Evidence against Wolbachia symbiosis in Loa loa

BACKGROUND: The majority of filarial nematode species are host to Wolbachia bacterial endosymbionts, although a few including Acanthocheilonema viteae, Onchocerca flexuosa and Setaria equina have been shown to be free of infection. Comparisons of species with and without symbionts can provide important information on the role of Wolbachia symbiosis in the biology of the nematode hosts and the contribution of the bacteria to the development of disease. Previous studies by electron microscopy and PCR have failed to detect intracellular bacterial infection in Loa loa. Here we use molecular and immunohistological techniques to confirm this finding. METHODS: We have used a combination of PCR amplification of bacterial genes (16S ribosomal DNA [rDNA], ftsZ and Wolbachia surface protein [WSP]) on samples of L. loa adults, third-stage larvae (L3) and microfilariae (mf) and immunohistology on L. loa adults and mf derived from human volunteers to determine the presence or absence of Wolbachia endosymbionts. Samples used in the PCR analysis included 5 adult female worms, 4 adult male worms, 5 mf samples and 2 samples of L3. The quality and purity of nematode DNA was tested by PCR amplification of nematode 5S rDNA and with diagnostic primers from the target species and used to confirm the absence of contamination from Onchocerca sp., Mansonella perstans, M. streptocerca and Wuchereria bancrofti. Immunohistology was carried out by light and electron microscopy on L. loa adults and mf and sections were probed with rabbit antibodies raised to recombinant Brugia malayi Wolbachia WSP. Samples from nematodes known to be infected with Wolbachia (O. volvulus, O. ochengi, Litomosoides sigmodontis and B. malayi) were used as positive controls and A. viteae as a negative control. RESULTS: Single PCR analysis using primer sets for the bacterial genes 16S rDNA, ftsZ, and WSP were negative for all DNA samples from L. loa. Positive PCR reactions were obtained from DNA samples derived from species known to be infected with Wolbachia, which confirmed the suitability of the primers and PCR conditions. The quality and purity of nematode DNA samples was verified by PCR amplification of 5S rDNA and with nematode diagnostic primers. Additional analysis by 'long PCR' failed to produce any further evidence for Wolbachia symbiosis. Immunohistology of L. loa adults and mf confirmed the results of the PCR with no evidence for Wolbachia symbiosis. CONCLUSION: DNA analysis and immunohistology provided no evidence for Wolbachia symbiosis in L. loa

Regulation of skeletal muscle oxidative capacity and insulin signaling by the Mitochondrial Rhomboid Protease PARL

Type 2 diabetes mellitus (T2DM) and aging are characterized by insulin resistance and impaired mitochondrial energetics. In lower organisms, remodeling by the protease pcp1 (PARL ortholog) maintains the function and lifecycle of mitochondria. We examined whether variation in PARL protein content is associated with mitochondrial abnormalities and insulin resistance. PARL mRNA and mitochondrial mass were both reduced in elderly subjects and in subjects with T2DM. Muscle knockdown of PARL in mice resulted in malformed mitochondrial cristae, lower mitochondrial content, decreased PGC1&alpha; protein levels, and impaired insulin signaling. Suppression of PARL protein in healthy myotubes lowered mitochondrial mass and insulin-stimulated glycogen synthesis and increased reactive oxygen species production. We propose that lower PARL expression may contribute to the mitochondrial abnormalities seen in aging and T2DM.<br /

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

Integrating Bayesian networks and Simpson's paradox in data mining

This paper proposes to integrate two very different kinds of methods for data mining, namely the construction of Bayesian networks from data and the detection of occurrences of Simpson’s paradox. The former aims at discovering potentially causal knowledge in the data, whilst the latter aims at detecting surprising patterns in he data. By integrating these two kinds of methods we can hopefully discover patterns which are more likely to be useful to the user, a challenging data mining goal which is under-explored in the literature. The proposed integration method involves two approaches. The first approach uses the detection of occurrences of Simpson’s paradox as a preprocessing for a more effective construction of Bayesian networks; whilst the second approach uses the construction of a Bayesian network from data as a preprocessing for the detection of occurrences of Simpson’s paradox

Training without data: Knowledge Insertion into RBF Neural Networks

Often, in real-world situations no actual data is available for training neural networks but the domain expert has a good idea of what to expect in terms of input and output parameter values. If the expert can express these relationships in the form of rules, this would provide a resource too valuable to ignore. Fuzzy logic is used to handle the imprecision and vagueness of natural language and provides this additional advantage to a system. This paper investigates the development of a novel knowledge insertion algorithm that explores the benefits of pre-structuring RBF neural networks by using prior fuzzy domain knowledge and previous training experiences. Pre-structuring is accomplished by using fuzzy rules gained from a domain expert and using them to modify existing RBF networks. The benefits and novel achievements of this work enable RBF neural networks to be trained without actual data but to rely on input to output mappings defined through expert knowledge.

Auto-Extraction, Representation and Integration of a Diabetes Ontology using Bayesian Networks

This paper describes how high level biological knowledge obtained from ontologies such as the Gene Ontology (GO) can be integrated with low level information extracted from a Bayesian network trained on protein interaction data. We can automatically generate a biological ontology by text mining the type II diabetes research literature. The ontology is populated with the entities and relationships from protein-to-protein interactions. New, previously unrelated information is extracted from the growing body of research literature and incorporated with knowledge already known on this subject from the gene ontology and databases such as BIND and BioGRID. We integrate the ontology within the probabilistic framework of Bayesian networks which enables reasoning and prediction of protein function.