Interferometric detection of prostate specific antigen based on enzyme immunoassay

AbstractInterferometric detection of Prostate-specific antigen (PSA) based on enzyme immunoassay are investigated. Refractive index changes of substrate are measured for PSA detection. Michelson scheme of optical interferometer was used so as to be applicable to a disposable fluidic chip. When interferometer is used for the measurements of refractive index changes, the detection is over 8 times more sensitive than that of absorbance changes for the same amount of target protein

Readout-segmented echo-planar imaging in diffusion-weighted mr imaging in breast cancer: comparison with single-shot echo-planar imaging in image quality

Objective: The purpose of this study was to compare the image quality of standard single-shot echo-planar imaging (ss-EPI) and that of readout-segmented EPI (rs-EPI) in patients with breast cancer. Materials and Methods: Seventy-one patients with 74 breast cancers underwent both ss-EPI and rs-EPI. For qualitative comparison of image quality, three readers independently assessed the two sets of diffusion-weighted (DW) images. To evaluate geometric distortion, a comparison was made between lesion lengths derived from contrast enhanced MR (CE-MR) images and those obtained from the corresponding DW images. For assessment of image parameters, signal-to-noise ratio (SNR), lesion contrast, and contrast-to-noise ratio (CNR) were calculated. Results: The rs-EPI was superior to ss-EPI in most criteria regarding the qualitative image quality. Anatomical structure distinction, delineation of the lesion, ghosting artifact, and overall image quality were significantly better in rs-EPI. Regarding the geometric distortion, lesion length on ss-EPI was significantly different from that of CE-MR, whereas there were no significant differences between CE-MR and rs-EPI. The rs-EPI was superior to ss-EPI in SNR and CNR. Conclusion: Readout-segmented EPI is superior to ss-EPI in the aspect of image quality in DW MR imaging of the breast

Etanercept treatment for pediatric toxic epidermal necrolysis induced by deflazacort: a case report and literature review

Toxic epidermal necrolysis (TEN) is a life-threatening mucocutaneous disorder commonly caused by drugs. TEN is often treated with corticosteroids, intravenous immunoglobulin (IVIG), or cyclosporine; however, the efficacy of these treatments is controversial. Etanercept (a TNF-α antagonist) was proven to decrease skin-healing time in a randomized clinical trial. Herein, we report the case of a 44-month-old boy who developed TEN due to deflazacort as the probable culprit drug and was successfully treated with etanercept. The patient presented to the emergency department complaining of erythematous maculopapular rashes and vesicles all over the face and body, with vesicles on the hands, feet, and trunk. Symptoms started 4 days before presentation, with edema of the upper lip, which progressed to erythematous macules over the body. He was started on deflazacort for nephrotic syndrome 21 days before the visit. Approximately 20% of the body surface area (BSA) was covered by vesicular lesions. Under the diagnosis of Steven Johnson syndrome/TEN, deflazacort was discontinued, and intravenous dexamethasone (1.5 mg/kg/day), a 5-day course of IVIG (0.4 mg/kg/day), and cyclosporine (3 mg/kg/day) were administered. The lesions seemed to be stationary for 3 days, but on the 6th day of hospitalization, when IVIG was discontinued, the vesicular lesions progressed to approximately 60% of the BSA. Etanercept 0.8 mg/kg was administered subcutaneously. Lesions stopped progressing, and bullous lesions started epithelialization. However, on the 15th day, around 30% of the BSA was still involved; thus, a second dose of etanercept was administered. No acute or sub-acute complications were observed. In conclusion, the use of etanercept in children with TEN that is not controlled with conventional therapy is both effective and safe

Effect of donor–recipient size mismatch on long-term graft survival in pediatric kidney transplantation: a multicenter cohort study

Background Donor–recipient size mismatching is commonly occurs in pediatric kidney transplantation (KT). However, its effect on graft survival remains unknown. This study aimed to determine the effect of donor–recipient size mismatch on the long-term survival rate of transplant kidneys in pediatric KT. Methods A total of 241 pediatric patients who received KT were enrolled. The medical records of all patients were retrospectively reviewed, and the correlation between donor–recipient size mismatch and graft function and long-term graft outcome was analyzed according to donor–recipient size mismatch. Results Recipients and donors’ mean body weight at the time of KT were 34.31 ± 16.85 and 56.53 ± 16.73 kg, respectively. The mean follow-up duration was 96.49 ± 52.98 months. A significant positive correlation was observed between donor–recipient body weight ratio (DRBWR) or donor–recipient body surface area ratio (DRBSR) and graft function until 1 year after KT. However, this correlation could not be confirmed at the last follow-up. The results of long-term survival analysis using Fine and Gray’s subdistribution hazard model showed no significant difference of the survival rate of the transplant kidney according to DRBWR or DRBSR. Conclusion Donor–recipient size mismatch in pediatric KT is not an important factor in determining the long-term prognosis of transplant kidneys

Study on Threat Modeling in Smart Greenhouses

In the era of agriculture 4.0, cutting-edge technologies including Information and communication technology (ICT) is being introduced into traditional agriculture. As farm intelligence emerges as a key area of smart agriculture, the scope of agriculture has expanded from the seed industry to distribution and logistics, however the area that is still most directly connected to the physical agricultural environment is smart farming. Cybersecurity incidents or cybercrimes in smart farming can directly damage crops and harm human safety. Research on individual technical elements that constitute smart farming has been ongoing for a long time relatively, however it has not been long since the work of systematically identifying and classifying threats to smart agriculture as a whole. In this study, STRIDE threat modeling is used to identify cyber threats to greenhouse and make system design more robust. Through this work, we have derived 126 threats and have created 4 types of attack trees. It will be the basis to allow systematic threat classification more clearly in smart greenhouse

Effect of High Glucose on MUC5B expression in Human Airway Epithelial Cells

Objectives Excessive production of mucus results in plugging of the airway tract, which can increase morbidity and mortality in affected patients. In patients with diabetes, inflammatory airway disease appears with more frequent relapse and longer duration of symptoms. However, the effects of high glucose (HG) on the secretion of mucin in inflammatory respiratory diseases are not clear. Therefore, this study was conducted in order to investigate the effect and the brief signaling pathway of HG on MUC5B expression in human airway epithelial cells. Methods The effect and signaling pathway of HG on MUC5B expression were investigated using reverse transcriptase-polymerase chain reaction (RT-PCR), real-time PCR, enzyme immunoassay, and immunoblot analysis with specific inhibitors and small interfering RNA. Results HG increased MUC5B expression and epidermal growth factor receptor (EGFR) expression, and activated the phosphorylation of EGFR and p38 mitogen-activated protein kinase (MAPK). Pretreatment with EGFR inhibitor significantly attenuated the HG-induced phosphorylation of p38 MAPK, and pretreatments with p38 inhibitor or EGFR inhibitor significantly attenuated HG-induced MUC5B expression. In addition, knockdown of p38 MAPK by p38 MAPK siRNA significantly blocked HG-induced MUC5B expression. Conclusion These findings suggest that HG induces MUC5B expression via the sequential activations of the EGFR/p38 MAPK signaling pathway in human airway epithelial cells

Shrinking a large dataset to identify variables associated with increased risk of Plasmodium falciparum infection in Western Kenya

Large datasets are often not amenable to analysis using traditional single-step approaches. Here, our general objective was to apply imputation techniques, principal component analysis (PCA), elastic net and generalized linear models to a large dataset in a systematic approach to extract the most meaningful predictors for a health outcome. We extracted predictors for Plasmodium falciparum infection, from a large covariate dataset while facing limited numbers of observations, using data from the People, Animals, and their Zoonoses (PAZ) project to demonstrate these techniques: data collected from 415 homesteads in western Kenya, contained over 1500 variables that describe the health, environment, and social factors of the humans, livestock, and the homesteads in which they reside. The wide, sparse dataset was simplified to 42 predictors of P. falciparum malaria infection and wealth rankings were produced for all homesteads. The 42 predictors make biological sense and are supported by previous studies. This systematic data-mining approach we used would make many large datasets more manageable and informative for decision-making processes and health policy prioritization