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.

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.

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.

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.

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.

Obesity and perioperative outcomes in older surgical patients undergoing elective spine and major arthroplasty surgery.

STUDY OBJECTIVE: To determine whether obesity status is associated with perioperative complications, discharge outcomes and hospital length of stay in older surgical patients. DESIGN: Secondary analysis of five independent study cohorts (N = 1262). SETTING: An academic medical center between 2001 and 2017 in the United States. PATIENTS: Patients aged 65 years or older who were scheduled to undergo elective spine, knee, or hip surgery with an expected hospital stay of at least 2 days. MEASUREMENTS: Body mass index (BMI) was stratified as nonobese (BMI ≤ 30 kg/m2), obesity class 1 (30 kg/m2 ≤ BMI < 35 kg/m2) or obesity class 2-3 (BMI ≥ 35 kg/m2). Primary outcomes included predefined intraoperative and postoperative complications, hospital length of stay (LOS), and discharge location. Univariate and multivariate logistic regression was performed. MAIN RESULTS: Obesity status was not associated with intraoperative adverse events. However, obesity class 2-3 significantly increased the risk for postoperative complications (IRR 1.43, 95% CI 1.03-1.95, P = 0.03), hospital LOS (IRR 1.13, 95% CI 1.02-1.25, P = 0.02) and non-home discharge destination (OR 1.95, 95% CI 1.35-2.81, P < 0.001) after accounting for patient related factors and surgery type. CONCLUSIONS: Obesity class 2-3 status has prognostic value in predicting an increased incidence of postoperative complications, increased hospital LOS, and non-home discharge location. These results have important clinical implications for preoperative informed consent and provide areas to target for care improvement for the older obese individual.

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.

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.

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.

ISREA: An Efficient Peak-Preserving Baseline Correction Algorithm for Raman Spectra.

A critical step in Raman spectroscopy is baseline correction. This procedure eliminates the background signals generated by residual Rayleigh scattering or fluorescence. Baseline correction procedures relying on asymmetric loss functions have been employed recently. They operate with a reduced penalty on positive spectral deviations that essentially push down the baseline estimates from invading Raman peak areas. However, their coupling with polynomial fitting may not be suitable over the whole spectral domain and can yield inconsistent baselines. Their requirement of the specification of a threshold and the non-convexity of the corresponding objective function further complicates the computation. Learning from their pros and cons, we have developed a novel baseline correction procedure called the iterative smoothing-splines with root error adjustment (ISREA) that has three distinct advantages. First, ISREA uses smoothing splines to estimate the baseline that are more flexible than polynomials and capable of capturing complicated trends over the whole spectral domain. Second, ISREA mimics the asymmetric square root loss and removes the need of a threshold. Finally, ISREA avoids the direct optimization of a non-convex loss function by iteratively updating prediction errors and refitting baselines. Through our extensive numerical experiments on a wide variety of spectra including simulated spectra, mineral spectra, and dialysate spectra, we show that ISREA is simple, fast, and can yield consistent and accurate baselines that preserve all the meaningful Raman peaks.

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.

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.

Raman chemometric urinalysis (Rametrix) as a screen for bladder cancer.

Bladder cancer (BCA) is relatively common and potentially recurrent/progressive disease. It is also costly to detect, treat, and control. Definitive diagnosis is made by examination of urine sediment, imaging, direct visualization (cystoscopy), and invasive biopsy of suspect bladder lesions. There are currently no widely-used BCA-specific biomarker urine screening tests for early BCA or for following patients during/after therapy. Urine metabolomic screening for biomarkers is costly and generally unavailable for clinical use. In response, we developed Raman spectroscopy-based chemometric urinalysis (Rametrix™) as a direct liquid urine screening method for detecting complex molecular signatures in urine associated with BCA and other genitourinary tract pathologies. In particular, the RametrixTM screen used principal components (PCs) of urine Raman spectra to build discriminant analysis models that indicate the presence/absence of disease. The number of PCs included was varied, and all models were cross-validated by leave-one-out analysis. In Study 1 reported here, we tested the Rametrix™ screen using urine specimens from 56 consented patients from a urology clinic. This proof-of-concept study contained 17 urine specimens with active BCA (BCA-positive), 32 urine specimens from patients with other genitourinary tract pathologies, seven specimens from healthy patients, and the urinalysis control SurineTM. Using a model built with 22 PCs, BCA was detected with 80.4% accuracy, 82.4% sensitivity, 79.5% specificity, 63.6% positive predictive value (PPV), and 91.2% negative predictive value (NPV). Based on the number of PCs included, we found the RametrixTM screen could be fine-tuned for either high sensitivity or specificity. In other studies reported here, RametrixTM was also able to differentiate between urine specimens from patients with BCA and other genitourinary pathologies and those obtained from patients with end-stage kidney disease (ESKD). While larger studies are needed to improve RametrixTM models and demonstrate clinical relevance, this study demonstrates the ability of the RametrixTM screen to differentiate urine of BCA-positive patients. Molecular signature variances in the urine metabolome of BCA patients included changes in: phosphatidylinositol, nucleic acids, protein (particularly collagen), aromatic amino acids, and carotenoids.

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.

Fate of pirlimycin and antibiotic resistance genes in dairy manure slurries in response to temperature and pH adjustment.

Quantifying the fate of antibiotics and antibiotic resistance genes (ARGs) in response to physicochemical factors during storage of manure slurries will aid in efforts to reduce the spread of resistance when manure is land-applied. The objectives of this study were to determine the effects of temperature (10, 35, and 55 °C) and initial pH (5, 7, 9, and 12) on the removal of pirlimycin and prevalence of ARGs during storage of dairy manure slurries. We collected and homogenized feces and urine from five lactating dairy cows treated with pirlimycin and prepared slurries by mixing manure and sterile water. Aliquots (200 mL) of slurry were transferred and incubated in 400 mL glass beakers under different temperatures (10, 35, and 55 °C) or initial pH (5, 7, 9, and 12). Pirlimycin concentration and abundances of 16S rRNA, mefA, tet(W), and cfxA as indicators of total bacteria and ARGs corresponding to macrolide, tetracycline, and ß-lactam resistance, respectively, were analyzed during manure incubation. The thermophilic environment (55 °C) increased the deconjugation and removal of pirlimycin, while the acidic shock at pH 5 increased deconjugation but inhibited removal of pirlimycin, suggesting that the chemical stability of pirlimycin could be affected by temperature and pH. The thermophilic environment decreased mefA relative abundance on day 7 and 28 (P = 0.02 and 0.04), which indicates that the bacteria that encoded mefA gene were not thermotolerant. Although mefA relative abundance was greater at the pH 9 shock than the rest of pH treatments on day 7 (P = 0.04), no significant pH effect was observed on day 28. The tet(W) abundance under initial pH 12 shock was less than other pH shocks on day 28 (P = 0.01), while no temperature effect was observed on day 28. There was no significant temperature and initial pH effect on cfxA abundance at any time point during incubation, implying that the bacteria that carrying cfxA gene are relatively insensitive to these environmental factors. Overall, directly raising temperature and pH can facilitate pirlimycin removal and decrease mefA and tet(W) relative abundances during storage of manure slurries.

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.