2011-2017年大连市输入性卵形疟原虫感染病例的实验室诊断

目的分析大连市2011-2017年7例输入性卵形疟原虫感染病例的病原、抗原及核酸检测结果,以提高卵形疟原虫诊断的准确率和效率。方法收集7例疑似疟疾患者的血样,对血样进行血涂片镜检、快速疟疾诊断试纸条检测(RDT)及荧光定量PCR检测。结果血涂片镜检结果显示7例患者均感染卵形疟原虫。RDT检测结果显示2例阴性,另外5例为泛疟原虫抗原阳性。荧光定量PCR结果显示7例患者均检出疟原虫属和卵形疟原虫核酸。结论综合镜检、RDT及荧光定量PCR检测结果确定该7例患者均为卵形疟原虫感染。     

人结核分枝杆菌特异性核酸探针制备及结核病PCR检测技术的初步临床应用

采用人结核分枝杆菌(Mycobacterium tuberculosis TB)染色体DNA为模板,选择位于插入片段IS6110中884～865和568～588碱基对处的两个片段为引物,扩增出317bp的特异性片段．将其克隆进pUCl9载体。酶切图谱分析和DNA序列测定证实为目的片段。该片段经DIG标记,分别与11种分枝杆菌DNA进行Southern杂交,结果证明只与人型复合分枝杆菌发生杂交反应。利用该对引物建立的PcR检测拄术对74份结核病痰液标本进行检测,并与临床细菌快速培养结果相比较,发现48份临床阳性均为PcR阳性,在26份临床阴性标本中亦发现11份PCR检测阳性。将标本PCR产物与克隆探针进行杂交,显示两者结果完全一致。说明PCR检测体系结果可靠,其灵敏度明显高于目前临床所采用的方法,可作为一种常规技术用于结核病的临床检测。     

Spectral imaging for precise surgical intervention in Hirschsprung's Disease

We used advanced spectral imaging for intrasurgical decision making in a preclinical study, on a mouse model of Hirschsprung's Disease. Our imaging device sampled areas from normal and abnormal (aganglionic) colon in these animals. Spectral segmentation and classification of the resulting images showed a clear distinction between the normal and aganglionic regions, as confirmed by pathological analysis and use of mutant mice. We developed a simple algorithm that could distinguish normal from aganglionic colon with high spatial resolution and reproducibility, and the following statistics: sensitivity = 97%, specificity = 94%, positive predictive value = 92%, negative predictive value = 98%. These studies showed translational proof of concept that spectral imaging could be used during operations, in real time, to help surgeons precisely distinguish normal from abnormal tissue without requiring traditional biopsy. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Use of milk electrical conductivity for the differentiation of mastitis causing pathogens in Holstein cows

Mastitis is one of the most prevalent and costly diseases in dairy cattle. Key components for adequate mastitis control are the detection of early stages of infection, as well as the selection of appropriate management interventions and therapies based on the causal pathogens associated with the infection. The objective was to characterize the pattern of electrical conductivity (EC) in milk during intramammary infection, considering specific mastitis-causing pathogen groups involvement. Cows (n = 200) identified by an in-line mastitis detection system with a positive deviation ≥15% in the manufacturer’s proprietary algorithm for EC (high electrical conductivity (HEC)) were considered cases and enrolled in the study at the subsequent milking. One control (CON) cow, within normal ranges for EC, was matched to each case. A composite milk sample was collected aseptically from each cow for bacteriological culture. Milk yield (MY) and EC were recorded for each milking during ±7 days relative to enrollment. Milk cultures were categorized into gram positive (GP), gram negative (GN), other (OTH) and no growth (NOG). Data were submitted for repeated-measures analysis with EC as the dependent variable and EC status at day −1, bacteriological culture category, parity number, stage of lactation and days relative to sampling as main independent variables. Average (± standard error (SE)) EC was greater in HEC than in CON cows (12.5 ± 0.5 v. 10.8 ± 0.5 mS/cm) on the day of identification (day −1). Milk yield on day −1 was greater in CON than in HEC (37.6 ± 5.1 v. 33.5 ± 5.2 kg). For practical management purposes, average EC on day −1 was similar for the different bacteriological culture categories: 11.4 ± 0.6, 11.7 ± 0.5, 12.3 ± 0.8 and 11.7 ± 0.5 mS/cm in GN, GP, OTH and NOG, respectively. Parity number was only associated with day −1 EC in HEC group, with the greatest EC values in parity 3 (12.3 ± 0.3 mS/cm), followed by parity 2 (11.9 ± 0.2 mS/cm), parity >3 (11.6 ± 0.5 mS/cm) and primiparous cows (11.2 ± 0.2 mS/cm). An effect on EC for the interaction of day relative to identification by pathogen gram category was observed. The same interaction effect was observed on daily MY. Overall, the level of variation for MY and EC between- and within-cows was substantial, and as indicated by the model diagnostic procedures, the magnitude of the variance in the cows in the CON group resulted in deviations from normality in the residuals. We concluded that characteristic temporal patterns in EC and MY in particular pathogen groups may provide indications for differentiation of groups of mastitis-causing pathogens. Further research to build detection models including EC, MY and cow-level factors is required for accurate differentiation.     

Machine Learning Approach to Detect Cardiac Arrhythmias in ECG Signals: A Survey

Cardiac arrhythmia is a condition when the heart rate is irregular either the beat is too slow or too fast. It occurs due to improper electrical impulses that coordinates the heart beats. Sudden cardiac death may occurs due to some dangerous arrhythmias conditions. Hence the main objective of the electrocardiogram (ECG) analysis is to detect the life-threatening arrhythmias accurately for appropriate treatment in order to save life. Since the last decades, several methods were reported for automatic ECG beat classifications. In this work, we present a systematic review of the current state-of-the-art methods used to detect cardiac arrhythmia using on ECG signals. It includes the signal decomposition, feature extraction and machine learning approaches used for automatic detection and decision making process. The articles covers the pre-processing, detection of QRS complex, feature extraction and classification of ECG beats. Based on the past studies, it is understood that the automated approach using computer-aided decision making process is highly required for real-time detection of cardiac arrhythmias. The advantages and limitations of different methods are discussed and also the future scopes is highlighted in the process of effective detection of cardiac arrhythmias. This study could be beneficial for researchers to analyze the existing state-of-art techniques used in detection of arrhythmia conditions.     

Using vaginal discharge score (VDS) grading system to evaluate the effect of clinical endometritis on reproductive performance of dairy cows in China

Clinical endometritis (CE) is a major cause in affecting the reproductive performance of dairy cows. The objectives of this study were to ascertain the prevalence of CE and to evaluate the effect of CE on reproductive performance in dairy cows using vaginal discharge score (VDS) grading system. 803 dairy cows were examined by vaginoscope with 4-point VDS at 26 ± 3 days in milk (DIM) and classified into six groups: non-endometritis with VDS 0 (control; CON), endometritis with VDS 1 (MEM), non-treated endometritis with VDS 2 (NTME), treated endometritis with VDS 2 (TME), non-treated endometritis with VDS 3 (NTPE), and treated endometritis with VDS 3 (TPE). Cows in TME and TPE groups were treated with 200 mL of 50% dextrose solution by intrauterine infusion. The prevalence of CE was 33% at 26 ± 3 DIM. Binary logistic regression analysis revealed cows in MEM, NTME and NTPE groups had a less likelihood of first artificial insemination (AI) pregnancy than those in CON group (P < 0.05). Kaplan-Meier survival curves for days open were statistically different (P = 0.004). In Cox regression model, cows in NTME and NTPE groups had a reduced pregnancy rate than those in CON group (P < 0.05). The hazard of pregnancy in NTME group was lower than that in TME group (P = 0.044). Similarly, it was lower for the hazard of pregnancy in NTPE group than in TPE group (P = 0.048). Cows in MEM, NTME, and NTPE groups required more services per pregnancy than those in CON group (P < 0.05). In conclusion, CE examined by the VDS grading system impaired reproductive performance, and mild endometritis with VDS 1 should be treated in the early postpartum period to ameliorate fertility in dairy herds.     

Serological tests for COVID-19: Potential opportunities

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel betacoronavirus, caused a pandemic leading to a standstill of nearly all global activities. There are some controversies on the production of specific immunoglobulin M (IgM) and IgG antibodies after the infection with SARS-CoV-2. This paper seeks to elaborate on the potential application of IgM and IgG antibodies and the viral antigens for the diagnosis and the course of the disease as well as the recurrence of positive nucleic acid tests after discharge.     

An Approach Based on Mutually Informed Neural Networks to Optimize the Generalization Capabilities of Decision Support Systems Developed for Heart Failure Prediction

Available clinical methods for heart failure (HF) diagnosis are expensive and require a high-level of experts intervention. Recently, various machine learning models have been developed for the prediction of HF where most of them have an issue of over-fitting. Over-fitting occurs when machine learning based predictive models show better performance on the training data yet demonstrate a poor performance on the testing data and the other way around. Developing a machine learning model which is able to produce generalization capabilities (such that the model exhibits better performance on both the training and the testing data sets) could overall minimize the prediction errors. Hence, such prediction models could potentially be helpful to cardiologists for the effective diagnose of HF. This paper proposes a two-stage decision support system to overcome the over-fitting issue and to optimize the generalization factor. The first stage uses a mutual information based statistical model while the second stage uses a neural network. We applied our approach to the HF subset of publicly available Cleveland heart disease database. Our experimental results show that the proposed decision support system has optimized the generalization capabilities and has reduced the mean percent error (MPE) to 8.8% which is significantly less than the recently published studies. In addition, our model exhibits a 93.33% accuracy rate which is higher than twenty eight recently developed HF risk prediction models that achieved accuracy in the range of 57.85% to 92.31%. We can hope that our decision support system will be helpful to cardiologists if deployed in clinical setup.     

Short communication: Single tube allele specific (STAS) PCR for direct determination of the mutation in the porcine ryanodine receptor gene associated with malignant hyperthermia

The ability to add or delete specific genes in swine will likely provide considerable benefits not just to agriculture but also to medicine, where pigs have potential as models for human disease and as organ donors. Here we have transferred nuclei from a genetically modified fibroblast cell line to porcine oocytes, matured in vitro under defined culture conditions, to create piglets expressing enhanced green fluorescent protein. The nuclear transfer-derived piglets were of normal size, although some mild symptoms of “large offspring syndrome” were evident. These experiments represent a next step towards creating swine with more useful genetic modifications.     

Proteomics in translational cancer research: biomarker discovery for clinical applications

This special focus issue of Expert Review of Proteomics invites key opinion leaders to report their recent findings and views on the important topic of translating potential proteomic biomarkers to clinically useful, regulator-approved biomarkers: a challenging journey. The issue also highlights the difficulties associated with and the way forward in the discovery of proteomic cancer biomarkers for clinical applications, as well as presenting recent original research in the field.