Likelihood ratio combination of multiple biomarkers via smoothing spline estimated densities.

The diagnostic accuracy of multiple biomarkers in medical research is crucial for detecting diseases and predicting patient outcomes. An optimal method for combining these biomarkers is essential to maximize the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC). Although the optimality of the likelihood ratio has been proven by Neyman and Pearson, challenges persist in estimating the likelihood ratio, primarily due to the estimation of multivariate density functions. In this study, we propose a non-parametric approach for estimating multivariate density functions by utilizing Smoothing Spline density estimation to approximate the full likelihood function for both diseased and non-diseased groups, which compose the likelihood ratio. Simulation results demonstrate the efficiency of our method compared to other biomarker combination techniques under various settings for generated biomarker values. Additionally, we apply the proposed method to a real-world study aimed at detecting childhood autism spectrum disorder (ASD), showcasing its practical relevance and potential for future applications in medical research.

Presence of Preoperative Neurodegeneration Biofluid Markers in Patients with Postoperative Delirium.

BACKGROUND: The pathophysiology of delirium is incompletely understood, including what molecular pathways are involved in brain vulnerability to delirium. This study examined whether preoperative plasma neurodegeneration markers were elevated in patients who subsequently developed postoperative delirium through a retrospective case-control study. METHODS: Inclusion criteria were patients of 65 yr of age or older, undergoing elective noncardiac surgery with a hospital stay of 2 days or more. Concentrations of preoperative plasma P-Tau181, neurofilament light chain, amyloid ß1-42 (Aß42), and glial fibrillary acidic protein were measured with a digital immunoassay platform. The primary outcome was postoperative delirium measured by the Confusion Assessment Method. The study included propensity score matching by age and sex with nearest neighbor, such that each patient in the delirium group was matched by age and sex with a patient in the no-delirium group. RESULTS: The initial cohort consists of 189 patients with no delirium and 102 patients who developed postoperative delirium. Of 291 patients aged 72.5 ± 5.8 yr, 50.5% were women, and 102 (35%) developed postoperative delirium. The final cohort in the analysis consisted of a no-delirium group (n = 102) and a delirium group (n = 102) matched by age and sex using the propensity score method. Of the four biomarkers assayed, the median value for neurofilament light chain was 32.05 pg/ml for the delirium group versus 23.7 pg/ml in the no-delirium group. The distribution of biomarker values significantly differed between the delirium and no-delirium groups (P = 0.02 by the Kolmogorov-Smirnov test) with the largest cumulative probability difference appearing at the biomarker value of 32.05 pg/ml. CONCLUSIONS: These results suggest that patients who subsequently developed delirium are more likely to be experiencing clinically silent neurodegenerative changes before surgery, reflected by changes in plasma neurofilament light chain biomarker concentrations, which may identify individuals with a preoperative vulnerability to subsequent cognitive decline.

Manure-Based Amendments Influence Surface-Associated Bacteria and Markers of Antibiotic Resistance on Radishes Grown in Soils with Different Textures.

A controlled greenhouse study was performed to determine the effect of manure or compost amendments, derived during or in the absence of antibiotic treatment of beef and dairy cattle, on radish taproot-associated microbiota and indicators of antibiotic resistance when grown in different soil textures. Bacterial beta diversity, determined by 16S rRNA gene amplicon sequencing, bifurcated according to soil texture (P < 0.001, R = 0.501). There was a striking cross-effect in which raw manure from antibiotic-treated and antibiotic-free beef and dairy cattle added to loamy sand (LS) elevated relative (16S rRNA gene-normalized) (by 0.9 to 1.9 log10) and absolute (per-radish) (by 1.1 to 3.0 log10) abundances of intI1 (an integrase gene and indicator of mobile multiantibiotic resistance) on radishes at harvest compared to chemical fertilizer-only control conditions (P < 0.001). Radishes tended to carry fewer copies of intI1 and sul1 when grown in silty clay loam than LS. Composting reduced relative abundance of intI1 on LS-grown radishes (0.6 to 2.4 log10 decrease versus corresponding raw manure; P < 0.001). Effects of antibiotic use were rarely discernible. Heterotrophic plate count bacteria capable of growth on media containing tetracycline, vancomycin, sulfamethazine, or erythromycin tended to increase on radishes grown in turned composted antibiotic-treated dairy or beef control (no antibiotics) manures relative to the corresponding raw manure in LS (0.8- to 2.3-log10 increase; P < 0.001), suggesting that composting sometimes enriches cultivable bacteria with phenotypic resistance. This study demonstrates that combined effects of soil texture and manure-based amendments influence the microbiota of radish surfaces and markers of antibiotic resistance, illuminating future research directions for reducing agricultural sources of antibiotic resistance.IMPORTANCE In working toward a comprehensive strategy to combat the spread of antibiotic resistance, potential farm-to-fork routes of dissemination are gaining attention. The effects of preharvest factors on the microbiota and corresponding antibiotic resistance indicators on the surfaces of produce commonly eaten raw is of special interest. Here, we conducted a controlled greenhouse study, using radishes as a root vegetable grown in direct contact with soil, and compared the effects of manure-based soil amendments, antibiotic use in the cattle from which the manure was sourced, composting of the manure, and soil texture, with chemical fertilizer only as a control. We noted significant effects of amendment type and soil texture on the composition of the microbiota and genes used as indicators of antibiotic resistance on radish surfaces. The findings take a step toward identifying agricultural practices that aid in reducing carriage of antibiotic resistance and corresponding risks to consumers.

DLG1-AS1 is activated by MYC and drives the proliferation and migration of hepatocellular carcinoma cells through miR-497-5p/SSRP1 axis.

BACKGROUND: Long non-coding RNAs (lncRNAs) have been reported to be biological regulators in hepatocellular carcinoma (HCC). DLG1 antisense RNA 1 (DLG1-AS1) has been found to be up-regulated in cervical cancer. However, its function and underlying mechanism in HCC remains unknown. METHODS: DLG1-AS1 expression was assessed in HCC cells and normal cell by RT-qPCR. Luciferase reporter assay, RNA pull down assay and RIP assay were used to demonstrate the interaction between DLG1-AS1 and miR-497-5p. RESULTS: DLG1-AS1 was highly expressed in HCC cells. Silencing of DLG1-AS1 led to the inhibition of HCC cell growth and migration. Besides, MYC induced the transcriptional activation of DLG1-AS1. MYC could facilitate HCC cellular processes by up-regulating DLG1-AS1. MiR-497-5p could interact with DLG1-AS1 in HCC cells. Down-regulation of miR-497-5p could reverse the impacts of DLG1-AS1 silencing on HCC cells. SSRP1 expression could be positively regulated by DLG1-AS1 but was negatively regulated by miR-497-5p. Knockdown of DLG1-AS1 suppressed tumor growth in nude mice. CONCLUSIONS: DLG1-AS1 is activated by MYC and functions as an oncogene in HCC via miR-497-5p/SSRP1 axis.

Automated quantification of COVID-19 severity and progression using chest CT images.

OBJECTIVE: To develop and test computer software to detect, quantify, and monitor progression of pneumonia associated with COVID-19 using chest CT scans. METHODS: One hundred twenty chest CT scans from subjects with lung infiltrates were used for training deep learning algorithms to segment lung regions and vessels. Seventy-two serial scans from 24 COVID-19 subjects were used to develop and test algorithms to detect and quantify the presence and progression of infiltrates associated with COVID-19. The algorithm included (1) automated lung boundary and vessel segmentation, (2) registration of the lung boundary between serial scans, (3) computerized identification of the pneumonitis regions, and (4) assessment of disease progression. Agreement between radiologist manually delineated regions and computer-detected regions was assessed using the Dice coefficient. Serial scans were registered and used to generate a heatmap visualizing the change between scans. Two radiologists, using a five-point Likert scale, subjectively rated heatmap accuracy in representing progression. RESULTS: There was strong agreement between computer detection and the manual delineation of pneumonic regions with a Dice coefficient of 81% (CI 76-86%). In detecting large pneumonia regions (> 200 mm3), the algorithm had a sensitivity of 95% (CI 94-97%) and specificity of 84% (CI 81-86%). Radiologists rated 95% (CI 72 to 99) of heatmaps at least "acceptable" for representing disease progression. CONCLUSION: The preliminary results suggested the feasibility of using computer software to detect and quantify pneumonic regions associated with COVID-19 and to generate heatmaps that can be used to visualize and assess progression. KEY POINTS: • Both computer vision and deep learning technology were used to develop computer software to quantify the presence and progression of pneumonia associated with COVID-19 depicted on CT images. • The computer software was tested using both quantitative experiments and subjective assessment. • The computer software has the potential to assist in the detection of the pneumonic regions, monitor disease progression, and assess treatment efficacy related to COVID-19.

MOSS-Multi-Modal Best Subset Modeling in Smart Manufacturing.

Smart manufacturing, which integrates a multi-sensing system with physical manufacturing processes, has been widely adopted in the industry to support online and real-time decision making to improve manufacturing quality. A multi-sensing system for each specific manufacturing process can efficiently collect the in situ process variables from different sensor modalities to reflect the process variations in real-time. However, in practice, we usually do not have enough budget to equip too many sensors in each manufacturing process due to the cost consideration. Moreover, it is also important to better interpret the relationship between the sensing modalities and the quality variables based on the model. Therefore, it is necessary to model the quality-process relationship by selecting the most relevant sensor modalities with the specific quality measurement from the multi-modal sensing system in smart manufacturing. In this research, we adopted the concept of best subset variable selection and proposed a new model called Multi-mOdal beSt Subset modeling (MOSS). The proposed MOSS can effectively select the important sensor modalities and improve the modeling accuracy in quality-process modeling via functional norms that characterize the overall effects of individual modalities. The significance of sensor modalities can be used to determine the sensor placement strategy in smart manufacturing. Moreover, the selected modalities can better interpret the quality-process model by identifying the most correlated root cause of quality variations. The merits of the proposed model are illustrated by both simulations and a real case study in an additive manufacturing (i.e., fused deposition modeling) process.

Any unique image biomarkers associated with COVID-19?

OBJECTIVE: To define the uniqueness of chest CT infiltrative features associated with COVID-19 image characteristics as potential diagnostic biomarkers. METHODS: We retrospectively collected chest CT exams including n = 498 on 151 unique patients RT-PCR positive for COVID-19 and n = 497 unique patients with community-acquired pneumonia (CAP). Both COVID-19 and CAP image sets were partitioned into three groups for training, validation, and testing respectively. In an attempt to discriminate COVID-19 from CAP, we developed several classifiers based on three-dimensional (3D) convolutional neural networks (CNNs). We also asked two experienced radiologists to visually interpret the testing set and discriminate COVID-19 from CAP. The classification performance of the computer algorithms and the radiologists was assessed using the receiver operating characteristic (ROC) analysis, and the nonparametric approaches with multiplicity adjustments when necessary. RESULTS: One of the considered models showed non-trivial, but moderate diagnostic ability overall (AUC of 0.70 with 99% CI 0.56-0.85). This model allowed for the identification of 8-50% of CAP patients with only 2% of COVID-19 patients. CONCLUSIONS: Professional or automated interpretation of CT exams has a moderately low ability to distinguish between COVID-19 and CAP cases. However, the automated image analysis is promising for targeted decision-making due to being able to accurately identify a sizable subsect of non-COVID-19 cases. KEY POINTS: • Both human experts and artificial intelligent models were used to classify the CT scans. • ROC analysis and the nonparametric approaches were used to analyze the performance of the radiologists and computer algorithms. • Unique image features or patterns may not exist for reliably distinguishing all COVID-19 from CAP; however, there may be imaging markers that can identify a sizable subset of non-COVID-19 cases.

Retracted: Low-expressed GAS5 injure myocardial cells and progression of chronic heart failure via regulation of miR-223-3P.

Chronic heart failure (CHF) is a common disease in clinical practice, and its incidence has been increasing in recent years. Understanding the pathogenesis of CHF is the key to its future clinical diagnosis and treatment. Molecular research is a hot topic in modern hospitals, and long non-coding RNA (LncRNA) has been gradually understood and applied in many diseases. The situation of LncRNA GAS5 in CHF is still unclear, so this experiment will investigate the situation of GAS5 in CHF and its effect on myocardial cells, aiming to gain a preliminary understanding of the mechanism of GAS5's effect on CHF. In this study, the expression of GAS5 and miR-223-3p in peripheral blood of CHF patients and healthy subjects was first detected, GAS5 was low in CHF while miR-223-3p was high (P < 0.05). Subsequently, ROC curve analysis showed that GAS5 and miR-223-3p had good predictive value for the occurrence and recurrence of CHF. Secondly, through in vitro experiments, we found that inhibition of GAS5 with elevated expression of miR-223-3p decreased the proliferative capacity of cardiomyocytes and increased apoptotic capacity and inflammatory factors (P < 0.050). Through dual luciferase reporter and RNA immunoprecipitation experiment, we found that miR-223-3p was regulated by GAS5 in a targeted manner.

Fetal brain development at 25-39 weeks gestational age: A preliminary study using intravoxel incoherent motion diffusion-weighted imaging.

BACKGROUND: The fetal brain developmental changes of water diffusivity and perfusion has not been extensively explored. PURPOSE/HYPOTHESIS: To evaluate the fetal brain developmental changes of water diffusivity and perfusion using intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI). STUDY TYPE: Prospective. POPULATION: Seventy-nine normal singleton fetuses were scanned without sedation of healthy pregnant women. FIELD STRENGTH/SEQUENCE: 5 T MRI/T1 /2 -weighted image and IVIM-DWI. ASSESSMENT: Pure diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction (f) values were calculated in the frontal (FWM), temporal (TWM), parietal (PWM), and occipital white matter (OWM) as well as cerebellar hemisphere (CH), basal ganglia region (BGR), thalamus (TH), and pons using an IVIM model. STATISTICAL TESTS: One-way analysis of variable (ANOVA) followed by Bonferroni post-hoc multiple comparison was employed to reveal the difference of IVIM parameters among the investigated brain regions. The linear and the nonlinear polynomial regression analyses were utilized to reveal the correlation between gestational age (GA) and IVIM parameters. RESULTS: There were significant differences in both D (F(7,623) = 96.64, P = 0.000) and f values (F(7,623) = 2.361, P = 0.0219), but not D* values among the varied brain regions. D values from TWM (r2 = 0.1402, P = 0.0002), PWM (r2 = 0.2245, P = 0.0002), OWM (r2 = 0.2519, P = 0.0002), CH (r2 = 0.2245, P = 0.0002), BGR (r2 = 0.3393, P = 0.0001), TH (r2 = 0.1259, P = 0.0001), and D* value from pons (r2 = 0.2206, P = 0.0002) were significantly correlated with GA using linear regression analysis. Quadratic regression analysis led to results similar to those using the linear regression model. DATA CONCLUSION: IVIM-DWI parameters may indicate fetal brain developmental alterations but the conclusion is far from reached due to the not as high-powered correlation between IVIM parameters and GA. LEVEL OF EVIDENCE: 2 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:899-909.

Promotion time cure rate model with nonparametric form of covariate effects.

Survival data with a cured portion are commonly seen in clinical trials. Motivated from a biological interpretation of cancer metastasis, promotion time cure model is a popular alternative to the mixture cure rate model for analyzing such data. The existing promotion cure models all assume a restrictive parametric form of covariate effects, which can be incorrectly specified especially at the exploratory stage. In this paper, we propose a nonparametric approach to modeling the covariate effects under the framework of promotion time cure model. The covariate effect function is estimated by smoothing splines via the optimization of a penalized profile likelihood. Point-wise interval estimates are also derived from the Bayesian interpretation of the penalized profile likelihood. Asymptotic convergence rates are established for the proposed estimates. Simulations show excellent performance of the proposed nonparametric method, which is then applied to a melanoma study.

Antibiotics and Antibiotic Resistance Genes in Bulk and Rhizosphere Soils Subject to Manure Amendment and Vegetable Cultivation.

There is interest in understanding effects of amending soil with manure in a cultivation setting and if composting can provide benefits. Raw or composted manure from cattle administered with and without sulfamethazine, chlortetracycline, and tylosin was amended to loamy sand and silty clay loam soils, where lettuce ( L.), radish ( L.), and broccoli ( L. var. ) were cultivated and compared with those grown in soil amended with fertilizer as a control. Upon plant maturation, rhizosphere and bulk soils were analyzed for antibiotics, and 1, B, (W), and I1 genes were quantified. Antibiotic concentrations in compost-amended soils were below detection limits. For soils amended with manure containing antibiotics, sulfamethazine ranged from 1.1 to 3.1 µg kg in the bulk soils but was below detection limits in the rhizosphere soils. Chlortetracycline (2.8-9.3 µg kg) was two times lower in the rhizosphere than in the bulk soil. Levels of tylosin in the rhizosphere soil were similar to the bulk soil. Soil texture or vegetable type did not have significant influence on antibiotic concentration differences between the bulk and rhizosphere soils. Relative abundances of (W) and I1 in the fertilizer-amended soil were significantly lower than in those amended with manure or compost ( < 0.05), whereas B was not detected in any soils. Rhizosphere zone has no significant effect on the detected antibiotic resistance genes. It is suggested that plant roots may have a substantial effect on the fate of certain antibiotics in manure-amended fields, but less of an effect on antibiotic resistance and mobility genes.

Fate of Pirlimycin and Antibiotic-Resistant Fecal Coliforms in Field Plots Amended with Dairy Manure or Compost during Vegetable Cultivation.

Identification of agricultural practices that mitigate the environmental dissemination of antibiotics is a key need in reducing the prevalence of antibiotic-resistant bacteria of human health concern. Here, we aimed to compare the effects of crop (lettuce [ L.] or radish [ L.]), soil amendment type (inorganic fertilizer, raw dairy manure, composted dairy manure, or no amendment), and prior antibiotic use history (no antibiotics during previous lactation cycles vs. manure mixed from cows administered pirlimycin or cephapirin) of manure-derived amendments on the incidence of culturable antibiotic-resistant fecal coliforms in agricultural soils through a controlled field-plot experiment. Antibiotic-resistant culturable fecal coliforms were recoverable from soils across all treatments immediately after application, although persistence throughout the experiment varied by antibiotic class and time. The magnitude of observed coliform counts differed by soil amendment type. Compost-amended soils had the highest levels of cephalosporin-resistant fecal coliforms, regardless of whether the cows from which the manure was derived were administered antibiotics. Samples from control plots or those treated with inorganic fertilizer trended toward lower counts of resistant coliforms, although these differences were not statistically significant. No statistical differences were observed between soils that grew leafy (lettuce) versus rooted (radish) crops. Only pirlimycin was detectable past amendment application in raw manure-amended soils, dissipating 12 to 25% by Day 28. Consequently, no quantifiable correlations between coliform count and antibiotic magnitude could be identified. This study demonstrates that antibiotic-resistant fecal coliforms can become elevated in soils receiving manure-derived amendments, but that a variety of factors likely contribute to their long-term persistence under typical field conditions.

Plasma SOMAmer proteomics of postoperative delirium.

BACKGROUND: Postoperative delirium is prevalent in older adults and has been shown to increase the risk of long-term cognitive decline. Plasma biomarkers to identify the risk for postoperative delirium and the risk of Alzheimer's disease and related dementias are needed. METHODS: This biomarker discovery case-control study aimed to identify plasma biomarkers associated with postoperative delirium. Patients aged ≥65 years undergoing major elective noncardiac surgery were recruited. The preoperative plasma proteome was interrogated with SOMAmer-based technology targeting 1433 biomarkers. RESULTS: In 40 patients (20 with vs. 20 without postoperative delirium), a preoperative panel of 12 biomarkers discriminated patients with postoperative delirium with an accuracy of 97.5%. The final model of five biomarkers delivered a leave-one-out cross-validation accuracy of 80%. Represented biological pathways included lysosomal and immune response functions. CONCLUSION: In older patients who have undergone major surgery, plasma SOMAmer proteomics may provide a relatively non-invasive benchmark to identify biomarkers associated with postoperative delirium.

Two-component mixture cure rate model with spline estimated nonparametric components.

In some survival analysis of medical studies, there are often long-term survivors who can be considered as permanently cured. The goals in these studies are to estimate the noncured probability of the whole population and the hazard rate of the susceptible subpopulation. When covariates are present as often happens in practice, to understand covariate effects on the noncured probability and hazard rate is of equal importance. The existing methods are limited to parametric and semiparametric models. We propose a two-component mixture cure rate model with nonparametric forms for both the cure probability and the hazard rate function. Identifiability of the model is guaranteed by an additive assumption that allows no time-covariate interactions in the logarithm of hazard rate. Estimation is carried out by an expectation-maximization algorithm on maximizing a penalized likelihood. For inferential purpose, we apply the Louis formula to obtain point-wise confidence intervals for noncured probability and hazard rate. Asymptotic convergence rates of our function estimates are established. We then evaluate the proposed method by extensive simulations. We analyze the survival data from a melanoma study and find interesting patterns for this study.

VARIABLE SELECTION IN PARTLY LINEAR REGRESSION MODEL WITH DIVERGING DIMENSIONS FOR RIGHT CENSORED DATA.

Recent biomedical studies often measure two distinct sets of risk factors: low-dimensional clinical and environmental measurements, and high-dimensional gene expression measurements. For prognosis studies with right censored response variables, we propose a semiparametric regression model whose covariate effects have two parts: a nonparametric part for low-dimensional covariates, and a parametric part for high-dimensional covariates. A penalized variable selection approach is developed. The selection of parametric covariate effects is achieved using an iterated Lasso approach, for which we prove the selection consistency property. The nonparametric component is estimated using a sieve approach. An empirical model selection tool for the nonparametric component is derived based on the Kullback-Leibler geometry. Numerical studies show that the proposed approach has satisfactory performance. Application to a lymphoma study illustrates the proposed method.

Peritraumatic Stress From a Disaster Increases Risk for Onset of Chronic Diseases Among Older Adults.

BACKGROUND AND OBJECTIVES: Our understanding of the impact of disaster exposure on the physical health of older adults is largely based on hospital admissions for acute illnesses in the weeks following a disaster. Studies of longer-term outcomes have centered primarily on mental health. Missing have been studies examining whether exposure to disaster increases the risk for the onset of chronic diseases. We examined the extent to which 2 indicators of disaster exposure (geographic exposure and peritraumatic stress) were associated with new onset of cardiovascular disease, diabetes, arthritis, and lung disease to improve our understanding of the long-term physical health consequences of disaster exposure. RESEARCH DESIGN AND METHODS: We linked self-reported data collected prior to and following Hurricane Sandy from a longitudinal panel study with Medicare data to assess time to new onset of chronic diseases in the 4 years after the hurricane. RESULTS: We found that older adults who reported high levels of peritraumatic stress from Hurricane Sandy had more than twice the risk of experiencing a new diagnosis of lung disease, diabetes, and arthritis in the 4 years after the hurricane compared to older adults who did not experience high levels of peritraumatic stress. Geographic proximity to the hurricane was not associated with these outcomes. Analyses controlled for known risk factors for the onset of chronic diseases, including demographic, psychosocial, and health risks. DISCUSSION AND IMPLICATIONS: Findings reveal that physical health effects of disaster-related peritraumatic stress extend beyond the weeks and months after a disaster and include new onset of chronic diseases that are associated with loss of functioning and early mortality.

Long term impact of Hurricane Sandy on hospital admissions of older adults.

RATIONALE: In the weeks and months following a disaster, acute illness and injuries requiring hospital admission increase. It is not known whether disaster exposure is associated with increased risk for hospitalization in the years after a disaster. OBJECTIVE: We examined the extent to which disaster exposure is associated with hospitalization two years after Hurricane Sandy. The analyses fill a clinical gap in our understanding of long-term physical health consequences of disaster exposure by identifying older adults at greatest risk for hospitalization two years after disaster exposure. METHOD: Survey data from a longitudinal panel study collectedbefore and after Hurricane Sandy were linked with Medicare inpatient files in order to assess the impact of Hurricane Sandy on hospital admissions two years following the hurricane. RESULTS: We found that people who reported experiencing a lot of fear and distress in the midst of Hurricane Sandy were at an increased risk of being hospitalized two years after the hurricane [Hazard Ratio = 1.75; 95% CI (1.12-2.73)]. Findings held after controlling for pre-disaster demographics, social risks, chronic conditions, hospitalizations during the year before the hurricane, and decline in physical functioning. CONCLUSIONS: These findings are the first to show that disaster exposure increases the risk for hospital admissions two years after a disaster. Controlling for known risk factors for hospitalization, older adults who experience high levels of fear and distress during a disaster are more likely to be hospitalized two years following the disaster than older adults who do not have this experience.

Smooth Muscle Organization and Nerves in the Rat Vagina: A First Look Using Tissue Clearing and Immunolabeling.

Smooth muscle fibers within the vagina, as well as the nerve fibers that contribute to their control mechanisms, are important for the maintenance and alteration of vaginal length and tone. Vaginal smooth muscle (VaSM) is typically described as being arranged into two distinct concentric layers: an inner circular muscular layer and an outer longitudinal muscular layer. However, the distribution of VaSM oriented in the longitudinal direction (LD) and circumferential direction (CD) has never been quantified. In this study, tissue clearing and immunohistochemistry were performed so that the VaSM, and surrounding nerves, within whole rat vaginas ([Formula: see text]) could be imaged without tissue sectioning, preserving the three-dimensional architecture of the organs. Using these methods, the vagina was viewed through the full thickness of the muscularis layer, from the distal to the proximal regions. The VaSM orientation in the proximal and distal regions and the VaSM content along the LD and CD were quantified. Additionally, a qualitative assessment of vaginal nerves was performed. When compared using a permuted version of the Watson [Formula: see text] test, the orientation of VaSM in the proximal and distal regions were found to be significantly different in 4 of the 6 imaged rat vaginas ([Formula: see text]). While the distal vagina contained a similar amount of VaSM oriented within [Formula: see text] of the LD and within [Formula: see text] of the CD, the proximal vagina contained significantly more VaSM oriented towards the LD than towards the CD. Nerve fibers were found to be wavy, running both parallel and perpendicular to vascular and non-vascular smooth muscle within the vagina. Micro-structural analyses, like the one conducted here, are necessary to understand the physiological function and pathological changes of the vagina.

Alterations in the molecular composition of COVID-19 patient urine, detected using Raman spectroscopic/computational analysis.

We developed and tested a method to detect COVID-19 disease, using urine specimens. The technology is based on Raman spectroscopy and computational analysis. It does not detect SARS-CoV-2 virus or viral components, but rather a urine 'molecular fingerprint', representing systemic metabolic, inflammatory, and immunologic reactions to infection. We analyzed voided urine specimens from 46 symptomatic COVID-19 patients with positive real time-polymerase chain reaction (RT-PCR) tests for infection or household contact with test-positive patients. We compared their urine Raman spectra with urine Raman spectra from healthy individuals (n = 185), peritoneal dialysis patients (n = 20), and patients with active bladder cancer (n = 17), collected between 2016-2018 (i.e., pre-COVID-19). We also compared all urine Raman spectra with urine specimens collected from healthy, fully vaccinated volunteers (n = 19) from July to September 2021. Disease severity (primarily respiratory) ranged among mild (n = 25), moderate (n = 14), and severe (n = 7). Seventy percent of patients sought evaluation within 14 days of onset. One severely affected patient was hospitalized, the remainder being managed with home/ambulatory care. Twenty patients had clinical pathology profiling. Seven of 20 patients had mildly elevated serum creatinine values (>0.9 mg/dl; range 0.9-1.34 mg/dl) and 6/7 of these patients also had estimated glomerular filtration rates (eGFR) <90 mL/min/1.73m2 (range 59-84 mL/min/1.73m2). We could not determine if any of these patients had antecedent clinical pathology abnormalities. Our technology (Raman Chemometric Urinalysis-Rametrix®) had an overall prediction accuracy of 97.6% for detecting complex, multimolecular fingerprints in urine associated with COVID-19 disease. The sensitivity of this model for detecting COVID-19 was 90.9%. The specificity was 98.8%, the positive predictive value was 93.0%, and the negative predictive value was 98.4%. In assessing severity, the method showed to be accurate in identifying symptoms as mild, moderate, or severe (random chance = 33%) based on the urine multimolecular fingerprint. Finally, a fingerprint of 'Long COVID-19' symptoms (defined as lasting longer than 30 days) was located in urine. Our methods were able to locate the presence of this fingerprint with 70.0% sensitivity and 98.7% specificity in leave-one-out cross-validation analysis. Further validation testing will include sampling more patients, examining correlations of disease severity and/or duration, and employing metabolomic analysis (Gas Chromatography-Mass Spectrometry [GC-MS], High Performance Liquid Chromatography [HPLC]) to identify individual components contributing to COVID-19 molecular fingerprints.