BackgroundAtrial fibrillation (AF) is the most common arrhythmia in hypertrophic cardiomyopathy (HCM) and is associated with adverse outcomes in HCM patients. Although the left atrial (LA) diameter has consistently been identified as a strong predictor of AF in HCM patients, the relationship between LA dysfunction and AF still remains unclear. The aim of this study is to evaluate the LA function in patients with non-obstructive HCM (NOHCM) utilizing cardiovascular magnetic resonance feature tracking (CMR-FT).MethodsThirty-three patients with NOHCM and 28 healthy controls were studied. The global and regional LA function and left ventricular (LV) function were compared between the two groups. The following LA global functional parameters were quantitively analyzed: reservoir function (total ejection fraction [LA total EF], total strain [εs], peak positive strain rate [SRs]), conduit function (passive ejection fraction [LA passive EF], passive strain [εe], peak early-negative SR [SRe]), and booster pump function (active ejection fraction [LA active EF], active strain [εa], peak late-negative SR [SRa]). The LA wall was automatically divided into 6 segments: anterior, antero-roof, inferior, septal, septal-roof and lateral. Three LA strain parameters (εs, εe, εa) and their corresponding strain rate parameters (SRs, SRe, SRa) during the reservoir, conduit and booster pump LA phases were segmentally measured and analyzed.ResultsThe LA reservoir (LA total EF: 57.6 ± 8.2% vs. 63.9 ± 6.4%, p < 0.01; εs: 35.0 ± 12.0% vs. 41.5 ± 11.2%, p = 0.03; SRs: 1.3 ± 0.4 s− 1 vs. 1.5 ± 0.4 s− 1, p = 0.02) and conduit function (LA passive EF: 28.7 ± 9.1% vs. 37.1 ± 10.0%, p < 0.01; εe: 18.7 ± 7.9% vs. 25.9 ± 10.0%, p < 0.01; SRe: − 0.8 ± 0.3 s− 1 vs. -1.1 ± 0.4 s− 1, p < 0.01) were all impaired in patients with NOHCM when compared with healthy controls, while LA booster pump function was preserved. The LA segmental strain and strain rate analysis demonstrated that the εs, εe, SRe of inferior, SRs, SRe of septal-roof, and SRa of antero-roof walls (all p < 0.05) were all decreased in the NOHCM cohort. Correlations between LA functional parameters and LV conventional function and LA functional parameters and baseline parameters (age, body surface area and NYHA classification) were weak. The two strongest relations were between εs and LA total EF(r = 0.84, p < 0.01), εa and LA active EF (r = 0.83, p < 0.01).ConclusionsCompared with healthy controls, patients with NOHCM have LA reservoir and conduit dysfunction, and regional LA deformation before LA enlargement. CMR-FT identifies LA dysfunction and deformation at an early stage.

(a) Topography and major land features in central Asia. (b) Distribution of the 586 meteorological stations where observed precipitation is used in this study. The grey areas are the mountainous areas and the grid boxes are 0.5 × 0.5° resolution.

The "dry gets drier, wet gets wetter" (DGDWGW) paradigm well describes the pattern of precipitation changes over the oceans. However, it has also been usually considered as a simplified pattern of regional changes in wet/dry under global warming, although GCMs mostly do not agree this pattern over land. To examine the validity of this paradigm over land and evaluate how usage of drought indices estimated from different hydrological variables affects detection of regional wet/dry trends, we take the arid regions of central Asia as a case study area and estimate the drying and wetting trends during the period of 1950-2015 based on multiple drought indices. These indices include the standardized precipitation index (SPI), the standardized precipitation evapotranspiration index (SPEI), the Palmer drought severity index (PDSI) and selfcalibrating PDSI (sc_PDSI) with both the Thornthwaite (th) and Penman-Monteith (pm) equations in PDSI calculation (namely, PDSI_th, PDSI_pm, sc_PDSI_th and sc_PDSI_pm). The results show that there is an overall agreement among the indices in terms of inter-annual variation, especially for the PDSIs. All drought indices except SPI show a drying trend over the five states of central Asia (CAS5: including Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan). The four PDSIs and SPEI reveal a wetting tendency over the northwestern China (NW; including Xinjiang Uygur Autonomous Region and Hexi Corridor). The contrasting trends between CAS5 and NW can also be revealed in soil moisture (SM) variations. The nonlinear wet and dry variations are dominated by the 3-7 years oscillations for the indices. Relationships between the six indices and climate variables show the major drought drivers have regional features: with mean temperature (TMP), precipitation total (PRE) and potential evapotranspiration (PET) for CAS5, and PRE and PET for NW. Finally, our analyses indicate that the dry and wet variations are strongly correlated with the El Niño/Southern Oscillation (ENSO). K E Y W O R D Scentral Asia, dry and wet, SPI, SPEI, PDSI

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The outbreak of COVID‐19 coronavirus disease around the end of 2019 has become a pandemic. The preferred method for COVID‐19 detection is the real‐time polymerase chain reaction (RT‐PCR)‐based technique; however, it also has certain limitations, such as sample‐dependent procedures with a relatively high false negative ratio. We propose a safe and efficient method for screening COVID‐19 based on Raman spectroscopy. A total of 177 serum samples are collected from 63 confirmed COVID‐19 patients, 59 suspected cases, and 55 healthy individuals as a control group. Raman spectroscopy is adopted to analyze these samples, and a machine learning support‐vector machine (SVM) method is applied to the spectrum dataset to build a diagnostic algorithm. Furthermore, 20 independent individuals, including 5 asymptomatic COVID‐19 patients and 5 symptomatic COVID‐19 patients, 5 suspected patients, and 5 healthy patients, were sampled for external validation. In these three groups—confirmed COVID‐19, suspected, and healthy individuals—the distribution of statistically significant points of difference showed highly consistency for intergroups after repeated sampling processes. The classification accuracy between the COVID‐19 cases and the suspected cases is 0.87 (95% confidence interval [CI]: 0.85–0.88), and the accuracy between the COVID‐19 and the healthy controls is 0.90 (95% CI: 0.89–0.91), while the accuracy between the suspected cases and the healthy control group is 0.68 (95% CI: 0.67–0.73). For the independent test dataset, we apply the obtained SVM model to the classification of the independent test dataset to have all the results correctly classified. Our model showed that the serum‐level classification results were all correct for independent test dataset. Our results suggest that Raman spectroscopy could be a safe and efficient technique for COVID‐19 screening.