No abstract

Antibiotics increase the frequency of resistant bacteria by providing them a competitive advantage over sensitive strains. Here, we develop a versatile assay for differential chemical inhibition of competing microbial strains, and use it to identify compounds that preferentially inhibit tetracycline-resistant relative to sensitive bacteria, thus “inverting” selection for resistance. Our assay distinguishes compounds selecting directly against specific resistance mechanisms and compounds whose selection against resistance is based on their physiological interaction with tetracycline and is more general with respect to resistance mechanism. A pilot screen indicates that both types of selection-inverting compounds are secreted by soil microbes, suggesting that nature has evolved a repertoire of chemicals that counteracts antibiotic resistance. Finally, we show that our assay can more generally permit simple, direct screening for drugs based on their differential activity against different strains or targets.

Bone health of the elderly is a major global health concern, since about 1 in 3 women and 1 in 5 men suffer from bone loss and fractures, often called osteoporosis, in old age. Bone health is a complex issue affected by multiple hormones and minerals. Among all the hormones involved in bone health, calcitriol (also vitamin D), parathyroid, and sex hormones (especially estrogen) have been discussed in this review paper. We have discussed the metabolism of these hormones and their effects on bone health. Vitamin D can be obtained from diet or formed from 7-dehydrocholesterol found under the skin in the presence of sunlight. The active form, calcitriol, causes dimerization of vitamin D receptor and acts on the bones, intestine, and kidney to regulate the level of calcium in blood. Similarly, parathyroid hormone is secreted when the serum level of calcium is low. It helps regulate the level of blood calcium through calcitriol. Sex hormones regulate bone modeling at an early age and remodeling later in life. Loss of ovarian function and a decrement in the level of production of estrogen are marked by bone loss in elderly women. In the elderly, various changes in the calcium and vitamin D metabolism, such as decrease in the production of vitamin D, decrease in dietary vitamin D, decreased renal production, increased production of excretory products, decrease in the level of VDR, and decreased calcium absorption by the intestines, can lead to bone loss. When the elderly are diagnosed with osteoporosis, medications that directly target bone such as bisphosphonates, RANK ligand inhibitors, estrogen and estrogen analogues, estrogen receptor modulators, and parathyroid hormone receptor agonists are used. Additionally, calcium and vitamin D supplements are prescribed.

Background Standard measures of kidney function are only modestly useful for accurate prediction of risk for acute kidney injury (AKI). Hypothesis Clinical and biomarker data can predict AKI more accurately. Methods Using Luminex xMAP technology, we measured 109 biomarkers in blood from 889 patients prior to undergoing coronary angiography. Procedural AKI was defined as an absolute increase in serum creatinine of ≥0.3 mg/dL, a percentage increase in serum creatinine of ≥50%, or a reduction in urine output (documented oliguria of <0.5 mL/kg per hour for >6 hours) within 7 days after contrast exposure. Clinical and biomarker predictors of AKI were identified using machine learning and a final prognostic model was developed with least absolute shrinkage and selection operator (LASSO). Results Forty‐three (4.8%) patients developed procedural AKI. Six predictors were present in the final model: four (history of diabetes, blood urea nitrogen to creatinine ratio, C‐reactive protein, and osteopontin) had a positive association with AKI risk, while two (CD5 antigen‐like and Factor VII) had a negative association with AKI risk. The final model had a cross‐validated area under the receiver operating characteristic curve (AUC) of 0.79 for predicting procedural AKI, and an in‐sample AUC of 0.82 ( P < 0.001). The optimal score cutoff had 77% sensitivity, 75% specificity, and a negative predictive value of 98% for procedural AKI. An elevated score was predictive of procedural AKI in all subjects (odds ratio = 9.87; P < 0.001). Conclusions We describe a clinical and proteomics‐supported biomarker model with high accuracy for predicting procedural AKI in patients undergoing coronary angiography.