Modeling dynamic regulatory processes in stroke.

The ability to examine the behavior of biological systems in silico has the potential to greatly accelerate the pace of discovery in diseases, such as stroke, where in vivo analysis is time intensive and costly. In this paper we describe an approach for in silico examination of responses of the blood transcriptome to neuroprotective agents and subsequent stroke through the development of dynamic models of the regulatory processes observed in the experimental gene expression data. First, we identified functional gene clusters from these data. Next, we derived ordinary differential equations (ODEs) from the data relating these functional clusters to each other in terms of their regulatory influence on one another. Dynamic models were developed by coupling these ODEs into a model that simulates the expression of regulated functional clusters. By changing the magnitude of gene expression in the initial input state it was possible to assess the behavior of the networks through time under varying conditions since the dynamic model only requires an initial starting state, and does not require measurement of regulatory influences at each time point in order to make accurate predictions. We discuss the implications of our models on neuroprotection in stroke, explore the limitations of the approach, and report that an optimized dynamic model can provide accurate predictions of overall system behavior under several different neuroprotective paradigms.

Does climate help modeling COVID-19 risk and to what extent?

A growing number of studies suggest that climate may impact the spread of COVID-19. This hypothesis is supported by data from similar viral contagions, such as SARS and the 1918 Flu Pandemic, and corroborated by US influenza data. However, the extent to which climate may affect COVID-19 transmission rates and help modeling COVID-19 risk is still not well understood. This study demonstrates that such an understanding is attainable through the development of regression models that verify how climate contributes to modeling COVID-19 transmission, and the use of feature importance techniques that assess the relative weight of meteorological variables compared to epidemiological, socioeconomic, environmental, and global health factors. The ensuing results show that meteorological factors play a key role in regression models of COVID-19 risk, with ultraviolet radiation (UV) as the main driver. These results are corroborated by statistical correlation analyses and a panel data fixed-effect model confirming that UV radiation coefficients are significantly negatively correlated with COVID-19 transmission rates.

Predicting individual affect of health interventions to reduce HPV prevalence.

Recently, human papilloma virus (HPV) has been implicated to cause several throat and oral cancers and HPV is established to cause most cervical cancers. A human papilloma virus vaccine has been proven successful to reduce infection incidence in FDA clinical trials, and it is currently available in the USA. Current intervention policy targets adolescent females for vaccination; however, the expansion of suggested guidelines may extend to other age groups and males as well. This research takes a first step toward automatically predicting personal beliefs, regarding health intervention, on the spread of disease. Using linguistic or statistical approaches, sentiment analysis determines a text's affective content. Self-reported HPV vaccination beliefs published in web and social media are analyzed for affect polarity and leveraged as knowledge inputs to epidemic models. With this in mind, we have developed a discrete-time model to facilitate predicting impact on the reduction of HPV prevalence due to arbitrary age- and gender-targeted vaccination schemes.

Bleeding in orthopaedic surgery: the role of blood transfusion and erythropoietin alpha.

High energy trauma is often responsible for acute bleeding. Long bone and pelvis fractures are correlated with increased blood loss. Hypovolaemia could become a life threatening complication especially in elderly patients because of the reduced physiological response. Furthermore it could compromise the course of associated morbidities. Haemorrage is also associated in both comminuted fractures and osteoporosis. An increased intraoperative bleeding often occurs when a prolonged surgical time is required to obtain an appropriate ostheosynthesis. The final consequence of a mayor bleeding is hypovolaemic shock. The reduced oxygen tension of the tissue may be responsible for heart attack, arrhythmia, stroke, multi organ deficiency. For these reasons, it is important to immediately recognize and correct all potential bleeding in order to avoid complications.

Combining hierarchical and associative gene ontology relations with textual evidence in estimating gene and gene product similarity.

Two approaches have recently emerged where the similarity between two genes or gene products is obtained by comparing Gene Ontology (GO) annotations associated with the genes or gene products. One approach captures GO-based similarity in terms of hierarchical relations within each gene subontology, while the other relies on associative relations across the three gene subontologies. We propose a novel methodology where the two approaches can be merged and enriched by textual evidence extracted from biomedical literature with ensuing benefits in coverage and stronger correlation with sequence-based similarity.

α-Lipoic acid and superoxide dismutase in the management of chronic neck pain: a prospective randomized study.

BACKGROUND AND OBJECTIVE: Since oxidative stress plays a pathogenetic role in chronic neck pain (CNP), we investigated whether a combination of α-lipoic acid (ALA) and superoxide dismutase (SOD) might improve pain control and the efficacy of physiotherapy ("multimodal therapy") in patients with CNP. SETTING: This study was conducted in the Rehabilitation Unit of the Department of Surgical and Oncological Sciences at the University Policlinic in Palermo, Italy. DESIGN AND PATIENTS: This was a prospective, randomized, open study in outpatients. INTERVENTION: Patients randomly received either physiotherapy alone (group 2; n = 45) or a combination of ALA 600 mg and SOD 140 IU daily in addition to physiotherapy (group 1; n = 51), for 60 days. Pain was assessed by a visual analogue scale (VAS) and a modified Neck Pain Questionnaire (mNPQ). Treatment compliance and safety were also evaluated. RESULTS: Both groups experienced a significant reduction in the VAS and mNPQ scores after 1 month; however, while no further improvement was observed in group 2 at 60 days, group 1 showed a further VAS reduction (p < 0.001). In addition, in the mNPQ at 60 days, more patients in group 1 than in group 2 reported that their neck pain was improved (p < 0.01), and they showed greater compliance with prescribed physiotherapy (p = 0.048). No drug reaction was observed. CONCLUSION: Use of ALA/SOD in combination with physiotherapy may be a useful approach to CNP, being antioxidants that act on nerve inflammation and disease progression. CLINICAL REHABILITATION IMPACT: These preliminary observations suggest that some interesting goals (better pain control and physical wellbeing) can be achieved by multimodal therapy in CNP patients.

Identification and validation of Ifit1 as an important innate immune bottleneck.

The innate immune system plays important roles in a number of disparate processes. Foremost, innate immunity is a first responder to invasion by pathogens and triggers early defensive responses and recruits the adaptive immune system. The innate immune system also responds to endogenous damage signals that arise from tissue injury. Recently it has been found that innate immunity plays an important role in neuroprotection against ischemic stroke through the activation of the primary innate immune receptors, Toll-like receptors (TLRs). Using several large-scale transcriptomic data sets from mouse and mouse macrophage studies we identified targets predicted to be important in controlling innate immune processes initiated by TLR activation. Targets were identified as genes with high betweenness centrality, so-called bottlenecks, in networks inferred from statistical associations between gene expression patterns. A small set of putative bottlenecks were identified in each of the data sets investigated including interferon-stimulated genes (Ifit1, Ifi47, Tgtp and Oasl2) as well as genes uncharacterized in immune responses (Axud1 and Ppp1r15a). We further validated one of these targets, Ifit1, in mouse macrophages by showing that silencing it suppresses induction of predicted downstream genes by lipopolysaccharide (LPS)-mediated TLR4 activation through an unknown direct or indirect mechanism. Our study demonstrates the utility of network analysis for identification of interesting targets related to innate immune function, and highlights that Ifit1 can exert a positive regulatory effect on downstream genes.

The treatment of wrist fractures with Epibloc system.

BACKGROUND: Fractures of the distal extremity of the radius are the most common bone lesions of the superior extremity of the body, representing 17% of the fractures treated in First Aid facilities. The restoration of anatomical relationships in the radiocarpal joint is considered to be a prerequisite for good functional recovery. There are numerous methods of treatment of these fractures. The Epibloc system is a percutaneous endomedullary synthesis alternative to the plaster cast in wrist fracture treatment. MATERIALS AND METHODS: The authors describe their experience with 81 fractures (63 type A2-3 fractures and 18 type C1) of the wrist treated with the Epibloc System (ES). The results were evaluated clinically, functionally and radiographically on the 7th, 14th, 35th, 90th day. The Gartland and Werley score modified by Sarmiento was used for the functional assessment of the wrist injuries RESULTS: Twenty-nine patients (35.8%) had excellent results, 43 (53.1%) had good results. Forty-seven of the patients (58%) had a ROM of 100% of normal, with a dorsiflexion-palmar flexion (DF-PF) arc of 120 degrees or more. Seven (8.6 %) of the patients developed Sudeck's osteodystrophy. A "very moderate" or "moderate" malunion was observed in 19 cases (23.4% CONCLUSION: 1. The ES is a surgical technique for the treatment of numerous wrist fractures. These fractures can result in excellent mobility as well as a low rate of complications and secondary loss of reduction. 2. This method enables early functional recovery with low incidence of rigidity and algodystrophy. 3. This technique is best indicated for AO type A2-3 and type C1. 4. The ES is inadequate for patients with severe osteopaenia and markedly comminuted intra-articular fractures.

Enriching regulatory networks by bootstrap learning using optimised GO-based gene similarity and gene links mined from PubMed abstracts.

Increasingly, reverse engineering methods have been employed to infer transcriptional regulatory networks from gene expression data. Enrichment with independent evidence from sources such as the biomedical literature and the Gene Ontology (GO) is desirable to corroborate, annotate and expand these networks as well as manually constructed networks. In this paper, we explore a novel approach for computer-assisted enrichment of regulatory networks. GO-based gene similarity is first tuned to an initial network augmented with gene links mined from PubMed and then used to drive network construction using a bootstrapping algorithm. We describe two applications of this approach and discuss its added value in terms of corroboration, annotation and expansion of manually constructed and reversed engineered networks.

Defining the players in higher-order networks: predictive modeling for reverse engineering functional influence networks.

Determining biological network dependencies that can help predict the behavior of a system given prior observations from high-throughput data is a very valuable but difficult task, especially in the light of the ever-increasing volume of experimental data. Such an endeavor can be greatly enhanced by considering regulatory influences on co-expressed groups of genes representing functional modules, thus constraining the number of parameters in the system. This allows development of network models that are predictive of system dynamics. We first develop a predictive network model of the transcriptomics of whole blood from a mouse model of neuroprotection in ischemic stroke, and show that it can accurately predict system behavior under novel conditions. We then use a network topology approach to expand the set of regulators considered and show that addition of topological bottlenecks improves the performance of the predictive model. Finally, we explore how improvements in definition of functional modules may be achieved through an integration of inferred network relationships and functional relationships defined using Gene Ontology similarity. We show that appropriate integration of these two types of relationships can result in models with improved performance.

Social Web mining and exploitation for serious applications: Technosocial Predictive Analytics and related technologies for public health, environmental and national security surveillance.

This paper explores Technosocial Predictive Analytics (TPA) and related methods for Web "data mining" where users' posts and queries are garnered from Social Web ("Web 2.0") tools such as blogs, micro-blogging and social networking sites to form coherent representations of real-time health events. The paper includes a brief introduction to commonly used Social Web tools such as mashups and aggregators, and maps their exponential growth as an open architecture of participation for the masses and an emerging way to gain insight about people's collective health status of whole populations. Several health related tool examples are described and demonstrated as practical means through which health professionals might create clear location specific pictures of epidemiological data such as flu outbreaks.

Using the gene ontology to enrich biological pathways.

Most current approaches to automatic pathway generation are based on a reverse engineering approach in which pathway plausibility is solely derived from gene expression data and not independently validated. Alternative approaches use prior biological knowledge to validate automatically inferred pathways, but the prior knowledge is usually not sufficiently tuned to the pathology of focus. We present a novel pathway generation approach that combines insights from the reverse engineering and knowledge-based approaches to increase the biological plausibility of automatically generated regulatory networks and describe an application of this approach to transcriptional data from a mouse model of neuroprotection during stroke.