Functional exchangeability of the nuclear localization signal (NLS) of capsid protein between PCV1 and PCV2 in vitro: Implications for the role of NLS in viral replication

<p>Abstract</p> <p>Background</p> <p>Porcine circovirus type 2 (PCV2) is believed to be the primary causative agent of postweaning multisystemic wasting syndrome (PMWS). It is supposed that capsid protein of PCV may contribute to replication control via interaction between Cap and Rep in the nucleoplasm. In this study, we described the construction and in vitro characterization of NLS-exchanged PCV DNA clones based on a PMWS-associated PCV2b isolate from China to determine the role of ORF2 NLS in PCV replication.</p> <p>Results</p> <p>The PCV1, PCV2, PCV2-NLS1 and PCV1-NLS2 DNA clone were generated by ligating a copy of respective genome in tandem with a partial duplication. The PCV2-NLS1 and PCV1-NLS2 DNA clone contained a chimeric genome in which the ORF2 NLS was exchanged. The four DNA clones were all confirmed to be infectious in vitro when transfected into PK-15 cells, as PCV capsid protein were expressed in approximately 10-20% of the transfected cells. The in vitro growth characteristics of the DNA clones were then determined and compared. All the recovered progeny viruses gave rise to increasing infectious titers during passages and were genetically stable by genomic sequencing. The chimeric PCV1-NLS2 and PCV2-NLS1 viruses had the final titers of about 10^4.2and 10^3.8TCID₅₀/ml, which were significantly lower than that of PCV1 and PCV2 (10^5.6and 10^5.0TCID₅₀/ml, respectively). When the ORF2 NLS exchanged, the mutant PCV2 (PCV2-NLS1) still replicated less efficiently and showed lower infectious titer than did PCV1 mutant (PCV1-NLS2), which was consistent with the distinction between wild type PCV1 and PCV2.</p> <p>Conclusions</p> <p>Recovery of the chimeiric PCV1-NLS2 and PCV2-NLS1 progeny viruses indicate that the nuclear localization signal sequence of capsid protein are functionally exchangeable between PCV1 and PCV2 with respect to the role of nuclear importing and propagation. The findings also reveal that ORF2 NLS play an accessory role in the replication of PCV. However, we found that ORF2 NLS was not responsible for the distinction of in vitro growth characteristic between PCV1 and PCV2. Further studies are required to determine the in vivo viral replication and pathogenicity of the NLS chimeric DNA clones.</p

Prognostic and recurrent significance of SII in patients with pancreatic head cancer undergoing pancreaticoduodenectomy

BackgroundTo investigate the clinical significance of preoperative inflammatory status in patients with pancreatic head carcinoma (PHC), we performed a single-center study to assess it.MethodWe studied a total of 164 patients with PHC undergoing PD surgery (with or without allogeneic venous replacement) from January 2018 to April 2022. Systemic immune-inflammation index (SII) was the most important peripheral immune index in predicting the prognosis according to XGBoost analysis. The optimal cutoff value of SII for OS was calculated according to Youden index based on the receiver operating characteristic (ROC) curve and the cohort was divided into Low SII group and High SII group. Demographic, clinical data, laboratory data, follow-up data variables were obtained and compared between the two groups. Kaplan-Meier curves, univariable and multivariable Cox regression models were used to determine the association between preoperative inflammation index, nutritional index and TNM staging system with OS and DFS respectively.ResultsThe median follow-up time was 16 months (IQR 23), and 41.4% of recurrences occurred within 1 year. The cutoff value of SII was 563, with a sensitivity of 70.3%, and a specificity of 60.7%. Peripheral immune status was different between the two groups. Patients in High SII group had higher PAR, NLR than those in Low SII group (P &lt;0.01, &lt;0.01, respectively), and lower PNI (P &lt;0.01). Kaplan–Meier analysis showed significantly poorer OS and DFS (P &lt; 0.001, &lt;0.001, respectively) in patients with high SII. By using the multivariable Cox regression model, high SII (HR, 2.056; 95% CI, 1.082–3.905, P=0.028) was significant predictor of OS. Of these 68 high-risk patients who recurrence within one year, patients with widespread metastasis had lower SII and worse prognosis (P &lt;0.01).ConclusionHigh SII was significantly associated with poor prognosis in patients with PHC. However, in patients who recurrence within one year, SII was lower in patients at TNM stage III. Thus, care needs to be taken to differentiate those high-risk patients

Cryoballoon ablation for paroxysmal atrial fibrillation in a case of persistent left superior vena cava

Abstract Background Atrial fibrillation (AF) usually originates from pulmonary veins (PVs) but can also be caused by pulmonary veins outside, such as the coronary sinus (CS), the superior vena cava (SVC), and the ligament of Marshall. Case presentation A 69-year-old male with a history of palpitations for 10 years was referred to our institute because of its recurrence for half a day. A dynamic electrocardiogram revealed sinus rhythm (SR) and paroxysmal AF. Echocardiography demonstrated normal cardiac structure, and physical examination results were unremarkable. However, computed tomography angiography (CTA) showed a persistent left superior vena cava (LSVC) but no indication of thrombosis in the left atria. A cryoablation catheter was inserted into the PV. After the PV was successfully isolated, AF was still observed. After cardioversion was synchronized, SR was detected, but AF occurred again in less than a minute. Finally, we observed ectopic atrial electrical activity originating from the LSVC and successfully ablated it. Conclusions An LSVC may be a substrate for initiating or perpetuating atrial arrhythmia. Cryoballoon ablation can help treat AF originating from the LSVC

The Electrochemical Performance of Al-Mg-Ga-Sn-xBi Alloy Used as the Anodic Material for Al-Air Battery in KOH Electrolytes

The effects of Bi content (0–0.12 wt.%) on the self-corrosion properties, the open-circuit potential, the electrochemical impedance spectroscopy, the potentiodynamic polarization curves, and the battery performances of Al-0.4Mg-0.05Sn-0.015Ga alloys were investigated; meanwhile, the microstructures and the post-discharge surfaces of the alloy were also examined by SEM. The results show that Bi can increase the amount of the precipitated phase containing Bi. The Al-0.4Mg-0.05Sn-0.015Ga-0.10Bi alloy exhibits the optimum electrochemical properties; it has the greatest negative open circuit potential of −1.780 V, the highest constant current discharge voltage of 1.287 V, and a minimum self-corrosion rate of 0.132 mL cm−2 min−1; a uniform corrosive surface is obtained after discharge

Using machine learning to detect the turbulent region in flow past a circular cylinder

Detecting the turbulent/non-turbulent interface is a challenging topic in turbulence research. In the present study, machine learning methods are used to train detectors for identifying turbulent regions in the flow past a circular cylinder. To ensure that the turbulent/non-turbulent interface is independent of the reference frame of coordinates and is physics-informed, we propose to use invariants of tensors appearing in the transport equations of velocity fluctuations, strain-rate tensor and vortical tensor as the input features to identify the flow state. The training samples are chosen from numerical simulation data at two Reynolds numbers, and 3900. Extreme gradient boosting (XGBoost) is utilized to train the detector, and after training, the detector is applied to identify the flow state at each point of the flow field. The trained detector is found robust in various tests, including the applications to the entire fields at successive snapshots and at a higher Reynolds number . The objectivity of the detector is verified by changing the input features and the flow region for choosing the turbulent training samples. Compared with the conventional methods, the proposed method based on machine learning shows its novelty in two aspects. First, no threshold value needs to be specified explicitly by the users. Second, machine learning can treat multiple input variables, which reflect different properties of turbulent flows, including the unsteadiness, vortex stretching and three-dimensionality. Owing to these advantages, XGBoost generates a detector that is more robust than those obtained from conventional methods