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Since 1997, the Fellowship Council (FC) has evolved into a robust organization responsible for the advanced training of nearly half of the US residency graduates entering general surgery practice. While FC fellowships are competitive (55% match rate) and offer outstanding educational experiences, funding is arguably vulnerable. This study aimed to investigate the current funding models of FC fellowships. Under an IRB-approved protocol, an electronic survey was administered to 167 FC programs with subsequent phone interviews to collect data on total cost and funding sources. De-identified data were also obtained via 2020–2021 Foundation for Surgical Fellowships (FSF) grant applications. Means and ranges are reported. Data were obtained from 59 programs (35% response rate) via the FC survey and 116 programs via FSF applications; the average cost to train one fellow per year was $107,957 and $110,816, respectively. Most programs utilized departmental and grants funds. Additionally, 36% (FC data) to 39% (FSF data) of programs indicated billing for their fellow, generating on average $74,824 ($15,000–200,000) and $33,281 ($11,500–66,259), respectively. FC data documented that 14% of programs generated net positive revenue, whereas FSF data documented that all programs were budget-neutral. Both data sets yielded similar overall results, supporting the accuracy of our findings. Expenses varied widely, which may, in part, be due to regional cost differences. Most programs relied on multiple funding sources. A minority were able to generate a positive revenue stream. Although fewer than half of programs billed for their fellow, this source accounted for substantial revenue. Institutional support and external grant funding have continued to be important sources for the majority of programs as well. Given the value of these fellowships and inherent vulnerabilities associated with graduate medical education funding, alternative grant funding models and standardization of annual financial reporting are encouraged.

This paper introduces an intellectual image fusion technique which is much focused on Medical Image Integrated Possessions assisted Soft Computing Techniques (MIPSCT) with fuzzy sets. This dual image fusion design uses a fuzzy mid matrix method and a smooth adjustment process which helps to eliminate impulsive noise from extremely distorted images that is included in a smart image agent when fusing image on various image processing environment. The fuzzy function used in the filter is intended to remove impulses without losing fine details and textures which are more important in image fusion modelling. Furthermore, adjust filter parameters from a set of exercise data with an image culture process based on genetic algorithm has been implemented on MIPSCT to improve contour detection. The experimental results have been analyzed based on intelligent soft computing tools in assistance with matrix laboratory to achieve better output in accordance with SDROM, AWFM, SFVQ, and DCT modelling for brain image datasets. The validation at lab scale shows promising results on Peak Signal to Noise Ratio and Absolute Mean Error (AME) parameters in accordance with conventional methods.

BACKGROUND: Brain monitors tracking quantitative brain activities from electroencephalogram (EEG) to predict hypnotic levels have been proposed as a labor-saving alternative to behavioral assessments. Expensive clinical trials are required to validate any newly developed processed EEG monitor for every drug and combinations of drugs due to drug-specific EEG patterns. There is a need for an alternative, efficient, and economical method.METHODS: Using deep learning algorithms, we developed a novel data-repurposing framework to predict hypnotic levels from sleep brain rhythms. We used an online large sleep data set (5723 clinical EEGs) for training the deep learning algorithm and a clinical trial hypnotic data set (30 EEGs) for testing during dexmedetomidine infusion. Model performance was evaluated using accuracy and the area under the receiver operator characteristic curve (AUC).RESULTS: The deep learning model (a combination of a convolutional neural network and long short-term memory units) trained on sleep EEG predicted deep hypnotic level with an accuracy (95% confidence interval [CI]) = 81 (79.2-88.3)%, AUC (95% CI) = 0.89 (0.82-0.94) using dexmedetomidine as a prototype drug. We also demonstrate that EEG patterns during dexmedetomidine-induced deep hypnotic level are homologous to nonrapid eye movement stage 3 EEG sleep.CONCLUSIONS: We propose a novel method to develop hypnotic level monitors using large sleep EEG data, deep learning, and a data-repurposing approach, and for optimizing such a system for monitoring any given individual. We provide a novel data-repurposing framework to predict hypnosis levels using sleep EEG, eliminating the need for new clinical trials to develop hypnosis level monitors.

Background: Necrotizing soft tissue infections (NSTI) are a surgical emergency with significant morbidity and mortality rates. It has been thought that NSTIs are best treated in large tertiary centers. However, the effect of transfer has been under-studied. We examined whether transfer status is associated with a higher mortality rate in NSTIs. Methods: We conducted a retrospective review of patients with an International Classification of Disease (ICD) code associated with NSTI seen from 2012–2015 at two tertiary care institutions. Patients transferred to a tertiary center (T-NSTI) were compared with those who were treated initially at a tertiary center (P-NSTI). The primary endpoint was in-hospital death. Results: A total of 138 patients with NSTI met our study criteria, 39 transfer patients (28.0%) and 99 (72.0%) who were treated primarily at our institutions. The mortality rate was significantly higher for T-NSTI patients than P-NSTI patients (35.9% versus 14.1%; p

ObjectiveHandoffs by emergency medical services (EMS) personnel suffer from poor structure, inattention, and interruptions. The relationship between the quality of EMS communication and the non-technical performance of trauma teams remains unknown.MethodsWe analyzed 3 months of trauma resuscitation videos (highest acuity activations or patients with an Injury Severity Score [ISS] of ≥15). Handoffs were scored using the mechanism-injury-signs-treatment (MIST) framework for completeness (0-20), efficiency (category jumps), interruptions, and timeliness. Trauma team non-technical performance was scored using the Trauma Non-Technical Skills (T-NOTECHS) scale (5-15).ResultsWe analyzed 99 videos. Handoffs lasted a median of 62 seconds [IQR: 43-74], scored 11 [10-13] for completeness, and had 2 [1-3] interruptions. Most interruptions were verbal (85.2%) and caused by the trauma team (64.9%). Most handoffs (92%) were efficient with 2 or fewer jumps. Patient transfer during handoff occurred in 53.5% of the videos; EMS providers giving handoff helped transfer in 69.8% of the Primary surveys began during handoff in 42.4% of the videos. Resuscitation teams who scored in the top quartile on the T-NOTECHS (>11) had higher MIST scores than teams in lower quartiles (13 [11.25-14.75] vs. 11 [10-13]; P < .01). There were no significant differences in ISS, efficiency, timeliness, or interruptions between top- and lower-quartile groups.ConclusionsThere is a relationship between EMS MIST completeness and high performance of non-technical skill by trauma teams. Trauma video review (TVR) can help identify modifiable behaviors to improve EMS handoff and resuscitation efforts and therefore trauma team performance.

BACKGROUND AND OBJECTIVE: With aging, patients with diabetic kidney disease (DKD) show progressive decrease in kidney function. We investigated whether the deviation of biological age (BA) from the chronological age (CA) due to DKD can be used (denoted as Kidney Age Index; KAI) to quantify kidney function using machine learning algorithms.METHODS: Three large datasets were used in this study to develop KAI. The machine learning algorithms were trained on PREVEND dataset with healthy subjects (N = 7963) using 13 clinical markers to predict the CA. The trained model was then used to predict the BA of patients with DKD using RENAAL (N = 1451) and IDNT (N = 1706). The performance of four traditional machine learning algorithms were evaluated and the KAI = BA-CA was estimated for each patient.RESULTS: The neural network model achieved the best performance and predicted the CA of healthy subjects in PREVEND dataset with a mean absolute deviation (MAD) = 6.5 ± 3.5 years and pearson correlation = 0.62. Patients with DKD showed a significant higher KAI of 15.4 ± 11.8 years and 13.6 ± 12.3 years in RENAAL and IDNT datasets, respectively.CONCLUSIONS: Our findings suggest that for a given CA, patients with DKD shows excess BA when compared to their healthy counterparts due to disease severity. With further improvement, the proposed KAI can be used as a complementary easy-to-interpret tool to give a more inclusive idea into disease state.

Introduction In primary care, identifying patients with type 2 diabetes (T2D) who are at increased risk of hypoglycaemia is important for the prevention of hypoglycaemic events. We aimed to develop a screening tool based on machine learning to identify such patients using routinely available demographic and medication data. Methods We used a cohort study design and the Groningen Initiative to ANalyse Type 2 diabetes Treatment (GIANTT) medical record database to develop models for hypoglycaemia risk. The first hypoglycaemic event in the observation period (2007-2013) was the outcome. Demographic and medication data were used as predictor variables to train machine learning models. The performance of the models was compared with a model using additional clinical data using 5-fold cross validation with the area under the receiver operator characteristic curve (AUC) as a metric. Results We included 13,876 T2D patients. The best performing model including only demographic and medication data was logistic regression with least absolute shrinkage and selection operator, with an AUC of 0.71. Ten variables were included (odds ratio): male gender (0.997), age (0.990), total drug count (1.012), glucose-lowering drug count (1.039), sulfonylurea use (1.62), insulin use (1.769), premixed insulin use (1.109), insulin count (1.827), insulin duration (1.193), and antidepressant use (1.05). The proposed model obtained a similar performance to the model using additional clinical data. Conclusion Using demographic and medication data, a model for identifying patients at increased risk of hypoglycaemia was developed using machine learning. This model can be used as a tool in primary care to screen for patients with T2D who may need additional attention to prevent or reduce hypoglycaemic events. This article is protected by copyright. All rights reserved.

The mouse is one of the wonderful inventions of Human-Computer Interaction (HCI) technology. Currently, wireless mouse or a Bluetooth mouse still uses devices and is not free of devices completely since it uses a battery for power and a dongle to connect it to the PC. In the proposed AI virtual mouse system, this limitation can be overcome by employing webcam or a built-in camera for capturing of hand gestures and hand tip detection using computer vision. The algorithm used in the system makes use of the machine learning algorithm. Based on the hand gestures, the computer can be controlled virtually and can perform left click, right click, scrolling functions, and computer cursor function without the use of the physical mouse. The algorithm is based on deep learning for detecting the hands. Hence, the proposed system will avoid COVID-19 spread by eliminating the human intervention and dependency of devices to control the computer.

OBJECTIVE Social distancing restrictions due to COVID-19 challenged our ability to educate incoming surgery interns who depend on early simulation training for basic skill acquisition. This study aimed to create a proficiency-based laparoscopic skills curriculum using remote learning. DESIGN Content experts designed 5 surgical tasks to address hand-eye coordination, depth perception, and precision cutting. A scoring formula was used to measure performance: cutoff time - completion time - (K × errors) = score; the constant K was determined for each task. As a benchmark for proficiency, a fellowship-trained laparoscopic surgeon performed 3 consecutive repetitions of each task; proficiency was defined as the surgeon's mean score minus 2 standard deviations. To train remotely, PGY1 surgery residents (n = 29) were each issued a donated portable laparoscopic training box, task explanations, and score sheets. Remote training included submitting a pre-test video, self-training to proficiency, and submitting a post-test video. Construct validity (expert vs. trainee pre-tests) and skill acquisition (trainee pre-tests vs. post-tests) were compared using a Wilcoxon test (median [IQR] reported). SETTING The University of Texas Southwestern Medical Center in Dallas, Texas PARTICIPANTS Surgery interns RESULTS Expert and trainee pre-test performance was significantly different for all tasks, supporting construct validity. One trainee was proficient at pre-test. After 1 month of self-training, 7 additional residents achieved proficiency on all 5 tasks after 2-18 repetitions; trainee post-test scores were significantly improved versus pre-test on all tasks (p = 0.01). CONCLUSIONS This proficiency-based curriculum demonstrated construct validity, was feasible as a remote teaching option, and resulted in significant skill acquisition. The remote format, including video-based performance assessment, facilitates effective at-home learning and may allow additional innovations such as video-based coaching for more advanced curricula.

Objective Electroencephalogram (EEG) reactivity is a robust predictor of neurological recovery after cardiac arrest, however interrater-agreement among electroencephalographers is limited. We sought to evaluate the performance of machine learning methods using EEG reactivity data to predict good long-term outcomes in hypoxic-ischemic brain injury. Methods We retrospectively reviewed clinical and EEG data of comatose cardiac arrest subjects. Electroencephalogram reactivity was tested within 72 h from cardiac arrest using sound and pain stimuli. A Quantitative EEG (QEEG) reactivity method evaluated changes in QEEG features (EEG spectra, entropy, and frequency features) during the 10 s before and after each stimulation. Good outcome was defined as Cerebral Performance Category of 1–2 at six months. Performance of a random forest classifier was compared against a penalized general linear model (GLM) and expert electroencephalographer review. Results Fifty subjects were included and sixteen (32%) had good outcome. Both QEEG reactivity methods had comparable performance to expert EEG reactivity assessment for good outcome prediction (mean AUC 0.8 for random forest vs. 0.69 for GLM vs. 0.69 for expert review, respectively; p non-significant). Conclusions Machine-learning models utilizing quantitative EEG reactivity data can predict long-term outcome after cardiac arrest. Significance A quantitative approach to EEG reactivity assessment may support prognostication in cardiac arrest.

Aim To assess the potential of supervised machine‐learning techniques to identify clinical variables for predicting short‐term and long‐term glycated haemoglobin (HbA1c) response after insulin treatment initiation in patients with type 2 diabetes mellitus (T2DM). Materials and methods We included patients with T2DM from the Groningen Initiative to Analyse Type 2 diabetes Treatment (GIANTT) database who started insulin treatment between 2007 and 2013 and had a minimum follow‐up of 2 years. Short‐ and long‐term responses at 6 (±2) and 24 (±2) months after insulin initiation, respectively, were assessed. Patients were defined as good responders if they had a decrease in HbA1c ≥ 5 mmol/mol or reached the recommended level of HbA1c ≤ 53 mmol/mol. Twenty‐four baseline clinical variables were used for the analysis and an elastic net regularization technique was used for variable selection. The performance of three traditional machine‐learning algorithms was compared for the prediction of short‐ and long‐term responses and the area under the receiver‐operating characteristic curve (AUC) was used to assess the performance of the prediction models. Results The elastic net regularization‐based generalized linear model, which included baseline HbA1c and estimated glomerular filtration rate, correctly classified short‐ and long‐term HbA1c response after treatment initiation, with AUCs of 0.80 (95% CI 0.78–0.83) and 0.81 (95% CI 0.79–0.84), respectively, and outperformed the other machine‐learning algorithms. Using baseline HbA1c alone, an AUC = 0.71 (95% CI 0.65–0.73) and 0.72 (95% CI 0.66–0.75) was obtained for predicting short‐term and long‐term response, respectively. Conclusions Machine‐learning algorithm performed well in the prediction of an individual's short‐term and long‐term HbA1c response using baseline clinical variables.

PURPOSE In this ongoing national case series, we document 25 new genetic testing cases in which tests were recommended, ordered, interpreted, or used incorrectly. METHODS An invitation to submit cases of adverse events in genetic testing was issued to the general National Society of Genetic Counselors Listserv, the National Society of Genetic Counselors Cancer Special Interest Group members, private genetic counselor laboratory groups, and via social media platforms (i.e., Facebook, Twitter, LinkedIn). Examples highlighted in the invitation included errors in ordering, counseling, and/or interpretation of genetic testing and did not limit submissions to cases involving genetic testing for hereditary cancer predisposition. Clinical documentation, including pedigree, was requested. Twenty-six cases were accepted, and a thematic analysis was performed. Submitters were asked to approve the representation of their cases before manuscript submission. RESULTS All submitted cases took place in the United States and were from cancer, pediatric, preconception, and general adult settings and involved both medical-grade and direct-to-consumer genetic testing with raw data analysis. In 8 cases, providers ordered the wrong genetic test. In 2 cases, multiple errors were made when genetic testing was ordered. In 3 cases, patients received incorrect information from providers because genetic test results were misinterpreted or because of limitations in the provider's knowledge of genetics. In 3 cases, pathogenic genetic variants identified were incorrectly assumed to completely explain the suspicious family histories of cancer. In 2 cases, patients received inadequate or no information with respect to genetic test results. In 2 cases, result interpretation/documentation by the testing laboratories was erroneous. In 2 cases, genetic counselors reinterpreted the results of people who had undergone direct-to-consumer genetic testing and/or clarifying medical-grade testing was ordered. DISCUSSION As genetic testing continues to become more common and complex, it is clear that we must ensure that appropriate testing is ordered and that results are interpreted and used correctly. Access to certified genetic counselors continues to be an issue for some because of workforce limitations. Potential solutions involve action on multiple fronts: new genetic counseling delivery models, expanding the genetic counseling workforce, improving genetics and genomics education of nongenetics health care professionals, addressing health care policy barriers, and more. Genetic counselors have also positioned themselves in new roles to help patients and consumers as well as health care providers, systems, and payers adapt to new genetic testing technologies and models. The work to be done is significant, but so are the consequences of errors in genetic testing.

Cancer incidence is rising, and accurate prediction of incident cancers could be relevant to understanding and reducing cancer incidence. The aim of this study was to develop machine learning (ML) models that could predict an incident diagnosis of cancer. Participants without any history of cancer within the Lifelines population-based cohort were followed for a median of 7 years. Data were available for 116,188 cancer-free participants and 4232 incident cancer cases. At baseline, socioeconomic, lifestyle, and clinical variables were assessed. The main outcome was an incident cancer during follow-up (excluding skin cancer), based on linkage with the national pathology registry. The performance of three ML algorithms was evaluated using supervised binary classification to identify incident cancers among participants. Elastic net regularization and Gini index were used for variables selection. An overall area under the receiver operator curve (AUC) &lt, 0.75 was obtained, the highest AUC value was for prostate cancer (random forest AUC = 0.82 (95% CI 0.77–0.87), logistic regression AUC = 0.81 (95% CI 0.76–0.86), and support vector machines AUC = 0.83 (95% CI 0.78–0.88), respectively), age was the most important predictor in these models. Linear and non-linear ML algorithms including socioeconomic, lifestyle, and clinical variables produced a moderate predictive performance of incident cancers in the Lifelines cohort.

Sleep scoring is an important step for the detection of sleep disorders and usually performed by visual analysis. Since manual sleep scoring is time consuming, machine-learning based approaches have been proposed. Though efficient, these algorithms are black-box in nature and difficult to interpret by clinicians. In this paper, we propose a deep learning architecture for multi-modal sleep scoring, investigate the model?s decision making process, and compare the model?s reasoning with the annotation guidelines in the AASM manual. Our archi-tecture, called STQS, uses convolutional neural networks (CNN) to automatically extract spatio-temporal features from 3 modalities (EEG, EOG and EMG), a bidirectional long short-term memory (Bi-LSTM) to extract sequential information, and residual connections to combine spatio-temporal and sequential features. We evaluated our model on two large datasets, obtaining an accuracy of 85% and 77% and a macro F1 score of 79% and 73% on SHHS and an in-house dataset, respectively. We further quantify the contribution of various architectural components and conclude that adding LSTM layers improves performance over a spatio-temporal CNN, while adding residual connections does not. Our interpretability results show that the output of the model is well aligned with AASM guidelines, and therefore, the model?s decisions correspond to domain knowledge. We also compare multi-modal models and single-channel models and suggest that future research should focus on improving multi-modal models.

The advancement of genetic testing technologies has allowed for better diagnosis and management of patients, but also results in more variants of uncertain significance (VUSs) due to the increased number of genes being analyzed. There are more genetic tests available and more providers who do not specialize in genetics ordering genetic testing, but few studies examining how providers who do not specialize in genetics interpret VUSs. This study surveyed pediatric providers at a midwestern pediatric care center who do not specialize in genetics about their understanding of a mock genetic test report with a VUS result and whether their understanding of the result was associated with experience ordering genetic tests. Participants' preferences about content of the report and steps taken to understand the result were also examined. Of the 51 participants, 33% correctly answered both knowledge questions about the VUS result: one asking them to interpret the result and one asking them how they would explain the result to the patient. There was no association between answering both knowledge questions correctly and types of previous genetic tests ordered (p > .1 for 8 types of genetic tests), having received a genetic test report with a VUS result (p = .58), having referred patients to a genetics professional (p = .74), or feeling comfortable discussing a positive, negative, or VUS genetic test result (p > .4). This suggests that having previous experience ordering genetic tests does not contribute to the participants' knowledge about a variant of uncertain significance. Most participants reported that the amount of information in each section of the mock report was adequate. Participants were likely to reference multiple resources to better understand a VUS result, including published literature (82%), gene-specific databases (67%), and colleagues (63%). While these results cannot be generalized to all institutions, institutions can use the two knowledge questions to determine participants' understanding of genetic test results. This will help healthcare institutions determine methods that will best aide their providers who order genetic testing but do not specialize in genetics in learning more about the genetic testing process and better utilize results to improve patient care.

Study Objectives Dexmedetomidine-induced electroencephalogram (EEG) patterns during deep sedation are comparable with natural sleep patterns. Using large-scale EEG recordings and machine learning techniques, we investigated whether dexmedetomidine-induced deep sedation indeed mimics natural sleep patterns. Methods We used EEG recordings from three sources in this study: 8,707 overnight sleep EEG and 30 dexmedetomidine clinical trial EEG. Dexmedetomidine-induced sedation levels were assessed using the Modified Observer’s Assessment of Alertness/Sedation (MOAA/S) score. We extracted 22 spectral features from each EEG recording using a multitaper spectral estimation method. Elastic-net regularization method was used for feature selection. We compared the performance of several machine learning algorithms (logistic regression, support vector machine, and random forest), trained on individual sleep stages, to predict different levels of the MOAA/S sedation state. Results The random forest algorithm trained on non-rapid eye movement stage 3 (N3) predicted dexmedetomidine-induced deep sedation (MOAA/S = 0) with area under the receiver operator characteristics curve >0.8 outperforming other machine learning models. Power in the delta band (0–4 Hz) was selected as an important feature for prediction in addition to power in theta (4–8 Hz) and beta (16–30 Hz) bands. Conclusions Using a large-scale EEG data-driven approach and machine learning framework, we show that dexmedetomidine-induced deep sedation state mimics N3 sleep EEG patterns. Clinical Trials Name—Pharmacodynamic Interaction of REMI and DMED (PIRAD), URL—https://clinicaltrials.gov/ct2/show/NCT03143972, and registration—NCT03143972.

Cryptography is a method for secure communication by hiding information with secret keys so that only authorised users can read and process it. Efficient random sequence generators provide robust cipher design for cryptographic applications; further, these sequences are used for data encryption. In this paper, the highly chaotic nature of hybrid chaos maps and neural network is combined to build a random number generator for cryptographic applications. A custom neural network with a user-defined layer transfer function is built to increase the generator’s randomness. In this work, the two-hybrid chaotic map’s control parameters and iteration value are designed as a layer transfer function to obtain high randomness. Colour image encryption is performed with the extracted sequences and deoxyribonucleic acid encoding technique. Various tests like NIST, attractor test and correlation are applied to the generator to show the degree of randomness. Simulation analysis such as keyspace, key sensitivity, statistical, differential analysis, and chosen-plaintext attack shows the encryption algorithm’s strength.

Testing and implementation of integrated and intelligent transport systems (IITS) of an electrical vehicle need many high-performance and high-precision subsystems. The existing systems confine themselves with limited features and have driving range anxiety, charging and discharging time issues, and inter- and intravehicle communication problems. The above issues are the critical barriers to the penetration of EVs with a smart grid. This paper proposes the concepts which consist of connected vehicles that exploit vehicular ad hoc network (VANET) communication, embedded system integrated with sensors which acquire the static and dynamic parameter of the electrical vehicle, and cloud integration and dig data analytics tools. Vehicle control information is generated based on machine learning-based control systems. This paper also focuses on improving the overall performance (discharge time and cycle life) of a lithium ion battery, increasing the range of the electric vehicle, enhancing the safety of the battery that acquires the static and dynamic parameter and driving pattern of the electrical vehicle, establishing vehicular ad hoc network (VANET) communication, and handling and analyzing the acquired data with the help of various artificial big data analytics techniques.

ObjectiveElectroencephalogram (EEG) monitors are often used to monitor depth of general anesthesia. EEG monitoring is less well developed for lighter levels of anesthesia. Here we present an automated method to monitor the depth of anesthesia for office based procedures using EEG spectral features.MethodsWe analyze EEG recordings from 30 patients undergoing sedation using a multimodal anesthesia strategy. Level of sedation during the procedure is coded using the Richmond Agitation and Sedation Scale (RASS). The power spectrum from the frontal EEG is used to infer the level of sedation, by training a logistic regression model with elastic net regularization. Area under the receiver operator characteristic curve (AUC) is used to evaluate how well the automated system distinguishes awake from sedated EEG epochs.ResultsEEG power spectral characteristics vary systematically and consistently across patients with the levels of light anesthesia and relatively healthy patients encountered during office-based anesthesia procedures. The logistic regression model using spectral EEG features distinguishes awake and sedated states with an AUC of 0.85 (± 0.14).ConclusionsOur results demonstrate that frontal EEG spectral features can reliably monitor sedation levels during office based anesthesia.

Necrotizing soft tissue infections (NSTIs) are life-threatening surgical emergencies associated with high morbidity and mortality. Fungal NSTIs are considered rare and have been largely understudied. The purpose of this study was to study the impact of fungal NSTIs and antifungal therapy on mortality after NSTIs.A retrospective chart review was performed on patients with NSTIs from 2012 to 2018. Patient baseline characteristics, microbiologic data, antimicrobial therapy, and clinical outcomes were collected. Patients were excluded if they had comfort care before excision. The primary outcome measured was in-hospital mortality.A total of 215 patients met study criteria with a fungal species identified in 29 patients (13.5%). The most prevalent fungal organism was Candida tropicalis (n = 11). Fungal NSTIs were more prevalent in patients taking immunosuppressive medications (17.2% versus 3.2%, P = 0.01). A fungal NSTI was significantly associated with in-hospital mortality (odds ratio, 3.13; 95% confidence interval, 1.16-8.40; P = 0.02). Furthermore, fungal NSTI patients had longer lengths of stay (32 d [interquartile range, 16-53] versus 19 d [interquartile range, 11-31], P 0.01), more likely to require initiation of renal replacement therapy (24.1% versus 8.6%, P = 0.02), and more likely to require mechanical ventilation (64.5% versus 42.0%, P = 0.02). Initiation of antifungals was associated with a significantly lower rate of in-hospital mortality (6.7% versus 57.1%, P = 0.01).Fungal NSTIs are more common in patients taking immunosuppressive medications and are significantly associated with in-hospital mortality. Antifungal therapy is associated with decreased in-hospital mortality in those with fungal NSTIs. Consideration should be given to adding antifungals in empiric treatment regimens, especially in those taking immunosuppressive medications.

Aim To investigate which metabolic pathways are targeted by the sodium‐glucose co‐transporter‐2 inhibitor dapagliflozin to explore the molecular processes involved in its renal protective effects. Methods An unbiased mass spectrometry plasma metabolomics assay was performed on baseline and follow‐up (week 12) samples from the EFFECT II trial in patients with type 2 diabetes with non‐alcoholic fatty liver disease receiving dapagliflozin 10 mg/day (n = 19) or placebo (n = 6). Transcriptomic signatures from tubular compartments were identified from kidney biopsies collected from patients with diabetic kidney disease (DKD) (n = 17) and healthy controls (n = 30) from the European Renal cDNA Biobank. Serum metabolites that significantly changed after 12 weeks of dapagliflozin were mapped to a metabolite‐protein interaction network. These proteins were then linked with intra‐renal transcripts that were associated with DKD or estimated glomerular filtration rate (eGFR). The impacted metabolites and their protein‐coding transcripts were analysed for enriched pathways. Results Of all measured (n = 812) metabolites, 108 changed (P

Aim: To predict end-stage renal disease (ESRD) in patients with type 2 diabetes by using machine-learning models with multiple baseline demographic and clinical characteristics. Materials and methods: In total, 11 789 patients with type 2 diabetes and nephropathy from three clinical trials, RENAAL (n = 1513), IDNT (n = 1715) and ALTITUDE (n = 8561), were used in this study. Eighteen baseline demographic and clinical characteristics were used as predictors to train machine-learning models to predict ESRD (doubling of serum creatinine and/or ESRD). We used the area under the receiver operator curve (AUC) to assess the prediction performance of models and compared this with traditional Cox proportional hazard regression and kidney failure risk equation models. Results: The feed forward neural network model predicted ESRD with an AUC of 0.82 (0.76-0.87), 0.81 (0.75-0.86) and 0.84 (0.79-0.90) in the RENAAL, IDNT and ALTITUDE trials, respectively. The feed forward neural network model selected urinary albumin to creatinine ratio, serum albumin, uric acid and serum creatinine as important predictors and obtained a state-of-the-art performance for predicting long-term ESRD. Conclusions: Despite large inter-patient variability, non-linear machine-learning models can be used to predict long-term ESRD in patients with type 2 diabetes and nephropathy using baseline demographic and clinical characteristics. The proposed method has the potential to create accurate and multiple outcome prediction automated models to identify high-risk patients who could benefit from therapy in clinical practice.

The goal of this study was to evaluate the impact of genetic counselor (GC) review of incoming test orders received in an academic diagnostic molecular genetics laboratory. The GC team measured the proportion of orders that could be modified to improve efficiency or sensitivity, tracked provider uptake of GC proposed testing changes, and calculated the health care dollar savings resulting from GC intervention. During this 6-month study, the GC team reviewed 2367 incoming test orders. Of these, 109 orders (4.6%) were flagged for review for potentially inefficient or inappropriate test ordering. These flagged orders corresponded to a total of 51 cases (1-5 orders for each patient), representing 54 individuals and including 3 sibling pairs. The GC team proposed a modification for each flagged case and the ordering providers approved the proposed change for 49 of 51 cases (96.08%). For the 49 modifications, the cost savings totaled $98,750.64, for an average of $2015.32 saved per modification. This study provides evidence of the significant contribution of genetic counselors in a laboratory setting and demonstrates the benefit of laboratories working with ordering providers to identify the best test for their patients. The review of test orders by a genetic counselor both improves genetic test ordering strategies and decreases the amount of health care dollars spent on genetic testing.

Iron being a basic raw material for iron and steel industry, mining of iron ore has become a very important economic activity in many countries including India. One of the various adverse effects of mining to the surrounding environment near mining area is piling up of overburden dumps, leading to difficulty of disposal of the waste. For sustainable development, various researchers have tried to utilize different types of industrial by-products like fly ash, blast furnace slag, sewage sludge, marble dust, and the like in the preparation of stabilized blocks. In a similar way, comprehensive utilization of mining waste is important in saving resources, improving surroundings of mining area and also leading to sustainable development. With an intention of finding a possible solution for the bulk utilization of the Iron Mine Spoil Waste (MSW) accumulated as silt in the upstream portion of an earthen dam located in one of the mining area in Sandur region, Karnataka, India, an exploratory study was taken up to prepare CSEBs utilizing various proportions of MSW, quarry dust and stabilizers (Cement and lime). Attempts made in this study to utilize MSW in preparation of CSEBs with varying amounts of MSW (30% to 50%), cement and lime have shown that wet compressive strength of blocks to be more than 5 MPa at 6 months of ageing. This value of wet compressive strength is good enough for residential buildings. The present study brings out green building blocks suitable for construction and thereby promote sustainability.

BACKGROUND: Sedation indicators based on a single quantitative EEG (QEEG) feature have been criticised for their limited performance. We hypothesised that integration of multiple QEEG features into a single sedation-level estimator using a machine learning algorithm could reliably predict levels of sedation, independent of the sedative drug used.METHODS: In total, 102 subjects receiving propofol (N=36; 16 male/20 female), sevoflurane (N=36; 16 male/20 female), or dexmedetomidine (N=30; 15 male/15 female) were included in this study of healthy volunteers. Sedation level was assessed using the Modified Observer's Assessment of Alertness/Sedation (MOAA/S) score. We used 44 QEEG features estimated from the EEG data in a logistic regression algorithm, and an elastic-net regularisation method was used for feature selection. The area under the receiver operator characteristic curve (AUC) was used to assess the performance of the logistic regression model.RESULTS: The performances obtained when the system was trained and tested as drug-dependent mode to distinguish between awake and sedated states (mean AUC [standard deviation]) were propofol=0.97 (0.03), sevoflurane=0.74 (0.25), and dexmedetomidine=0.77 (0.10). The drug-independent system resulted in mean AUC=0.83 (0.17) to discriminate between the awake and sedated states.CONCLUSIONS: The incorporation of large numbers of QEEG features and machine learning algorithms is feasible for next-generation monitors of sedation level. Different QEEG features were selected for propofol, sevoflurane, and dexmedetomidine groups, but the sedation-level estimator maintained a high performance for predicting MOAA/S independent of the drug used.CLINICAL TRIAL REGISTRATION: NCT02043938; NCT03143972.

Over- and under-sedation are common in the ICU, and contribute to poor ICU outcomes including delirium. Behavioral assessments, such as Richmond Agitation-Sedation Scale (RASS) for monitoring levels of sedation and Confusion Assessment Method for the ICU (CAM-ICU) for detecting signs of delirium, are often used. As an alternative, brain monitoring with electroencephalography (EEG) has been proposed in the operating room, but is challenging to implement in ICU due to the differences between critical illness and elective surgery, as well as the duration of sedation. Here we present a deep learning model based on a combination of convolutional and recurrent neural networks that automatically tracks both the level of consciousness and delirium using frontal EEG signals in the ICU. For level of consciousness, the system achieves a median accuracy of 70% when allowing prediction to be within one RASS level difference across all patients, which is comparable or higher than the median technician–nurse agreement at 59%. For delirium, the system achieves an AUC of 0.80 with 69% sensitivity and 83% specificity at the optimal operating point. The results show it is feasible to continuously track level of consciousness and delirium in the ICU.

Objective Seizure detection is a major facet of electroencephalography (EEG) analysis in neurocritical care, epilepsy diagnosis and management, and the instantiation of novel therapies such as closed-loop stimulation or optogenetic control of seizures. It is also of increased importance in high-throughput, robust, and reproducible pre-clinical research. However, seizure detectors are not widely relied upon in either clinical or research settings due to limited validation. In this study, we create a high-performance seizure-detection approach, validated in multiple data sets, with the intention that such a system could be available to users for multiple purposes. Methods We introduce a generalized linear model trained on 141 EEG signal features for classification of seizures in continuous EEG for two data sets. In the first (Focal Epilepsy) data set consisting of 16 rats with focal epilepsy, we collected 1012 spontaneous seizures over 3 months of 24/7 recording. We trained a generalized linear model on the 141 features representing 20 feature classes, including univariate and multivariate, linear and nonlinear, time, and frequency domains. We tested performance on multiple hold-out test data sets. We then used the trained model in a second (Multifocal Epilepsy) data set consisting of 96 rats with 2883 spontaneous multifocal seizures. Results From the Focal Epilepsy data set, we built a pooled classifier with an Area Under the Receiver Operating Characteristic (AUROC) of 0.995 and leave-one-out classifiers with an AUROC of 0.962. We validated our method within the independently constructed Multifocal Epilepsy data set, resulting in a pooled AUROC of 0.963. We separately validated a model trained exclusively on the Focal Epilepsy data set and tested on the held-out Multifocal Epilepsy data set with an AUROC of 0.890. Latency to detection was under 5 seconds for over 80% of seizures and under 12 seconds for over 99% of seizures. Significance This method achieves the highest performance published for seizure detection on multiple independent data sets. This method of seizure detection can be applied to automated EEG analysis pipelines as well as closed loop interventional approaches, and can be especially useful in the setting of research using animals in which there is an increased need for standardization and high-throughput analysis of large number of seizures.

Electroencephalogram (EEG)-based prediction systems are used to target anesthetic-states in patients undergoing procedures with general anesthesia (GA). These systems are not widely employed in resource-limited settings because they are cost-prohibitive. Although anesthetic-drugs induce highly-structured, oscillatory neural dynamics that make EEG-based systems a principled approach for anesthetic-state monitoring, anesthetic-drugs also significantly modulate the autonomic nervous system (ANS). Because ANS dynamics can be inferred from electrocardiogram (ECG) features such as heart rate variability, it may be possible to develop an ECG-based system to infer anesthetic-states as a low-cost and practical alternative to EEG-based anesthetic-state prediction systems. In this work, we demonstrate that an ECG-based system using ANS features can be used to discriminate between non-GA and GA states in sevoflurane, with a GA F 1 score of 0.834, [95% CI, 0.776, 0.892], and in sevoflurane-plus-ketamine, with a GA F 1 score of 0.880 [0.815, 0.954]. With further refinement, ECG-based anesthetic-state systems could be developed as a fully automated system for anesthetic-state monitoring in resource-limited settings.

Simple SummaryHealth behaviors affect health status in cancer survivors. We aimed to identify such key health behaviors using nonlinear algorithms and compare their classification performance with logistic regression, for distinguishing cancer survivors from those cancer-free in a population-based cohort. We used health behaviors and socioeconomic factors for analysis. Participants from the Lifelines population-based cohort were binary classified as cancer survivors or cancer-free using nonlinear algorithms or logistic regression. Data were collected for 107,624 cancer-free participants and 2760 cancer survivors. Using all variables, algorithms obtained an area under the receiver operator curve (AUC) of 0.75 +/- 0.01. Using only health behaviors, the algorithms differentiated cancer survivors from cancer-free participants with AUCs of 0.62 +/- 0.01 and 0.60 +/- 0.01, respectively. In the case-control analyses, both algorithms produced AUCs of 0.52 +/- 0.01. The main distinctive classifier was age. No key health behaviors were identified by linear and nonlinear algorithms to differentiate cancer survivors from cancer-free participants.Health behaviors affect health status in cancer survivors. We hypothesized that nonlinear algorithms would identify distinct key health behaviors compared to a linear algorithm and better classify cancer survivors. We aimed to use three nonlinear algorithms to identify such key health behaviors and compare their performances with that of a logistic regression for distinguishing cancer survivors from those without cancer in a population-based cohort study. We used six health behaviors and three socioeconomic factors for analysis. Participants from the Lifelines population-based cohort were binary classified into a cancer-survivors group and a cancer-free group using either nonlinear algorithms or logistic regression, and their performances were compared by the area under the curve (AUC). In addition, we performed case-control analyses (matched by age, sex, and education level) to evaluate classification performance only by health behaviors. Data were collected for 107,624 cancer free participants and 2760 cancer survivors. Using all variables resulted an AUC of 0.75 +/- 0.01, using only six health behaviors, the logistic regression and nonlinear algorithms differentiated cancer survivors from cancer-free participants with AUCs of 0.62 +/- 0.01 and 0.60 +/- 0.01, respectively. The main distinctive classifier was age. Though not relevant to classification, the main distinctive health behaviors were body mass index and alcohol consumption. In the case-control analyses, algorithms produced AUCs of 0.52 +/- 0.01. No key health behaviors were identified by linear and nonlinear algorithms to differentiate cancer survivors from cancer-free participants in this population-based cohort.

To explore the potential value of heart rate variability features for automated monitoring of sedation levels in mechanically ventilated ICU patients.Multicenter, pilot study.Several ICUs at Massachusetts General Hospital, Boston, MA.Electrocardiogram recordings from 40 mechanically ventilated adult patients receiving sedatives in an ICU setting were used to develop and test the proposed automated system.Richmond Agitation-Sedation Scale scores were acquired prospectively to assess patient sedation levels and were used as ground truth. Richmond Agitation-Sedation Scale scores were grouped into four levels, denoted "unarousable" (Richmond Agitation- Sedation Scale = -5, -4), "sedated" (-3, -2, -1), "awake" (0), "agitated" (+1, +2, +3, +4). A multiclass support vector machine algorithm was used for classification. Classifier training and performance evaluations were carried out using leave-oneout cross validation. An overall accuracy of 69% was achieved for discriminating between the four levels of sedation. The proposed system was able to reliably discriminate (accuracy = 79%) between sedated (Richmond Agitation-Sedation Scale0) and nonsedated states (Richmond Agitation-Sedation Scale0).With further refinement, the methodology reported herein could lead to a fully automated system for depth of sedation monitoring. By enabling monitoring to be continuous, such technology may help clinical staff to monitor sedation levels more effectively and to reduce complications related to over- and undersedation.

Background: Iodine is an essential micronutrient with an RDA of 100-150 μg for normal human growth and mental development. Iodine deficiency disorders (IDD) constitute the single largest cause of preventable brain damage worldwide. Majority of consequences of IDD are invisible and irreversible but at the same time these are preventable. The study was conducted to assess the prevalence of goiter in school children aged 6-12 years and to assess the level of iodine concentration in salt samples obtained from households of selected school children. Methods: Population proportionate to size sampling. Sample size: 90 primary school-going children of age 6-12 years in each selected village, total 2700 from 30 villages in Ramanagara district, Karnataka, India. Results: The prevalence of goiter among the 6 - 12 years children was found to be 8.6%. Females had higher prevalence compared to males in all the age groups but the difference was not statistically significant (0.437). Of the 540 salt samples, 518(95.3%) had iodine concentration ≥15 ppm at household level. Conclusions: IDD is a mild public health problem in Ramanagara district. There is a need of periodic surveys to assess the change in magnitude of the IDD with respect to impact of iodized salt (IS) intervention.

Synchronous machines are dedicated to the specific application. They are generally employed in rolling mills, pumps, fans, and compressors like reprobating and centrifugal drives, pulp and paper processing, water treatment, mining, and in cement industries. As a synchronous motor, the performance is reduced for the given excitation while the load increases. When operated as synchronous generators, both power loads and lighting loads depend on the output from the armature winding. This paper presents an alternative choice in which by providing an additional winding in the stationary armature, when operated as a Double Winding Synchronous Motor (DWSyM), it becomes possible to operate in maximum power factor by adjusting the loads on both the stator windings. When operated as conventional motor, for the load current of 3.5 A, the efficiency is 55% and power factor is 0.55, for the same excitation when second winding is connected to a load current of 1 A, the efficiency is improved to 77.6% and power factor is improved to 0.66. The main focus of this machine is to improve the performance of the machine for the reduced excitation and minimum load. For the reduced excitation, the performance can be improved by loading both the windings. While operated as Double Winding Synchronous Generator (DWSyG), two stator outputs are available which help to separate the power and lighting circuits. Hence, interruption in the lighting circuit can be limited, this machine can be considered as Twin generator.

Objective: Analysis of the electroencephalogram (EEG) background pattern helps predicting neurological outcome of comatose patients after cardiac arrest (CA). Visual analysis may not extract all discriminative information. We present predictive values of the revised Cerebral Recovery Index (rCRI), based on continuous extraction and combination of a large set of evolving quantitative EEG (qEEG) features and machine learning techniques.Methods: We included 551 subsequent patients from a prospective cohort study on continuous EEG after CA in two hospitals. Outcome at six months was classified as good (Cerebral Performance Category (CPC) 1-2) or poor (CPC 3-5). Forty-four qEEG features (from time, frequency and entropy domain) were selected by the least absolute shrinkage and selection operator (LASSO) method and used in a Random Forests classification system. We trained and evaluated the system with 10-fold cross validation. For poor outcome prediction, the sensitivity at 100% specificity (Se-100) and the area under the receiver operator curve (AUC) were used as performance of the prediction model. For good outcome, we used the sensitivity at 95% specificity (Se-95).Results: Two hundred fifty-six (47%) patients had a good outcome. The rCRI predicted poor outcome with AUC = 0.94 (95% CI: 0.83-0.91), Se-100 = 0.66 (0.65-0.78), and AUC = 0.88 (0.78-0.93), Se-100 = 0.60 (0.510.75) at 12 and 24 h after CA, respectively. The rCRI predicted good outcome with Se-95 = 0.72 (0.61-0.85) and 0.40 (0.30-0.51) at 12 and 24 h after CA, respectively.Conclusions: Results obtained in this study suggest that with machine learning algorithms and large set of qEEG features, it is possible to efficiently monitor patient outcome after CA. We also demonstrate the importance of selection of optimal performance metric to train a classifier model for outcome prediction.Significance: The rCRI is a sensitive, reliable predictor of neurological outcome of comatose patients after CA. (C) 2018 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Over- and under-sedation are common in critically ill patients admitted to the Intensive Care Unit. Clinical assessments provide limited time resolution and are based on behavior rather than the brain itself. Existing brain monitors have been developed primarily for non-ICU settings. Here, we use a clinical dataset from 154 ICU patients in whom the Richmond Agitation-Sedation Score is assessed about every 2 hours. We develop a recurrent neural network (RNN) model to discriminate between deep vs. no sedation, trained end-to-end from raw EEG spectrograms without any feature extraction. We obtain an average area under the ROC of 0.8 on 10-fold cross validation across patients. Our RNN is able to provide reliable estimates of sedation levels consistently better compared to a feed-forward model with simple smoothing. Decomposing the prediction error in terms of sedatives reveals that patient-specific calibration for sedatives is expected to further improve sedation monitoring.

Introduction EEG background reactivity is a strong predictor of coma recovery after cardiac arrest. The current clinical value of EEG background reactivity testing is limited by inadequate inter-rater expert agreement, few number of daily assessments, and unsuitability for quantitative tracking by visual review. We hypothesized that a quantitative EEG reactivity method using actigraphy-triggered events from a wrist-worn wearable can predict long-term functional outcome in comatose cardiac arrest patients treated with targeted temperature management. Methods We prospectively recorded clinical, actigraphy, and EEG data of comatose cardiac arrest patients managed with targeted temperature management at a single U.S. academic hospital. Continuous actigraphy data obtained from a wrist-worn wearable (Affectiva 3) was synchronized to continuous EEG data for up to 96 h after cardiac arrest. Change in actigraphy across time was used as a surrogate of bedside external physical stimulation (i.e. actigraphy-triggered event). Our quantitative EEG method evaluated changes in EEG spectra, entropy, and frequency features during 30 s before and 30 s after each actigraphy-triggered event (46 EEG features used). Actigraphy threshold and EEG window duration (pre and post actigraphy-triggered events) were utilized as hyperparameters in the model. In addition to actigraphy-triggered quantitative EEG background reactivity, EEG reactivity was scored visually as present or absent once daily by a single expert electroencephalographer during clinical care. Good outcome was defined as Cerebral Performance Category of 1–2 at six months. Classification was carried out using extreme learning machine and leave-one-subject-out cross validation. Final outcome class prediction for each individual subject was determined using simple majority voting across all actigraphy-triggered events. Results Ten subjects were monitored. Mean age was 60 (standard deviation (SD) ± 14.6) years, 10% were female, 50% had a shockable rhythm, and 40% had good outcome at six months. A total of 19 (SD ± 8.9) actigraphy-triggered events per patient were detected. Best outcome prediction performance was achieved using 50-s-long EEG window (25-s pre and 25-s post each actigraphy-triggered event). The quantitative EEG reactivity method correctly predicted good outcome in 80% of cases (sensitivity and specificity 80%). Expert rating using once daily visual EEG reactivity assessments had 60% accuracy for good outcome prediction. Conclusion Quantitative EEG reactivity assessments using actigraphy-based triggered events is feasible and may support long-term outcome prediction in cardiac arrest coma. Integration of EEG and wearable sensors using machine learning methods might facilitate the deployment of dynamic multi-modal patient monitoring in the intensive care unit environment.

Objective : This study was performed to evaluate how well states of deep sedation in ICU patients can be detected from the frontal electroencephalogram (EEG) using features based on the method of atomic decomposition (AD). Methods : We analyzed a clinical dataset of 20 min of EEG recordings per patient from 44 mechanically ventilated adult patients receiving sedatives in an intensive care unit (ICU) setting. Several features derived from AD of the EEG signal were used to discriminate between awake and sedated states. We trained support vector machine (SVM) classifiers using AD features and compared the classification performance with SVM classifiers trained using standard spectral and entropy features using leave-one-subject-out validation. The potential of each feature to discriminate between awake and sedated states was quantified using area under the receiver operating characteristic curve (AUC). Results : The sedation level classification system using AD was able to reliably discriminate between sedated and awake states achieving an average AUC of 0.90, which was significantly better ( $p ) than performance achieved using spectral (AUC = 0.86) and entropy (AUC = 0.81) domain features. A combined feature set consisting of AD, entropy, and spectral features provided better discrimination (AUC = 0.91, $p ) than any individual feature set. Conclusions : Features derived from the atomic decomposition of EEG signals provide useful discriminative information about the depth of sedation in ICU patients. Significance : With further refinement and external validation, the proposed system may be able to assist clinical staff with continuous surveillance of sedation levels in mechanically ventilated critically ill ICU patients.

Background: Nutrition is a core pillar of human development, influences growth and development before as well as after birth, around 90% of the world undernourished children live in Asia and Africa. In India among under-five children 43% are underweight and 20% have wasting due to acute under-nutrition. The study was conducted to assess socio demographic characteristics of the family and to assess nutritional status of children attending under five clinics. Methods: Retrospective record based data was obtained from the register of under-five clinic, MIMS, Mandya from the mothers who attended under five clinics from September 2012 - August 2013 (1 year). Results: A total of 8506 children attended under-five clinic in the study period. 68.0% of mothers were in the 20-25 years age group. 82% of mothers had education above secondary school level, while 6.2% of mothers are illiterate. Majority of the families (88.4%) belongs to lower socio economic class and 67.9% were living in joint family. Malnutrition in the form of overweight and underweight has been observed in 2517 (74.0%) children, who were class IV SES, followed by 640 (18.8%) in class V. Conclusions: Majority of the children attending under five clinics belongs to joint family and low socio economic status. A significant of them were malnourished have been observed in lower socio economic people.

Silver was deposited on manganese-doped titanates (Mn–TiO2) by photoinduced deposition method. The catalyst shows enhanced photocatalytic activity due to the synergistic effect of bicrystalline framework of anatase and rutile structures with high intimate contact due to the similarity in their crystallite sizes. The deposited metal nanostructures help in the formation of resonant surface plasmons in response to a photon flux, localizing the electromagnetic energy close to their surfaces. Better charge separation is achieved near the semiconductor surface due to the localized field. Silver deposition was varied from 0.1 to 1.5% on the surface of Mn–TiO2. The mechanism of interfacial electron transfer at heterojunctions in mixed phase induced by the plasmonic catalysis is explained. The extent of band bending, the variation of potential field in the space charge region with respect to the size of the deposited Ag metal particles is discussed. The photocatalytic activity of silver deposited Mn–TiO2 was evaluated by taking resorcinol (Rs) as the model compound along with oxidants such as hydrogen peroxide (H2O2) and ammonium per sulfate (APS) under UV/solar light illumination. The electronic level of the dopant, high intimate contact between the anatase and rutile phases along with efficient electron trapping by silver particles, plays a significant role in the photocatalytic process.

Background/Objectives: Studies have shown that the general public is supportive of newborn screening (NBS) and supportive of the storage and use of residual bloodspots for quality assurance and biomedical research. However, the attitudes of parents of children with serious health conditions have not been assessed. In this study, we assessed attitudes of parents with children who have phenylketonuria (PKU) and leukemia towards NBS and storage and use of residual bloodspots for research. Methods: A total of 49 individuals were recruited and responded to a validated 41-item survey regarding NBS and the retention and use of residual bloodspots. Of these participants, 22 had a child with PKU and 27 had a child with leukemia. We compared their responses to those of 1,927 individuals from the general public obtained in a previous study using the same survey instrument. Results/Conclusions: We found that parents of children with a serious health condition had higher levels of support than the general public towards the use of residual NBS samples for research but similar attitudes regarding choice and privacy protections. It is important to assess the attitudes of various stakeholders for policy development.