Assessment of risk management and control measures against coronavirus disease

This article presents the COVID-19 situation and control measures taken by the Government of Pakistan. Two waves of pandemic are faced globally and similar in the study area. We have investigated the risk management decision in two phases. Primarily, strict lockdown was observed from March 2020 to July 2020 and smart lockdown was enforced from August 2020 to December 2020. It has been studied that during strict lockdown, COVID cases reduced gradually but reopening of institutes and smart lockdown strategy resulted gradual increase in confirmed cases and death rates. During first wave of COVID-19 in Pakistan, a total confirmed number of patients of COVID-19 were 263,496 till 18th of July 2020 with total deaths of 5,568 people and 204,276 recoveries, while total number of COVID-19 patients reached 555,511 till 9th of February 2021 with total deaths of 12,026 people. Province of Sindh was affected badly with total number of 251,434 COVID-19 cases followed by Punjab Province with total number of 161,347 COVID-19 till 9th of February 2020.     

Alpha (1,2)-Fucosyltransferase M307A Polymorphism Improves Piglet Survival

To confirm the beneficial effects of alpha (1,2)-fucosyltransferase (FUT1) M307 ^A on piglet survival on commercial farms, we performed PCR-RFLP analysis of FUT1 M307 in successfully marketed (n = 245) and disease affected/deceased pigs during weaning (n = 252) at a commercial farm. We also evaluated the FUT1 genotypes of 190 healthy pigs from three different genetic backgrounds. The distribution of genotypes differed between the successfully marketed and disease affected/deceased pig groups. The frequency of the A allele, associated with resistance to edema and post-weaning diarrhea, was higher in the post-weaning survival group (0.21) than in the non-survival group (0.16, P < 0.05). The odds ratio for piglet survival between AA and GG genotypes was 1.98; thus, piglet survival for individuals with the AA genotype was almost two-fold greater than for GG individuals. The FUT1 gene polymorphism can be used as an effective marker for selection programs to improve post-weaning piglet survival.     

Adhesion molecules in subjects with COPD and healthy non-smokers: a cross sectional parallel group study

Background

The aim of the study was to investigate how the expression of adhesion molecules changes as neutrophils migrate from the circulation to the lung and if these changes differ between non-smoking subjects and smokers with and without COPD.

Methods

Non-smoking healthy subjects (n=22), smokers without (n=21) and with COPD (n=18) were included. Neutrophils from peripheral blood, sputum and bronchial biopsies were analysed for cell surface expression of adhesion molecules (CD11b, CD62L, CD162). Serum, sputum supernatant and BAL-fluid were analysed for soluble adhesion molecules (ICAM-1, -3, E-selectin, P-selectin, VCAM-1, PECAM-1).

Results

Expression of CD11b was increased on circulating neutrophils from smokers with COPD. It was also increased on sputum neutrophils in both smokers groups, but not in non-smokers, as compared to circulating neutrophils.Serum ICAM-1 was higher in the COPD group compared to the other two groups (p<0.05) and PECAM-1 was lower in smokers without COPD than in non-smoking controls and the COPD group (p<0.05). In BAL-fluid ICAM-1 was lower in the COPD group than in the other groups (p<0.05).

Conclusions

Thus, our data strongly support the involvement of a systemic component in COPD and demonstrate that in smokers neutrophils are activated to a greater extent at the point of transition from the circulation into the lungs than in non-smokers.     

A closer look at prion strains

Prions are infectious proteins that are responsible for transmissible spongiform encephalopathies (TSEs) and consist primarily of scrapie prion protein (PrP^Sc), a pathogenic isoform of the host-encoded cellular prion protein (PrP^C). The absence of nucleic acids as essential components of the infectious prions is the most striking feature associated to these diseases. Additionally, different prion strains have been isolated from animal diseases despite the lack of DNA or RNA molecules. Mounting evidence suggests that prion-strain-specific features segregate with different PrP^Sc conformational and aggregation states.

Strains are of practical relevance in prion diseases as they can drastically differ in many aspects, such as incubation period, PrP^Sc biochemical profile (e.g., electrophoretic mobility and glycoform ratio) and distribution of brain lesions. Importantly, such different features are maintained after inoculation of a prion strain into genetically identical hosts and are relatively stable across serial passages.

This review focuses on the characterization of prion strains and on the wide range of important implications that the study of prion strains involves.     

Identification of PrP sequences essential for the interaction between the PrP polymers and Aβ peptide in a yeast-based assay

Alzheimer disease is associated with the accumulation of oligomeric amyloid β peptide (Aβ), accompanied by synaptic dysfunction and neuronal death. Polymeric form of prion protein (PrP), PrP^Sc, is implicated in transmissible spongiform encephalopathies (TSEs). Recently, it was shown that the monomeric cellular form of PrP (PrP^C), located on the neuron surface, binds Aβ oligomers (and possibly other β-rich conformers) via the PrP_23–27 and PrP_90–110 segments, acting as Aβ receptor. On the other hand, PrP^Sc polymers efficiently bind to Aβ monomers and accelerate their oligomerization. To identify specific PrP sequences that are essential for the interaction between PrP polymers and Aβ peptide, we have co-expressed Aβ and PrP (or its shortened derivatives), fused to different fluorophores, in the yeast cell. Our data show that the 90–110 and 28–89 regions of PrP control the binding of proteinase-resistant PrP polymers to the Aβ peptide, whereas the 23–27 segment of PrP is dispensable for this interaction. This indicates that the set of PrP fragments involved in the interaction with Aβ depends on PrP conformational state.     

The Citrobacter rodentium Mouse Model: Studying Pathogen and Host Contributions to Infectious Colitis

This protocol outlines the steps required to produce a robust model of infectious disease and colitis, as well as the methods used to characterize Citrobacter rodentium infection in mice. C. rodentium is a gram negative, murine specific bacterial pathogen that is closely related to the clinically important human pathogens enteropathogenic E. coli and enterohemorrhagic E. coli. Upon infection with C. rodentium, immunocompetent mice suffer from modest and transient weight loss and diarrhea. Histologically, intestinal crypt elongation, immune cell infiltration, and goblet cell depletion are observed. Clearance of infection is achieved after 3 to 4 weeks. Measurement of intestinal epithelial barrier integrity, bacterial load, and histological damage at different time points after infection, allow the characterization of mouse strains susceptible to infection.The virulence mechanisms by which bacterial pathogens colonize the intestinal tract of their hosts, as well as specific host responses that defend against such infections are poorly understood. Therefore the C. rodentium model of enteric bacterial infection serves as a valuable tool to aid in our understanding of these processes. Enteric bacteria have also been linked to Inflammatory Bowel Diseases (IBDs). It has been hypothesized that the maladaptive chronic inflammatory responses seen in IBD patients develop in genetically susceptible individuals following abnormal exposure of the intestinal mucosal immune system to enteric bacteria. Therefore, the study of models of infectious colitis offers significant potential for defining potentially pathogenic host responses to enteric bacteria. C. rodentium induced colitis is one such rare model that allows for the analysis of host responses to enteric bacteria, furthering our understanding of potential mechanisms of IBD pathogenesis; essential in the development of novel preventative and therapeutic treatments.     

少数民族中医体质类型研究现状

中医体质学以人为研究对象,为了研究不同体质构成特点、演变规律、影响因素、分类标准,从而指导疾病的预防、诊治、康复与养生。目前关于生理和病理状态下的中医体质研究日益受到关注,因为对其研究符合中医”治未病”的理念,而关于少数民族的中医体质类型研究开展较少,但对少数民族之间和汉族之间中医体质的比较研究及与生活习惯,生活环境的联系将能更好地说明先天因素和后天环境对中医体质形成的影响。本文就这一领域的研究现状进行总结,对少数民族中医体质的研究一方面可为少数民族人群的疾病防治提供依据,另一方面有助于中医体质形成机理的阐明。     

Stem cells: Insights into the secretome

Stem cells have been considered as possible therapeutic vehicles for different health related problems such as cardiovascular and neurodegenerative diseases and cancer. Secreted molecules are key mediators in cell–cell interactions and influence the cross talk with the surrounding tissues. There is strong evidence supporting that crucial cellular functions such as proliferation, differentiation, communication and migration are strictly regulated from the cell secretome. The investigation of stem cell secretome is accumulating continuously increasing interest given the potential use of these cells in regenerative medicine. The scope of the review is to report the main findings from the investigation of stem cell secretome by the use of contemporary proteomics methods and discuss the current status of research in the field. This article is part of a Special Issue entitled: An Updated Secretome.     

Rule extraction in gene–disease relationship discovery

Background

Biomedical data available to researchers and clinicians have increased dramatically over the past years because of the exponential growth of knowledge in medical biology. It is difficult for curators to go through all of the unstructured documents so as to curate the information to the database. Associating genes with diseases is important because it is a fundamental challenge in human health with applications to understanding disease properties and developing new techniques for prevention, diagnosis and therapy.

Methods

Our study uses the automatic rule-learning approach to gene–disease relationship extraction. We first prepare the experimental corpus from MEDLINE and OMIM. A parser is applied to produce some grammatical information. We then learn all possible rules that discriminate relevant from irrelevant sentences. After that, we compute the scores of the learned rules in order to select rules of interest. As a result, a set of rules is generated.

Results

We produce the learned rules automatically from the 1000 positive and 1000 negative sentences. The test set includes 400 sentences composed of 200 positives and 200 negatives. Precision, recall and F-score served as our evaluation metrics. The results reveal that the maximal precision rate is 77.8% and the maximal recall rate is 63.5%. The maximal F-score is 66.9% where the precision rate is 70.6% and the recall rate is 63.5%.

Conclusions

We employ the rule-learning approach to extract gene–disease relationships. Our main contributions are to build rules automatically and to support a more complete set of rules than a manually generated one. The experiments show exhilarating results and some improving efforts will be made in the future.