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Body Weight

Q: What are the key factors that influence body weight?

Body weight is influenced by a variety of factors, including diet, physical activity, genetics, and medical conditions. Diet plays a crucial role, with the balance between caloric intake and expenditure being a primary determinant. Physical activity level also significantly impacts body weight, as an active lifestyle can help burn calories and maintain a healthy weight. Genetic factors can predispose individuals to certain body weight patterns, while medical conditions like thyroid disorders or hormonal imbalances can also affect weight management. Understanding these multifaceted influences is key to optimizing body weight through evidence-based protocols and products.

Q: How can PubCompare.ai help with body weight research?

PubCompare.ai is an AI-driven platform that can greatly assist in body weight research. The platform allows you to screen protocol literature more efficiently, leveraging AI to pinpoint critical insights. PubCompare.ai can help researchers identify the most effective protocols related to body weight for their specific research goals. The platform's AI-driven analysis can highlight key differences in protocol effectiveness, enabling researchers to choose the best option for reproducibility and accuracy. This empowers researchers to make informed decisions and optimize their body weight research.

Q: What are some common challenges in using body weight-related protocols and products?

One of the main challenges in using body weight-related protocols and products is ensuring their reproducibility and accuracy. With the vast amount of research and products available, it can be difficult to determine which ones are truly effective and supported by strong evidence. Additionally, individual differences in factors like genetics, metabolism, and medical conditions can lead to varying responses to the same protocol or product. Properly evaluating and comparing the available options is crucial to finding the most suitable solution for one's specific needs. PubComprate.ai can help overcome these challenges by providing a streamlined, AI-driven platform for identifying and comparing the most effective body weight protocols and products.

Q: How can PubCompare.ai assist in optimizing body weight research?

PubCompare.ai can be an invaluable tool for optimizing body weight research. The platform's advanced comparison capabilities allow researchers to quickly and accurately evaluate the effectiveness of different protocols and products related to body weight. By leveraging AI-driven analysis, PubCompare.ai can highlight the key differences between various approaches, enabling researchers to make informed decisions and choose the most suitable options for their specific research goals. This helps ensure reproducibility, accuracy, and the identification of the most effective body weight-related protocols and products. The platform's streamlined workflow and data-driven insights empower researchers to optimize their body weight research and drive meaningful progress in this important field.

Q: What are the different types or variations of body weight-related protocols and products?

There is a wide range of body weight-related protocols and products available, each with its own unique characteristics and applications. Some common types include dietary interventions (e.g., calorie-restricted diets, macronutrient-focused plans), exercise programs (e.g., strength training, cardio-based workouts), and supplementaion (e.g., weight loss pills, meal replacements). Additionally, some protocols may combine multiple approaches, such as a comprehensive lifestyle modification program. The specific type or variation chosen should be based on the individual's needs, preferences, and the desired outcomes of the research. PupCompare.ai can help researchers navigate this diverse landscape and identify the most effective options for their body weight studies.

Body weight refers to the total mass of an individual's physical body.
It is an important indicator of health and nutrition status, and is commonly used to assess and monitor growth, development, and weight management.
Factors influencing body weight include diet, physical activity, genetics, and various medical conditions.
Optimizing body weight through evidence-based protocols and products can help support overall wellbeing.
PubCompare.ai, an AI-driven platform, facilitates this process by locating the best research and enabling reproducible, accrurate comparisons to inform decision-making for body weight research.

Most cited protocols related to «Body Weight»

Robust CTF Determination for High Astigmatism

Strong structure factors at low spatial frequencies can lead to CTF determination bias. Direct CTF determination at high frequency using the ‘1S2R’ procedure might fail in the case of large astigmatism due to severe oscillation of CTF. Two options are provided to deal with CTF determination at near-atomic resolution for micrographs that have very large astigmatism. They both make the ‘1S2R’ procedure more robust in such challenging case. One option is ‘resolution–extension (RE)’ and the other is ‘Bfactor-switch (BS)’. In the first method, Gctf determines initial CTF parameters using a relatively lower resolution ring (e.g. 50–10 Å by default). These parameters are passed as input to the next step of CTF refinement using a higher range (e.g. 15–4 Å). In the second method, Gctf uses a larger Bfactor (e.g. 500 Å²) to significantly down-weight high frequency for initial CTF determination. Then it switches to a smaller Bfactor (e.g. 50 Å²) to refine the previously determined CTF parameters. Either method shows its power to deal with some challenging cases (detailed results in Section 3.5). The combination (‘REBS’) can even work slightly better in certain cases.

, & Zhang K. (2016). Gctf: Real-time CTF determination and correction. Journal of Structural Biology, 193(1), 1-12.

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Publication 2016

Astigmatism

Restrained Macromolecular Structure Refinement

Model parameters, such as coordinates and ADPs, are not refined simultaneously but at separate steps (see §2.2 for details). phenix.refine uses the following refinement target function for restrained refinement of individual coordinates, A similar function is used in restrained ADP refinement, Here, T_exp is the crystallographic term that relates the experimental data to the model structure factors. It can be a least-squares target (LS; for example, as defined in Afonine et al., 2005a ▶ ), an amplitude-based maximum-likelihood target (ML; for example, as defined in Afonine et al., 2005a ▶ ) or a phased maximum-likelihood target (MLHL; Pannu et al., 1998 ▶ ). For refinement of coordinates, T_exp can also be defined in real space (see below).
T_{xyz_restraints} and T_{adp_restraints} are restraint terms that introduce a priori knowledge, thus helping to compensate for the insufficient amount of experimental data owing to finite resolution or incompleteness of the data set typically observed in macromolecular crystallography. Note that the restraint terms are not used in certain situations, for example rigid-body coordinate refinement, TLS refinement, occupancy refinement, f′/f′′ refinement or if the data-to-parameter ratio is extremely high. In these cases the total refinement target is reduced to T_exp.
The weights wxc_scale, wxc and wc (or wxu_scale, wxu and wu, correspondingly) are used to balance the relative contributions of experimental and restraints terms. The automatic weight-estimation procedure is implemented as described in Brünger et al. (1989 ▶ ) and Adams et al. (1997 ▶ ) with some variations and is used by default to calculate wxc and wxu. The long-term experience of using a similar scheme in CNS and PHENIX indicates that it is typically robust and provides a good estimate of weights in most cases, especially at medium to high resolution. In cases where this procedure fails to produce optimal weights, a more time-intensive automatic weight-optimization procedure may be used, as originally described by Brünger (1992 ▶ ) and further adopted by Afonine et al. (2011 ▶ ), in which an array of wxc_scale or wxu_scale values is systematically tested in order to find the value that minimizes R_free while keeping the overall model geometry deviations from ideality within a predefined range. The weight wc (or wu, correspondingly) is used to scale the restraints contribution, mostly duplicating the function of wxc_scale (or wxu_scale), while allowing an important unique option of excluding the restraints if necessary (for example, at subatomic resolution). Setting wc = 0 (or wu = 0) reduces the total refinement target to T_exp.
In maximum-likelihood (ML)-based refinement (Pannu & Read, 1996 ▶ ; Bricogne & Irwin, 1996 ▶ ; Murshudov et al., 1997 ▶ ; Adams et al., 1997 ▶ ; Pannu et al., 1998 ▶ ) the calculation of the ML target (Lunin & Urzhumtsev, 1984 ▶ ; Read, 1986 ▶ , 1990 ▶ ; Lunin & Skovoroda, 1995 ▶ ) requires an estimation of model error parameters, which depend on the current atomic parameters and bulk-solvent model and scales. Since the atomic parameters and the bulk-solvent model are updated during refinement, the ML error model has to be updated correspondingly, as described in Lunin & Skovoroda (1995 ▶ ), Urzhumtsev et al. (1996 ▶ ) and Afonine et al. (2005a ▶ ).

Afonine P.V., Grosse-Kunstleve R.W., Echols N., Headd J.J., Moriarty N.W., Mustyakimov M., Terwilliger T.C., Urzhumtsev A., Zwart P.H, & Adams P.D. (2012). Towards automated crystallographic structure refinement with phenix.refine. Acta Crystallographica Section D: Biological Crystallography, 68(Pt 4), 352-367.

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Publication 2012

Crystallography Dietary Fiber Human Body Muscle Rigidity Solvents

Suction Blister Induction and Wound Healing

A negative pressure instrument (Electronic Diversities, Finksburg, MD, USA) constructed to produce standard suction blisters upon application of negative pressure, was used on healthy skin (ex vivo: abdominal skin; in vivo: lower forearm). Subcutaneous fat was partially removed from ex vivo skin using a scissor. Subsequently, skin (10 × 10 cm²) was placed (not fixed, not kept in medium) on a styrofoam lid that was covered with aluminium foil to provide (at least partial) backpressure. Suction chambers with 5 openings (Ø = 5 mm) on the orifice plate were attached to skin, topped with a styrofoam lid and pressed with 1 kg weight in order to avoid movement of the plate. A pressure of 200–250 millimeter (mm) mercury (Hg) (ex vivo) or 150–200 mm Hg (in vivo) caused the skin to be drawn through the openings creating typical suction blisters of different size within 6–8 h (ex vivo) and 1–2 h (in vivo). Suction blister fluid (~110 µl/5 blisters) was collected using a syringe with a needle. Cells within the fluid were counted and placed on adhesion slides for staining and analysis. Blister roof epidermis was cut with a scissor, fixed with ice-cold acetone (10 minutes) and used for staining. For comparison and control, epidermal sheets were prepared from unwounded skin biopsy punches (Ø = 6 mm; 3.8% ammonium thiocyanate (Carl Roth GmbH + Co. KG, Germany) in PBS (Gibco, Thermo Fisher, Waltham, MA, USA), 1 h, 37 °C). Removal of the blister roof created a wound area. Biopsies (Ø = 6 mm) from wounded and unwounded areas were cultivated for 12 days in either duplicates or triplicates in 12 well culture plates and Dulbecco’s modified Eagle’s medium (DMEM) (Gibco) supplemented with 10% fetal bovine serum (FBS) (Gibco) and 1% penicillin-streptomycin (Gibco) and were cultured at the air-liquid interphase. Medium was changed every second day.

Rakita A., Nikolić N., Mildner M., Matiasek J, & Elbe-Bürger A. (2020). Re-epithelialization and immune cell behaviour in an ex vivo human skin model. Scientific Reports, 10, 1.

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Publication 2020

Abdomen Acetone Aluminum ammonium thiocyanate Biopsy Cells Cold Temperature Eagle Epidermis Fetal Bovine Serum Forearm Interphase Mercury-200 Movement Needles Penicillins Pressure Skin Streptomycin styrofoam Subcutaneous Fat Suction Drainage Syringes

EPA-PTP: Open Reference Species Delimitation

In the following, we describe the open reference species delimitation pipeline that combines the EPA with the PTP (EPA-PTP). The EPA initially places a large number of query sequences (short reads) into the branches of a given reference phylogeny. Thereafter, we execute PTP separately and independently for the query sequences assigned to each branch. This allows to annotate the branches of the reference tree by the number of species induced by the query sequences that were placed into each branch. The input of our pipeline is a reference alignment where each sequence represents one species and a reference phylogeny for that alignment. The PTP method and the pipeline are implemented in Python and rely on the python Environment for Tree Exploration package (Huerta-Cepas et al., 2010 (link)) for tree manipulation and visualization.
Our pipeline executes the following steps:

Run UCHIME (Edgar et al., 2011 (link)) against the reference alignment to remove chimeric query sequences.

Use EPA to place the query sequences onto the reference tree. Sequences that have a maximum placement likelihood weight of <0.5 (i.e. an uncertain placement, see Berger et al., 2011 (link) for details) are discarded.

For each branch in the reference tree, we extract the set of query sequences that have been placed into that branch and infer a tree on them using RAxML (Stamatakis, 2006 (link)). Because the PTP method requires a correctly rooted tree, we use the following two rooting strategies: if the branch leads to a tip, apart from the query sequences, we extend the alignment by including the reference tree tip sequence and that reference sequence that is furthest away from the current tip. The most distant sequence is used as outgroup. Keep in mind, that thereby the tree will be rooted at the longest branch (see the discussion below). To analyze query sequence placements at internal branches, we use the RAxML −g constraint tree option to obtain a rooted tree of the query sequences. The constraint tree consists of the bifurcating reference tree and a polytomy comprising the query sequences attached to the reference tree branch under consideration. The result of this constrained ML tree search is a resolved tree of query sequences that are attached to the reference tree branch. The attachment point is used as root.

Because we assume that the reference phylogeny is a species tree that reflects our knowledge about the speciation process and rate, we initially estimate only once on the reference phylogeny. Thereafter, we apply PTP to each query sequence (one for each branch of the reference phylogeny) tree to delimit species. Note that in this scenario we will only need to estimate , as remains fixed.

When PTP is applied to a placement of query sequences on a terminal branch, those queries that are delimited as one population with the reference sequence at the tip will be assigned taxonomically to the species represented by this reference sequence. Otherwise, they are identified as new species in the reference tree.

As mentioned previously, we also combined EPA with CROP (EPA-CROP). The method works as EPA-PTP, with the only difference that CROP is used instead of PTP to calculate the number of MOTUs for each placement.

Zhang J., Kapli P., Pavlidis P, & Stamatakis A. (2013). A general species delimitation method with applications to phylogenetic placements. Bioinformatics, 29(22), 2869-2876.

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Publication 2013

Chimera Crop, Avian Plant Roots Python Respiratory Diaphragm Sequence Insertion Trees

Carcinogenic Risk Assessment of PM2.5-bound PAHs

Carcinogenic and mutagenic risk assessments^{15 (link),60 (link)–63 (link),67 (link)–69 (link)} induced by inhalation of PM2.5-bound enriched with selected nitro-PAHs (1-NPYR, 2-NPYR, 2-NFLT, 3-NFLT, 2-NBA, and 3-NBA) and PAHs (PYR, FLT, BaP, and BaA) were estimated in the bus station and coastal site samples according to calculations done by Wang et al.^{60 (link)}, Nascimento et al.^{61 (link)}, and Schneider et al.^{67 (link)} PAH and PAH derivatives risk assessment is done in terms of BaP toxicity, which is well established^{67 (link)–73 (link)}. The daily inhalation levels (E_I) were calculated as:

E_{I} = B a P_{e q} \times I R = (\sum C_{i} \times T E F_{i}) \times I R

where E_I (ng person⁻¹ day⁻¹) is the daily inhalation exposure, IR (m³ d⁻¹) is the inhalation rate (m³ d⁻¹), BaP_eq is the equivalent of benzo[a]pyrene (BaP_eq = Σ C_i × TEF_i) (in ng m⁻³), C_i is the PM2.5 concentration level for a target compound i, and TEF_i is the toxic equivalent factor of the compound i. TEF values were considered those from Tomaz et al.^{15 (link)}, Nisbet and LaGoy^{69 (link)}, OEHHA⁷², Durant et al.^{73 (link)}, and references therein. E_I in terms of mutagenicity was calculated using equation (1), just replacing the TEF data by the mutagenic potency factors (MEFs) data, published by Durant et al.^{73 (link)}. Individual TEFs and MEFs values and other data used in this study are described in SI, Table S4.
The incremental lifetime cancer risk (ILCR) was used to assess the inhalation risk for the population in the Greater Salvador, where the bus station and the coastal site are located. ILCR is calculated as:

I L C R = (E_{I} \times S F \times E_{D} \times c f \times E F) / (A T \times B W)

where SF is the cancer slope factor of BaP, which was 3.14 (mg kg⁻¹ d⁻¹)⁻¹ for inhalation exposure^{60 (link)}, EF (day year⁻¹) represents the exposure frequency (365 days year⁻¹), E_D (year) represents exposure duration to air particles (year), cf is a conversion factor (1 × 10⁻⁶), AT (days) means the lifespan of carcinogens in 70 years (70 × 365 = 25,550 days)^{70 ,72}, and BW (kg) is the body weight of a subject in a target population⁷¹.
The risk assessment was performed considering four different target groups in the population: adults (>21 years), adolescents (11–16 years), children (1–11 years), and infants (<1 year). The IR for adults, adolescents, children, and infants were 16.4, 21.9, 13.3, 6.8 m³ day⁻¹, respectively. The BW was considered 80 kg for adults, 56.8 kg for adolescents, 26.5 kg for children and 6.8 kg for infants⁷⁰.

Santos A.G., da Rocha G.O, & de Andrade J.B. (2019). Occurrence of the potent mutagens 2- nitrobenzanthrone and 3-nitrobenzanthrone in fine airborne particles. Scientific Reports, 9, 1.

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Publication 2019

Adolescent Adult Benzo(a)pyrene Body Weight Carcinogens Child derivatives Factor X Fibrinogen fluoromethyl 2,2-difluoro-1-(trifluoromethyl)vinyl ether Health Risk Assessment Infant Inhalation Inhalation Exposure Malignant Neoplasms Mutagens Polycyclic Hydrocarbons, Aromatic Population at Risk Population Group Respiratory Rate

Most recents protocols related to «Body Weight»

Molded Silicone Pressure Sensitive Adhesive

Not available on PMC !

Example 3

Moulded Silicone Pressure Sensitive Adhesive Body:

Dow Corning 7-9800 A&B (mixing ration between A and Bis 1:1 by weight) were used for production of a PDMS based adhesive body. A mould having a triangular shape (each side of the triangular mould having a distance of 300 mm, the center part having a thickness of 0.5 mm and the edge having a thickness of 0.1 mm) was used. The components were thoroughly mixed and applied on a 50 μm cover layer of silicone rubber lining in the female part of a triangular mould and a male mould part was placed on top, said part lined with a low density polyethylene release liner. The adhesive was cured in an oven at 100 degree C. for 15 minutes. After curing the adhesive was punched out of the mould and a dent in the centre of the adhesive body device for embedment of an electronic sensing system was punched out.

US11864916B2. Three-dimensional adhesive device having a microelectronic system embedded therein (2024-01-09). Braemar Manufacturing, LLC [US]. Inventors: Susanne Holm Faarbaek [DK], Karsten Hoppe [DK], Peter Boman Samuelsen [DK], Jens Branebjerg [DK].

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Patent 2024

A 300 Dental Cavity Liner Females Fungus, Filamentous Human Body Males Polyethylene, Low-Density Pressure Silicone Elastomers Silicones

Measurement of ICP Using Samba Sensor

Not available on PMC !

Example 6

ICP is monitored using a Samba 420 Sensor, pressure transducer, with a Samba 202 control unit (Harvard Apparatus, Holliston, MA). This ICP monitoring system consists of a 0.42 mm silicon sensor element mounted on an optical fiber. A 20-gauge syringe needle is implanted through the cisterna magna to a depth of ˜1 cm. The needle then acts as a guide for insertion of the Samba Sensor and the site of implantation and the open end of the needle are sealed with 100% silicone sealant. A baseline ICP reading is established followed by a water bolus IP injection (20% weight of animal) with or without Compound 1. ICP is monitored until the animal expires from the water load.

Adjusting for the slight rise in ICP observed in the animals when they are monitored without the water bolus injection (FIG. 9, No Water Toxicity), Compound 1 at 0.76 mg/kg reduces the relative rate of ICP rise by 36%, from 3.6×10⁻³min⁻¹to 2.3×10⁻³min⁻¹(n=6 mice/treatment, mean±SEM).

US11873266B2. Methods of treating or controlling cytotoxic cerebral edema consequent to an ischemic stroke (2024-01-16). Aeromics, Inc. [US]. Inventors: Marc F. Pelletier [US], George William Farr [US], Paul Robert McGuirk [US], Christopher H Hall [US], Walter F. Boron [US].

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Patent 2024

Acceptance and Commitment Therapy Animals Injections, Intraperitoneal Intracranial Pressure Magna, Cisterna Mice, Laboratory Needles Ovum Implantation Silicon Silicones Syringes Transducers, Pressure

Formulation and Manufacture of Mifepristone Tablets

Not available on PMC !

Example 4

TABLE 15

Composition of mifepristone tablet 240 mg

Composition H

Ingredientsmg/unit

Mifepristone nano-suspension

Mifepristone240.00

HPMC20.00

Sodium lauryl sulphate6.40

Docusate sodium0.80

Purified waterQ.S.

Intra-granular material

Silicified microcrystalline cellulose280.40

Sodium starch glycolate27.20

Extra-granular material

Microcrystalline cellulose119.6

Sodium starch glycolate20.40

Colloidal silicon dioxide1.8

Magnesium Stearate3.40

Core tablet weight (mg)720.00

Film-coating blend

OPADRY ® II Complete Film Coating21.60

System 85F18422 white

Purified WaterQ.S.

Coated Tablet Weight (mg)741.60

Manufacturing Procedure of Composition H:

Composition H was manufactured according to the following procedure:

a) Specified amount of purified water was taken in a suitable container and specified quantity of docusate sodium was added and stirred continuously to obtain a solution.
b) Sodium lauryl sulphate was added to the step (a) solution and stirred continuously to obtain a solution.
c) Hydroxypropyl methyl cellulose was added to the step (b) solution and stirred continuously to obtain a solution.
d) Mifepristone was added to the step (c) solution and stirred for 5 minutes to obtain Mifepristone dispersion.
e) Mifepristone dispersion was homogenized using IKA's Ultra TURRAX® homogenizer at 1000 RPM for 15 minutes.
f) The above homogenized mifepristone slurry was nano-sized in ball-mill chamber to obtain nano-suspension containing desired particle size of mifepristone. The particle size distribution was measured by using Mastersizer 3000 particle analyser.
g) Specified quantities of the silicified microcrystalline cellulose and sodium starch glycolate were dispensed in a bowl and warmed to reach 28° C. to 30° C. temperature.
h) The nano-sized mifepristone suspension according to step (f) was sprayed onto the warmed intra-granular material according to step (g). The sprayed granules were dried at a temperature of 50° C. to 65° C. and sieved through 30 number mesh sieve.
i) Specified quantities of milled granules of step (h), sodium starch glycolate, microcrystalline cellulose, colloidal silicon dioxide and magnesium stearate were blended and compressed using tablet compression machine. The tablets according to step (i) were coated with suitable coating materials.

US11878025B2. Pharmaceutical compositions of mifepristone (2024-01-23). SLAYBACK PHARMA LLC [US]. Inventors: Paras P. Jain [IN], Somnath Devidas Navgire [IN], Sumitra Ashokkumar Pillai [IN].

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Patent 2024

Cytoplasmic Granules Docusate Sodium Hypromellose magnesium stearate microcrystalline cellulose Mifepristone Pharmaceutical Preparations Silicon Silicon Dioxide sodium starch glycolate Sulfate, Sodium Dodecyl

Lyophilized Silk Powder Formulations

Not available on PMC !

Example 19

TABLE 37

Embodiments of lyophilized silk powders

Silk SolutionTreatmentSoluble

~60 kDa silk, 6% silk, pH = 7-8lyopholize and cut withno

blender

~60 kDa silk, 6% silk, pH = 10lyopholize and cut withno

blender

~25 kDa silk, 6% silk, pH = 7-8lyopholize and cut withyes

blender

~25 kDa silk, 6% silk, pH = 10lyopholize and cut withyes

blender

The above silk solutions were transformed to a silk powder through lyophilization to remove bulk water and chopping to small pieces with a blender. pH was adjusted with sodium hydroxide. Low molecular weight silk (−25 kDa) was soluble while high molecular weight silk (−60 kDa) was not.

The lyophilized silk powder can be advantageous for enhanced storage control ranging from 10 days to 10 years depending on storage and shipment conditions. The lyophilized silk powder can also be used as a raw ingredient in the pharmaceutical, medical, consumer, and electronic markets. Additionally, lyophilized silk powder can be re-suspended in water, HFIP, or an organic solution following storage to create silk solutions of varying concentrations, including higher concentration solutions than those produced initially.

In an embodiment, aqueous pure silk fibroin-based protein fragment solutions of the present disclosure comprising 1%, 3%, and 5% silk by weight were each dispensed into a 1.8 L Lyoguard trays, respectively. All 3 trays were placed in a 12 ft²lyophilizer and a single run performed. The product was frozen with a shelf temperature of ≤−40° C. and held for 2 hours. The compositions were then lyophilized at a shelf temperature of −20° C., with a 3 hour ramp and held for 20 hours, and subsequently dried at a temperature of 30° C., with a 5 hour ramp and held for about 34 hours. Trays were removed and stored at ambient conditions until further processing. Each of the resultant lyophilized silk fragment compositions were able to dissolve in aqueous solvent and organic solvent to reconstitute silk fragment solutions between 0.1 wt % and 8 wt %. Heating and mixing were not required but were used to accelerate the dissolving rate. All solutions were shelf-stable at ambient conditions.

In an embodiment, an aqueous pure silk fibroin-based protein fragment solution of the present disclosure, fabricated using a method of the present disclosure with a 30 minute boil, has a molecular weight of about 57 kDa, a polydispersity of about 1.6, inorganic and organic residuals of less than 500 ppm, and a light amber color.

In an embodiment, an aqueous pure silk fibroin-based protein fragment solution of the present disclosure, fabricated using a method of the present disclosure with a 60 minute boil, has a molecular weight of about 25 kDa, a polydispersity of about 2.4, inorganic and organic residuals of less than 500 ppm, and a light amber color.

US11878070B2. Silk-based moisturizer compositions and methods thereof (2024-01-23). Evolved By Nature, Inc. [US]. Inventors: Gregory H. Altman [US], Dylan S. Haas [US], Kevin T. Healy [US].

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Patent 2024

Amber ARID1A protein, human Dietary Fiber Fibroins Freeze Drying Freezing Furuncles Light Pharmaceutical Preparations Powder Proteins Silk Sodium Hydroxide Solvents

Fabrication of Fibrous Acetaminophen Dosage Forms

Not available on PMC !

Example 2

Dosage forms B and C were prepared as follows. 20 wt % acetaminophen drug particles were first mixed with the excipient, 80 wt % HPMC of molecular weight 120 kg/mol. The mixture was then combined with a solvent, either DMSO (for preparing dosage form B) or water (for dosage form C). The volume of solvent per mass of excipient was 5.5 ml/g and 3.33 ml/g, respectively, for preparing dosage forms B and C. The drug-excipient-solvent mixture was then extruded through a laboratory extruder to form a uniform viscous paste. The viscous paste was put in a syringe equipped with a hypodermic needle of inner radius, R_n=130 μm (for preparing dosage form B) or R_n500 μm (for preparing dosage form C). The paste was then extruded through the needle and patterned as a fibrous dosage form with cross-ply arrangement of fibers. The nominal inter-fiber distance in a ply was uniform and equal to 730 μm (for preparing dosage form B) or 2800 μm (for preparing dosage form C). During and after patterning, warm air at a temperature of 60° C. and a velocity of about 2.3 m/s was blown over the fibrous dosage forms for a time, t_dry˜40 minutes, to evaporate the solvent and freeze the structure. The process parameters to prepare the dosage forms are summarized in Table 1. After drying, the structure was trimmed to a square disk shaped dosage form of side length, L₀˜8 mm. The thickness, H₀, of the dosage forms B and C was about 3 mm.

Single fibers B and C were prepared as dosage forms B and C, but without structuring the fibrous extrudate to a dosage form.

TABLE1

Process parameters to prepare the single fibers and fibrous dosage forms.

v'_sR_nλ_nt_dry

solvent(ml/g)(μm)(μm)R_n/λ_n(min)

ADMSO0.90130 7300.1835

BDMSO5.50130 7300.1840

Cwater3.3350028000.1840

v'_s: volume of solvent/ mass of excipient,

R_n: inner radius of needle,

λ_n: nominal inter-fiber spacing,

t_d: drying time.

The microstructural parameters of dry dosage forms differ from the nominal parameters because the dosage form shrinks during drying (Table 2, later). In all formulations the drug weight fraction in the drug-excipient mixture was 0.2.

US11865216B2. Expandable structured dosage form for immediate drug delivery (2024-01-09). None [US]. Inventors: Aron H. Blaesi [US], Nannaji Saka [US].

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Patent 2024

Acetaminophen Cocaine Dosage Forms Excipients Fibrosis Freezing Hypodermic Needles Needles Pastes Pharmaceutical Preparations Radius Solvents Sulfoxide, Dimethyl Syringes Viscosity

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Prism 8 by GraphPad

Sourced in United States, Austria, Canada, Belgium, United Kingdom, Germany, China, Japan, Poland, Israel, Switzerland, New Zealand, Australia, Spain, Sweden

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

D12492 by Research Diets

Sourced in United States, China, Canada, Japan, Denmark, Montenegro, United Kingdom

The D12492 is a powdered rodent diet formulated by Research Diets. It is a highly palatable, nutrient-dense diet that provides a standardized nutritional profile for research purposes. The diet is designed to be easily administered and consumed by laboratory rodents.

Sprague dawley rat by Charles River Laboratories

Sourced in United States, China, Germany, Canada, United Kingdom, Japan, France, Italy, Morocco, Hungary, New Caledonia, Montenegro, India

Sprague-Dawley rats are an outbred albino rat strain commonly used in laboratory research. They are characterized by their calm temperament and reliable reproductive performance.

Dextran sulfate sodium (dss) by MP Biomedicals

Sourced in United States, France, Germany, China, Canada, United Kingdom, Australia, Japan, Panama, Philippines

The DSS is a laboratory instrument designed for the separation and analysis of molecules and particles in complex samples. It utilizes a specialized technique called differential sedimentation to achieve precise separation and characterization of the components within a sample. The core function of the DSS is to provide accurate and reliable data on the size, distribution, and concentration of the analytes present, without interpretation or extrapolation on its intended use.

Matrigel by BD

Sourced in United States, United Kingdom, Germany, China, Canada, Japan, Italy, France, Belgium, Australia, Uruguay, Switzerland, Israel, India, Spain, Denmark, Morocco, Austria, Brazil, Ireland, Netherlands, Montenegro, Poland

Matrigel is a solubilized basement membrane preparation extracted from the Engelbreth-Holm-Swarm (EHS) mouse sarcoma, a tumor rich in extracellular matrix proteins. It is widely used as a substrate for the in vitro cultivation of cells, particularly those that require a more physiologically relevant microenvironment for growth and differentiation.

What are the key factors that influence body weight?

Body weight is influenced by a variety of factors, including diet, physical activity, genetics, and medical conditions. Diet plays a crucial role, with the balance between caloric intake and expenditure being a primary determinant. Physical activity level also significantly impacts body weight, as an active lifestyle can help burn calories and maintain a healthy weight. Genetic factors can predispose individuals to certain body weight patterns, while medical conditions like thyroid disorders or hormonal imbalances can also affect weight management. Understanding these multifaceted influences is key to optimizing body weight through evidence-based protocols and products.

How can PubCompare.ai help with body weight research?

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What are some common challenges in using body weight-related protocols and products?

One of the main challenges in using body weight-related protocols and products is ensuring their reproducibility and accuracy. With the vast amount of research and products available, it can be difficult to determine which ones are truly effective and supported by strong evidence. Additionally, individual differences in factors like genetics, metabolism, and medical conditions can lead to varying responses to the same protocol or product. Properly evaluating and comparing the available options is crucial to finding the most suitable solution for one's specific needs. PubComprate.ai can help overcome these challenges by providing a streamlined, AI-driven platform for identifying and comparing the most effective body weight protocols and products.

How can PubCompare.ai assist in optimizing body weight research?

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What are the different types or variations of body weight-related protocols and products?

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More about "Body Weight"

Body mass, weight management, nutrition status, diet, physical activity, genetics, medical conditions, BMI, adiposity, obesity, overweight, underweight, growth, development, wellness, health optimization, research protocols, products, PubCompare.ai, AI-driven platform, reproducibility, accuracy, decision-making, Tamoxifen, SAS 9.4, STZ, SAS version 9.4, C57BL/6J mice, Prism 8, D12492, Sprague-Dawley rats, DSS, Matrigel.