UV Method Development and Validation of Ellagic Acid for its Rapid Quantitative Estimation

Development and validation of a simple UV- Spectroscopy method was done for the quantitative analysis of Ellagic Acid (EA). The stock solution of 50μg/ml was prepared and scanned, for which absorption maxima was found to be 277nm. Further dilutions to different concentrations (1-5μg/ml) were prepared and analyzed at 277nm. The method so developed was validated as per ICH guidelines for: linearity, robustness, precision, accuracy, limit of detection and quantification. The Lambert- Beer’s law is followed in the range (1-5μg/ml) with correlation coefficient value 0.9994. It was observed that the method is precise and accurate for EA analysis with good recovery percent of 94.47% to 106.83%. The method developed was further employed for determining the entrapment efficiency of ellagic acid and its release from its nanoparticle dosage form. The method may be utilized for determining the concentration of EA when present as formulation and in combination with other drugs

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Comparison of staff and family perceptions of causes of noise pollution in the Pediatric Intensive Care Unit and suggested intervention strategies

Noise and excessive, unwanted sound in the Pediatric Intensive Care Unit (PICU) is common and has a major impact on patients′ sleep and recovery. Previous research has focused mostly on absolute noise levels or included only staff as respondents to acknowledge the causes of noise and to plan for its reduction. Thus far, the suggested interventions have not ameliorated noise, and it continues to serve as a barrier to recovery. In addition to surveying PICU providers through internet-based software, patients′ families were evaluated through in-person interviews utilizing a pretested instrument over 3 months. Families of patients admitted for more than 24 h were considered eligible for evaluation. Participants were asked to rank causes of noise from 1 to 8, with eight being highest, and identified potential interventions as effective or ineffective. In total, 50 families from 251 admissions and 65 staff completed the survey. Medical alarms were rated highest (mean ± standard deviation [SD], 4.9 ± 2.1 [2.8-7.0]), followed by noise from medical equipment (mean ± SD, 4.7 ± 2.1 [2.5-6.8]). This response was consistent among PICU providers and families. Suggested interventions to reduce noise included keeping a patient′s room door closed, considered effective by 93% of respondents (98% of staff; 88% of families), and designated quiet times, considered effective by 82% (80% of staff; 84% of families). Keeping the patient′s door closed was the most effective strategy among survey respondents. Most families and staff considered medical alarms an important contributor to noise level. Because decreasing the volume of alarms such that it cannot be heard is inappropriate, alternative strategies to alert staff of changes in vital signs should be explored

Approach, complications, and outcomes for 37 consecutive pediatric patients undergoing laser ablation for medically refractory epilepsy at Stanford Children\u27s Health

Objective:The objective of this study was to better understand the safety and efficacy of laser interstitial thermal therapy (LITT) for children with medically refractory epilepsy. Methods:Thirty-seven consecutive pediatric epilepsy patients at a single pediatric center who underwent LITT ablation of epileptogenic foci between May 2017 and December 2021 were retrospectively reviewed. Patient demographics, medication use, seizure frequency, prior surgical interventions, procedural details, and pre- and postoperative seizure history were analyzed. Results:Thirty-seven pediatric patients (24 male, 13 female) with severe medically refractory epilepsy were included; all underwent stereo-electroencephalography (SEEG) prior to LITT. The SEEG electrode placement was based on the preoperative workup and tailored to each patient by the epileptologist and neurosurgeons working together to identify the epileptic network and hopefully quiet borders. Seizure onset was at a mean age of 2.70 ± 2.82 years (range 0.25-12 years), and the mean age at the time of LITT was 9.46 ± 5.08 years (range 2.41-17.86 years). Epilepsy was lesional in 23 patients (18 tuberous sclerosis, 4 focal cortical dysplasia, 1 gliosis) and nonlesional in 14. Eighteen patients had prior surgical interventions including open resections (n = 13: 11 single and 2 multiple), LITT (n = 4), or both (n = 1). LITT targeted a region adjacent to the previous target in 5 cases. The median number of lasers placed during the procedure was 3 (range 1-5). Complications occurred in 14 (37.8%) cases, only 3 (8.11%) of which resulted in a permanent deficit: 1 venous hemorrhage requiring evacuation following laser ablation, 1 aseptic meningitis, 2 immediate postoperative seizures, and 10 neurological deficits (7 transient and 3 permanent). Postoperatively, 22 (59.5%) patients were seizure free at the last follow-up (median follow-up 18.35 months, range 7.40-48.76 months), and the median modified Engel class was I (Engel class I in 22 patients, Engel class II in 2, Engel class III in 2, and Engel class IV in 11). Patients having tried a greater number of antiseizure medications before LITT were less likely to achieve seizure improvement (p = 0.046) or freedom (p = 0.017). Seizure improvement following LITT was associated with a shorter duration of epilepsy prior to LITT (p = 0.044), although postoperative seizure freedom was not associated with a shorter epilepsy duration (p = 0.667). Caregivers reported postoperative neurocognitive improvement in 17 (45.9%) patients. Conclusions:In this large single-institution cohort of pediatric patients with medically refractory seizures due to various etiologies, LITT was a relatively safe and effective surgical approach for seizure reduction and seizure freedom at 1 year of follow-up

Disparities in time to treatment for skin cancer

Background/aim: Skin cancer is the most common cancer worldwide. This study aimed to identify factors contributing to the disparities in skin cancer treatment.Patients and methods: Data from The National Cancer Database (NCDB) spanning 2004 to 2019 were utilized. Variables including age, sex, race, Hispanic origin, Charlson-Deyo Comorbidity (CDC) score, geographic location, insurance status, income, grade and stage of cancer, and type of treatment facility impacting the time to treatment, surgery, radiation, and chemotherapy were analyzed.Results: Trends of longer time to treatment were seen with older age, non-Hispanic white, uninsured, those with a higher CDC score, and treated at academic facilities. Additionally, annual income and clinicopathology of cancer were also significantly associated with time to treatment.Conclusion: Our findings contribute to the expanding body of evidence pointing to the influence of socioeconomic and demographic factors in treatment disparities across diverse patient populations

Disparities in Time to Treatment for Skin Cancer

Background/aim: Skin cancer is the most common cancer worldwide. This study aimed to identify factors contributing to the disparities in skin cancer treatment.Patients and methods: Data from The National Cancer Database (NCDB) spanning 2004 to 2019 were utilized. Variables including age, sex, race, Hispanic origin, Charlson-Deyo Comorbidity (CDC) score, geographic location, insurance status, income, grade and stage of cancer, and type of treatment facility impacting the time to treatment, surgery, radiation, and chemotherapy were analyzed.Results: Trends of longer time to treatment were seen with older age, non-Hispanic white, uninsured, those with a higher CDC score, and treated at academic facilities. Additionally, annual income and clinicopathology of cancer were also significantly associated with time to treatment.Conclusion: Our findings contribute to the expanding body of evidence pointing to the influence of socioeconomic and demographic factors in treatment disparities across diverse patient population

Automated chart review utilizing natural language processing algorithm for asthma predictive index

Abstract Background Thus far, no algorithms have been developed to automatically extract patients who meet Asthma Predictive Index (API) criteria from the Electronic health records (EHR) yet. Our objective is to develop and validate a natural language processing (NLP) algorithm to identify patients that meet API criteria. Methods This is a cross-sectional study nested in a birth cohort study in Olmsted County, MN. Asthma status ascertained by manual chart review based on API criteria served as gold standard. NLP-API was developed on a training cohort (n = 87) and validated on a test cohort (n = 427). Criterion validity was measured by sensitivity, specificity, positive predictive value and negative predictive value of the NLP algorithm against manual chart review for asthma status. Construct validity was determined by associations of asthma status defined by NLP-API with known risk factors for asthma. Results Among the eligible 427 subjects of the test cohort, 48% were males and 74% were White. Median age was 5.3 years (interquartile range 3.6–6.8). 35 (8%) had a history of asthma by NLP-API vs. 36 (8%) by abstractor with 31 by both approaches. NLP-API predicted asthma status with sensitivity 86%, specificity 98%, positive predictive value 88%, negative predictive value 98%. Asthma status by both NLP and manual chart review were significantly associated with the known asthma risk factors, such as history of allergic rhinitis, eczema, family history of asthma, and maternal history of smoking during pregnancy (p value < 0.05). Maternal smoking [odds ratio: 4.4, 95% confidence interval 1.8–10.7] was associated with asthma status determined by NLP-API and abstractor, and the effect sizes were similar between the reviews with 4.4 vs 4.2 respectively. Conclusion NLP-API was able to ascertain asthma status in children mining from EHR and has a potential to enhance asthma care and research through population management and large-scale studies when identifying children who meet API criteria