategies for improved ED care with reduced ED utilization. A decision tree based model with discriminant Electronic Medical Record (EMR) features was developed and validated, estimating patient ED 30 day revisit risk. A retrospective cohort of 293,461 ED encounters from HealthInfoNet (HIN), Maine's Health Information Exchange (HIE), between January 1, 2012 and December 31, 2012, was assembled with the associated patients' demographic information and one-year clinical histories before the discharge date as the inputs. To validate, a prospective cohort of 193,886 encounters between January 1, 2013 and June 30, 2013 was constructed. The c-statistics for the retrospective and prospective predictions were 0.710 and 0.704 respectively. Clinical resource utilization, including ED use, was analyzed as a function of the ED risk score. Cluster analysis of high-risk patients identified discrete sub-populations with distinctive demographic, clinical and resource utilization patterns. Our ED 30-day revisit model was prospectively validated on the Maine State HIN secure statewide data system. Future integration of our ED predictive analytics into the ED care work flow may lead to increased opportunities for targeted care intervention to reduce ED resource burden and overall healthcare expense, and improve outcomes.

Necrotizing enterocolitis (NEC) is a major source of neonatal morbidity and mortality. Since there is no specific diagnostic test or risk of progression model available for NEC, the diagnosis and outcome prediction of NEC is made on clinical grounds. The objective in this study was to develop and validate new NEC scoring systems for automated staging and prognostic forecasting. A six-center consortium of university based pediatric teaching hospitals prospectively collected data on infants under suspicion of having NEC over a 7-year period. A database comprised of 520 infants was utilized to develop the NEC diagnostic and prognostic models by dividing the entire dataset into training and testing cohorts of demographically matched subjects. Developed on the training cohort and validated on the blind testing cohort, our multivariate analyses led to NEC scoring metrics integrating clinical data. Machine learning using clinical and laboratory results at the time of clinical presentation led to two nec models: (1) an automated diagnostic classification scheme; (2) a dynamic prognostic method for risk-stratifying patients into low, intermediate and high NEC scores to determine the risk for disease progression. We submit that dynamic risk stratification of infants with NEC will assist clinicians in determining the need for additional diagnostic testing and guide potential therapies in a dynamic manner. http://translationalmedicine.stanford.edu/cgi-bin/NEC/index.pl and smartphone application upon request.

Background Estimating patient risk of future emergency department (ED) revisits can guide the allocation of resources, e.g. local primary care and/or specialty, to better manage ED high utilization patient populations and thereby improve patient life qualities. Methods We set to develop and validate a method to estimate patient ED revisit risk in the subsequent 6 months from an ED discharge date. An ensemble decision-tree-based model with Electronic Medical Record (EMR) encounter data from HealthInfoNet (HIN), Maine’s Health Information Exchange (HIE), was developed and validated, assessing patient risk for a subsequent 6 month return ED visit based on the ED encounter-associated demographic and EMR clinical history data. A retrospective cohort of 293,461 ED encounters that occurred between January 1, 2012 and December 31, 2012, was assembled with the associated patients’ 1-year clinical histories before the ED discharge date, for model training and calibration purposes. To validate, a prospective cohort of 193,886 ED encounters that occurred between January 1, 2013 and June 30, 2013 was constructed. Results Statistical learning that was utilized to construct the prediction model identified 152 variables that included the following data domains: demographics groups (12), different encounter history (104), care facilities (12), primary and secondary diagnoses (10), primary and secondary procedures (2), chronic disease condition (1), laboratory test results (2), and outpatient prescription medications (9). The c -statistics for the retrospective and prospective cohorts were 0.742 and 0.730 respectively. Total medical expense and ED utilization by risk score 6 months after the discharge were analyzed. Cluster analysis identified discrete subpopulations of high-risk patients with distinctive resource utilization patterns, suggesting the need for diversified care management strategies. Conclusions Integration of our method into the HIN secure statewide data system in real time prospectively validated its performance. It promises to provide increased opportunity for high ED utilization identification, and optimized resource and population management.

Background Necrotizing enterocolitis (NEC) is a major source of neonatal morbidity and mortality. Since there is no specific diagnostic test or risk of progression model available for NEC, the diagnosis and outcome prediction of NEC is made on clinical grounds. The objective in this study was to develop and validate new NEC scoring systems for automated staging and prognostic forecasting. Study design A six-center consortium of university based pediatric teaching hospitals prospectively collected data on infants under suspicion of having NEC over a 7-year period. A database comprised of 520 infants was utilized to develop the NEC diagnostic and prognostic models by dividing the entire dataset into training and testing cohorts of demographically matched subjects. Developed on the training cohort and validated on the blind testing cohort, our multivariate analyses led to NEC scoring metrics integrating clinical data. Results Machine learning using clinical and laboratory results at the time of clinical presentation led to two NEC models: (1) an automated diagnostic classification scheme; (2) a dynamic prognostic method for risk-stratifying patients into low, intermediate and high NEC scores to determine the risk for disease progression. We submit that dynamic risk stratification of infants with NEC will assist clinicians in determining the need for additional diagnostic testing and guide potential therapies in a dynamic manner. Algorithm availability http://translationalmedicine.stanford.edu/cgi-bin/NEC/index.pl and smartphone application upon request.

The majority of the investments went into solar PV power plant and wind farm development, while a few investments went into manufacturing or sales support. [...]the United States is currently experiencing close-to-flat energy demand growth. [...]increasing renewable energy generation capacity will need to be driven by a shift from fossil energy to renewable energy. On the other side of the Pacific, fast renewable energy development in China has caused many growing pains for the industry, such as the current inability of the electric grid to simultaneously assimilate all the installed wind power in China.

[...]the country has provided nearly $40 billion dollars to other countries’ solar and wind industries over the past decade. [...]direct investments overseas are seen as a way of retaining and expanding market share, typically through creating demand for the export of products. With its low-cost financing and a robust renewable energy industry, China has the potential to advance clean energy development in countries across the world.

Background Dysmenorrhea has a significant negative impact on teenagers’ quality of life, and its prevalence is increasing annually. Although studies have explored the factors affecting dysmenorrhea, it remains unclear how these factors interact with one another. This study aimed to explore the mediating role of binge eating and sleep quality between depression and dysmenorrhea. Methods This cross-sectional study recruited adolescent girls from the Health Status Survey of adolescents in Jinan, Shandong Province, and used multistage stratified cluster random sampling. Data was collected using an electronic questionnaire between March 9, 2022, and June 20, 2022. The Numerical Rating Scale and Cox Menstrual Symptom Scale were used to assess dysmenorrhea and the Patient Health Questionnaire-9 to assess depression. The mediation model was tested by Mplus 8.0, and the mediating effect was analyzed using the Product of Coefficients approach and the Bootstrap method. Results Among the total of 7818 adolescent girls included in this study, the prevalence of dysmenorrhea is 60.5%. A significant positive association was found between dysmenorrhea and depression. Binge eating and sleep quality seemingly mediate this association. The mediating effect of sleep quality (21.31%) was greater than that of binge eating (6.18%). Conclusions The findings of this study point in the right direction for preventing and treating dysmenorrhea in adolescents. For adolescent dysmenorrhea, mental health should be considered and proactive steps taken for educating adolescents on healthy lifestyles to reduce negative consequences of dysmenorrhea. Longitudinal studies on the causal link and influence mechanisms between depression and dysmenorrhea should be conducted in the future.

Lanthanum (La) ions are generally recognized to cause a decline of the catalytic performance for Cu-SSZ-13 zeolite in the selective catalytic reduction of NO x with NH 3 (NH 3 -SCR). Herein, we demonstrate that the NH 3 -SCR performance and hydrothermal stability of Cu-La-SSZ-13 zeolites can be enhanced with the incorporation of a small amount of La ions. The incorporation of La ions into SSZ-13 favors more Z 2 Cu 2+ ions at six-membered rings (6MRs), which results in higher hydrothermal stability of Cu-La-SSZ-13 than that of Cu-SSZ-13. The NO conversion of Cu-La-SSZ-13 achieves 5%–10% higher than that of Cu-SSZ-13 at the temperature range of 400–550 °C after hydrothermal ageing. While introducing excess amount of La ions in SSZ-13 may cause the formation of inactive CuO x , leading to the decrease of catalytic activity and hydrothermal stability. Notably, the low-temperature activity of Cu-SSZ-13 with a low Cu content (≤ 2 wt.%) can be boosted by the introduction of La ions, which is largely due to the improved redox ability of Cu active sites modified by La ions. Density functional theory (DFT) calculations indicate that La ions prefer to locate at eight-membered rings (8MRs) and thus promoting the formation of more Z 2 Cu 2+ ions. Meanwhile, the existence of La ions in SSZ-13 inhibits the dealumination process and the transformation from Z 2 Cu 2+ to CuO x , resulting in its enhanced hydrothermal stability. The present work sheds a new insight into the regulation of secondary metal cations for promoting high NH 3 -SCR performance over Cu-SSZ-13 zeolite catalysts.

Pseudostellaria heterophylla (Miq.) Pax is a well-known medicinal and ecologically important plant. Effectively distinguishing its different genetic resources is essential for its breeding. Plant chloroplast genomes can provide much more information than traditional molecular markers and provide higher-resolution genetic analyses to distinguish closely related planting materials. Here, seventeen P. heterophylla samples from Anhui, Fujian, Guizhou, Hebei, Hunan, Jiangsu, and Shandong provinces were collected, and a genome skimming strategy was employed to obtain their chloroplast genomes. The P. heterophylla chloroplast genomes ranged from 149,356 bp to 149,592 bp in length, and a total of 111 unique genes were annotated, including 77 protein-coding genes, 30 tRNA genes, and four rRNA genes. Codon usage analysis showed that leucine had the highest frequency, while UUU (encoding phenylalanine) and UGC (encoding cysteine) were identified as the most and least frequently used codons, respectively. A total of 75–84 SSRs, 16–21 short tandem repeats, and 27–32 long repeat structures were identified in these chloroplast genomes. Then, four primer pairs were revealed for identifying SSR polymorphisms. Palindromes are the dominant type, accounting for an average of 47.86% of all long repeat sequences. Gene orders were highly collinear, and IR regions were highly conserved. Genome alignment indicated that there were four intergenic regions ( psaI - ycf4 , ycf3 - trnS , ndhC - trnV , and ndhI - ndhG ) and three coding genes ( ndhJ , ycf1 , and rpl20 ) that were highly variable among different P. heterophylla samples. Moreover, 10 SNP/MNP sites with high polymorphism were selected for further study. Phylogenetic analysis showed that populations of Chinese were clustered into a monophyletic group, in which the non-flowering variety formed a separate subclade with high statistical support. In this study, the comparative analysis of complete chloroplast genomes revealed intraspecific variations in P. heterophylla and further supported the idea that chloroplast genomes could elucidate relatedness among closely related cultivation materials.

Background The Meyers-McKeever two-dimensional classification has been widely used in diagnosing and treating posterior cruciate ligament avulsion fractures since its introduction in 1959. However, because of image overlap and obscuration on knee radiographs, the type of avulsion fracture injury is often not identified, which can lead to misdiagnosis. CT has a higher overall confidence in size assessment, displacement, and degree of comminution of the posterior cruciate ligament avulsion fracture block compared with X-ray. Therefore, this study aims to propose a new classification system based on CT for posterior cruciate ligament avulsion fractures. Methods This multicenter study evaluated 523 patients with posterior cruciate ligament avulsion fractures admitted between July 2018 and July 2023 at nine medical centers. The new CT-based classification system, the Dong classification system, includes 2 major types and 7 subtypes. Furthermore, the inter- and intra-observer reliability of this classification system were compared to the Meyers - McKeever classification system by 4 observers (Kappa coefficient, K-value) to assess the reproducibility of the Dong-classification system for posterior cruciate ligament avulsion fractures. Results A total of 446 patients were eligible for inclusion criteria; 29 patients (6%) could not be classified through the Meyers - McKeever classification system, but all the cases were classifiable through the Dong classification system. The most common type of posterior cruciate ligament avulsion fracture is Type Ic, with 176 cases, accounting for 40%, followed by Type Ib, with 141 cases, accounting for 32%. Non-displaced fractures, including Type Ia and Type IIa, totaled 55 cases, representing 12%. The Meyers-McKeever classification had a mean K-value of 0.593 for inter-observer reliability and a mean K-value of 0.896 for intra-observer reliability. However, the Dong classification had a mean K-value of 0.768 for inter-observer reliability and 0.910 for intra-observer reliability. Conclusion The CT-based 3D Dong classification is the first proposed 3D CT classification of posterior cruciate ligament avulsion fractures, which helps analyze the type of avulsion fracture and whether it is combined with a tibial plateau fracture from a 3D level. This classification shows higher inter- and intra-observer reliability than the Meyers - McKeever classification and can be used as a complement to the Meyers - McKeever and Schatzker classifications.