SMARCA4-deficient non-small cell lung carcinoma: clinicodemographic, computed tomography, and positron emission tomography-computed tomography features.

Background: SMARCA4-deficient non-small cell lung carcinoma (SD-NSCLC) is a relatively rare tumor, which occurs in 5-10% of NSCLC. Based on World Health Organization thoracic tumor classification system, SMARCA4-deficient undifferentiated tumor (SD-UT) is recognized as a separate entity from SD-NSCLC. Differentiation between SD-NSCLC and SD-UT is often difficult due to shared biological continuum, but often required for choosing appropriate treatment regimen. Therefore, the aim of our study was to identify the clinicopathologic, computed tomography (CT), and positron emission tomography (PET)-CT imaging features of SD-NSCLC. Methods: Nine patients of pathologically confirmed SD-NSCLC were included in our analysis. We reviewed electronic medical records for clinical information, demographic features, CT, and PET-CT imaging features were analyzed. Results: Smoking history and male predominance are observed in all patients with SD-NSCLC (n=9). On CT, SD-NSCLC appeared as relatively well-defined masses with lobulated contour (n=8) and peripheral location (n=7). Invasion of adjacent pleura or chest wall (n=7) were frequently observed, regardless of small tumor size. Four cases showed lymph node metastases. Among nine patients, three patients showed multiple bone metastases, and one patient showed lung-to-lung metastases. Conclusions: In patient with SD-NSCLC, there was tendency for male smokers, peripheral location and invasion of adjacent pleural or chest wall invasion regardless of small tumor size, when compared to SD-UT.

Effect of the sodium-glucose cotransporter-2 inhibitor, DWP16001, as an add-on therapy to insulin for diabetic dogs: A pilot study.

BACKGROUND: Sodium-glucose cotransporter-2 (SGLT2) inhibitors are a novel class of anti-hyperglycaemic agents. OBJECTIVE: This study aimed to evaluate the safety and the adjuvant glycaemic control effect of an SGLT2 inhibitor, DWP16001, in diabetic dogs receiving insulin treatment. METHODS: Nineteen diabetic dogs receiving insulin treatment (NPH, porcine lente and glargine insulin) were divided into two groups according to dosing frequency: DWP TOD group (n = 10) and DWP SID group (n = 9). In the DWP TOD group, 0.025 mg/kg of DWP16001 was administered once every 3 days, whereas, in the DWP SID group, 0.025 mg/kg of DWP16001 was administered once a day. Food intake was maintained during the trial period. Hypoglycaemia, ketoacidosis or unexpected life-threatening reactions were assessed as adverse effects before and after DWP16001 administration. We compared insulin requirement reduction and blood glucose level control between two groups. RESULTS: No specific adverse effects were observed during the clinical trial, and haematological parameter remained unchanged. Moreover, the fasting glucose levels and daily insulin dose in the DWP TOD group were lower than the pre-administration values, but not significantly different for 8 weeks. Systolic blood pressure, fructosamine and insulin dose decreased significantly in the DWP SID group compared to the DWP TOD group at 8 weeks (p < 0.05) without affecting food consumption. Among these patients, 10 patients were monitored while receiving DWP16001 for 12 months (DWP TOD group n = 5, DWP SID group n = 5). The fasting glucose and fructosamine levels and daily insulin dose were reduced in both groups at 12 months compared with those before receiving DWP16001. CONCLUSION: When DWP16001, an SGLT2 inhibitor, was supplied to dogs with type 1 diabetes, no adverse effects were observed, and it was confirmed that the administered insulin dose can be reduced in controlling blood glucose.

Corrective feedback guides human perceptual decision-making by informing about the world state rather than rewarding its choice.

Corrective feedback received on perceptual decisions is crucial for adjusting decision-making strategies to improve future choices. However, its complex interaction with other decision components, such as previous stimuli and choices, challenges a principled account of how it shapes subsequent decisions. One popular approach, based on animal behavior and extended to human perceptual decision-making, employs "reinforcement learning," a principle proven successful in reward-based decision-making. The core idea behind this approach is that decision-makers, although engaged in a perceptual task, treat corrective feedback as rewards from which they learn choice values. Here, we explore an alternative idea, which is that humans consider corrective feedback on perceptual decisions as evidence of the actual state of the world rather than as rewards for their choices. By implementing these "feedback-as-reward" and "feedback-as-evidence" hypotheses on a shared learning platform, we show that the latter outperforms the former in explaining how corrective feedback adjusts the decision-making strategy along with past stimuli and choices. Our work suggests that humans learn about what has happened in their environment rather than the values of their own choices through corrective feedback during perceptual decision-making.

Antioxidant mitoquinone suppresses benign prostatic hyperplasia by regulating the AR-NLRP3 pathway.

Mitoquinone (MitoQ), a mitochondria-targeted antioxidant, has been used to treat several diseases. The present study aimed to investigate the therapeutic effects of MitoQ in benign prostatic hyperplasia (BPH) models and their underlying molecular mechanisms. In this study, we determined that MitoQ inhibited dihydrotestosterone (DHT)-induced cell proliferation and mitochondrial ROS by inhibiting androgen receptor (AR) and NOD-like receptor family pyrin domain-containing 3 (NLRP3) signaling in prostate epithelial cells. Molecular modeling revealed that DHT may combine with AR and NLRP3, and that MitoQ inhibits both AR and NLRP3. AR and NLRP3 downregulation using siRNA showed the linkage among AR, NLRP3, and MitoQ. MitoQ administration alleviated pathological prostate enlargement and exerted anti-proliferative and antioxidant effects by suppressing the AR and NLRP3 signaling pathways in rats with BPH. Hence, our findings demonstrated that MitoQ is an inhibitor of NLPR3 and AR and a therapeutic agent for BPH treatment.

Estimating the Volume of Nodules and Masses on Serial Chest Radiography Using a Deep-Learning-Based Automatic Detection Algorithm: A Preliminary Study.

BACKGROUND: The purpose of this study was to assess the volume of the pulmonary nodules and masses on serial chest X-rays (CXRs) from deep-learning-based automatic detection algorithm (DLAD)-based parameters. METHODS: In a retrospective single-institutional study, 72 patients, who obtained serial CXRs (n = 147) for pulmonary nodules or masses with corresponding chest CT images as the reference standards, were included. A pre-trained DLAD based on a convolutional neural network was developed to detect and localize nodules using 13,710 radiographs and to calculate a localization map and the derived parameters (e.g., the area and mean probability value of pulmonary nodules) for each CXR, including serial follow-ups. For validation, reference 3D CT volumes were measured semi-automatically. Volume prediction models for pulmonary nodules were established through univariable or multivariable, and linear or non-linear regression analyses with the parameters. A polynomial regression analysis was performed as a method of a non-linear regression model. RESULTS: Of the 147 CXRs and 208 nodules of 72 patients, the mean volume of nodules or masses was measured as 9.37 ± 11.69 cm3 (mean ± standard deviation). The area and CT volume demonstrated a linear correlation of moderate strength (i.e., R = 0.58, RMSE: 9449.9 mm3 m3 in a linear regression analysis). The area and mean probability values exhibited a strong linear correlation (R = 0.73). The volume prediction performance based on a multivariable regression model was best with a mean probability and unit-adjusted area (i.e. , RMSE: 7975.6 mm3, the smallest among the other variable parameters). CONCLUSIONS: The prediction model with the area and the mean probability based on the DLAD showed a rather accurate quantitative estimation of pulmonary nodule or mass volume and the change in serial CXRs.

Assessment of main pancreatic duct cutoff with dilatation, but without visible pancreatic focal lesion on MDCT: a novel diagnostic approach for malignant stricture using a CT-based nomogram.

OBJECTIVES: To identify useful features to predict hidden pancreatic malignancies in patients with main pancreatic duct (MPD) abrupt cutoff and dilatation, but without visible focal pancreatic lesions on CT. METHODS: This retrospective study included 92 patients (mean age, 63.4 ± 10.6 years, 63 men and 29 women) with MPD abrupt cutoff and dilatation, but without visible focal pancreatic lesion on contrast-enhanced CT between 2009 and 2021. Two radiologists independently evaluated the CT imaging features. Multivariable logistic regression analysis was performed to identify clinical and CT imaging features for hidden pancreatic malignancies. A nomogram was developed based on these results and assessed its performance. RESULTS: Thirty-eight (41.3%) and 54 (58.7%) were classified into the malignant and benign groups, respectively. In the multivariable analysis, CA19-9 elevation (odds ratio [OR] 7.5, p = 0.003), duct cutoff site at the head/neck (OR 7.6, p = 0.006), parenchymal contour abnormality at the duct cutoff site (OR 13.7, p < 0.001), and presence of acute pancreatitis (OR 11.5, p = 0.005) were independent predictors of pancreatic malignancy. A combination of any two significant features showed an accuracy of 77.2%, and a combination of any three features exhibited a specificity of 100%. The CT-based nomogram showed an area under the curve (AUC) of 0.84 (95% confidence interval, 0.77-0.90). CONCLUSIONS: The three CT imaging features and CA19-9 elevation translated into a nomogram permit a reliable estimation of hidden pancreatic malignancies in patients with MPD abrupt cutoff without visible focal pancreatic lesion. It may facilitate determining whether to proceed to further diagnostic tests. KEY POINTS: • Isoattenuating pancreatic ductal adenocarcinoma can manifest only as an isolated main pancreatic duct (MPD) dilatation with abrupt cutoff, making it difficult to distinguish from benign strictures. • Along with the serum CA 19-9 elevation, MPD cutoff site at the pancreas head or neck, parenchymal contour abnormality at the duct cutoff site, and associated acute pancreatitis indicated a higher probability of the malignant MPD strictures. • The CT-based nomogram provided excellent diagnostic performance (AUC of 0.84) for hidden pancreatic malignancies in patients with MPD abrupt cutoff and dilatation.

A Spatiotemporal Analytical Outlook of the Exposure to Air Pollution and COVID-19 Mortality in the USA.

The world is experiencing a pandemic due to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), also known as COVID-19. The USA is also suffering from a catastrophic death toll from COVID-19. Several studies are providing preliminary evidence that short- and long-term exposure to air pollution might increase the severity of COVID-19 outcomes, including a higher risk of death. In this study, we develop a spatiotemporal model to estimate the association between exposure to fine particulate matter PM2.5 and mortality accounting for several social and environmental factors. More specifically, we implement a Bayesian zero-inflated negative binomial regression model with random effects that vary in time and space. Our goal is to estimate the association between air pollution and mortality accounting for the spatiotemporal variability that remained unexplained by the measured confounders. We applied our model to four regions of the USA with weekly data available for each county within each region. We analyze the data separately for each region because each region shows a different disease spread pattern. We found a positive association between long-term exposure to PM2.5 and the mortality from the COVID-19 disease for all four regions with three of four being statistically significant. Data and code are available at our GitHub repository. Supplementary materials accompanying this paper appear on-line. Supplementary Information: The online version contains supplementary material available at 10.1007/s13253-022-00487-1.

A robust multivariate structure of interindividual covariation between psychosocial characteristics and arousal responses to visual narratives.

People experience the same event but do not feel the same way. Such individual differences in emotion response are believed to be far greater than those in any other mental functions. Thus, to understand what makes people individuals, it is important to identify the systematic structures of individual differences in emotion response and elucidate how such structures relate to what aspects of psychological characteristics. Reflecting this importance, many studies have attempted to relate emotions to psychological characteristics such as personality traits, psychosocial states, and pathological symptoms across individuals. However, systematic and global structures that govern the across-individual covariation between the domain of emotion responses and that of psychological characteristics have been rarely explored previously, which limits our understanding of the relationship between individual differences in emotion response and psychological characteristics. To overcome this limitation, we acquired high-dimensional data sets in both emotion-response (8 measures) and psychological-characteristic (68 measures) domains from the same pool of individuals (86 undergraduate or graduate students) and carried out the canonical correlation analysis in conjunction with the principal component analysis on those data sets. For each participant, the emotion-response measures were quantified by regressing affective-rating responses to visual narrative stimuli onto the across-participant average responses to those stimuli, while the psychological-characteristic measures were acquired from 19 different psychometric questionnaires grounded in personality, psychosocial-factor, and clinical-problem taxonomies. We found a single robust mode of population covariation, particularly between the 'accuracy' and 'sensitivity' measures of arousal responses in the emotion domain and many 'psychosocial' measures in the psychological-characteristics domain. This mode of covariation suggests that individuals characterized with positive social assets tend to show polarized arousal responses to life events.

Association of vortical structures and hemodynamic parameters for regional thrombus accumulation in abdominal aortic aneurysms.

The intraluminal thrombus (ILT) has been shown to negatively impact the progression of the abdominal aortic aneurysms (AAAs). The formation of this thrombus layer has been connected to the local flow environment within AAAs, but the specific mechanisms leading to thrombus formation are still not fully understood. Our study investigated the association between vortical structures, near-wall hemodynamic metrics (e.g., time averaged wall shear stress (TAWSS) and oscillatory shear index (OSI)), and ILT accumulation in a longitudinal cohort of 14 AAAs (53 scans total). Vortices and hemodynamic parameters were estimated using hemodynamic simulations performed to each scan of each patient and compared to local 3D changes of ILT thickness between two consecutive scans (ΔILT). Results showed that vortices formed and remained strong and close to the lumen surface in AAAs without an ILT, while in AAAs with ILTs these detached from the lumen surface and dissipated nearby wall region where an increase in ILT thickness was observed. Although low TAWSS was observed in regions with and without ILT accumulation, an inverse correlation between ∆ILT and TAWSS was observed within the regions that experienced a thrombus growth. Our results support the idea that vortical structures might be playing a role modulating ILT accumulation into specific wall regions. Also, it submits the idea that the low TAWSS will be modulating the growth of thrombus within these preferred ILT accumulated regions.

Intraluminal thrombus effect on the progression of abdominal aortic aneurysms by using a multistate continuous-time Markov chain model.

OBJECTIVE: To investigate the relationship between the characteristics of intraluminal thrombus (ILT) with abdominal aortic aneurysm (AAA) expansion. METHODS: This retrospective clinical study applied homogeneous multistate continuous-time Markov chain models to longitudinal computed tomography (CT) data from Korean patients with AAA. Four AAA states were considered (early, mild, severe, fatal) and the maximal thickness of the ILT (maxILT), the fraction of the wall area covered by the ILT (areafrac) and the fraction of ILT volume (volfrac) were used as covariates. RESULTS: The analysis reviewed longitudinal CT images from 26 patients. Based on likelihood-ratio statistics, the areafrac was the most significant biomarker and maxILT was the second most significant. In addition, within AAAs that developed an ILT layer, the analysis found that the AAA expands relatively quickly during the early stage but the rate of expansion reduces once the AAA has reached a larger size. CONCLUSION: The results recommend surgical intervention when a patient has an areafrac more than 60%. Although this recommendation should be considered with caution given the limited sample size, physicians can use the proposed model as a tool to find such recommendations with their own data.

Multi-group analysis using generalized additive kernel canonical correlation analysis.

Multivariate analysis has been widely used and one of the popular multivariate analysis methods is canonical correlation analysis (CCA). CCA finds the linear combination in each group that maximizes the Pearson correlation. CCA has been extended to a kernel CCA for nonlinear relationships and generalized CCA that can consider more than two groups. We propose an extension of CCA that allows multi-group and nonlinear relationships in an additive fashion for a better interpretation, which we termed as Generalized Additive Kernel Canonical Correlation Analysis (GAKCCA). In addition to exploring multi-group relationship with nonlinear extension, GAKCCA can reveal contribution of variables in each group; which enables in-depth structural analysis. A simulation study shows that GAKCCA can distinguish a relationship between groups and whether they are correlated or not. We applied GAKCCA to real data on neurodevelopmental status, psychosocial factors, clinical problems as well as neurophysiological measures of individuals. As a result, it is shown that the neurophysiological domain has a statistically significant relationship with the neurodevelopmental domain and clinical domain, respectively, which was not revealed in the ordinary CCA.

Patient-Specific Prediction of Abdominal Aortic Aneurysm Expansion Using Bayesian Calibration.

Translating recent advances in abdominal aortic aneurysm (AAA) growth and remodeling (G&R) knowledge into a predictive, patient-specific clinical treatment tool requires a major paradigm shift in computational modeling. The objectives of this study are to develop a prediction framework that first calibrates the physical AAA G&R model using patient-specific serial computed tomography (CT) scan images, predicts the expansion of an AAA in the future, and quantifies the associated uncertainty in the prediction. We adopt a Bayesian calibration method to calibrate parameters in the G&R computational model and predict the magnitude of AAA expansion. The proposed Bayesian approach can take different sources of uncertainty; therefore, it is well suited to achieve our aims in predicting the AAA expansion process as well as in computing the propagated uncertainty. We demonstrate how to achieve the proposed aims by solving the formulated Bayesian calibration problems for cases with the synthetic G&R model output data and real medical patient-specific CT data. We compare and discuss the performance of predictions and computation time under different sampling cases of the model output data and patient data, both of which are simulated by the G&R computation. Furthermore, we apply our Bayesian calibration to real patient-specific serial CT data and validate our prediction. The accuracy and efficiency of the proposed method is promising, which appeals to computational and medical communities.

Analysis of conversion of Alzheimer's disease using a multi-state Markov model.

With rapid aging of world population, Alzheimer's disease is becoming a leading cause of death after cardiovascular disease and cancer. Nearly 10% of people who are over 65 years old are affected by Alzheimer's disease. The causes have been studied intensively, but no definitive answer has been found. Genetic predisposition, abnormal protein deposits in brain, and environmental factors are suspected to play a role in the development of this disease. In this paper, we model progression of Alzheimer's disease using a multi-state Markov model to investigate the significance of known risk factors such as age, apolipoprotein E4, and some brain structural volumetric variables from magnetic resonance imaging scans (e.g., hippocampus, etc.) while predicting transitions between different clinical diagnosis states. With the Alzheimer's Disease Neuroimaging Initiative data, we found that the model with age is not significant (p = 0.1733) according to the likelihood ratio test, but the apolipoprotein E4 is a significant risk factor, and the examination of apolipoprotein E4-by-sex interaction suggests that the apolipoprotein E4 link to Alzheimer's disease is stronger in women. Given the estimated transition probabilities, the prediction accuracy is as high as 0.7849.

Assessing the association of diabetes self-management education centers with age-adjusted diabetes rates across U.S.: Aspatial cluster analysis approach.

The purpose of this study is to identify regions with diabetes health-service shortage. American Diabetes Association (ADA)-accredited diabetes self-management education (DSME) is recommended for all those with diabetes. In this study, we focus on demographic patterns and geographic regionalization of the disease by including accessibility and availability of diabetes education resources as a critical component in understanding and confronting differences in diabetes prevalence, as well as addressing regional or sub-regional differences in awareness, treatment and control. We conducted an ecological county-level study utilizing publicly available secondary data on 3,109 counties in the continental U.S. We used a Bayesian spatial cluster model that enabled spatial heterogeneities across the continental U.S. to be addressed. We used the American Diabetes Association (ADA) website to identify 2012 DSME locations and national 2010 county-level diabetes rates estimated by the Centers for Disease Control and Prevention and identified regions with low DSME program availability relative to their diabetes rates and population density. Only 39.8% of the U.S. counties had at least one ADA-accredited DSME program location. Based on our 95% credible intervals, age-adjusted diabetes rates and DSME program locations were associated in only seven out of thirty five identified clusters. Out of these seven, only two clusters had a positive association. We identified clusters that were above the 75th percentile of average diabetes rates, but below the 25th percentile of average DSME location counts and found that these clusters were all located in the Southeast portion of the country. Overall, there was a lack of relationship between diabetes rates and DSME center locations in the U.S., suggesting resources could be more efficiently placed according to need. Clusters that were high in diabetes rates and low in DSME placements, all in the southeast, should particularly be considered for additional DSME programming.

Variable selection for binary spatial regression: Penalized quasi-likelihood approach.

We consider the problem of selecting covariates in a spatial regression model when the response is binary. Penalized likelihood-based approach is proved to be effective for both variable selection and estimation simultaneously. In the context of a spatially dependent binary variable, an uniquely interpretable likelihood is not available, rather a quasi-likelihood might be more suitable. We develop a penalized quasi-likelihood with spatial dependence for simultaneous variable selection and parameter estimation along with an efficient computational algorithm. The theoretical properties including asymptotic normality and consistency are studied under increasing domain asymptotics framework. An extensive simulation study is conducted to validate the methodology. Real data examples are provided for illustration and applicability. Although theoretical justification has not been made, we also investigate empirical performance of the proposed penalized quasi-likelihood approach for spatial count data to explore suitability of this method to a general exponential family of distributions.

Evaluation of autologous bone marrow-derived mesenchymal stem cells on renal regeneration after experimentally induced acute kidney injury in dogs.

OBJECTIVE: To evaluate the usefulness of autologous bone marrow-derived mesenchymal stem cell (BM-MSC) therapy for the treatment of dogs with experimentally induced acute kidney injury. ANIMALS: 6 healthy dogs. PROCEDURES: After induction of kidney injury (day 0) with cisplatin (5 mg/kg, IV), dogs immediately received saline (0.9% NaCl) solution (10 mL; n = 3) or BM-MSCs (1 × 10(6) cells/kg in 10 mL of saline solution; 3) IV. A CBC, serum biochemical analysis, and urinalysis were performed for each dog before administration of cisplatin and on days 1 through 4. Glomerular filtration rate was determined for all dogs on days -7 and 2; BM-MSC tracking by MRI was performed on BM-MSC-treated dogs on days -14 and 4. After sample collection and BM-MSC tracking on day 4, all dogs were euthanized; kidney tissue samples underwent histologic evaluation, immunohistochemical analysis, and cytokine profiling via reverse transcriptase PCR assays. RESULTS: Kidney tissue from both groups had mononuclear inflammatory cell infiltration, tubular necrosis, dilated tubules, and glomerular damage. However, there was less fibrotic change and increased proliferation of renal tubular epithelial cells in the BM-MSC-treated dogs, compared with findings for the control dogs. Expressions of tumor necrosis factor-α and transforming growth factor-ß were lower in the BM-MSC-treated group, compared with findings for the control group. Laboratory data revealed no improvement in the renal function in BM-MSC-treated dogs. CONCLUSIONS AND CLINICAL RELEVANCE: Results of this study suggested that autologous BM-MSCs may accelerate renal regeneration after experimentally induced acute kidney injury in dogs.

Effect of granulocyte colony-stimulating factor administration on renal regeneration after experimentally induced acute kidney injury in dogs.

OBJECTIVE: To evaluate the effects of granulocyte colony-stimulating factor (GCSF) administration in dogs with experimentally induced acute kidney injury. ANIMALS: 6 healthy dogs. PROCEDURES: After induction of kidney injury (day 0) with cisplatin (5 mg/kg, IV), the dogs were randomly assigned into 2 groups (n = 3 dogs/group). Then dogs immediately received GCSF (10 µg/kg) or 1 mL of saline (0.9% NaCl) solution (control group) SC; this treatment was repeated once daily for 4 additional days (days 1 through 4). A once-daily CBC (day 0 to 4), serum biochemical analysis (day 0 to 3), and urinalysis (day 0 to 3) were performed for each dog; samples were collected before administration of cisplatin (day 0) and before treatment with GCSF or saline solution (days 1 through 4). After sample collection and treatment on day 4, all dogs were euthanized; kidney tissue samples underwent histologic evaluation, immunohistochemical analyses, and cytokine profiling via reverse transcriptase PCR assay. RESULTS: In the GCSF-treated group, the histologic evaluation and immunohistochemical analyses of kidney tissue revealed less fibrotic change and greater proliferation of renal tubular epithelial cells, compared with findings in the control group. The mRNA profiles of kidney tissue from the GCSF-treated group indicated lower expression of tumor necrosis factor-α and tumor growth factor-ß, compared with findings in the control group; however, concentrations of factors related to renal regeneration were not greater in the GCSF-treated group. CONCLUSIONS AND CLINICAL RELEVANCE: Results indicated that GCSF treatment can impede renal fibrosis and increase proliferation of renal tubules after experimentally induced acute kidney injury in dogs.

Uveodermatologic syndrome concurrent with keratoconjunctivitis sicca in a miniature poodle dog.

A 5-year-old, intact female miniature poodle dog was presented with chronic ophthalmic problems, facial poliosis, nasal depigmentation and multiple areas of alopecia over the trunk. Bilateral uveitis preceded dermatologic signs by 5 months and gradually worsened. The dog was diagnosed as having uveodermatologic syndrome (UDS) concurrent with keratoconjunctivitis sicca (KCS). Treatment with immunosuppressive drugs resolved the dermatologic lesions, but uveitis continued to progress.

Syndrome uvéodermatologique concomitant à une kératoconjonctivite sèche chez un caniche miniature. Une chienne caniche miniature intacte âgée de 5 ans a été présentée avec des problèmes ophtalmiques chroniques, une poliose faciale, une dépigmentation nasale et plusieurs plaques d'alopécie sur le tronc. L'uvéite bilatérale a précédé les signes dermatologiques 5 mois auparavant et elle s'est graduellement aggravée. La chienne a été diagnostiquée comme atteinte du syndrome uvéodermatologique concomitant à une kératoconjonctivite sèche. Le traitement avec des immunodépresseurs a résolu les lésions dermatologiques, mais l'uvéite a continué de progresser.(Traduit par Isabelle Vallières).

A wearable healthcare system for cardiac signal monitoring using conductive textile electrodes.

Accurate cardiac signal monitoring feasible for long-term monitoring is important for a practical, cost-effective health monitoring system. In this study, we propose a wearable healthcare system based on conductive fabric-based electrodes allowing monitoring of electrocardiogram (ECG) waveforms and demonstrated the potential for arrhythmia detection using the system. The measurement system uses conductive fabric-based electrodes arranged in a modified bipolar electrode configuration on the chest area of the patient. An adaptive impulse correlation filter (AICF) algorithm and a band pass filter to enable accurate R-peak detection in noisy environments.

Clinical and magnetic resonance imaging (MRI) findings of idiopathic aplastic pancytopenia in a dog treated with cyclosporine and azathioprine.

A dog was referred for pancytopenia. Bone marrow biopsy showed hypocellular fatty marrow. Magnetic resonance imaging had a high signal on T1-weighted image in the metaphyseal region of the femur. We suggest that MRI could be a valuable supportive diagnostic method and introduce a strategy of treatment with cyclosporine and azathioprine in canine aplastic pancytopenia.