Prevalence, Molecular Characteristics and Virulence Identification of Bovine Parainfluenza Virus Type 3 in China.

Bovine parainfluenza virus type 3 (BPIV-3) is one of the major pathogens of the bovine respiratory disease complex (BRDC). BPIV-3 surveillance in China has been quite limited. In this study, we used PCR to test 302 cattle in China, and found that the positive rate was 4.64% and the herd-level positive rate was 13.16%. Six BPIV-3C strains were isolated and confirmed by electron microscopy, and their titers were determined. Three were sequenced by next-generation sequencing (NGS). Phylogenetic analyses showed that all isolates were most closely related to strain NX49 from Ningxia; the genetic diversity of genotype C strains was lower than strains of genotypes A and B; the HN, P, and N genes were more suitable for genotyping and evolutionary analyses of BPIV-3. Protein variation analyses showed that all isolates had mutations at amino acid sites in the proteins HN, M, F, and L. Genetic recombination analyses provided evidence for homologous recombination of BPIV-3 of bovine origin. The virulence experiment indicated that strain Hubei-03 had the highest pathogenicity and could be used as a vaccine candidate. These findings apply an important basis for the precise control of BPIV-3 in China.

Volatile organic compounds (VOCs) as a rapid means for assessing the source of coprolites.

The odor of rehydrated coprolites can be used as an informal means of fecal identification. To date, the analysis of volatiles emitted by coprolites from different sources has not been attempted, and the possibility of utilizing volatile organic compounds (VOCs) as fecal biomarkers unexplored. VOCs released by coprolites from the Paisley Caves, were analyzed using solid-phase microextraction (SPME), to assess the variance of results from different coprolites (carnivores, herbivores, or humans). Coprolites from carnivores can be clearly distinguished from those produced by herbivores and humans; these latter two are separated to a lesser degree. Eight discriminatory compounds differentiated between the coprolite sources, and their identities were verified using reference standards. Coprolites and their associated sediments could not be differentiated between using this method, suggesting leaching of VOCs into the burial matrix. This work provides an alternative, more rapid way to assess coprolite origin.

Influencing factors for decision-making delay in seeking medical care among acute ischemic stroke patients in rural areas.

OBJECTIVE: To explore the factors influencing decision-making delay in seeking medical care for patients with acute ischemic stroke (AIS) in rural areas. METHODS: From September to December 2021, we conducted a questionnaire survey of 260 patients with AIS who were hospitalized in the neurology departments of four county-level hospitals in Daqing. A decision-tree and logistic regression model was used to investigate the elements contributing to decision-making delays. RESULTS: This study found that the decision-making delay rate for rural patients with AIS was 71.5%. The results of the univariate analysis showed that factors associated with decision-making delay included educational level, National Institute of Health stroke scale (NIHSS) score, self-assessed health, monthly income, social support, attitude toward medical help-seeking, health belief, and family dynamics (P < 0.05). Further, we combined logistic regression (LR) and decision-tree (DT) models for multivariate analysis, and finally obtained five factors affecting decision-making delay in AIS patients in rural areas: disease severity, health belief, monthly income (common factors), educational level (only DT model), and social support (only LR model). CONCLUSIONS: This study found that a few variables, including disease severity, educational level, monthly income, health belief, and social support, affected rural AIS patients' decision-making delay in seeking medical care. PRACTICE IMPLICATIONS: To achieve the goal of reducing decision-delay and increasing thrombolysis rate, this study thoroughly examined the influencing factors of decision-making delay in seeking medical care of rural AIS patients from various angles. This analysis provides guidance for medical and healthcare professionals on how to best provide future health education for the high-risk population for stroke in rural areas.

Factors associated with patient delay for older adults with benign prostatic hyperplasia: A descriptive qualitative study.

Based on the theory of planned behavior, the aim of this study was to describe the influencing factors of patient delay intentions and behaviors in benign prostatic hyperplasia (BPH) patients and to provide a reference for the development of a patient delay intention scale. This study was carried out over 4 months in 2021 in Daqing, Heilongjiang, China. The participants were 20 patients with BPH who were aged 60 to 82 years and experienced patient delay; participants were selected through a purposive sampling method. The data were collected via face-to-face semistructured interviews. Five main themes emerged from the interviews, including an insufficient understanding of symptoms, experiences of coping instead of seeking health care, negative attitudes toward care-seeking, the influence of others on decision-making for care-seeking, and obstacles to seeking health care. In conclusion, the patient delay intentions and behaviors of BPH patients are the result of a combination of many factors.

Fast intelligent cell phenotyping for high-throughput optofluidic time-stretch microscopy based on the XGBoost algorithm.

SIGNIFICANCE: The use of optofluidic time-stretch flow cytometry enables extreme-throughput cell imaging but suffers from the difficulties of capturing and processing a large amount of data. As significant amounts of continuous image data are generated, the images require identification with high speed. AIM: We present an intelligent cell phenotyping framework for high-throughput optofluidic time-stretch microscopy based on the XGBoost algorithm, which is able to classify obtained cell images rapidly and accurately. The applied image recognition consists of density-based spatial clustering of applications with noise outlier detection, histograms of oriented gradients combining gray histogram fused feature, and XGBoost classification. APPROACH: We tested the ability of this framework against other previously proposed or commonly used algorithms to phenotype two groups of cell images. We quantified their performances with measures of classification ability and computational complexity based on AUC and test runtime. The tested cell image datasets were acquired from high-throughput imaging of over 20,000 drug-treated and untreated cells with an optofluidic time-stretch microscope. RESULTS: The framework we built beats other methods with an accuracy of over 97% and a classification frequency of 3000 cells / s. In addition, we determined the optimal structure of training sets according to model performances under different training set components. CONCLUSIONS: The proposed XGBoost-based framework acts as a promising solution to processing large flow image data. This work provides a foundation for future cell sorting and clinical practice of high-throughput imaging cytometers.

High-speed cell recognition algorithm for ultrafast flow cytometer imaging system.

An optical time-stretch flow imaging system enables high-throughput examination of cells/particles with unprecedented high speed and resolution. A significant amount of raw image data is produced. A high-speed cell recognition algorithm is, therefore, highly demanded to analyze large amounts of data efficiently. A high-speed cell recognition algorithm consisting of two-stage cascaded detection and Gaussian mixture model (GMM) classification is proposed. The first stage of detection extracts cell regions. The second stage integrates distance transform and the watershed algorithm to separate clustered cells. Finally, the cells detected are classified by GMM. We compared the performance of our algorithm with support vector machine. Results show that our algorithm increases the running speed by over 150% without sacrificing the recognition accuracy. This algorithm provides a promising solution for high-throughput and automated cell imaging and classification in the ultrafast flow cytometer imaging platform.