Development and Validation of a UPLC-MS/MS Method to Monitor Cephapirin Excretion in Dairy Cows following Intramammary Infusion

Cephapirin, a cephalosporin antibiotic, is used by the majority of dairy farms in the US. Fecal and urinary excretion of cephapirin could introduce this compound into the environment when manure is land applied as fertilizer, and may cause development of bacterial resistance to antibiotics critical for human health. The environmental loading of cephapirin by the livestock industry remains un-assessed, largely due to a lack of appropriate analytical methods. Therefore, this study aimed to develop and validate a cephapirin quantification method to capture the temporal pattern of cephapirin excretion in dairy cows following intramammary infusion. The method includes an extraction with phosphate buffer and methanol, solid-phase extraction (SPE) clean-up, and quantification using ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). The LOQ values of the developed method were 4.02 µg kg⁻¹ and 0.96 µg L⁻¹ for feces and urine, respectively. This robust method recovered >60% and >80% cephapirin from spiked blank fecal and urine samples, respectively, with acceptable intra- and inter-day variation (<10%). Using this method, we detected trace amounts (µg kg⁻¹) of cephapirin in dairy cow feces, and cephapirin in urine was detected at very high concentrations (133 to 480 µg L⁻¹). Cephapirin was primarily excreted via urine and its urinary excretion was influenced by day (P = 0.03). Peak excretion (2.69 mg) was on day 1 following intramammary infusion and decreased sharply thereafter (0.19, 0.19, 0.08, and 0.17 mg on day 2, 3, 4, and 5, respectively) reflecting a quadratic pattern of excretion (Quadratic: P = 0.03). The described method for quantification of cephapirin in bovine feces and urine is sensitive, accurate, and robust and allowed to monitor the pattern of cephapirin excretion in dairy cows. This data will help develop manure segregation and treatment methods to minimize the risk of antibiotic loading to the environment from dairy farms.     

Mechanism of endotoxin-induced reduction in the number of beta-adrenergic receptors in dog livers: role of phospholipase A

The role of phospholipase A on the endotoxin-induced reduction in the number of beta-adrenergic receptors in dog liver plasma membranes was investigated. The results show that digestion of control liver plasma membranes with exogenous phospholipase A2 (0.2 unit/200 micrograms protein) decreased the specific binding of (-)-[3H]dihydroalprenolol by 37.3% (P less than 0.01) and reduced the number of receptor sites by 31.7% (P less than 0.05). These decreases in the specific binding and the number of beta-adrenergic receptors were completely reversible by the addition of phosphatidylcholine (0.2 mM). Endotoxin administration (2 hr postendotoxin) decreased the specific binding by 36% (P less than 0.05) and reduced the number of beta-adrenergic receptors by 33% (P less than 0.05), and these decreases were completely reversible by the addition of 0.2 mM phosphatidylcholine. Digestion of control liver membranes with exogenous phospholipase A2 decreased phosphatidylcholine and phosphatidylethanolamine levels by 50.6 and 51.2%, respectively, but increased lysophosphatidylcholine and lysophosphatidylethanolamine levels by 12- and 8.4-fold, respectively. Endotoxin administration decreased phosphatidylcholine and phosphatidylethanolamine contents by 21.4 and 23.8%, respectively, but increased lysophosphatidylcholine and lysophosphatidylethanolamine contents by 2.1- and 1.4-fold, respectively. In addition, endotoxin administration increased endogenous phospholipase A activity by 73.5%. Based on these results, it is suggested that the decreases in the specific binding and the number of beta-adrenergic receptors in dog livers during endotoxic shock are a result of phospholipase A activation.     

A cross-talk between epithelium and endothelium mediates human alveolar–capillary injury during SARS-CoV-2 infection

COVID-19, caused by SARS-CoV-2, is an acute and rapidly developing pandemic, which leads to a global health crisis. SARS-CoV-2 primarily attacks human alveoli and causes severe lung infection and damage. To better understand the molecular basis of this disease, we sought to characterize the responses of alveolar epithelium and its adjacent microvascular endothelium to viral infection under a co-culture system. SARS-CoV-2 infection caused massive virus replication and dramatic organelles remodeling in alveolar epithelial cells, alone. While, viral infection affected endothelial cells in an indirect manner, which was mediated by infected alveolar epithelium. Proteomics analysis and TEM examinations showed viral infection caused global proteomic modulations and marked ultrastructural changes in both epithelial cells and endothelial cells under the co-culture system. In particular, viral infection elicited global protein changes and structural reorganizations across many sub-cellular compartments in epithelial cells. Among the affected organelles, mitochondrion seems to be a primary target organelle. Besides, according to EM and proteomic results, we identified Daurisoline, a potent autophagy inhibitor, could inhibit virus replication effectively in host cells. Collectively, our study revealed an unrecognized cross-talk between epithelium and endothelium, which contributed to alveolar–capillary injury during SARS-CoV-2 infection. These new findings will expand our understanding of COVID-19 and may also be helpful for targeted drug development.Subject terms: Mechanisms of disease, Viral infection     

Semiparametric Regression in Size-Biased Sampling

Summary .  Size-biased sampling arises when a positive-valued outcome variable is sampled with selection probability proportional to its size. In this article, we propose a semiparametric linear regression model to analyze size-biased outcomes. In our proposed model, the regression parameters of covariates are of major interest, while the distribution of random errors is unspecified. Under the proposed model, we discover that regression parameters are invariant regardless of size-biased sampling. Following this invariance property, we develop a simple estimation procedure for inferences. Our proposed methods are evaluated in simulation studies and applied to two real data analyses.