સહકારી ધિરાણ મંડળીઓની લાભાર્થી પર થયેલી આર્થિક અને સામાજીક અસરો (અમરેલી જિલ્લાને ધ્યાનમાં રાખીને)

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Association Between Cytokines and Liver Histology in Children with Nonalcoholic Fatty Liver Disease.

BackgroundReliable non-invasive markers to characterize inflammation, hepatocellular ballooning, and fibrosis in nonalcoholic fatty liver disease (NAFLD) are lacking. We investigated the relationship between plasma cytokine levels and features of NAFLD histology to gain insight into cellular pathways driving NASH and to identify potential non-invasive discriminators of NAFLD severity and pattern.MethodsCytokines were measured from plasma obtained at enrollment in pediatric participants in NASH Clinical Research Network studies with liver biopsy-proven NAFLD. Cytokines were chosen a priori as possible discriminators of NASH and its components. Minimization of Akaike Information Criterion (AIC) was used to determine cytokines retained in multivariable models.ResultsOf 235 subjects, 31% had "Definite NASH" on liver histology, 43% had "Borderline NASH", and 25% had NAFLD but not NASH. Total plasminogen activator inhibitor 1 (PAI1) and activated PAI1 levels were higher in pediatric participants with Definite NASH and with lobular inflammation. Interleukin-8 (IL-8) was higher in those with stage 3-4 fibrosis and lobular inflammation. sIL-2rα was higher in children with stage 3-4 fibrosis and portal inflammation. In multivariable analysis, PAI1 variables were discriminators of Borderline/Definite NASH, definite NASH, lobular inflammation and ballooning. IL-8 increased with steatosis and fibrosis severity; sIL-2rα increased with fibrosis severity and portal inflammation. IL-7 decreased with portal inflammation and fibrosis severity.ConclusionsPlasma cytokines associated with histology varied considerably among NASH features, suggesting promising avenues for investigation. Future, more targeted analysis is needed to identify the role of these markers in NAFLD and to evaluate their potential as non-invasive discriminators of disease severity

Multi-Scale Simulation Modeling for Prevention and Public Health Management of Diabetes in Pregnancy and Sequelae

Diabetes in pregnancy (DIP) is an increasing public health priority in the Australian Capital Territory, particularly due to its impact on risk for developing Type 2 diabetes. While earlier diagnostic screening results in greater capacity for early detection and treatment, such benefits must be balanced with the greater demands this imposes on public health services. To address such planning challenges, a multi-scale hybrid simulation model of DIP was built to explore the interaction of risk factors and capture the dynamics underlying the development of DIP. The impact of interventions on health outcomes at the physiological, health service and population level is measured. Of particular central significance in the model is a compartmental model representing the underlying physiological regulation of glycemic status based on beta-cell dynamics and insulin resistance. The model also simulated the dynamics of continuous BMI evolution, glycemic status change during pregnancy and diabetes classification driven by the individual-level physiological model. We further modeled public health service pathways providing diagnosis and care for DIP to explore the optimization of resource use during service delivery. The model was extensively calibrated against empirical data.Comment: 10 pages, SBP-BRiMS 201

An HMM-Based Framework for Supporting Accurate Classification of Music Datasets

open3In this paper, we use Hidden Markov Models (HMM) and Mel-Frequency Cepstral Coecients (MFCC) to build statistical models of classical music composers directly from the music datasets. Several musical pieces are divided by instruments (String, Piano, Chorus, Orchestra), and, for each instrument, statistical models of the composers are computed.We selected 19 dierent composers spanning four centuries by using a total number of 400 musical pieces. Each musical piece is classied as belonging to a composer if the corresponding HMM gives the highest likelihood for that piece. We show that the so-developed models can be used to obtain useful information on the correlation between the composers. Moreover, by using the maximum likelihood approach, we also classied the instrumentation used by the same composer. Besides as an analysis tool, the described approach has been used as a classier. This overall originates an HMM-based framework for supporting accurate classication of music datasets. On a dataset of String Quartet movements, we obtained an average composer classication accuracy of more than 96%. As regards instrumentation classication, we obtained an average classication of slightly less than 100% for Piano, Orchestra and String Quartet. In this paper, the most signicant results coming from our experimental assessment and analysis are reported and discussed in detail.openCuzzocrea, Alfredo; Mumolo, Enzo; Vercelli, GianniCuzzocrea, Alfredo; Mumolo, Enzo; Vercelli, Giann

What factors determine the severity of hepatitis A‐related acute liver failure?

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87020/1/j.1365-2893.2010.01410.x.pd

Multiseriate cortical sclerenchyma enhance root penetration in compacted soils

Mechanical impedance limits soil exploration and resource capture by plant roots. We examine the role of root anatomy in regulating plant adaptation to mechanical impedance and identify a root anatomical phene in maize (Zea mays) and wheat (Triticum aestivum) associated with penetration of hard soil: multiseriate cortical sclerenchyma (MCS). We characterize this trait and evaluate the utility of MCS for root penetration in compacted soils. Roots with MCS had a greater cell wall to lumen ratio and a distinct UV emission spectrum in outer cortical cells. Genome-wide association mapping revealed that MCS is heritable and genetically controlled. We identified a candidate gene associated with MCS. Across all root classes and nodal positions, maize genotypes with MCS had 13% greater root lignin concentration compared to genotypes without MCS. Genotypes without MCS formed MCS upon exogenous ethylene exposure. Genotypes with MCS had greater lignin concentration and bending strength at the root tip. In controlled environments, MCS in maize and wheat was associated improved root tensile strength and increased penetration ability in compacted soils. Maize genotypes with MCS had root systems with 22% greater depth and 39% greater shoot biomass in compacted soils in the field compared to lines without MCS. Of the lines we assessed, MCS was present in 30-50% of modern maize, wheat, and barley cultivars but was absent in teosinte and wild and landrace accessions of wheat and barley. MCS merits investigation as a trait for improving plant performance in maize, wheat, and other grasses under edaphic stress

The non-Verbal Structure of Patient Case Discussions in Multidisciplinary Medical Team Meetings

Meeting analysis has a long theoretical tradition in social psychology, with established practical rami?cations in computer science, especially in computer supported cooperative work. More recently, a good deal of research has focused on the issues of indexing and browsing multimedia records of meetings. Most research in this area, however, is still based on data collected in laboratories, under somewhat arti?cial conditions. This paper presents an analysis of the discourse structure and spontaneous interactions at real-life multidisciplinary medical team meetings held as part of the work routine in a major hospital. It is hypothesised that the conversational structure of these meetings, as indicated by sequencing and duration of vocalisations, enables segmentation into individual patient case discussions. The task of segmenting audio-visual records of multidisciplinary medical team meetings is described as a topic segmentation task, and a method for automatic segmentation is proposed. An empirical evaluation based on hand labelled data is presented which determines the optimal length of vocalisation sequences for segmentation, and establishes the competitiveness of the method with approaches based on more complex knowledge sources. The effectiveness of Bayesian classi?cation as a segmentation method, and its applicability to meeting segmentation in other domains are discusse

Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

Background Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide.Methods A multimethods analysis was performed as part of the GlobalSurg 3 study-a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital.Findings Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3.85 [95% CI 2.58-5.75]; p<0.0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63.0% vs 82.7%; OR 0.35 [0.23-0.53]; p<0.0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer.Interpretation Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised