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Snup

Snup is a term used to describe a sudden, unexpected event or occurrence.
It is often used in informal or conversational contexts to express surprise or excitement.
The term may refer to a brief, abrupt action or moment, such as a quick sneeze or a sudden realization.
Snup can also be used to describe a sudden change or shift in a situation or environment.
While not a formal scientific or medical term, the use of Snup can add a sense of spontaneity and informality to language, reflecting the unpredictable nature of certain events or experiences.
Teh term is commonly used in colloquial speech and may vary in its specific meaning or connotation depending on the context.

Most cited protocols related to «Snup»

1
Lung Tumor STAS Identification and Characterization
Cited 34 times
Tumor slides from the internal training cohort were reviewed by 2 pathologists (K.K. and W.D.T.) who were blinded to patient clinical outcomes; they used an Olympus BX51 microscope (Olympus Optical Co. Ltd., Tokyo, Japan) with a standard 22-mm diameter eyepiece.
Tumor STAS was defined as tumor cells within air spaces in the lung parenchyma beyond the edge of the main tumor (Figure 1A and 1D) and was composed of 3 morphological patterns: 1) micropapillary structures consisting of papillary structures without central fibrovascular cores (Figure 1A and 1B),15 (link), 16 (link) which occasionally form ring-like structures within air spaces (Figure 1C); 2) solid nests or tumor islands consisting of solid collections of tumor cells filling air spaces (Figure 1D and 1E)17 (link); and 3) single cells consisting of scattered discohesive single cells (Figure 1F). The edge of the main tumor was defined as the smooth surface of the tumor which is easily recognizable at gross or at low-power field examination as highlighted with the dotted line in Figure 1A. Tumor STAS was considered present when tumor STAS, as defined above, was identified beyond the edge of the main tumor even if it existed only in the first alveolar layer from the tumor edge. Lesions of STAS consist of tumor cells which morphologically appear to be situated within air spaces as micropapillary clusters, solid nests or single cells that are detached from alveolar walls. This differs from lepidic growth where tumor cells grow in a linear fashion along the surface of alveolar walls. Extent of air space filling by tumor cells varied from abundant cellular infiltrates to very inconspicuous single cells or micropapillary clusters that were sometimes difficult to distinguish from alveolar macrophages. In addition, distance between tumor surface and farthest STAS from tumor edge was measured by a ruler. Since lung specimens were not consistently inflated during processing, in order to account for artifactual atelectasis, we also measured according to the number of alveolar spaces.
Tumor cells of STAS were distinguished from alveolar macrophages using the following methods. Macrophages in smokers typically have cytoplasm containing faint brown pigment and black carbon granules while in nonsmokers the pigment is lacking and cytoplasm is sometimes foamy. Nuclei are small, uniform, and regular, without atypia. Nuclear folds are frequent and nucleoli are inconspicuous or absent. In contrast, tumor cells of STAS typically lack cytoplasmic pigment or foamy cytoplasm. They often grow in cohesive clusters and nuclei are atypical with hyperchromasia and frequent nucleoli. The distinction of STAS from artifacts was done in the following way. Tumor floaters were favored, by the presence of clusters of cells often randomly scattered over tissue and at the edges of the tissue section. Presence of jagged edges of tumor cell clusters suggested tumor fragmentation or edges of a knife cut during specimen processing rather than STAS. Linear strips of cells that were lifted off of alveolar walls also favored the presence of artifact. Identification of tumor cells distant from the main tumor was regarded as an artifact unless intraalveolar tumor cells could be demonstrated in a continuum of airspaces containing intraalveolar tumor cells back to the tumor edge.
According to the International Association for the Study of Lung Cancer, American Thoracic Society, and European Respiratory Society histological classification, the percentage of each histologic pattern—lepidic, acinar, papillary, solid, and micropapillary—was recorded in 5% increments and tumors were classified by their predominant pattern.1 (link) Each histologic pattern was considered present in the tumor when it comprised ≥5% of the overall tumor.7 (link) Presence of visceral pleural, lymphatic, and vascular invasion was also recorded.
Kadota K., Nitadori J.I., Sima C.S., Ujiie H., Rizk N.P., Jones D.R., Adusumilli P.S, & Travis W.D. (2015). Tumor Spread Through Air Spaces is an Important Pattern of Invasion and Impacts the Frequency and Location of Recurrences Following Limited Resection for Small Stage I Lung Adenocarcinomas. Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer, 10(5), 806-814.
Publication 2015
Atelectasis Blood Vessel Carbon Black Cell Nucleolus Cell Nucleus Cells Cytoplasm Cytoplasmic Granules Europeans Lung Lung Cancer Macrophage Macrophages, Alveolar Microscopy Neoplasms Non-Smokers Pathologists Patients Pigmentation Pleura, Visceral Respiratory Rate Snup Syncope Tissues Vision
2
Longitudinal Evaluation of Palliative Care Outcomes
Cited 5 times
Demographic/clinical information for patient participants at baseline included age, gender, ethnicity, marital status, if living alone, presence/absence of family caregiver(s), performance status using Australia-modified Karnofsky Performance Status (AKPS),26 (link) and primary diagnosis.
Data (see Table 1 for data collection measures) were collected at two time-points; 2–5 days apart within in-patient settings, and 7–21 days apart in community settings. For patient participants, IPOS (patient version, 3-day recall period), Edmonton Symptom Assessment System–revised (ESASr)30 (link)–32 (link, link) and the Functional Assessment of Cancer Therapy-General (FACT-G)33 (link) were collected at first time-point, and IPOS (patient-version) and global change question (see Table 1)34 (link),40 (link) were collected at the second time-point. For staff participants, the staff-version of IPOS, the Support Team Assessment Schedule (STAS),41 (link) AKPS26 (link) and Phase of Illness38 (link) were collected at the first time point, and IPOS (staff-version), AKPS, and Phase of Illness were collected at the second time point.
Murtagh F.E., Ramsenthaler C., Firth A., Groeneveld E.I., Lovell N., Simon S.T., Denzel J., Guo P., Bernhardt F., Schildmann E., van Oorschot B., Hodiamont F., Streitwieser S., Higginson I.J, & Bausewein C. (2019). A brief, patient- and proxy-reported outcome measure in advanced illness: Validity, reliability and responsiveness of the Integrated Palliative care Outcome Scale (IPOS). Palliative Medicine, 33(8), 1045-1057.
Publication 2019
Diagnosis Ethnicity Family Caregivers Gender Malignant Neoplasms Mental Recall Patients Snup Symptom Assessment Therapeutics
3
Tumor Spread Through Air Spaces (STAS) Assessment
Cited 5 times

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Publication 2016
Alveolar Epithelial Cells Atelectasis Carbon Cell Nucleus Cells Cytokeratin Cytoplasm Dissection Inflammation Lung Lung Neoplasms Macrophage Macrophages, Alveolar Malignant Neoplasms Microscopy Necrosis Neoplasms Pathologists Patients Pigmentation Pulmonary Fibrosis Shock Snup Squamous Cell Carcinoma Vacuole Vision
4
Comparative Analysis of Appraisal Processes
Cited 5 times
NICE has 2 appraisal processes, multiple- and single-technology appraisal (MTA, STA). In
an MTA, a set of competing new products are compared with each other and with standard
comparators, whereas in an STA a single new product is under scrutiny. Although an NMA is
almost always used in MTAs, STAs may be based on pairwise synthesis or NMA. However, the
methods guide requires that the methods of assessment be the same. The evidence synthesis
models and the WinBUGS software for implementing them presented throughout this tutorial
series10 (link),11 (link) have precisely this
property: The software will run either type of analysis without distinction, and the
pairwise model is simply a special case of the NMA model in which only 2 treatments are
being compared. This is not necessarily the case in other models, or software, for NMA.
The Lumley model22 (link)for NMA cannot be run on pairwise meta-analysis or on indirect comparisons, nor in its
published form can it deal correctly with trials comparing more than 2 treatments. These
are serious shortcomings, as manufacturers, clinicians, and decision makers will want to
be assured that there is a single, fair way of estimating treatment effects, regardless of
the structure of the evidence.
Dias S., Welton N.J., Sutton A.J, & Ades A.E. (2013). Evidence Synthesis for Decision Making 1: Introduction. Medical Decision Making, 33(5), 597-606.
Publication 2013
Anabolism Pemetrexed Snup
5
Analyzing Neuronal Responses with Robust Electrophysiology
Cited 5 times
Some electrodes in the array yielded weak and inconsistent responses and were excluded from analysis. For Monkey 1, one region of the array did not yield usable signals. The data reported here were obtained from 27 electrodes in Monkey 1 and 62 electrodes in Monkey 2. Each of these electrodes yielded reliable responses and stable estimates of the receptive field centers (SD less than 0.1° across days, mapped by flashing small Gabor stimuli on a rectangular grid that spanned the receptive fields of all the electrodes). Only electrodes with reliable estimates of the receptive field center were used so that we could limit the stimulus period analysis to sites within 0.2 degrees of the stimulus center (see below). This analysis also provided an independent measure of the signal quality recorded from each electrode. In comparison, Nauhaus and colleagues used electrodes for which the time-to-peak versus distance plots were significant (p<0.001), thereby removing all electrodes that failed to provide supporting evidence for traveling waves.
For pre-stimulus STA, any electrode from which at least 25 spikes were obtained was used for analysis. For Monkey 1 this yielded 116 electrodes from 10 recording sessions (23 unique electrodes - many electrodes were recorded on multiple sessions; median spikes per electrode per session: 314, inter-quartile range: 111-778). For Monkey 2 this yielded 655 electrodes from 17 sessions (60 unique electrodes; median spikes: 236, inter-quartile range: 89-596). For stimulus STA, we used electrodes from which at least 25 spikes were obtained, and whose receptive fields were within 0.2 degrees of the center of the stimulus. For Monkeys 1 and 2 this yielded 22 (12 unique; median spikes: 193, inter-quartile range: 75-335) and 46 (31 unique; median spikes: 98, inter-quartile range: 45-185) electrodes. To account for the multiplicity of some electrodes, the STAs from the same electrode were averaged across sessions.
LFP and multiunits were extracted using commercial hardware and software (Blackrock Microsystems). Raw data were filtered between 0.3 Hz (Butterworth filter, 1st order, analog) and 500 Hz (Butterworth, 4th order, digital) to extract the LFP, and digitized at 2 kHz (16 bit resolution). Multiunits were extracted by filtering the raw signal between 250 Hz (Butterworth, 4th order, digital) and 7500 Hz (Butterworth, 3rd order, analog) followed by an amplitude threshold (set at ∼6.25 and ∼4.25 of the signal SD for the two monkeys). To improve the quality of the isolation, the multiunits were further sorted offline (Offline Sorter, Plexon Inc.), although the results were similar when unsorted multiunits were used. LFP was used either raw (no additional filtering) or after filtering between 3-90 or 15-90 Hz (4th order high-pass and 5th order low-pass Butterworth filters; filtered twice in original and time-reversed order to achieve zero phase distortion). LFP from each electrode was independently z-scored. Because the spikes for STA computation were taken at a fixed time relative to stimulus onset, the stimulus locked LFP signal (i.e., the evoked response) was subtracted from each LFP trace before the STA computation.
Ray S, & Maunsell J.H. (2011). Network Rhythms Influence the Relationship between Spike-Triggered Local Field Potential and Functional Connectivity. The Journal of Neuroscience, 31(35), 12674-12682.
Publication 2011
Debility Fingers isolation Monkeys Snup

Most recents protocols related to «Snup»

1
Kinematic Calibration Procedure for the KneeKG System
The KneeKG™ system consists of a harness that reduces STAs and a calibration method that combines anatomical calibration, i.e. manual identification of anatomical points, and functional calibration, i.e. specific movements to identify axis and joint centres [5 (link)]. To avoid discrepancies in axis definition that would result in kinematic differences, the lower-limb geometry measured with the CAS system was introduced into the gait measurements using the calibration procedure described below.
First, a kinematic chain was defined based on the calibration of the CAS system, with a pivot at the knee and spherical joints at the hip and ankle. The pivot’s axis was based on the flexion–extension axis (wf) estimated during CAS. The kinematic chain was introduced into the treadmill gait measurements using a two-level multi-body kinematics optimisation [13 (link)], where the variables to optimise were the fully Cartesian coordinates (uf, rPf, rDf, wf, ut, rPt, rDt, wt) and the positions of the reflective markers with respect to this kinematic chain. The outcome of the optimisation determined the optimal position of the kinematic chain, i.e. joint centres and flexion-extension axis, with respect to the thigh and shank clusters of the KneeKG™ system. These positions were used as the final calibration for the KneeKG™ system. In this way, the six degrees of freedom (DoF) kinematics were computed with the femur and tibia LCS matching those used during the CAS measurement.
Gasparutto X., Bonnefoy-Mazure A., Attias M., Dumas R., Armand S, & Miozzari H. (2023). Comparison between passive knee kinematics during surgery and active knee kinematics during walking: A preliminary study. PLOS ONE, 18(3), e0282517.
Full text: Click here
Publication 2023
Epistropheus Femur Hip Joint Human Body Joints Joints, Ankle Knee Joint Lower Extremity Movement Snup Thigh Tibia
2
Knee Kinematics Comparison During Gait
Each DoF measured using the CAS system was expressed as a function of the knee flexion–extension angle (coupling curves) [14 (link)]. Then, the CAS kinematics were expressed as a theoretical gait cycle by matching the CAS knee flexion measurement with the corresponding average knee flexion angle measured using the KneeKG™ system during gait (Fig 2). The matching was performed in four steps: 1) at each frame of the treadmill gait cycle, the knee flexion-extension angle was identified, 2) all the frames from the CAS measurements with this knee flexion-extension angle were identified, 3) the values of the different degrees of freedom of the knee at those frames were identified, 4) those values were reported as the CAS values at this instant of the gait cycle. In this way, the theoretical CAS kinematics during treadmill gait are determined and can be compared to the knee kinematics assessed with the KneeKGTM. To increase the number of corresponding points between systems, the kinematic measurements from the CAS and the KneeKG™ were upsampled from 100 Hz to 300 Hz, and a distinction was made between the extension and flexion phases.
Each patient’s adduction–abduction (AA) angle, internal–external rotation (IER) angle and anterior–posterior (AP) displacement [5 (link)], as measured during the treadmill gait test were averaged over the gait cycles and compared using a Bland–Altman analysis [15 (link)] to the corresponding CAS measurements averaged over the gait cycles. Next, bias and limits of agreement tests assessed how well their anatomical axes corresponded, while Spearman’s correlation coefficient assessed the consistency of their kinematics pattern. Correlation coefficients were categorised as weak (0–0.30), moderate (0.31–0.50), good (0.51–0.70) and high (> 0.70) [16 ]. Each DoF’s RoM was assessed as a reference for the limits of agreement. Finally, each system and patient’s variability was assessed using the square root of the standard deviation (SD). A non-parametric Wilcoxon test was performed to assess differences in variability between systems. These analyses were performed over the whole gait cycle, for the single support phase and for the swing phase at two timepoints: before and after definitive TKA. Analyses at the two latter phases were selected to avoid any STAs due to foot contact in KneeKG™ measurements.
Calculations were made using the open-source Biomechanical ToolKit package [17 (link)], the 3D Kinematics and Inverse Dynamics toolbox [18 ], the Bland–Altman and Correlation Plot toolbox [19 ], and Matlab R2016b (MathWorks, USA). The workflow of measurements and data analysis is summarised in Fig 2.
Gasparutto X., Bonnefoy-Mazure A., Attias M., Dumas R., Armand S, & Miozzari H. (2023). Comparison between passive knee kinematics during surgery and active knee kinematics during walking: A preliminary study. PLOS ONE, 18(3), e0282517.
Full text: Click here
Publication 2023
Debility Epistropheus Foot Knee Patients Plant Roots Reading Frames Snup Treadmill Test
3
Polypropylene-enhanced Asphalt Concrete for Sustainable Roads
The type of asphalt mixture used in this study was wear layers (rolling) called asphalt concrete with crushed stone BA 8, where 8 represent the maximum size of the granule3 . The hot asphalt mixture samples type BA8 were made according to standard STAS 11348-8718 obtained in the road-testing laboratory within the Tancrad Company. The weight proportions of the components of standard asphalt mixture sample consists of 33.5% crushed siliceous stone chipping with a granulation ranging between 4.0 and 8.0 mm, 50% crushed sand with a granulation ranging between 0.1 and 4.0 mm, 10% sort limestone filler and 6.5% road bitumen type 50/7018 ,19 . For Marshall test were prepared three recipes by replacing 25% crushed sand with a granulation ranging between 0.1 and 4.0 mm with 25% waste grit from blasting process with a granulation ranging between 0.1–2.00 mm and by adding weight percentage of polypropylene (PP)-based microplastics with granulation ranging between 0.1 and 2 mm. Thus, the weight proportions of the components of asphalt mixtures samples consists of 33.5% crushed siliceous stone chipping with a granulation ranging between 4.0 and 8.0 mm, 25% crushed sand with a granulation ranging between 0.1 and 4.0 mm, 25% waste grit from blasting process with a granulation ranging between 0.1 and 2.00 mm, 10% sort limestone filler and 6.4% road bitumen type 50/70 with 0.1% polypropylene-based microplastics for Sample 1; 6.2% road bitumen type 50/70 with 0.3% polypropylene-based microplastics for Sample 2 and 5.9% road bitumen type 50/70 with 0.6% polypropylene-based microplas-tics for Sample 3, as can be seen listed in Table 1. The recipe of Sample 2 was the subject of the patent in collaboration with economic environment20 .

Recipes for hot asphalt mixture samples type BA8.

ComponentStandardSample 1Sample 2Sample 3
Crushed siliceous stone chipping, (%)33.533.533.533.5
Crushed sand, (%)50252525
Waste grit, (%)–252525
Sort limestone filler, (%)10101010
Road bitumen type 50/70, (%)6.56.46.25.9
Polypropylene-based microplastics, (%)–0.10.30.6
The obtained samples have cylindrical form with a diameter of 10 cm and a height of 6.3 cm.
The asphalt mixture BA 8 was used in this study, which is a typical asphalt concrete with crushed stone widely employed, its gradation is shown in Fig. 9. The gradation of the asphalt mixture was made for standard sample and for Sample 2 with 0.3% polypropylene-based microplastics. The content of the binder was determined by the Marshall method according to Romanian Standard SR 174-1/200921 .

Mixture gradation of standard and Sample 2 (0.3% PP) hot asphalt mixture type BA 8.

Buruiana D.L., Georgescu P.L., Carp G.B, & Ghisman V. (2023). Recycling micro polypropylene in modified hot asphalt mixture. Scientific Reports, 13, 3639.
Full text: Click here
Publication 2023
asphalt Calculi Limestone Microplastics Polypropylenes Snup Tic Disorder Vision
4
Physicochemical Analysis of Hydrolyzed Collagen
The concentrated hydrolysed collagen was analysed by physical–chemical methods according to the standards in force and methods such as ash content (EN ISO 4047:2008), dry matter (EN ISO 4684:2006), total nitrogen and protein (ISO 5397:1996), pH (STAS 8619/3:1990), amino nitrogen (according to ICPI method), and electrical conductivity (EN ISO 27883:1997). The viscosity of concentrated hydrolysed collagen hydrolysate was determined with a Brookfield AMETEK DV2T TC-550 Viscometer (Brookfield, Toronto, ON, Canada) at a temperature of 25 °C. The particle size and zeta potential of collagen were evaluated by using a Dynamic Light Scattering (DLS) instrument from Malvern (Zetasizer Nano-ZS, Malvern Hills, UK).
Berechet M.D., Gaidau C., Nešić A., Constantinescu R.R., Simion D., Niculescu O., Stelescu M.D., Sandulache I, & Râpă M. (2023). Antioxidant and Antimicrobial Properties of Hydrolysed Collagen Nanofibers Loaded with Ginger Essential Oil. Materials, 16(4), 1438.
Full text: Click here
Publication 2023
Collagen Electric Conductivity Nitrogen Physical Examination Proteins Snup Viscosity
5
Mineral Elements Analysis of Wheat-Quinoa Composite Flour and Bread
Cited 1 time
The K, Ca, Mg, Na, Zn, Fe, Mn, and Cu elements contents of the wheat flour, optimal wheat–quinoa composite flour and bread were determined by flame atomic absorption spectrometry (FAAS) (AA-6300 Shimadzu, Kyoto, Japan). The analysis of the sample involved two stages: the mineralization of the sample and the metal dosage by spectrophotometry. During mineralization, the organic matter from the sample (5.00 ± 0.001 g) is destroyed by carbonization and combustion in a muffle furnace (Nabertherm, LE 2/11/R6, Bremen, Germany), with the temperature gradually increasing from 250 to 450 °C, up to 900 °C, for 8 h. 5 mL HCl 6 mol/L (STAS 13013/1-91) is added to the ash obtained, and then the acid is evaporated using a sand bath, and the residue is dissolved with 730 µL HNO3 69% and brought to the mark (50 mL) with deionized water. Deionized water was used as a control sample, following the same procedure. The spectrophotometric determination involved the following steps: activating the hollow cathode lamp corresponding to the elements (K, Ca, Mg, Na, Fe, Zn, Mn, Cu), adjusting the operational parameters (wavelength, sensitivity), activating and adjusting the flame, as well as establishing the curve standard by absorbing four working standard solutions of different concentrations. The calibration curve made for each element covers the range of 0.5–5.0 mg/L Ca, 0.5–2.5 mg/L Cu, 0.5–5.0 mg/L Fe, 0.05–0.30 mg/L Mg, 0.5–3.0 mg/L Mn, 0.05–0.60 mg/L Zn, 0.1–0.5 mg/L Na, and 0.2–1.0 mg/L K. The wavelengths taken into account when determining Ca, Cu, Fe, Mg, Mn, Zn, K and Na elements correspond to 422.7, 342.7, 248.3, 285.2, 279.5, 213.8, 589.0, and 766.5 nm. Air-acetylene as the flame type, a gas flow rate of 15.0 L/min, a pre-spray time of 10 s, an integration time of 5 s, and a response time of 1 s were also included as working conditions. The mineral elements are expressed as mg/100 g of flour and were calculated with Equation (1): E=C·F·VM
where: E—Mineral element concentration, mg/100 g; C—The concentration measured on the calibration curve, mg/L; F—Dilution factor; V—Sample volume, mL; M—Sample mass taken in the analysis, g.
Coţovanu I., Mironeasa C, & Mironeasa S. (2023). Nutritionally Improved Wheat Bread Supplemented with Quinoa Flour of Large, Medium and Small Particle Sizes at Typical Doses. Plants, 12(4), 698.
Full text: Click here
Publication 2023
Acetylene Acids Baths, Sand Bread Copper F Factor Flour Hypersensitivity Metals Minerals Physiologic Calcification Quinoa Snup Sodium Spectrophotometry Spectrophotometry, Atomic Absorption Technique, Dilution Wheat Flour

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More about "Snup"

Snup is a colloquial term used to describe a sudden, unexpected event or occurrence.
It is often used in informal or conversational contexts to express surprise, excitement, or the spontaneous nature of a situation.
The term may refer to a brief, abrupt action or moment, such as a quick sneeze, a sudden realization, or a surprising shift in an environment or scenario.
While not a formal scientific or medical term, the use of Snup can add a sense of informality and unpredictability to language, reflecting the unpredictable nature of certain experiences.
The term is commonly used in casual speech and may have varying connotations depending on the context.
Synonyms and related terms for Snup include abrupt, abrupt event, unexpected happening, sudden occurrence, spontaneous moment, and surprise incident.
Abbreviations such as 'snu' or 'snp' may also be used to reference the concept.
Subtopics related to Snup may include the study of sudden physiological events (e.g., Somatom Definition AS 64 for medical imaging), the analysis of unexpected environmental changes (e.g., Stata 15 for data analysis), or the investigation of spontaneous cellular reactions (e.g., MTT assay with DMSO for cell viability assessment).
Techniques like microscopy (e.g., BX51) and microbial community profiling (e.g., ZymoBIOMICS Microbial Community Standard) may also be relevant in the study of Snup-like phenomena.
Overall, the term Snup reflects the unpredictable and spontaneous nature of certain events and experiences, and its use can add a sense of informality and excitement to language.