Genome assembly of autotetraploid Actinidia arguta highlights adaptive evolution and enables dissection of important economic traits.

Actinidia arguta, the most widely distributed Actinidia species and the second cultivated species in the genus, can be distinguished from the currently cultivated Actinidia chinensis on the basis of its small and smooth fruit, rapid softening, and excellent cold tolerance. Adaptive evolution of tetraploid Actinidia species and the genetic basis of their important agronomic traits are still unclear. Here, we generated a chromosome-scale genome assembly of an autotetraploid male A. arguta accession. The genome assembly was 2.77 Gb in length with a contig N50 of 9.97 Mb and was anchored onto 116 pseudo-chromosomes. Resequencing and clustering of 101 geographically representative accessions showed that they could be divided into two geographic groups, Southern and Northern, which first diverged 12.9 million years ago. A. arguta underwent two prominent expansions and one demographic bottleneck from the mid-Pleistocene climate transition to the late Pleistocene. Population genomics studies using paleoclimate data enabled us to discern the evolution of the species' adaptation to different historical environments. Three genes (AaCEL1, AaPME1, and AaDOF1) related to flesh softening were identified by multi-omics analysis, and their ability to accelerate flesh softening was verified through transient expression assays. A set of genes that characteristically regulate sexual dimorphism located on the sex chromosome (Chr3) or autosomal chromosomes showed biased expression during stamen or carpel development. This chromosome-level assembly of the autotetraploid A. arguta genome and the genes related to important agronomic traits will facilitate future functional genomics research and improvement of A. arguta.

A descriptive analysis of injury triage, surge of medical demand, and resource use in an university hospital after 8.12 Tianjin Port Explosion, China.

OBJECTIVE: The 8.12 Tianjin Port Explosion in 2015 caused heavy casualties. Pingjin Hospital, an affiliated college hospital in Tianjin, China participated in the rescue activities. This study aims to analyze the emergency medical response to this event and share experience with trauma physicians to optimize the use of medical resource and reduce mortality of critical patients. METHODS: As a trauma centre at the accident city, our hospital treated 298 patients. We retrospectively analyzed the data of emergency medical response, including injury triage, injury type, ICU patient flow, and medical resource use. RESULTS: There were totally 165 deaths, 8 missing, and 797 non-fatal injuries in this explosion. Our hospital treated 298 casualties in two surges of medical demand. The first one appeared at 1 h after explosion when 147 wounded were received and the second one at 4 h when 31 seriously injured patients were received, among whom 29 were transferred from Tianjin Emergency Center which was responsible for the scene injury triage. After reexamination and triage, only 11 cases were defined as critical ill patients. The over-triage rate reached as high as 62.07%. Seventeen patients underwent surgery and 17 patients were admitted to the intensive care unit. CONCLUSIONS: The present pre-hospital system is incomplete and may induce two surges of medical demand. The first one has a much larger number of casualties than predicted but the injury level is mild; while the second one has less wounded but almost all of them are critical patients. The over-triage rate is high. The hospital emergency response can be improved by an effective re-triage and implementation of a hospital-wide damage control.

A decision-tree model for predicting extubation outcome in elderly patients after a successful spontaneous breathing trial.

BACKGROUND: The commonly used single tests, based on a 1-time measurement of a physiologic variable, are often poorly predictive of tracheal extubation outcome because they examine only a single aspect of physiological function that affects the extubation outcome. We hypothesized that the construction of a decision-tree model, which includes multiple variables and considers the changes of these variables, may more accurately predict successful extubation. METHODS: This was a prospective observational study. From 2007 to 2008, 113 elderly patients in the medical intensive care unit on ventilation for >48 hours were enrolled. All patients underwent a 60-minute spontaneous breathing trial (SBT) [positive end-expiratory pressure of 5 cm H(2)O; automatic tube compensation, 100%]. Patients tolerating the trial were extubated immediately. The mouth occlusion pressure (P(0.1)), rapid shallow breathing index (RSBI,) and their combination (P(0.1) × RSBI) were recorded at the first, 30th, and 60th minute of the SBT. The changes in RSBI, which were determined at the 30th and 60th minute of the SBT (ΔRSBI30, ΔRSBI60), were assessed as the ratio (of RSBI30 or RSBI60) to RSBI at the first minute of the SBT. RESULTS: Twenty-two patients (19.5%) failed the SBT and were not included in the analysis, and 91 tolerated the trial and were extubated. At 48 hours, 73 (80.2%) remained extubated (successful extubation), and 18 (19.8%) required reintubation (extubation failure). Although theΔRSBI(30) was significantly higher in the extubation failure patients (118% ± 34%) than that in the successful extubation patients (93% ± 35%, P = 0.01), the receiver operating characteristic (ROC) analysis demonstrated that this index, with the threshold of <98%, presented poor performance in predicting successful extubation with area under the ROC curve (AUC) of only 0.76. The classification and regression-tree analysis selected 3 variables (P(0.1) × RSBI(30), RSBI(1), ΔRSBI(30)) and began with P(0.1) × RSBI(30). For patients with P(0.1) × RSBI-(30) >474 cmH(2)O*breaths/min/L, ΔRSBI(30) >98% defined a group including all failure patients but no success patients, whereas ΔRSBI(30) ≤98% included all success patients with no failure patients. For patients with P(0.1) × RSBI(30) ≤474 cm H(2)O*breaths/min/L, the combination of both a P(0.1) × RSBI(30) >328 cm H(2)O*breaths/min/L and RSBI(1) >112 breaths/min/L also defined a group including all success patients but no failure patients. Indeed, the diagnostic accuracy (DA) of the tree model, which was 89.1% with only the P(0.1) × RSBI(30) included, increased to 94.5% when both the P(0.1) × RSBI(30) and ▵RSBI(30) were included. The final tree model with the inclusion of all 3 discriminators could capture the successful extubation with diagnostic accuracy of 96.7%, AUC of 0.94 (95% confidence interval [CI], 0.87 to 0.98). CONCLUSION: If the current tree model is confirmed by a prospective study with a larger sample size, it would be useful in guiding physicians making extubation decisions in elderly medical intensive care unit patients.