Daily Clinical Data Research Articles

Dogs Health Care Management System

Web based applications have become an important part of daily life as it improves inefficient resources management while saving time. With the advancement of technology, every business sector tries to introduce software systems with smart features Life has become too busy to schedule and keep up with pets' necessary medical appointments and care. There are currently a smaller number of online applications for veterinary systems. So, this study designs an online system to fill up that research gap. In this project, a JavaScript based application is designed with features such as booking an appointment by checking a doctor's availability and all those details are going to store in a database. This also records all the details of the dogs that come to the hospital for daily treatments. With the help of daily clinical data, veterinary health care owners can easily identify active clients and give offers easily. Moreover, newly introduced features are caused to increase the efficiency and maintenance of the system. However, this application addresses not only the pet lovers’ difficulties regarding the veterinary health, but it also addresses management side problems as well. This system is designed and implemented not only for the pets, but also considering orphan dogs. So, this system provides volunteer service as well.

Normalization of HE-stained histological images using cycle consistent generative adversarial networks

BackgroundHistological images show strong variance (e.g. illumination, color, staining quality) due to differences in image acquisition, tissue processing, staining, etc. This can impede downstream image analysis such as staining intensity evaluation or classification. Methods to reduce these variances are called image normalization techniques.MethodsIn this paper, we investigate the potential of CycleGAN (cycle consistent Generative Adversarial Network) for color normalization in hematoxylin-eosin stained histological images using daily clinical data with consideration of the variability of internal staining protocol variations. The network consists of a generator network GB that learns to map an image X from a source domain A to a target domain B, i.e. GB:XA→XB. In addition, a discriminator network DB is trained to distinguish whether an image from domain B is real or generated. The same process is applied to another generator-discriminator pair (GA,DA), for the inverse mapping GA:XB→XA. Cycle consistency ensures that a generated image is close to its original when being mapped backwards (GA(GB(XA))≈XA and vice versa). We validate the CycleGAN approach on a breast cancer challenge and a follicular thyroid carcinoma data set for various stain variations. We evaluate the quality of the generated images compared to the original images using similarity measures. In addition, we apply stain normalization on pathological lymph node data from our institute and test the gain from normalization on a ResNet classifier pre-trained on the Camelyon16 data set.ResultsQualitative results of the images generated by our network are compared to original color distributions. Our evaluation indicates that by mapping images to a target domain, the similarity training images from that domain improves up to 96%. We also achieve a high cycle consistency for the generator networks by obtaining similarity indices greater than 0.9. When applying the CycleGAN normalization to HE-stain images from our institute the kappa-value of the ResNet-model that is only trained on Camelyon16 data is increased more than 50%.ConclusionsCycleGANs have proven to efficiently normalize HE-stained images. The approach compensates for deviations resulting from image acquisition (e.g. different scanning devices) as well as from tissue staining (e.g. different staining protocols), and thus overcomes the staining variations in images from various institutions.The code is publicly available at https://github.com/m4ln/stainTransfer_CycleGAN_pytorch. The data set supporting the solutions is available at https://doi.org/10.11588/data/8LKEZF.

Bioimpedance Spectroscopy in Critically Ill Children – Feasibility and Association with Protein Intake and Functional Status

Abstract Objectives Bioimpedance spectroscopy (BIS) is used to assess body composition, but its application in critically ill children remains scant. In this pilot study, we evaluated the feasibility of BIS in patients admitted to a pediatric intensive care unit (PICU), and aimed to examine the association between body composition changes, macronutrient intake, and functional outcomes in this population. Methods Children requiring invasive mechanical ventilation with an expected length of stay of >5 days were enrolled. Functional status scale (FSS) was obtained from medical records and BIS measurements were completed within 3 days of admission; BIS was repeated weekly and FSS was repeated at PICU discharge. Daily clinical data and nutrient intake were recorded by dietitians. Nonparametric statistics were used to describe the characteristics of the cohort. Results Of the 20 patients enrolled to date, 18 (50% female, 56% medical, 44% surgical) had complete baseline data and 12 had follow-up BIS. Median (IQR) age was 4.1 (1.4, 7.8) years. Admission weight was 16.0 (11.2, 24.0) kg and mid-arm circumference Z-score was −0.3 (−1.8, 1.0). BIS measured total body fluid (TBF) was 11.0 (7.9, 17.5) liters, the ratio of extracellular to intracellular fluid (ECF: ICF) was 0.9 (0.8, 1.1), and fat free mass (FFM) was 15.1 (10.8, 23.9) kg. Age (r = 0.77, P < 0.001) and weight (r = 0.84, P < 0.001) were strongly correlated with TBF at enrollment, but not with ECF: ICF. Initial phase angle at 50 kHz (PA) was 2.8 (2.2, 3.8) and was negatively correlated with ECF: ICF (r = −0.79, P < 0.001). Average intake during PICU stay was 0.7 (0.4, 1.3) g protein/kg/day and 20 (10, 44) kcal/kg/day. After 1 week in PICU, median change in FFM among patients with 2 plausible measurements (n = 5) was −0.8 (−1.8, 0.5) kg. Median (IQR) length of stay (days) was 12.2 (6.9, 18.9) in the PICU, and 23.9 (14.7, 33.6) in the hospital. FSS improved from 15 (12, 22) to 9 (7, 11) at PICU discharge (P = 0.001). Intake and body composition measurements by BIS were not significantly correlated with FSS. Conclusions BIS measurements were feasible and ECF: ICF ratio was high in this population, possibly due to fluid shifts. FSS improved over the course of PICU admission. The interaction between nutrient intake, body composition, phase angle and functional status during pediatric critical illness remains unknown. Funding Sources ASPEN Rhoads Research Foundation.

Social Media Surveillance for Outbreak Projection via Transmission Models: Longitudinal Observational Study.

BackgroundAlthough dynamic models are increasingly used by decision makers as a source of insight to guide interventions in order to control communicable disease outbreaks, such models have long suffered from a risk of rapid obsolescence due to failure to keep updated with emerging epidemiological evidence. The application of statistical filtering algorithms to high-velocity data streams has recently demonstrated effectiveness in allowing such models to be automatically regrounded by each new set of incoming observations. The attractiveness of such techniques has been enhanced by the emergence of a new generation of geospatially specific, high-velocity data sources, including daily counts of relevant searches and social media posts. The information available in such electronic data sources complements that of traditional epidemiological data sources.ObjectiveThis study aims to evaluate the degree to which the predictive accuracy of pandemic projection models regrounded via machine learning in daily clinical data can be enhanced by extending such methods to leverage daily search counts.MethodsWe combined a previously published influenza A (H1N1) pandemic projection model with the sequential Monte Carlo technique of particle filtering, to reground the model bu using confirmed incident case counts and search volumes. The effectiveness of particle filtering was evaluated using a norm discrepancy metric via predictive and dataset-specific cross-validation.ResultsOur results suggested that despite the data quality limitations of daily search volume data, the predictive accuracy of dynamic models can be strongly elevated by inclusion of such data in filtering methods.ConclusionsThe predictive accuracy of dynamic models can be notably enhanced by tapping a readily accessible, publicly available, high-velocity data source. This work highlights a low-cost, low-burden avenue for strengthening model-based outbreak intervention response planning using low-cost public electronic datasets.

Machine-Learning Approach for Modeling Myelosuppression Attributed to Nimustine Hydrochloride.

A major adverse effect arising from nimustine hydrochloride (ACNU) therapy for brain tumors is myelosuppression. Because its timing and severity vary among individual patients, the ACNU dose level has been adjusted in an empiric manner at individual medical facilities. To our knowledge, ours is the first study to develop a machine-learning approach to estimate myelosuppression through analysis of patient factors before treatment and attempts to clarify the relationship between myelosuppression and hematopoietic stem cells from daily clinical data. Adverse effect prediction will allow ACNU dose adjustment for patients predicted to have decreases in blood cell counts and will enable focused follow-up of patients undergoing chemoradiotherapy. Patients were newly pathologically diagnosed with WHO grade 2 or 3 tumors and were treated with ACNU-based chemoradiotherapy. For detailed analysis of the timing and intensity of adverse effects in patients, we developed a data-weighted support vector machine (SVM) based on adverse event criteria (nadir-weighted SVM [NwSVM]). To evaluate the estimation accuracy of blood cell count dynamics, the determination coefficient ( r2) between real and estimated data was calculated by three regression methods: polynomial, SVM, and NwSVM. Only the NwSVM-based regression enabled estimation of the dynamics of all blood cell types with high accuracy (mean r2 = 0.81). The mean timing of nadir arrival estimated using this regression was 35 days for platelets, 41 days for RBCs, 52 days for lymphocytes, 57 days for WBCs, and 62 days for neutrophils. The NwSVM can be used to predict myelosuppression and clearly depicts nadir timing differences between platelets and other blood cells.

Forecasting hand, foot, and mouth disease in Shenzhen based on daily level clinical data and multiple environmental factors.

Hand, foot, and mouth disease (HFMD) is caused by a group of enteroviruses. It infects millions of children in the Southeast Asian area. An accurate forecasting of outbreaks of HFMD could facilitate public health officials to suggest public health actions earlier. Many researchers tried to develop an early warning system for HFMD to lower the damage caused by a HFMD outbreak. The research data based on daily level could help figure out the relationship between HFMD and environmental factors, but nevertheless is difficult to collect. In this study, we collected the daily clinical data from the Shenzhen Health Information Center and multiple environmental factors to analyze the outbreaks of HFMD. Considering the incubation period of HFMD, we fed the previous 60 days' HFMD rates, 7 days' temperature factors and 7 days' air-quality factors into the tree model, XGBoost. The following conclusions were drawn in this study: i) Compared with the model only using the previous HFMD rate and temperature factors, the addition of the air-quality factors could make the model better, improving MAE nearly 16.7%. ii) By analyzing the Pearson correlation, we found that the temperature showed a positive correlation and the air quality showed a negative correlation for the HFMD outbreaks. Improving the air quality, especially decreasing PM2.5 and PM10 could decrease the risk of HFMD outbreaks.

Progrès récents de l’imagerie par résonance magnétique (IRM) chez le nouveau-né

The accurate morphological exploration of the brain is a major challenge in neonatology that advances in magnetic resonance imaging (MRI) can now provide. MRI is the gold standard if an hypoxic ischemic pathology is suspected in a full term neonate. In prematures, the specific role of MRI remains to be defined, secondary to US in any case. We present a state of the art of hardware and software technical developments in MRI. The increase in magnetic field strength (3 tesla) and the emergence of new MRI sequences provide access to new information. They both have positive and negative consequences on the daily clinical data acquisition use. The semiology of brain imaging in full term newborns and prematures is more extensive and complex and thereby more difficult to interpret. The segmentation of different brain structures in the newborn, even very premature, is now available. It is now possible to dissociate the cortex and basal ganglia from the cerebral white matter, to calculate the volume of anatomical structures, which improves the morphometric quantification and the understanding of the normal and abnormal brain development. MRI is a powerful tool to analyze the neonatal brain. The relevance of the diagnostic contribution requires an adaptation of the parameters of the sequences to acquire and of the image processing methods.

Draining Fluids through a Peritoneal Catheter in Newborns after Cardiac Surgery Helps to Control Fluid Balance

Fluid overload, a common complication following cardiac surgery in infants is often difficult to manage. Dialysis can be used in severe cases, but may not be well tolerated. In such patients, peritoneal drainage could be an alternative option for fluid removal. We report the case of a newborn with a truncus arteriosus who developed postoperatively a complicated clinical course with right ventricular dysfunction, prerenal condition as well as fluid overload despite diuretic therapy. Dialysis was indicated for fluid removal. Peritoneal dialysis was started using a surgically placed Tenckhoff catheter and stopped due to inefficacy and leaks and no other modalities of dialysis were used. However, the catheter was left in place over a period of two months for fluid drainage and removed because of unexplained fever. In order to determine the effect of peritoneal drainage, we selected a period of one week before and one week after the removal of the drain to compare daily clinical data, urine electrolytes and renal function and found a positive effect on fluid balance control. We conclude that the fluid removal by continuous peritoneal drainage is a simple and safe alternative that can be used to control fluid balance in infants after cardiac surgery.

Predictive Value of Intrathecal Interleukin-6 for Ventriculostomy-related Infection

Early diagnosis of ventriculostomy-related infection (VRI) is crucial for the early treatment and course of this disease. In neurosurgical patients the diagnostic criteria are equivocal, mostly because of bloodstained cerebrospinal fluid (CSF). The predictive value, sensitivity and specificity of intrathecal interleukin-6 (IL-6 (CSF)) has been proven for VRI compared with classical diagnostic CSF parameters, i.e. cell countCSF (CC (CSF)) and total protein (CSF). We prospectively analyzed the daily clinical data and CSF samples of 75 neurosurgical patients with an external ventricular drainage (EVD), which had been inserted predominantly because of poor-grade subarachnoid hemorrhage (SAH). The intrathecal interleukin-6 concentrations (IL-6 (CSF)) were correlated with the clinical course and VRI incidence, as diagnosed by the classical VRI criteria (CC (CSF), total protein (CSF), clinical symptoms). Based on classical criteria, bacterial meningitis occurred in 26.7% of patients. Patients with VRI manifested significantly (p<0.001) higher median values of IL-6 (CSF) (up to 2,000-fold increase) the day before (day -1) infection was diagnosed by conventional parameters. Using a cut-off value of IL-6 (CSF)>or=2,700 pg/ml [4,050 pg/ml after WHO standardization] on day -1, the relative risk for VRI was 6.09 (95% CI: 2.62-14.18%). A predictive value of IL-6 (CSF)>or=2,700 pg/ml [4,050 pg/ml] for VRI was calculated of 89% (95% CI: 79.6-98.0%), a sensitivity of 73.7% and a specificity of 91.4%. The amount of intrathecal blood was an independent risk factor for VRI occurrence, whereas the mean duration of EVD in place showed no impact on the rate of infection. Our data indicate that IL-6 (CSF) is a reliable marker for predicting VRI prior to clinically manifest meningitis, one day earlier than the common diagnostic criteria of CSF infection (CC (CSF), total protein (CSF), clinical symptoms).

Study on the fungal aeroallergen concentration in the Texas Panhandle using a Burkard Volumetric Spore Trap

Rationale This investigation was aimed at analyzing the relationship between the meteorological conditions, diurnal variations and the fungal aeroallergen data of the North West Texas Panhandle and to find the correlation between the aeroallergen index and frequency of allergic rhinitis recorded in the allergy clinic. Methods Daily mold spore count was determined by analyzing the Melinex tape from the Burkard Volumetric Spore Trap. Samples were analyzed in every 24 hours and mold spore concentration per cubic meter was determined. The data were correlated with maximum and minimum temperature, daily precipitation and peak wind speed. The daily clinical data were collected from the Allergy A.R.T.S. and were correlated with the daily fungal aeroallergen index. Results The most frequent fungal aeroallergens present during Sept. 2002 to Sept. 2003 were fungal spores like Alternaria, ascospores from Pezizales, Dreschlera, Cladosporium, and Curvularia. Mold spore count had an inverse relationship with the daily temperature. Alternaria conidia were the most abundant component of the dry air spora. We found an association between Alternaria concentration and the cases of severe asthma. Fungal spore concentrations did show more susceptibility to meteorological conditions on a daily basis than did pollen concentrations. Ascomycetes concentrations significantly increased in the hours just following a rain shower. Conclusions We found a very significant correlation between the mold spore concentration and the humidity. A little rain increased the spore count significantly. There was a positive correlation between the number of patients' visits with the increase spore index.