The authors would like to thank Khanh Nguyen, who was involved in the build of the electronic platform as part of his Bachelor of Engineering degree. We also acknowledge Alice Gibson, who developed the paper food diary. Finally, we would like to acknowledge Mackenzie Fong, James Gerofi, Fatima Ferkh, Cholris Leung, Lisa Leung, and Shaoyu Zhang for their help with experimental methods. The work was funded by the Commercial Development and Industry Partnership Funding Scheme at the University of Sydney.

NOTE: All experimental procedures described below were approved by the Sydney Local Health District (Royal Prince Alfred Zone) Ethics Review Committee.
1. Participant Recruitment
<ol>
	<li>Recruit participants from the institution databases and by advertising the study on the institution website and local newspapers. 
	NOTE: Criteria that was applied for this particular study are male and female, aged 25&#8211;55 years, pre-menopausal, with a BMI &#8805;25 to &#8804;35 kg/m2.</li>
	<li>Ensure that potential participants are computer literate.</li>
</ol>
2. Preparing Participants for Dietary Data Collection
<ol>
	<li>Arrange an initial individual screening visit by calling each participant.</li>
	<li>Obtain written informed consent from each participant prior to the screening visit.</li>
	<li>Train participants in the use of the electronic platform.
	<ol>
		<li>Provide participants with a step-by-step guide explaining how to use the electronic platform. Give participants sufficient time to read it.</li>
		<li>Show participants how to use the platform on the study laptop.</li>
		<li>Ask participants to enter in the platform some food and drink items in the presence of a trained study investigator.</li>
	</ol>
	</li>
	<li>Provide participants with a traditional paper-based food diary with instructions on how to use it included. Ask them to complete it before their baseline visit.</li>
	<li>Instruct participants to:
	<ol>
		<li>Read the instructions and the &#39;Example Day&#39; (Figure 1) included in the food diary thoroughly before starting to record any food or drink.</li>
		<li>Record all foods and beverages consumed for three consecutive days (two weekdays and one weekend). Ask them to report all items consumed both at home and outside home (e.g. at work or at a restaurant) and to insert them in the diary as close to the time of consumption as possible to avoid relying on memory.</li>
		<li>Refer to the portion sizes guide included in the food diary to estimate the measurement and amount of foods and drinks.</li>
		<li>Use the checklist of commonly forgotten foods and beverages included in the food diary to make sure no food and drink items are missing.</li>
		<li>Record items consumed as specific as possible (write &#39;chicken thigh with skin&#39; instead of &#39;chicken&#39;, or &#39;reduced fat milk&#39; instead of &#39;milk&#39;, for example).</li>
		<li>Indicate the preparation methods (fried, steamed, baked, and raw, for example).</li>
		<li>List the brand name of food and drink products.</li>
		<li>Include recipes for any unusual items prepared at home.</li>
	</ol>
	</li>
	<li>Instruct participants to enter the written information recorded on the paper food diary into the electronic diaryat the end of each of the three days.</li>
	<li>Arrange a baseline visit for each participant within one week after the screening visit.</li>
	<li>Instruct participants to bring the completed paper food diary at the baseline visit.</li>
	<li>At the week five visit, provide participants with a new paper food diary and instruct them to complete it for another three days before the week six visit and ask them to enter the data into the electronic diary over the same three days.</li>
</ol>
3. Written Food Diary
NOTE: Food intake was self-reported and collected by utilizing both the traditional written paper diary method and the electronic web-based platform (&#39;Boden Food Plate&#39;) over the same three-day period.
<ol>
	<li>Collecting the Dietary Data 
	NOTE: Participants complete the three-day paper food diary and return them at the baseline and week six visit.
	<ol>
		<li>Review the food diaries together with the participants to ensure all dietary data is legible and all of the measurements and amounts consumed are entered correctly.</li>
	</ol>
	</li>
	<li>Analysis of the Written Dietary Data
	<ol>
		<li>Enter the three-day written food diary data for each participant into dietary analysis software (see Table of Materials) for the analysis of nutrient intake. The software automatically calculates the nutritional intakes.</li>
		<li>Export the calculated values for energy (kJ) and macronutrient intake (protein, fat and carbohydrate) represented in absolute grams (g) and as a percentage of total energy intake (% kJ) as the average of six days (three days at baseline and three days at the week six visit) into a spreadsheet.</li>
		<li>Import mean values of the six days into statistical software for analysis.</li>
	</ol>
	</li>
</ol>
4. Electronic Food Diary (&#39;Boden Food Plate&#39;)
<ol>
	<li>Registration
	<ol>
		<li>Send an &#39;invitation email&#39; to the study participants with the link to register a new account in the electronic platform.</li>
		<li>Instruct participants to follow the registration process by entering their full name, date of birth, gender, email and create a username (email address) and password to log-in to the electronic platform (Figure 2).</li>
		<li>Instruct participants to access the visual guide on how to use the electronic food diary from the application homepage and to read it carefully (Figure 3).</li>
		<li>Provide phone and email support to assist participants who encounter difficulties when completing the electronic food diary.</li>
	</ol>
	</li>
	<li>To create an electronic food diary, have study participants:
	<ol>
		<li>Select the icon with the specific project title they are invited to join (Figure 4).</li>
		<li>Select &#39;Make a new entry&#39; to create a new day of the electronic food diary where they can enter food and drink items using the information from the written food diary (Figure 5).</li>
		<li>Use the &#39;Search&#39; tool to find food and drink items consumed during the day from the database (Figure 6).</li>
		<li>Click on the desired food and drink items and virtually drag them onto the plate to reflect the meals consumed at that time (Figure 6).</li>
		<li>Select one of the small plates at the bottom of the page to move to different meals (breakfast, morning snack, lunch, afternoon snack, dinner and supper). When the selected small plate starts to bounce, enter foods and beverages on the big plate in the middle of the screen which corresponds to the meal selected (Figure 6).</li>
		<li>Click the &#39;Save changes&#39; button to save all entries of the day (Figure 6).</li>
		<li>Visualize the bar graphs showing the comparison between the automatically calculated dietary intakes and the average population dietary intakes (Figure 7) by clicking on the &#39;Graph&#39; icon on the entries page (Figure 5). 
		NOTE: Participants create two separate three-day electronic food diaries for the baseline and week six visit.</li>
	</ol>
	</li>
	<li>Preparing dietary data for analysis
	<ol>
		<li>Export the average value of energy and nutrient intakes for each participant into a spreadsheet by accessing the platform managing portal.</li>
		<li>Import the spreadsheet into statistical software for analysis.</li>
	</ol>
	</li>
	<li>Overall satisfaction questionnaire
	<ol>
		<li>Administer a paper-based questionnaire to measure the overall satisfaction with the program for participants at week six.</li>
		<li>Instruct participants to score different elements of the web-based application on a scale of 0&#8211;10 (with 0 being a negative, 5 a neutral and 10 a positive evaluation), covering topics such as ease of use, completeness of the food list, and search function.</li>
	</ol>
	</li>
</ol>
5. Analysis of the Dietary Data
<ol>
	<li>Use only data from participants who entered three days of dietary data into the electronic platform and completed three days of written food diary at both baseline and week six visits.</li>
	<li>Obtain macronutrient percentages.
	<ol>
		<li>Multiply the total macronutrient amount (g) by kilojoules per gram (kJ/g) of macronutrient, with 17 kJ per gram for protein and carbohydrate and 37 kJ per gram for fat.</li>
		<li>Divide the value (kilojoules per total grams of macronutrient) by the total energy intake (kJ), then multiply the number obtained by 100 to find the macronutrient percentage of total energy intake (% kJ).</li>
	</ol>
	</li>
	<li>Calculate mean values (&#177;standard deviation (SD)) for total energy (kJ) and protein, fat and carbohydrate intake (in both absolute grams and as a percentage of energy intake) for the electronic and written food diaries separately.</li>
	<li>Use Pearson&#39;s product-moment correlation to determine the correlation between the two dietary collection methods.</li>
	<li>Obtain percentage differences between the two data collection methods.
	<ol>
		<li>Subtract the values determined for the written food diary from the electronic food diary.</li>
	</ol>
	</li>
	<li>Create Bland-Altman plots for mean total energy (kJ), protein, carbohydrate and fat intake (% kJ).
	<ol>
		<li>Plot the mean values of each data collection tool (x-axis) against the difference between the methods (y-axis)15,16.</li>
		<li>Plot the mean difference and upper and lower interval lines (mean (&#177;SD)*1.96). This allows for extensive examination of the agreement between the electronic and paper dietary collection methods.</li>
	</ol>
	</li>
</ol>
6. Analysis of the Questionnaires
<ol>
	<li>Calculate the mean (&#177;SD) score for each question of the overall satisfaction with data derived from the questionnaire.</li>
</ol>

<ol>
	<li>Buzzard, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=24-Hour+dietary+recall+and+food+record+methods.">24-Hour dietary recall and food record methods.</a> Nutritional Epidemiology. , 50-73 (1998).</li><li>MacIntyre, U. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Measuring+food+intake.">Measuring food intake.</a> Introduction to Human Nutrition. , 238-275 (2007).</li><li>Barrett-Connor, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Nutrition+Epidemiology+-+How+Do+We+Know+What+They+Ate?.">Nutrition Epidemiology - How Do We Know What They Ate?.</a> American Journal of Clinical Nutrition. 54 (1), S182-S187 (1991).</li><li>Black, A. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Measurements+of+Total-Energy+Expenditure+Provide+Insights+into+the+Validity+of+Dietary+Measurements+of+Energy-Intake.">Measurements of Total-Energy Expenditure Provide Insights into the Validity of Dietary Measurements of Energy-Intake.</a> Journal of the American Dietetic Association. 93 (5), 572-579 (1993).</li><li>Rebro, S. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+effect+of+keeping+food+records+on+eating+patterns.">The effect of keeping food records on eating patterns.</a> Journal of the American Dietetic Association. 98 (10), 1163-1165 (1998).</li><li>Johnson, R. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dietary+intake+-+How+do+we+measure+what+people+are+really+eating?.">Dietary intake - How do we measure what people are really eating?.</a> Obesity Research. 10, 63s-68s (2002).</li><li>Bonilla, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Use+of+electronic+dietary+assessment+tools+in+primary+care:+an+interdisciplinary+perspective.">Use of electronic dietary assessment tools in primary care: an interdisciplinary perspective.</a> BMC Medical Informatics and Decision Making. 15, 14 (2015).</li><li>Illner, A. K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Review+and+evaluation+of+innovative+technologies+for+measuring+diet+in+nutritional+epidemiology.">Review and evaluation of innovative technologies for measuring diet in nutritional epidemiology.</a> International Journal of Epidemiology. 41 (4), 1187-1203 (2012).</li><li>Cole, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+comparative+study+of+mobile+electronic+data+entry+systems+for+clinical+trials+data+collection.">A comparative study of mobile electronic data entry systems for clinical trials data collection.</a> International Journal of Medical Informatics. 75 (10-11), 722-729 (2006).</li><li>Carter, M. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term='My+Meal+Mate'+(MMM),+validation+of+the+diet+measures+captured+on+a+smartphone+application+to+facilitate+weight+loss.">'My Meal Mate' (MMM), validation of the diet measures captured on a smartphone application to facilitate weight loss.</a> British Journal of Nutrition. 109 (3), 539-546 (2013).</li><li>Woods, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparison+of+electronic+and+paper+data+collection.">Comparison of electronic and paper data collection.</a> American Journal of Epidemiology. 153 (11), S82 (2001).</li><li>Kerkenbush, N. L., Lasome, C. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+emerging+role+of+electronic+diaries+in+the+management+of+diabetes+mellitus.">The emerging role of electronic diaries in the management of diabetes mellitus.</a> The American Association of Critical-Care Nurses Clinical Issues. 14 (3), 371-378 (2003).</li><li>Burke, L. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=SMART+trial:+A+randomized+clinical+trial+of+self-monitoring+in+behavioral+weight+management-design+and+baseline+findings.">SMART trial: A randomized clinical trial of self-monitoring in behavioral weight management-design and baseline findings.</a> Contemporary Clinical Trials. 30 (6), 540-551 (2009).</li><li>Fuller, N. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparison+of+an+electronic+versus+traditional+food+diary+for+assessing+dietary+intake-A+validation+study.">Comparison of an electronic versus traditional food diary for assessing dietary intake-A validation study.</a> Obesity Research &amp; Clinical Practice. 11 (6), 647-654 (2017).</li><li>Bland, J. M., Altman, D. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Statistical+Methods+for+Assessing+Agreement+between+Two+Methods+of+Clinical+Measurement.">Statistical Methods for Assessing Agreement between Two Methods of Clinical Measurement.</a> Lancet. 1 (8476), 307-310 (1986).</li><li>Bland, J. M., Altman, D. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Statistical+methods+for+assessing+agreement+between+two+methods+of+clinical+measurement.">Statistical methods for assessing agreement between two methods of clinical measurement.</a> International Journal of Nursing Studies. 47 (8), 931-936 (2010).</li><li>Sharp, D. B., Allman-Farinelli, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Feasibility+and+validity+of+mobile+phones+to+assess+dietary+intake.">Feasibility and validity of mobile phones to assess dietary intake.</a> Nutrition. 30 (11-12), 1257-1266 (2014).</li><li>Carter, M. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Adherence+to+a+smartphone+application+for+weight+loss+compared+to+website+and+paper+diary:+pilot+randomized+controlled+trial.">Adherence to a smartphone application for weight loss compared to website and paper diary: pilot randomized controlled trial.</a> Journal of Medical Internet Research. 15 (4), e32 (2013).</li><li>Johannes, C. B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Electronic+versus+paper+instruments+for+daily+data+collection.">Electronic versus paper instruments for daily data collection.</a> Annals of Epidemiology. 10 (7), 457 (2000).</li></ol>

The authors have nothing to disclose.

The web-based food diary utilized for this study, the &#39;Boden Food Plate&#39;, is a newly developed method for the collection of dietary information, designed to provide research groups with faster access to data. The results demonstrate that the energy and macronutrient intake data collected on the electronic method were accurate when compared to the dietary data recorded on the written estimated food diary, which is considered the gold standard for this study. The visual electronic tool has also proven to be easier ...

The ability to accurately collect dietary data and assess diet is an essential component of nutrition and weight management research. Many methods to assess dietary intake, both retrospective and prospective, have already been established. However, these commonly used tools come with their own limitations. Hereby, there is a need for a new technological platform that can improve the accuracy of the data and reduce the burden for both researchers and participants1,2,3.
Twenty-four hour recalls (24 h recalls) and Food Frequency Questionnaires (FFQs) are dietary data collection methods that rely on the participants&#39; memory and suffer from potential recall bias and misreporting1,2,4. Written food records, such as FFQs and food diaries, require literacy and can place a high burden on the participants. Additionally, written records where food and drink items are reported at the time of consumption can lead to alterations in the habitual dietary intakes5. These traditional methods of nutritional data collection and coding can also be extremely time consuming and require highly trained personnel1. Seven-day weighed food diaries are considered the non-biomarker gold standard for dietary intake assessments. However, this method has been shown to increase the rate of under-reporting over a seven-day period due to a high participant burden6. Hence, a three-day estimated food diary using traditional household measures to quantify food and drink amounts is sought to be more feasible than seven-day written food records.
Previous studies have shown that collecting dietary intake using electronic tools is valid and efficient compared to traditional methods7,8. For this reason, there is an increased interest in technologies that can assist in the recording and analysis of dietary data. Despite the limitations of being costly and of requiring computer literacy from the participants9, electronic dietary assessment methods add the benefit of being more visual compared to traditional dietary collection tools. Hence, they are well received and found enjoyable by the users10. Additionally, electronic tools can significantly reduce researchers&#39; time and the costs associated with collecting and analyzing data8,11. Furthermore, they provide participants with immediate feedback allowing for self-monitoring of nutritional intakes and higher compliance to personal goals12,13.
The here presented protocol was developed to test the functions and practicality of the &#39;Boden Food Plate&#39;, a web-based electronic food diary, as well as its agreement with a traditional three-day estimated food diary method. This platform allows users to record food and drink items consumed throughout the day on virtual plates in the form of visual depictions, which are one of the strengths of the application. Foods and drinks are selected from an embedded database which contains approximately 1,200 items with fixed serving sizes. The dietary data entered in the electronic food diary is automatically analyzed and researchers can export the results. Additionally, the application generates graphs illustrating the adequacy or non-adequacy of the diet for total energy and some important nutrients which can be accessed by both researchers and users. Compared to other currently available nutritional platforms, this newly created electronic diary is innovative and practical to use. Therefore, it can be a useful dietary data collection tool to be used in future studies.
The present paper describes in detail the entire protocol for dietary data collection utilizing both electronic and paper-based food diaries and presents some results that are published in full in Fuller et al.14

<table><tbody><tr><td>Boden Food Plate</td><td>Boden Food Plate</td><td></td><td>Electronic dietary data collection tool.</td></tr><tr><td>Paper Food Diary</td><td>Any</td><td></td><td>Written dietary data collection tool.</td></tr><tr><td>Food Works 7 Professional</td><td>Xyris Software 2012</td><td></td><td>Software for the analysis of dietary intakes.</td></tr><tr><td>SPSS 19.0</td><td>SPSS</td><td></td><td>Statistical software.</td></tr><tr><td>Computer</td><td>Any</td><td></td><td>This should be used by the study participants to complete the electronic food diary from outside the research facility.</td></tr></tbody></table>

'boden food plate': novel interactive web-based method for the assessment of dietary intake

Different methods can be used in research to assess dietary intake, many of which are still paper-based. Written estimated food diaries are often utilized in clinical trials, despite being a burden for both study participants and researchers. This method requires participant literacy, it is time consuming, labor intensive, and can easily lead to under-reporting. With advancements in technology, there is a growing interest in electronic diaries that automate the dietary assessment process. These are focused on improving accuracy, reducing both time and cost and providing users with a visual and more enjoyable experience. The methodology presented here aimed to validate the &#39;Boden Food Plate&#39;, a novel web-based platform for self-recording of food and drink items, compared to a traditional estimated food diary. The application was also rated on a satisfaction scale by study participants using a paper-based questionnaire. Sixty-seven participants completed the dietary measures on both the three-day electronic and paper food diaries. For the analysis, only dietary data completed at both study time points (baseline and week six) was utilized. Despite small mean differences between dietary data collection methods, Bland Altman analysis showed fairly wide 95% limits of agreement between the electronic platform and the written estimated food diary and there were few cases which did not fall within the 95% confidence intervals. Overall, participants found the electronic food diary to be more fun than the paper method and as easy to use as hard copy diaries. The new platform has potential as a self-recording tool for the collection of dietary data, particularly when utilized in clinical trial settings. However, further validation studies are needed to improve the validity of this novel electronic dietary data collection tool.

Out of the 76 participants recruited, only 67 self-recorded their food intake for three days on both the electronic and paper-based food diary at both time periods (baseline and week six). Only the dietary data from these 67 subjects was utilized for the nutritional analysis. The 9 participants excluded from this study did not complete food intake records from the required time points.
Table 1 shows no significa...

Watch this Scientific Journal Video about 'Boden Food Plate': Novel Interactive Web-based Method for the Assessment of Dietary Intake at JoVE.com

'Boden Food Plate': Novel Interactive Web-based Method for the Assessment of Dietary Intake

The &#39;Boden Food Plate&#39; is an electronic food diary designed to be an interactive and fun method to collect dietary intake using visual depictions. The purpose of this study was to validate the web-based application against a traditional three-day estimated food diary method.

Different methods can be used in research to assess dietary intake, many of which are still paper-based. Written estimated food diaries are often utilized ...

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7.2K Views.  University of Sydney. The 'Boden Food Plate' is an electronic food diary designed to be an interactive and fun method to collect dietary intake using visual depictions. The purpose of this study was to validate the web-based application against a traditional three-day estimated food diary method.

Video: 'Boden Food Plate': Novel Interactive Web-based Method for the Assessment of Dietary Intake

Method for Novel Anti-Cancer Drug Development using Tumor Explants of Surgical Specimens

The current therapies for malignant glioma have only palliative effect. For therapeutic development, one hurdle is the discrepancy of efficacy determined by current drug efficacy tests and the efficacy on patients. Thus, novel and reliable methods for evaluating drug efficacy are warranted in pre-clinical phase. In vitro culture of tumor tissues, including cell lines, has substantial phenotypic, genetic, and epigenetic alterations of cancer cells caused by artificial environment of cell culture, which may not reflect the biology of original tumors in situ. Xenograft models with the immunodeficient mice also have limitations, i.e., the lack of immune system and interspecies genetic and epigenetic discrepancies in microenvironment. Here, we demonstrate a novel method using the surgical specimens of malignant glioma as undissociated tumor blocks to evaluate treatment effects. To validate this method, data with the current first-line chemotherapeutic agent, temozolomide (TMZ), are described.


We used the freshly-removed surgical specimen of malignant glioma for our experiments. We performed intratumoral injection of TMZ or other drug candidates, followed by incubation and analysis on surgical specimens. Here, we sought to establish a tumor tissue explant method as a platform to determine the efficacy of novel anti-cancer therapies so that we may be able to overcome, at least, some of the current limitations and fill the existing gap between the current experimental data and the efficacy on an actual patient's tumor. This method may have the potential to accelerate identifying novel chemotherapeutic agents for solid cancer treatment.

Here, we established a method for drug efficacy testing with surgical specimens of brain tumors, termed &#x201C;tumor explant method&#x201D;. With this method, we can evaluate drug efficacy without breaking the microenvironment of solid tumors. To validate reliability of this method, we describe representative data with our glioma specimen treated with the current first-line chemotherapeutic agent, temozolomide.


The current therapies for malignant glioma have only palliative effect. For therapeutic development, one hurdle is the discrepancy of efficacy determined ...

Use of Animal Model of Sepsis to Evaluate Novel Herbal Therapies

Sepsis refers to a systemic inflammatory response syndrome resulting from a microbial infection. It has been routinely simulated in animals by several techniques, including infusion of exogenous bacterial toxin (endotoxemia) or bacteria (bacteremia), as well as surgical perforation of the cecum by cecal ligation and puncture (CLP)1-3. CLP allows bacteria spillage and fecal contamination of the peritoneal cavity, mimicking the human clinical disease of perforated appendicitis or diverticulitis. The severity of sepsis, as reflected by the eventual mortality rates, can be controlled surgically by varying the size of the needle used for cecal puncture2. In animals, CLP induces similar, biphasic hemodynamic cardiovascular, metabolic, and immunological responses as observed during the clinical course of human sepsis3. Thus, the CLP model is considered as one of the most clinically relevant models for experimental sepsis1-3.
Various animal models have been used to elucidate the intricate mechanisms underlying the pathogenesis of experimental sepsis. The lethal consequence of sepsis is attributable partly to an excessive accumulation of early cytokines (such as TNF, IL-1 and IFN-&gamma;)4-6 and late proinflammatory mediators (e.g., HMGB1)7. Compared with early proinflammatory cytokines, late-acting mediators have a wider therapeutic window for clinical applications. For instance, delayed administration of HMGB1-neutralizing antibodies beginning 24 hours after CLP, still rescued mice from lethality8,9, establishing HMGB1 as a late mediator of lethal sepsis. The discovery of HMGB1 as a late-acting mediator has initiated a new field of investigation for the development of sepsis therapies using Traditional Chinese Herbal Medicine. In this paper, we describe a procedure of CLP-induced sepsis, and its usage in screening herbal medicine for HMGB1-targeting therapies.

Sepsis refers to a systemic inflammatory response syndrome resulting from a microbial infection, and can be simulated by a surgical technique termed cecal ligation and puncture (CLP). Here we describe a method to use CLP-induced animal model to screen medicinal herbs for therapeutic agents.

Sepsis refers to a systemic inflammatory response syndrome resulting from a microbial infection. It has been routinely simulated in animals by several ...

A Novel Vertebral Stabilization Method for Producing  Contusive Spinal Cord Injury

Clinically-relevant animal cervical spinal cord injury (SCI) models are essential for developing and testing potential therapies; however, producing reliable cervical SCI is difficult due to lack of satisfactory methods of vertebral stabilization. The conventional method to stabilize the spine is to suspend the rostral and caudal cervical spine via clamps attached to cervical spinous processes. &#160;However, this method of stabilization fails to prevent tissue yielding during the contusion as the cervical spinal processes are too short to be effectively secured by the clamps (Figure 1). &#160;Here we introduce a new method to completely stabilize the cervical vertebra at the same level of the impact injury. &#160;This method effectively minimizes movement of the spinal column at the site of impact, which greatly improves the production of consistent SCIs. &#160;We provide visual description of the equipment (Figure 2-4), methods, and a step-by-step protocol for the stabilization of the cervical 5 vertebra (C5) of adult rats, to perform laminectomy (Figure 5) and produce a contusive SCI thereafter. &#160;Although we only demonstrate a cervical hemi-contusion using the NYU/MASCIS impactor device, this vertebral stabilization technique can be applied to other regions of the spinal cord, or be adapted to other SCI devices. &#160;Improving spinal cord exposure and fixation through vertebral stabilization may be valuable for producing consistent and reliable injuries to the spinal cord. &#160;This vertebral stabilization method can also be used for stereotactic injections of cells and tracers, and for imaging using two-photon microscopy in various neurobiological studies.

Vertebral stabilization is necessary for minimizing variability, and for producing consistent experimental spinal cord injuries.&#160; Using a customized stabilizing apparatus in conjunction with the NYU/MASCIS impactor device, we have demonstrated here the proper equipment and procedure for generating reproducible hemi-contusive cervical (C5) spinal cord injuries in adult rats.

Clinically-relevant animal cervical spinal cord injury (SCI) models are essential for developing and testing potential therapies; however, producing ...

Control of Eating Behavior Using a Novel Feedback System

Subjects eat food from a plate that sits on a scale connected to a computer that records the weight loss of the plate during the meal and makes up a curve of food intake, meal duration and rate of eating modeled by a quadratic equation. The purpose of the method is to change eating behavior by providing visual feedback on the computer screen that the subject can adapt to because her/his own rate of eating appears on the screen during the meal. The data generated by the method is automatically analyzed and fitted to the quadratic equation using a custom made algorithm. The method has the advantage of recording eating behavior objectively and offers the possibility of changing eating behavior both in experiments and in clinical practice. A limitation may be that experimental subjects are affected by the method. The same limitation may be an advantage in clinical practice, as eating behavior is more easily stabilized by the method. A treatment that uses this method has normalized body weight and restored the health of several hundred patients with anorexia nervosa and other eating disorders and has reduced the weight and improved the health of severely overweight patients.

Subjects eat food from a plate placed on a scale connected to a computer that records the weight loss of the plate during the meal. Feedback on the computer screen allows the subject to adapt her/his eating behavior to reference curves thus normalizing body weight.

Subjects eat food from a plate that sits on a scale connected to a computer that records the weight loss of the plate during the meal and makes up a curve ...

Behavior

A Novel Application of Musculoskeletal Ultrasound Imaging

Ultrasound is an attractive modality for imaging muscle and tendon motion during dynamic tasks and can provide a complementary methodological approach for biomechanical studies in a clinical or laboratory setting. Towards this goal, methods for quantification of muscle kinematics from ultrasound imagery are being developed based on image processing. The temporal resolution of these methods is typically not sufficient for highly dynamic tasks, such as drop-landing. We propose a new approach that utilizes a Doppler method for quantifying muscle kinematics. We have developed a novel vector tissue Doppler imaging (vTDI) technique that can be used to measure musculoskeletal contraction velocity, strain and strain rate with sub-millisecond temporal resolution during dynamic activities using ultrasound. The goal of this preliminary study was to investigate the repeatability and potential applicability of the vTDI technique in measuring musculoskeletal velocities during a drop-landing task, in healthy subjects. The vTDI measurements can be performed concurrently with other biomechanical techniques, such as 3D motion capture for joint kinematics and kinetics, electromyography for timing of muscle activation and force plates for ground reaction force. Integration of these complementary techniques could lead to a better understanding of dynamic muscle function and dysfunction underlying the pathogenesis and pathophysiology of musculoskeletal disorders.

We describe a new ultrasound-based vector tissue Doppler imaging technique to measure muscle contraction velocity, strain and strain rate with sub-millisecond temporal resolution during dynamic activities. This approach provides complementary measurements of dynamic muscle function and could lead to a better understanding of mechanisms underlying musculoskeletal disorders.

Ultrasound is an attractive modality for imaging muscle and tendon  motion during dynamic tasks and can provide a complementary methodological  approach ...

Iterative Development of an Innovative Smartphone-Based Dietary Assessment Tool: Traqq

To collect dietary intake data in a fast and reliable manner, a flexible and innovative smartphone application (app) called Traqq was developed (iOS/Android). This app can be used as a food record and&#160;24-h recall (or shorter recall periods). Different sampling schemes can be created on either prespecified or random days/times within a predetermined period for both methods, with&#160;push notifications to urge the participants to register their food intake. In case of non-response, notifications are automatically rescheduled to ensure complete data collection. For use as a food record, respondents can access the app and log their food intake throughout the day. Food records close automatically at the end of the day; recalls close after submission of the consumed items. The recall as well as the food record module provide access to an extensive food list based on the Dutch food composition database (FCDB), which can be accustomed to fit different research purposes. When selecting a food item, respondents are simultaneously prompted to insert portion size, i.e., in household measures (e.g., cups, spoons, glasses), standard portion sizes (e.g., small, medium, large), or weight in grams, and eating occasion/time of consumption. Portion size options can be adjusted, e.g., only entry in grams in case of a weighed food record or time of consumption instead of eating occasion). The app also includes a My Dishes function, which allows the respondent to create their own recipes or product combinations (e.g., a daily breakfast) and only report the total quantity consumed. Subsequently, the app accounts for yield and retention factors. The data are stored on a secure server. If desired, additional questions, i.e., in general or those related to specific food items or eating occasions can be incorporated. This paper describes the development of the system (app and backend), including expert evaluations and usability testing.

This article describes the protocol for the development of an innovative smartphone-based dietary assessment application Traqq, including expert evaluations and usability testing.

To collect dietary intake data in a fast and reliable manner, a flexible and innovative smartphone application (app) called Traqq was developed ...

Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method

The vast majority of dietary and eating behavior assessment methods are based on self-reports. They are burdensome and also prone to measurement errors. Recent technological innovations allow for the development of more accurate and precise dietary and eating behavior assessment tools that require less effort for both the user and the researcher. Therefore, a new sensor-based device to assess food intake and eating behavior was developed. The device is a regular dining tray equipped with a video camera and three separate built-in weighing stations. The weighing stations measure the weight of the bowl, plate, and drinking cup continuously over the course of a meal. The video camera positioned to the face records eating behavior characteristics (chews, bites), which are analyzed using artificial intelligence (AI)-based automatic facial expression software. The tray weight and the video data are transported at real-time to a personal computer (PC) using a wireless receiver. The outcomes of interest, such as the amount eaten, eating rate and bite size, can be calculated by subtracting the data of these measures at the timepoints of interest. The information obtained by the current version of the tray can be used for research purposes, an upgraded version of the device would also facilitate the provision of more personalized advice on dietary intake and eating behavior. Contrary to the conventional dietary assessment methods, this dietary assessment device measures food intake directly within a meal and is not dependent on memory or the portion size estimation. Ultimately, this device is therefore suited for daily main meal food intake and eating behavior measures. In the future, this technology based dietary assessment method can be linked to health applications or smart watches to obtain a complete overview of exercise, energy intake, and eating behavior.

This protocol shows and explains a new technology-based dietary assessment method. The method consists of a dining tray with multiple built-in weighing scales and a video camera. The device is unique in the sense that it incorporates automated measures of food and drink intake and eating behavior over the course of a meal.

The vast majority of dietary and eating behavior assessment methods are based on self-reports. They are burdensome and also prone to measurement errors. ...

A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis

Eating disorders (anorexia nervosa, bulimia nervosa, binge-eating disorder, and other specified eating or feeding disorders) have a combined prevalence of 13% and are associated with severe physical and psychosocial problems. Early diagnosis, which is important for effective treatment and prevention of undesirable long-term health consequences, imposes problems among non-specialist clinicians unfamiliar with these patients, such as those working in primary care. Early, accurate diagnosis, particularly in primary care, allows expert interventions early enough in the disorder to facilitate positive treatment outcomes. Computer-assisted diagnostic procedures offer a possible solution to this problem by providing expertise via an algorithm that has been developed from a large number of cases that have been diagnosed in person by expert diagnosticians and expert caregivers. A web-based system for determining an accurate diagnosis for patients suspected to suffer from an eating disorder was developed based on these data. The process is automated using an algorithm that estimates the respondent's probability of having an eating disorder and the type of eating disorder the individual has. The system provides a report that works as an aid for clinicians during the diagnostic process and serves as an educational tool for new clinicians.

Diagnosing eating disorders in healthcare is challenging. Therefore, the present protocol develops an algorithm based on 949 patient responses to a questionnaire, with the diagnosis displayed on an easy-to-use web-based interface. This system facilitates the accurate diagnosis of eating disorders while excluding those believed to have an eating disorder.

Eating disorders (anorexia nervosa, bulimia nervosa, binge-eating disorder, and other specified eating or feeding disorders) have a combined prevalence of ...

Mobile Device-assisted Dietary Ecological Momentary Assessments for the Evaluation of the Adherence to the Mediterranean Diet in a Continuous Manner

Mobile device-assisted dietary ecological momentary assessments (EMAs) have emerged as a new tool allowing the evaluation of dietary intake in real time, in a real-world setting and in a continuous manner. They have the potential to minimize recall bias, participant, and investigator burden, and decrease economic and time investment while maximizing ecological validity.
We developed a set of EMAs aimed at evaluating continuous adherence to the MedDiet. Four multiple-choice EMAs are sent daily in a randomized manner from a total of eight questions. The EMAs enquire about the consumption of 11 key food groups of the Mediterranean diet in the last 24-48 h in a semi-quantitative way. EMAs capture the daily frequency of consumption of fruits, vegetables, and extra virgin olive oil on different days of the week. Additionally, EMAs capture the weekly frequency of consumption of whole grain products, sugary drinks, nuts, legumes, sweets, fish and seafood, and red and processed meats. A designed scoring system behind the EMAs extracts the percentage of adherence to the MedDiet recommendations and calculates a quality index of the diet every week. Individualized reports are sent periodically to the volunteers highlighting the strengths and weaknesses of their diet. EMAs are also expected to have a behavioral effect, reinforcing the choice of Mediterranean foods.

Presented here is a study protocol aimed at monitoring continuous adherence to the Mediterranean diet (MedDiet) by means of ecological momentary assessments. The method evaluates the intake of key food groups of the MedDiet and calculates an index of adherence.

Mobile device-assisted dietary ecological momentary assessments (EMAs) have emerged as a new tool allowing the evaluation of dietary intake in real time, ...

Integrated Spreadsheets for Nutritional Analysis of Population Diet Surveys

It can be challenging to calculate nutrient intakes in population diet surveys because existing nutritional analysis software is generally oriented toward analyzing intakes of individuals and may not allow users to input or easily modify the food composition data used in the analysis. These are drawbacks that are more problematic in low- and middle-income country settings. While there are numerous software-assisted dietary assessment platforms that conduct onboard nutritional analysis and are appropriate for use in large surveys, they are often similarly limited, and further restrict users to specific assessment modalities. This paper presents a multifunctional system of integrated spreadsheets for nutritional analysis of population diet surveys (ISNAPDS) that provides a solution for situations in which data have been collected but cannot be adequately analyzed with existing software. The protocol involves supplying the system with fully customizable data on food composition, food group classifications, and food intake (food intake in g/day may be entered directly or calculated based on user-supplied intake frequencies and either standard or variable serving sizes). Following data entry, the user modifies a set of simple pre-populated formulas to match them to the structure of the input data and the system applies these formulas to calculate nutrient and food group intakes, and the contributions of food groups to nutrient intakes for all members of the survey population. The flexibility of the ISNAPDS system allows it to accommodate the global diversity of foods consumed and analyze quantitative, semiquantitative, and nonquantitative food consumption data collected using prospective and retrospective assessment methods employing different reference periods and portion size estimation methods. To date, the system has been applied in published and ongoing analyses of 24 h recall, diet record, food frequency, and disaggregated household consumption data from population surveys in China, Ethiopia, India, Mongolia, Thailand, and a multi-country analysis of 10 sub-Saharan African countries.

This paper presents a system of integrated spreadsheets using simple formulas to calculate nutrient and food group intakes and the contributions of food groups to nutrient intakes for analysis of population diet survey data. The system accommodates quantitative, semiquantitative, and nonquantitative food intake data and a user-supplied food composition table.

It can be challenging to calculate nutrient intakes in population diet surveys because existing nutritional analysis software is generally oriented toward ...