Utility of the Human Papillomavirus Vaccination in Management of HPV-associated Cutaneous Lesions.

Human papillomavirus (HPV)-induced cutaneous disease is a common complaint for patients presenting for dermatology evaluation. Infection by HPV is the major etiologic factor in the development of cutaneous warts, epidermodysplasia verruciformis, and possibly a subset of cutaneous squamous cell carcinoma. Carcinoma of the genitourinary tract, most notably cervical carcinoma, is the most severe manifestation of infection with specific serotypes of HPV. For this reason, the HPV immunization (Gardasil) was developed in 2006 and upgraded in 2018 to a nonavalent formulation that includes serotypes 6, 11, 16, 18, 31, 33, 45, 52, 58. While immunization is highly effective at preventing infection with serotypes included in the formulation, it is less clear if the immunization can aid in managing active HPV infection. This review examines the available literature regarding the role of HPV immunization in managing common warts, genital warts, keratinocyte carcinoma, and epidermodysplasia verruciformis.

Renal Hypokalemia: An Endocrine Perspective.

The majority of disorders that cause renal potassium wasting present with abnormalities in adrenal hormone secretion. While these findings frequently lead patients to seek endocrine evaluation, clinicians often struggle to accurately diagnose these conditions, delaying treatment and adversely impacting patient care. At the same time, growing insight into the genetic and molecular basis of these disorders continues to improve their diagnosis and management. In this review, we outline a practical integrated approach to the evaluation of renal hypokalemia syndromes that are seen in endocrine practice while highlighting recent advances in understanding of the genetics and pathophysiology behind them.

Analysis of urine Raman spectra differences from patients with diabetes mellitus and renal pathologies.

Background: Chronic kidney disease (CKD) poses a major public health burden. Diabetes mellitus (DM) is one of the major causes of CKD. In patients with DM, it can be difficult to differentiate diabetic kidney disease (DKD) from other causes of glomerular damage; it should not be assumed that all DM patients with decreased eGFR and/or proteinuria have DKD. Renal biopsy is the standard for definitive diagnosis, but other less invasive methods may provide clinical benefit. As previously reported, Raman spectroscopy of CKD patient urine with statistical and chemometric modeling may provide a novel, non-invasive methodology for discriminating between renal pathologies. Methods: Urine samples were collected from renal biopsied and non-biopsied patients presenting with CKD secondary to DM and non-diabetic kidney disease. Samples were analyzed by Raman spectroscopy, baselined with the ISREA algorithm, and subjected to chemometric modeling. Leave-one-out cross-validation was used to assess the predictive capabilities of the model. Results: This proof-of-concept study consisted of 263 samples, including renal biopsied, non-biopsied diabetic and non-diabetic CKD patients, healthy volunteers, and the Surine™ urinalysis control. Urine samples of DKD patients and those with immune-mediated nephropathy (IMN) were distinguished from one another with 82% sensitivity, specificity, positive-predictive value (PPV), and negative-predictive value (NPV). Among urine samples from all biopsied CKD patients, renal neoplasia was identified in urine with 100% sensitivity, specificity, PPV, and NPV, and membranous nephropathy was identified with 66.7% sensitivity, 96.4% specificity, 80.0% PPV, and 93.1% NPV. Finally, DKD was identified among a population of 150 patient urine samples containing biopsy-confirmed DKD, other biopsy-confirmed glomerular pathologies, un-biopsied non-diabetic CKD patients (no DKD), healthy volunteers, and Surine™ with 36.4% sensitivity, 97.8% specificity, 57.1% PPV, and 95.1% NPV. The model was used to screen un-biopsied diabetic CKD patients and identified DKD in more than 8% of this population. IMN in diabetic patients was identified among a similarly sized and diverse population with 83.3% sensitivity, 97.7% specificity, 62.5% PPV, and 99.2% NPV. Finally, IMN in non-diabetic patients was identified with 50.0% sensitivity, 99.4% specificity, 75.0% PPV, and 98.3% NPV. Conclusions: Raman spectroscopy of urine with chemometric analysis may be able to differentiate between DKD, IMN, and other glomerular diseases. Future work will further characterize CKD stages and glomerular pathology, while assessing and controlling for differences in factors such as comorbidities, disease severity, and other lab parameters.

Dipstick analysis of urine chemistry: benefits and limitations of dry chemistry-based assays.

Urinalysis is a commonly utilized laboratory test, and analysis of urine has been studied and used since ancient times. Urine contains a wide array of metabolites that can provide information regarding the current physiologic state of the body and clinical manifestations of disease. In this review, we discuss the mechanics of the dry chemistry component of the urine dipstick such as the reaction principles underlying various assays and potential effects of collection and storage on results. Additionally, we discuss the benefits and limitations of the urine dipstick as it pertains to its use as a low-cost tool in point-of-care settings and the reasoning for a lack of its use as a broad screening tool.

Spectral characteristics of urine from patients with end-stage kidney disease analyzed using Raman Chemometric Urinalysis (Rametrix).

Raman Chemometric Urinalysis (RametrixTM) was used to discern differences in Raman spectra from (i) 362 urine specimens from patients receiving peritoneal dialysis (PD) therapy for end-stage kidney disease (ESKD), (ii) 395 spent dialysate specimens from those PD therapies, and (iii) 235 urine specimens from healthy human volunteers. RametrixTM analysis includes spectral processing (e.g., truncation, baselining, and vector normalization); principal component analysis (PCA); statistical analyses (ANOVA and pairwise comparisons); discriminant analysis of principal components (DAPC); and testing DAPC models using a leave-one-out build/test validation procedure. Results showed distinct and statistically significant differences between the three types of specimens mentioned above. Further, when introducing "unknown" specimens, RametrixTM was able to identify the type of specimen (as PD patient urine or spent dialysate) with better than 98% accuracy, sensitivity, and specificity. RametrixTM was able to identify "unknown" urine specimens as from PD patients or healthy human volunteers with better than 96% accuracy (with better than 97% sensitivity and 94% specificity). This demonstrates that an entire Raman spectrum of a urine or spent dialysate specimen can be used to determine its identity or the presence of ESKD by the donor.

Spectral characteristics of urine specimens from healthy human volunteers analyzed using Raman chemometric urinalysis (Rametrix).

Raman chemometric urinalysis (Rametrix™) was used to analyze 235 urine specimens from healthy individuals. The purpose of this study was to establish the "range of normal" for Raman spectra of urine specimens from healthy individuals. Ultimately, spectra falling outside of this range will be correlated with kidney and urinary tract disease. Rametrix™ analysis includes direct comparisons of Raman spectra but also principal component analysis (PCA), discriminant analysis of principal components (DAPC) models, multivariate statistics, and it is available through GitHub as the Rametrix™ LITE Toolbox for MATLAB®. Results showed consistently overlapping Raman spectra of urine specimens with significantly larger variances in Raman shifts, found by PCA, corresponding to urea, creatinine, and glucose concentrations. A 2-way ANOVA test found that age of the urine specimen donor was statistically significant (p < 0.001) and donor sex (female or male identification) was less so (p = 0.0526). With DAPC models and blind leave-one-out build/test routines using the Rametrix™ PRO Toolbox (also available through GitHub), an accuracy of 71% (sensitivity = 72%; specificity = 70%) was obtained when predicting whether a urine specimen from a healthy unknown individual was from a female or male donor. Finally, from female and male donors (n = 4) who contributed first morning void urine specimens each day for 30 days, the co-occurrence of menstruation was found statistically insignificant to Rametrix™ results (p = 0.695). In addition, Rametrix™ PRO was able to link urine specimens with the individual donor with an average of 78% accuracy. Taken together, this study established the range of Raman spectra that could be expected when obtaining urine specimens from healthy individuals and analyzed by Rametrix™ and provides the methodology for linking results with donor characteristics.