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Publication 2012

Alcoholic Beverages Amniotic Fluid Beer Beverages Black Tea Carbohydrates Coffee Diet Drinks Eating Energy Drinks Fat-Restricted Diet Food Light Macronutrient Milk Soft Drinks Vegetable Juices Water Consumption Wine

Dietary Intake Assessment in the Adventist Health Study-2

The 24 h dietary recalls were unannounced and obtained by telephone. A two-dimensional food portion visual (2D Food Portion Visual; Nutrition Consulting Enterprises, Framingham, MA, USA) was sent to each participant before the first recall to assist with portion size estimates. Trained research dietitians used standard probes and a multiple-pass approach methodology to collect detailed information on all foods, beverages and supplements consumed by each subject during the previous 24 h. Each recall interview was entered using Nutrition Data System for Research (NDS-R) version 4·06 or 5·0 (The Nutrition Coordinating Center, Minneapolis, MN, USA) and the conversation digitally recorded for subsequent quality check. An experienced research dietitian later evaluated randomly selected recall interviews ( ~5 %) and compared them with the recording, as a quality control measure.
The AHS-2 FFQ is a quantitative and comprehensive 22-page instrument consisting of 204 foods, fifty-four questions about food preparation and forty-six fields for open-ended questions. Frequency categories vary with food type to allow respondents to define their daily intake with greater specificity. Thus, the lowest category for most foods and beverages is never or rarely, and for cold cereals and vegetarian protein products, 1–3 per month. The highest frequency category for vegetables, soups, cereals, pasta, dressing, meats, fish, vegetarian protein products and soya milk is 2 or more per day; for nuts and seeds, 4 or more per day; for breads, eggs, dairy products, snacks and beverages, 6 or more per day. Portion sizes include three levels: standard,

\frac{1}{2}

or less, and

1 \frac{1}{2}

or more. Standard portions are based on serving sizes using familiar household units such as cup, tablespoon, slice, patty and others. Pictures of common foods or beverages typically served together were included with the questionnaire to assist subjects in estimating portion sizes. For example, to represent a standard portion, spaghetti, broccoli and steak are arranged on an 11-inch dinner plate, each food measured in a standard portion. Representations of

\frac{1}{2}

of standard and

1 \frac{1}{2}

times the standard portion size are provided as well, using the same foods and dinner plate.
The AHS-2 FFQ was designed to include foods commonly eaten by US Adventists and later modified to accommodate foods specific to black Adventists of US and Caribbean origin^{(6 (link))}. The questionnaire was sent to each subject, completed at home, and then mailed back to AHS-2. Respondents were asked to report on their intake over the previous one year. Upon receipt of the questionnaire, study personnel reviewed the questionnaire for completeness and as necessary followed-up by telephone to clarify any ambiguous or incomplete information.
Completed questionnaires were optically scanned using the NCS 5000i Image Scanner with ScanTools Plus software (Pearson NCS, Bloomington, MN, USA). Standardized processing of open-ended questions was done using the Food Write-In Processing software (Adventist Health Study-2, Loma Linda, CA, USA), a network-based application created in Microsoft® Access (Microsoft Corporation, Redmond, WA, USA).
Nutrient composition of foods reported from 24 h recalls and FFQ were based on the NDS-R 5·0_35 database (The Nutrition Coordinating Center), an analytic database of over 20 000 foods updated annually while maintaining nutrient profiles true to the version used for data collection^{(7 (link))}. Nutrient profiles of foods and supplements not found in the NDS database were obtained from the US Department of Agriculture, manufacturers, and the Caribbean Food and Nutrition Institute. Considerable attention was given to creating recipes for home-cooked vegetarian dishes (n > 500), home-made and commercial soya and nut milks (n > 180) and commercial meat analogues (n 309) frequently consumed among our study population. For the latter we contacted manufacturers or worked with a senior food technologist with experience in this industry, to create recipes.

Jaceldo-Siegl K., Knutsen S.F., Sabaté J., Beeson W.L., Chan J., Herring R.P., Butler T.L., Haddad E., Bennett H., Montgomery S., Sharma S.S., Oda K, & Fraser G.E. (2009). Validation of nutrient intake using an FFQ and repeated 24 h recalls in black and white subjects of the Adventist Health Study-2 (AHS-2). Public health nutrition, 13(6), 812-819.

Publication 2009

Attention Beverages Bread Broccoli Caribbean People Cereals Common Cold Conditioning, Psychology Dairy Products Diet Dietary Supplements Dietitian Eating Eggs Fishes Food Households Hyperostosis, Diffuse Idiopathic Skeletal Loma Meat Mental Recall Milk Nutrients Pastes Plant Embryos Proteins Snacks Soybeans Soy Milk Vegetables Vegetarians

SIPsmart Targeted Beverage Intervention

SIPsmartER targeted decreasing SSB consumption, with the primary goal of achieving the SSB recommendation of less than 8 fluid ounces per day [6 (link), 25 (link)]. To sufficiently target SSB reduction, participants were educated on recommendations for all beverage categories (e.g., water, noncalorically sweetened beverages, milk) [25 (link)]. A pragmatic approach was taken when developing a comparison condition that was matched for contact and structure, but focused on a behavior independent of SSB consumption. This approach ensured that all study participants in these medically-underserved counties had an opportunity to benefit from study participation [26 (link)]. As such, the comparison condition, MoveMore targeted PA promotion, with the primary goal of achieving 150 min of moderate-intensity aerobic activity and doing muscle-strengthening activities on two or more days per week [27 ].
Several formative research phases guided development of the culturally-sensitive SIPsmartER intervention [16 (link), 28 (link), 29 (link)]; and MoveMore was adapted from a previous research tested group-based PA intervention [30 (link)–32 (link)]. Prior to launching the Talking Health trial, a 5 week randomized-controlled pilot test was used to evaluate participant feedback on intervention content and structure, as well as understand the potential reach and preliminary effect sizes [29 (link)]. The final 6 month intervention structure, informed by the preliminary work, included three small-group classes, one live teach-back call, and 11 Interactive Voice Response (IVR) calls. SIPsmartER and MoveMore conditions were matched in duration and contact. Each of the small group classes were 90–120 min in duration, and delivered during weeks one, six, and seventeen. Participants who missed a class were mailed a packet that outlined key content information and then a research assistant called participants to verbally review and reinforce the content, using a semi-structured script. Approximately 1 week following the first class (or missed class call), a scripted teach-back call occurred, lasting an average of 18.6 (SD = 5.6) minutes. Participants were asked to teach-back key concepts from the first class and to explain how they tracked their behaviors and calculated weekly averages. When recalled incorrectly, participants were given correct answers and offered additional opportunities to recall concepts correctly [33 ]. Participants also received 11 IVR calls, weekly for the first 3 weeks and then bi-weekly for the remainder of the intervention. Each IVR call, lasting an average of 6.9 (SD = 1.9) minutes, reinforced key intervention messages, provided new content, and led participants through a personal action planning procedure [34 (link)–36 (link)].
A comprehensive overview of the intervention structure, theoretical constructs, and key learning objectives for SIPsmartER and MoveMore are published elsewhere [37 (link)]. In brief, the foundational program elements including the TPB [19 (link), 38 (link)–41 (link)] and concepts related to HL, media literacy, and numeracy [42 (link)–45 ]. Clear communication techniques are embedded throughout the interventions, including activity approaches (e.g., hands-on demonstrations, pictorial information); materials with simplified language; teach-back strategies to promote comprehension of learning objectives [33 ]; and non-written reinforcement of key intervention messages (i.e., IVR calls). Intervention content is aimed at building HL skills related to numeracy [41 (link)] and to interpreting SSB- and PA-specific media messages [46 (link)] as well as self-monitoring skills (e.g., personal action planning and behavior tracking) [47 (link)].
Three masters-level research staff (i.e., MPH, MS/RD, MS/MCHES) and two PhD investigators with expertise in media literacy delivered the classes. Trained graduate research assistants provided additional class support and completed the teach-back calls.
Implementation data was tracked via detailed bi-monthly research meeting minutes and the IVR system generated reports. Program engagement was tracked systematically in SPSS statistical analyses software and operationalized as attending small group classes or completing missed call, completing the teach-back call, and completing the IVR calls.

Zoellner J.M., Hedrick V.E., You W., Chen Y., Davy B.M., Porter K.J., Bailey A., Lane H., Alexander R, & Estabrooks P.A. (2016). Effects of a behavioral and health literacy intervention to reduce sugar-sweetened beverages: a randomized-controlled trial. The International Journal of Behavioral Nutrition and Physical Activity, 13, 38.

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Publication 2016

Beverages Exercise, Aerobic Mental Recall Milk Muscle Tissue Reinforcement, Psychological Sweetened Beverages Teaching

Comprehensive Dietary Assessment in Adventists

The AHS-2 FFQ is a quantitative and comprehensive instrument originally designed to include foods commonly consumed by US Adventists. The questionnaire was later revised to reduce the respondents’ fatigue and to accommodate foods specific to black Adventists of US and Caribbean origin^{(7 )} All versions of the FFQ consist of two major sections. Across all versions, the first section is a food list that includes 130–141 items of fruits, vegetables, legumes, grains, oils, dairy, fish, eggs and beverages, and the second consists of sixty-three to seventy-nine items of commercially prepared products, such as dietary supplements, dry cereals and vegetarian protein products that require respondents to examine food labels. Frequency categories range from never or rarely to ≥6 servings/d and vary with food type to allow respondents to define their daily intake with greater specificity. Portion sizes (e.g. cup, tablespoon, slice, patty) include a given standard,

\frac{1}{2}

or less and

1 \frac{1}{2}

or more of the standard serving. Pictures of common foods or beverages typically served together are included with the questionnaire to assist participants in estimating portion sizes. The questionnaire was sent to each participant, completed at home and then mailed back to AHS-2. Respondents were asked to report on their intake over the previous 1 year. Upon receipt of the questionnaire, study personnel reviewed the FFQ for completeness and, when necessary, followed up by telephone to clarify any ambiguous or incomplete information.
The 24HDR was administered unannounced and information was obtained by telephone. Each participant was provided a two-dimensional food portion visual (The Nutrition Consulting Enterprises, Framingham, MA, USA) to assist with portion size estimates. Trained research dietitians used standard probes and a multiple-pass approach method to collect detailed information on all foods, beverages and supplements consumed by each participant during the previous 24 h. All recall interviews were digitally recorded for subsequent quality check. Later, an experienced research dietitian evaluated randomly selected recall interviews (~5%) and compared them with the recording, as a quality control measure.
Recall and FFQ data were entered using the Nutrition Data System for Research version 4·06 or 5·0 (NDS-R, Nutrition Coordinating Center, Minneapolis, MN, USA); the analytic data used in the present study were based on the NDS-R 2008 database. Information on foods not found in the NDS-R database was obtained from the US Department of Agriculture, from individual manufacturers and from the Caribbean Food and Nutrition Institute. Considerable attention was given to creating recipes for home-cooked vegetarian dishes (n>500), homemade and commercial soya and nut milks (n>180) and for commercial meat analogues (n 309) that were frequently consumed by our study population. For the latter we contacted manufacturers or worked with a senior food technologist with experience in this industry in order to create recipes.

Jaceldo-Siegl K., Fan J., Sabaté J., Knutsen S.F., Haddad E., Beeson W.L., Herring R.P., Butler T.L., Bennett H, & Fraser G.E. (2011). Race-specific validation of food intake obtained from a comprehensive FFQ: the Adventist Health Study-2. Public health nutrition, 14(11), 1988-1997.

Publication 2011

Attention Beverages Caribbean People Cereals Conditioning, Psychology Dietary Supplements Dietitian Eggs Fabaceae Fatigue Fishes Food Food Labeling Fruit Hyperostosis, Diffuse Idiopathic Skeletal Meat Mental Recall Milk Oils Proteins Soybeans Vegetables Vegetarians

Lifestyle Risk Behaviors Assessment Protocol

Participants reported on a range of lifestyle risk behaviors in the questionnaire. Smoking status was derived from two questions: “Have you ever been a regular smoker?” and “Are you a regular smoker now?” Participants were asked, “About how many alcoholic drinks do you have each week?” with one drink defined as one glass of wine, one half pint of beer, or one shot of spirits. Dietary behavior was measured by a previously used index [18 (link)] of five food items (vegetable, fruit, fish, processed meat, and types of milk) based on the Dietary Guidelines for Australians [22 ], as an indicator for overall dietary behavior. Physical activity was measured using the Active Australia Survey [23 ], which has acceptable reliability (Spearman’s rho for test–retest reliability was 0.56–0.64, with 76% agreement on meeting the recommended physical activity level) and validity (Spearman’s rho for total minutes/week of moderate-to-vigorous physical activity was 0.52 against accelerometer measures) [24 (link)]. This instrument asked the total time one spent on walking, moderate-intensity, and vigorous-intensity physical activity (bouts of at least 10 min) in the previous week. Sedentary behavior was assessed using a single-item measure: hours spent sitting in a typical 24-h day. This question was adapted from the widely used International Physical Activity Questionnaire [25 (link)] and had acceptable reliability and validity [26 (link)]. A similar question was also asked about sleep duration in a 24-h day, and this question was comparable with single-item instruments of self-reported sleep duration used by previous studies [27 (link),28 (link)]. The specific coding of these lifestyle risk behaviors is presented in Table 1.
Each behavior was coded as 1 (at risk) or 0 (not at risk) and was summed as an index (total score range 0–6). Obesity was not included in the index because it was not considered a behavior, but rather an intermediate health outcome influenced by several of the included lifestyle behaviors.

Ding D., Rogers K., van der Ploeg H., Stamatakis E, & Bauman A.E. (2015). Traditional and Emerging Lifestyle Risk Behaviors and All-Cause Mortality in Middle-Aged and Older Adults: Evidence from a Large Population-Based Australian Cohort. PLoS Medicine, 12(12), e1001917.

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Publication 2015

Alcoholic Beverages Beer Diet Fishes Food Fruit Meat Milk Obesity Sleep Vegetables Wine

Most recents protocols related to «Milk»

Synthesis of Bis-Alkyne Compound

Not available on PMC !

Example 250

The structure of the compound of Example 250 is depicted in FIG. 1.

(+)-Sodium L-ascorbate (4.7 mg, 0.02 mmol) was added to a solution of the foregoing compound (25 mg, 0.01 mmol), N-but-3-yn-1-yl-N²,N⁶-dipent-4-ynoyl-L-lysinamide (2.103 mg, 5.88 μmol) and CuSO₄.5H₂O (5.88 mg, 0.02 mmol) in t-BuOH (5 mL)/H₂O (10 mL) under N₂atmosphere. The solution turned milky. After 4 hours the reaction was quenched with aqueous Na₂CO₃(10%, 0.050 mL, 0.05 mmol) and filtered. The filtrate was lyophilized and the residue was purified by preparative RP-HPLC (Column: Waters XSelect CSH C18 ODB 5 μm 150×19 mm; mobile phase: A—H₂O/TFA 100/0.15 and B—MeCN with a gradient 5% B for 0.5 min, 5-36% B in 1.5 min, 36-41% B in 14 min; flow 30 mL/min at rt, detection 230 nm) to give the title compound (6.8 mg, 9%). HRMS: calculated for (C₃₀₅H₄₃₂F₃N₈₁O₇₂S₉+4H)⁴+1682.5096; found (ESI [M+4H]⁴⁺) 1682.5154, purity 97%.

US11866518B2. Bicyclic peptide ligands specific for TSLP (2024-01-09). BicycleTx Limited [GB]. Inventors: Frank Narjes [SE], Tor Svensson [SE], Pavol Zlatoidsky [SE], Lotta Hidestal [SE], Liuhong Chen [GB], Michael Skynner [GB], Sophie Watcham [GB].

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Patent 2024

Atmosphere High-Performance Liquid Chromatographies Ligands Milk Peptides Sodium Ascorbate

Synthesis of Ascorbate-Functionalized Compound

Not available on PMC !

Example 251

The structure of the compound of Example 251 is depicted in FIG. 5.

(+)-Sodium L-ascorbate (16 mg, 0.08 mmol) was added to a solution of the foregoing compound (105 mg, 0.05 mmol), N²,N⁶-dipent-4-ynoyl-L-lysine (6 mg, 0.02 mmol) and CuSO₄.5H₂O (20 mg, 0.08 mmol) in t-BuOH (10 mL)/H₂O (20 mL) under N₂atmosphere. The solution turned milky. The reaction was stirred at rt for 6.5 h and more N²,N⁶-dipent-4-ynoyl-L-lysine (6 mg, 0.02 mmol) was added. After 23 h the reaction was quenched with aqueous Na₂CO₃(0.166 mL, 0.16 mmol) and filtered. The filtrate was freeze dried, and the crude product purified by preparative HPLC (Column: Waters Atlantis T3 ODB 5 μm 150×19 mm; mobile phase: A—H₂O/TFA 100/0.15 and B—MeCN with a gradient 5% B for 0.5 min, 5-38% B in 1.5 min, 38-43% B in 14 min; flow 30 mL/min at rt, detection 230 nm) to give the title compound obtained (5.5 mg, 3%). HRMS: calculated for (C₂₀₀H₂₈₀F₂N₃₄O₄₈S₆+3H)³⁺1479.6552; found (ESI [M+3H]³⁺) 1479.6583, purity 82%.

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Patent 2024

Atmosphere Freezing High-Performance Liquid Chromatographies Ligands Lysine Milk Peptides Sodium Ascorbate

Rapid Disintegrating Sweetener Blend

Not available on PMC !

Example 9

Example 9 was made by combining 541.46 g (54.15% by weight) sucrose, 332.7 g (33.3% by weight) corn syrup, 105.39 (10.5% by weight) water, 5.37 g (0.54% by weight) brown food coloring, 5.00 g (0.50% by weight) liquid chocolate flavoring, and 10.07 g (1.01% by weight) stevia.

Example 9 was an amorphous solid that disintegrated in less than one minute in milk at a refrigerated temperature of about 40° F. to about 32° F. (about 4° C. to about 0° C.).

US11856965B2. Solid foam products and methods of making the same (2024-01-02). THE HERSHEY COMPANY [US]. Inventors: Utkarsh Shah [US], Gagan Mongia [US], Yvette Thibault Pascua Cubides [US].

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Patent 2024

Chocolate Corns Milk Stevia Sucrose

Stevia-Sweetened Low-Fat Yogurt

Not available on PMC !

EXAMPLE 17

Yoghurt

Different glucosyl Stevia compositions (0.03%) and sucrose (4%) were dissolved in low fat milk. Glucosyl Stevia compositions were represented by Samples 1b, 2b, 3, and 5, obtained according to EXAMPLES 10, 11, 5, and 12, respectively. After pasteurizing at 82° C. for 20 minutes, the milk was cooled to 37° C. A starter culture (3%) was added and the mixture was incubated at 37° C. for 6 hours then at 5° C. for 12 hours.

The sensory properties were evaluated by 20 panelists. The best results were obtained in samples prepared by high purity short-chain glucosyl Stevia compositions (containing two or less α-1,4-glucosyl residues) derivatives (Samples 1b and 2b). The panelists noted rounded and complete flavor profile and mouthfeel in samples prepared with Samples 1b and 2b.

US11871771B2. Glucosyl Stevia composition (2024-01-16). PureCircle Sdn Bhd [MY]. Inventors: Avetik Markosyan [AM].

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Patent 2024

derivatives Fat-Restricted Diet Flavor Enhancers Milk Stevia Sucrose Yogurt

Aquafaba-Based Flavored Foam Beverage

Not available on PMC !

Example 3

Example 3 was made by combining 70 g of aquafaba (61% by weight), 40 g of sucrose (35% by weight), 1.2 g sucralose (1% by weight), 1.3 g vegetable juice liquid color (1% by weight), and 2.9 g natural mixed berry flavoring (3% by weight). The aquafaba contained about 90% water and about 2% protein, by weight. The combined ingredients were whipped to form a foam. The foam was piped onto a tray and baked to form a stable, baked solid foam.

The solid foam was added to 250-mL milk at a refrigerated temperature of about 40° F. to about 32° F. (about 4° C. to about 0° C.). The foam disintegrated quickly to form a purplish, mixed berry-flavored beverage.

US11856965B2. Solid foam products and methods of making the same (2024-01-02). THE HERSHEY COMPANY [US]. Inventors: Utkarsh Shah [US], Gagan Mongia [US], Yvette Thibault Pascua Cubides [US].

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Patent 2024

Berries Beverages Milk Proteins sucralose Sucrose Vegetable Juices

Top products related to «Milk»

Sas 9 by SAS Institute

Sourced in United States, Austria, Japan, Belgium, United Kingdom, Cameroon, China, Denmark, Canada, Israel, New Caledonia, Germany, Poland, India, France, Ireland, Australia

SAS 9.4 is an integrated software suite for advanced analytics, data management, and business intelligence. It provides a comprehensive platform for data analysis, modeling, and reporting. SAS 9.4 offers a wide range of capabilities, including data manipulation, statistical analysis, predictive modeling, and visual data exploration.

Sas version 9 by SAS Institute

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SAS version 9.4 is a statistical software package. It provides tools for data management, analysis, and reporting. The software is designed to help users extract insights from data and make informed decisions.

Milkoscan ft6000 by Foss

Sourced in Denmark, United Kingdom, Spain, Italy

The MilkoScan FT6000 is a laboratory instrument designed for the analysis of milk and dairy products. It utilizes Fourier Transform Infrared (FTIR) technology to measure the composition of various components in dairy samples, such as fat, protein, lactose, and solids.

Dimethyl sulfoxide (dmso) by Merck Group

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DMSO is a versatile organic solvent commonly used in laboratory settings. It has a high boiling point, low viscosity, and the ability to dissolve a wide range of polar and non-polar compounds. DMSO's core function is as a solvent, allowing for the effective dissolution and handling of various chemical substances during research and experimentation.

Pvdf membrane by Merck Group

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PVDF membranes are a type of laboratory equipment used for a variety of applications. They are made from polyvinylidene fluoride (PVDF), a durable and chemically resistant material. PVDF membranes are known for their high mechanical strength, thermal stability, and resistance to a wide range of chemicals. They are commonly used in various filtration, separation, and analysis processes in scientific and research settings.

Td 88137 by Inotiv

Sourced in United States, United Kingdom, Montenegro

The TD.88137 is a laboratory equipment product from Inotiv. It is designed for use in scientific research and analysis applications. The core function of the TD.88137 is to perform a specific task or measurement, but a detailed description is not available while maintaining an unbiased and factual approach.

Trizol reagent by Thermo Fisher Scientific

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TRIzol reagent is a monophasic solution of phenol, guanidine isothiocyanate, and other proprietary components designed for the isolation of total RNA, DNA, and proteins from a variety of biological samples. The reagent maintains the integrity of the RNA while disrupting cells and dissolving cell components.

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Prism 8 is a data analysis and graphing software developed by GraphPad. It is designed for researchers to visualize, analyze, and present scientific data.

Oxytocin by Merck Group

Sourced in United States

Oxytocin is a hormone produced in the hypothalamus and released by the posterior pituitary gland. It plays a crucial role in childbirth and lactation. This synthetic version of the hormone is used for research purposes in laboratories.

What are the different types of milk?

Milk can come from a variety of mammalian sources, including cows, goats, sheep, buffalo, and even humans. Each type of milk has its own unique nutritional profile and characteristics. Cow milk is the most common and widely consumed, but other animal milks like goat and sheep milk are also popular, especially in certain regions or for specific dietary needs. There are also plant-based milk alternatives made from sources like almonds, soy, oats, and rice, which can be good options for those with lactose intolerance or vegan diets.

How can PubCompare.ai help with milk research?

PubCompare.ai's AI-driven protocol comparison tool can be extremely helpful for researchers studying milk and its properties. The platform allows you to efficiently screen protocol literature from studies, pre-prints, and patents to identify the most effective and reproducible methods for your specific research goals. The AI analyzes key details like experimental design, materials used, and reported outcomes to highlight critical insights that can guide you towards the optimal protocols. This can save time, improve the quality of your research, and enhance the likelihood of getting consistent, accurate results when working with milk samples and products.

What are the health benefits of consuming milk?

Milk is a nutrient-rich food that provides a variety of health benefits. It is an excellent source of high-quality proteins, calcium, vitamins (like A, B12, and D), and minerals (like phosphorus and potassium) that are essential for strong bones, teeth, and overall growth and development. Consuming milk has been linked to reduced risk of osteoporosis, hypertension, cardiovascular disease, and type 2 diabetes. It also plays a role in supporting a healthy immune system. However, those with lactose intolerance or milk allergies may need to limit or avoid dairy products.

How can I determine the best protocols for my milk research using PubCompare.ai?

PubCompare.ai's AI-driven protocol comparison tool makes it easy to identify the most effective protocols for your milk research. First, you can use the platform to efficiently screen a wide range of protocol literature, including studies, pre-prints, and patents, to find relevant methods for analyzing milk composition, properties, or effects. The AI will then analyze key details about the protocols, such as experimental design, materials used, and reported outcomes, to highlight the critical insights that can guide you towards the optimal approach for your specific research goals. This can help you save time, improve the quality of your results, and enhance the reproducibility of your milk-related experiments.

More about "Milk"

Milk is a complex, nutrient-rich liquid produced by the mammary glands of female mammals, including cows, goats, and humans.
It provides essential nutrients such as proteins, fats, carbohydrates, vitamins, and minerals to support growth and development.
Milk consumption has been linked to various health benefits, including improved bone health, reduced risk of certain chronic diseases, and enhanced immune function.
Researchers utilize a variety of protocols and tools to study the composition, properties, and effects of milk.
SAS 9.4, a powerful data analysis software, can be used to process and analyze milk-related data.
MilkoScan FT6000, a state-of-the-art milk analyzer, can be employed to determine the chemical composition of milk samples.
DMSO (Dimethyl Sulfoxide) and PVDF (Polyvinylidene Fluoride) membranes are commonly used in milk research for sample preparation and analysis.
The TD.88137 protocol, a standardized procedure for milk protein extraction, and the TRIzol reagent, a widely used method for RNA isolation, are also important tools in milk research.
Prism 8, a data visualization and analysis software, can be utilized to present and interpret the findings from milk studies.
Furthermore, the hormone oxytocin, which plays a crucial role in milk let-down and milk production, is a key focus in milk research.
PubCompare.ai's AI-driven protocol comparison tool can help researchers optimize their milk research by enhancing reproducibility and identifying the most effective protocols and products.
By leveraging these resources and techniques, researchers can gain deeper insights into the complex composition, properties, and effects of milk, leading to advancements in our understanding of this vital nutrient and its impact on human health and development.