Immunoadsorption Columns Market Outlook

The global immunoadsorption columns market size was valued at approximately USD 400 million in 2023 and is projected to reach around USD 870 million by 2032, growing at a compound annual growth rate (CAGR) of 9%. This robust growth can be attributed to several factors including the rising prevalence of autoimmune diseases, advancements in biotechnologies, and increasing applications of immunoadsorption columns in clinical settings.

The growing prevalence of autoimmune diseases is one of the main drivers contributing to the expansion of the immunoadsorption columns market. Autoimmune diseases such as rheumatoid arthritis, lupus, and multiple sclerosis are becoming increasingly common, thereby necessitating advanced therapeutic interventions. Immunoadsorption columns provide a highly effective means of removing pathogenic antibodies from the blood, thereby offering significant therapeutic benefits. Additionally, the increased awareness and diagnosis of these conditions are further propelling the demand for these medical devices.

Technological advancements in biotechnologies have played a crucial role in the market's growth. Innovations in column materials, adsorption techniques, and automation have significantly improved the efficacy and safety profiles of immunoadsorption therapies. The development of single-use columns has notably decreased the risk of cross-contamination and has simplified the procedure, making it more attractive for healthcare providers. Furthermore, the integration of advanced control systems and data analytics into immunoadsorption columns has enabled more personalized and precise treatments, thereby broadening their application scope.

Another key growth factor is the increasing application of immunoadsorption columns in clinical settings. Beyond autoimmune diseases, these columns are being utilized for the treatment of various neurological and hematological disorders. For instance, in neurological conditions like Guillain-Barre syndrome and myasthenia gravis, immunoadsorption has shown promising results in removing autoantibodies. Similarly, in hematological disorders such as thrombotic thrombocytopenic purpura, these columns have provided effective therapeutic outcomes, further driving their adoption in diverse clinical applications.

Regionally, North America holds a significant share of the immunoadsorption columns market, driven by advanced healthcare infrastructure and high healthcare expenditure. Europe follows closely due to the rising cases of autoimmune diseases and supportive government initiatives. The Asia Pacific region is expected to exhibit the highest growth rate, owing to increasing healthcare investments, rising awareness, and improving healthcare facilities. Emerging markets in Latin America and the Middle East & Africa are also recognizing the potential benefits of immunoadsorption therapies, contributing to the overall market growth.

Product Type Analysis

The immunoadsorption columns market can be segmented by product type into single-use columns and reusable columns. Single-use columns are witnessing substantial growth due to their numerous advantages. These columns are designed for one-time use, which significantly reduces the risk of cross-contamination and infection. This is particularly important in clinical settings where patient safety is paramount. Moreover, single-use columns offer convenience and ease of use, as they do not require extensive cleaning and sterilization between uses. This can lead to reduced operational costs and time savings for healthcare providers, further driving their adoption.

Reusable columns, on the other hand, have traditionally been the standard in many therapeutic settings. These columns are designed for multiple uses and can be reprocessed and sterilized between treatments. While reusable columns have the advantage of being more cost-effective in the long run, they require rigorous cleaning protocols to ensure patient safety. Advances in materials and design have improved the durability and efficacy of reusable columns, making them a viable option for many healthcare facilities. However, the operational complexity associated with their use may limit their growth compared to single-use columns.

Innovation in both single-use and reusable columns is ongoing, with manufacturers focusing on enhancing adsorption capacity, biocompatibility, and ease of use. Technological advancements are leading to the development of columns with higher efficiency and specificity in removing pathogenic antibodies. As a r

Version 5 release notes:
Removes support for SPSS and Excel data.Changes the crimes that are stored in each file. There are more files now with fewer crimes per file. The files and their included crimes have been updated below.
Adds in agencies that report 0 months of the year.Adds a column that indicates the number of months reported. This is generated summing up the number of unique months an agency reports data for. Note that this indicates the number of months an agency reported arrests for ANY crime. They may not necessarily report every crime every month. Agencies that did not report a crime with have a value of NA for every arrest column for that crime.Removes data on runaways.
Version 4 release notes:
Changes column names from "poss_coke" and "sale_coke" to "poss_heroin_coke" and "sale_heroin_coke" to clearly indicate that these column includes the sale of heroin as well as similar opiates such as morphine, codeine, and opium. Also changes column names for the narcotic columns to indicate that they are only for synthetic narcotics.
Version 3 release notes:
Add data for 2016.Order rows by year (descending) and ORI.Version 2 release notes:
Fix bug where Philadelphia Police Department had incorrect FIPS county code.
The Arrests by Age, Sex, and Race data is an FBI data set that is part of the annual Uniform Crime Reporting (UCR) Program data. This data contains highly granular data on the number of people arrested for a variety of crimes (see below for a full list of included crimes). The data sets here combine data from the years 1980-2015 into a single file. These files are quite large and may take some time to load.
All the data was downloaded from NACJD as ASCII+SPSS Setup files and read into R using the package asciiSetupReader. All work to clean the data and save it in various file formats was also done in R. For the R code used to clean this data, see here. https://github.com/jacobkap/crime_data. If you have any questions, comments, or suggestions please contact me at jkkaplan6@gmail.com.

I did not make any changes to the data other than the following. When an arrest column has a value of "None/not reported", I change that value to zero. This makes the (possible incorrect) assumption that these values represent zero crimes reported. The original data does not have a value when the agency reports zero arrests other than "None/not reported." In other words, this data does not differentiate between real zeros and missing values. Some agencies also incorrectly report the following numbers of arrests which I change to NA: 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000, 99999, 99998.

To reduce file size and make the data more manageable, all of the data is aggregated yearly. All of the data is in agency-year units such that every row indicates an agency in a given year. Columns are crime-arrest category units. For example, If you choose the data set that includes murder, you would have rows for each agency-year and columns with the number of people arrests for murder. The ASR data breaks down arrests by age and gender (e.g. Male aged 15, Male aged 18). They also provide the number of adults or juveniles arrested by race. Because most agencies and years do not report the arrestee's ethnicity (Hispanic or not Hispanic) or juvenile outcomes (e.g. referred to adult court, referred to welfare agency), I do not include these columns.

To make it easier to merge with other data, I merged this data with the Law Enforcement Agency Identifiers Crosswalk (LEAIC) data. The data from the LEAIC add FIPS (state, county, and place) and agency type/subtype. Please note that some of the FIPS codes have leading zeros and if you open it in Excel it will automatically delete those leading zeros.

I created 9 arrest categories myself. The categories are:
Total Male JuvenileTotal Female JuvenileTotal Male AdultTotal Female AdultTotal MaleTotal FemaleTotal JuvenileTotal AdultTotal ArrestsAll of these categories are based on the sums of the sex-age categories (e.g. Male under 10, Female aged 22) rather than using the provided age-race categories (e.g. adult Black, juvenile Asian). As not all agencies report the race data, my method is more accurate. These categories also make up the data in the "simple" version of the data. The "simple" file only includes the above 9 columns as the arrest data (all other columns in the data are just agency identifier columns). Because this "simple" data set need fewer columns, I include all offenses.

As the arrest data is very granular, and each category of arrest is its own column, there are dozens of columns per crime. To keep the data somewhat manageable, there are nine different files, eight which contain different crimes and the "simple" file. Each file contains the data for all years. The eight categories each have crimes belonging to a major crime category and do not overlap in crimes other than with the index offenses. Please note that the crime names provided below are not the same as the column names in the data. Due to Stata limiting column names to 32 characters maximum, I have abbreviated the crime names in the data. The files and their included crimes are:

Index Crimes
MurderRapeRobberyAggravated AssaultBurglaryTheftMotor Vehicle TheftArsonAlcohol CrimesDUIDrunkenness
LiquorDrug CrimesTotal DrugTotal Drug SalesTotal Drug PossessionCannabis PossessionCannabis SalesHeroin or Cocaine PossessionHeroin or Cocaine SalesOther Drug PossessionOther Drug SalesSynthetic Narcotic PossessionSynthetic Narcotic SalesGrey Collar and Property CrimesForgeryFraudStolen PropertyFinancial CrimesEmbezzlementTotal GamblingOther GamblingBookmakingNumbers LotterySex or Family CrimesOffenses Against the Family and Children
Other Sex Offenses
ProstitutionRapeViolent CrimesAggravated AssaultMurderNegligent ManslaughterRobberyWeapon Offenses
Other CrimesCurfewDisorderly ConductOther Non-trafficSuspicion
VandalismVagrancy
Simple
This data set has every crime and only the arrest categories that I created (see above).
If you have any questions, comments, or suggestions please contact me at jkkaplan6@gmail.com.

This respository includes two datasets, a Document-Term Matrix and associated metadata, for 17,493 New York Times articles covering protest events, both saved as single R objects.

These datasets are based on the original Dynamics of Collective Action (DoCA) dataset (Wang and Soule 2012; Earl, Soule, and McCarthy). The original DoCA datset contains variables for protest events referenced in roughly 19,676 New York Times articles reporting on collective action events occurring in the US between 1960 and 1995. Data were collected as part of the Dynamics of Collective Action Project at Stanford University. Research assistants read every page of all daily issues of the New York Times to find descriptions of 23,624 distinct protest events. The text for the news articles were not included in the original DoCA data.

We attempted to recollect the raw text in a semi-supervised fashion by matching article titles to create the Dynamics of Collective Action Corpus. In addition to hand-checking random samples and hand-collecting some articles (specifically, in the case of false positives), we also used some automated matching processes to ensure the recollected article titles matched their respective titles in the DoCA dataset. The final number of recollected and matched articles is 17,493.

We then subset the original DoCA dataset to include only rows that match a recollected article. The "20231006_dca_metadata_subset.Rds" contains all of the metadata variables from the original DoCA dataset (see Codebook), with the addition of "pdf_file" and "pub_title" which is the title of the recollected article (and may differ from the "title" variable in the original dataset), for a total of 106 variables and 21,126 rows (noting that a row is a distinct protest events and one article may cover more than one protest event).

Once collected, we prepared these texts using typical preprocessing procedures (and some less typical procedures, which were necessary given that these were OCRed texts). We followed these steps in this order: We removed headers and footers that were consistent across all digitized stories and any web links or HTML; added a single space before an uppercase letter when it was flush against a lowercase letter to its right (e.g., turning "JohnKennedy'' into "John Kennedy''); removed excess whitespace; converted all characters to the broadest range of Latin characters and then transliterated to ``Basic Latin'' ASCII characters; replaced curly quotes with their ASCII counterparts; replaced contractions (e.g., turned "it's'' into "it is''); removed punctuation; removed capitalization; removed numbers; fixed word kerning; applied a final extra round of whitespace removal.

We then tokenized them by following the rule that each word is a character string surrounded by a single space. At this step, each document is then a list of tokens. We count each unique token to create a document-term matrix (DTM), where each row is an article, each column is a unique token (occurring at least once in the corpus as a whole), and each cell is the number of times each token occurred in each article. Finally, we removed words (i.e., columns in the DTM) that occurred less than four times in the corpus as a whole or were only a single character in length (likely orphaned characters from the OCRing process). The final DTM has 66,552 unique words, 10,134,304 total tokens and 17,493. The "20231006_dca_dtm.Rds" is a sparse matrix class object from the Matrix R package.

In R, use the load() function to load the objects `dca_dtm` and `dca_meta`. To associate the `dca_meta` to the `dca_dtm` , match the "pdf_file" variable in`dca_meta` to the rownames of `dca_dtm`.

This dataset includes ALL the abundance values, zero and non-zero. Taxonomic groups are diplayed in the 'taxon' column, rather than in separate columns, with abundances in the 'abund_L' column. For the original presentation of the data, see VPR_ashjian_orig. For a version of the data with only non-zero data, see VPR_ashjian_nonzero. In the 'nonzero' dataset, values of 0 in the abund_L column (taxon abundance) have been removed.

Methodology
The following information was extracted from C.J. Ashjian et al., Deep- Sea Research II 48(2001) 245-282 . An in-depth discussion of the data and sampling methods can be found there.

The Video Plankton Recorder was towed at 2 m/s, collecting data from the surface to the bottom (towyo). The VPR was equipped with 2-4 cameras, temperature and conductivity probes, fluorometer and transmissometer. Environmental data was collected at 0.25 Hz (CI9407) or 0.5 Hz (EN259, EN262). Video images were recorded at 60 fields per second (fps).

Video tapes were analyzed for plankton abundances using a semi-automated method discussed in Davis, C.S. et al., Deep-Sea Research II 43 (1996) 1946-1970. In-focus images were extracted from the video tapes and identified by hand to particle type, taxon, or species. Plankton and particle observations were merged with environmental and navigational data by binning the observations for each category into the time intervals at which the environmental data were collected (again see above Davis citation). Concentrations were calculated utilizing the total volume (liters) imaged during that period. For less-abundant categories, usually only a single organism was observed during each time interval so that the resulting concentrations are close to presence or absence data rather than covering a range of values.

GLP-1 Increases Pre-ingestive Satiation via Hypothalamic Circuits in Mice and Humans ## Description of data Figure 1, Figure S1 Path: Source Data and Codes\Figure 1, Figure S1 RAW_Control.xlsx : Raw data for Control test. RAW_GLP1.xlsx : Raw data for GLP1 test. Group: The group each person was designated, either A or B. Group number: The number each person was designated inside each group. Participant number: Number designated for those who participated in the test. P1_1 to P4_29: Each participant’s answers to Questionnaires 1-29. Questionnaires were given to participants in Korean language, and may be provided upon request. P1_1 to P1_29: Baseline, P2_1 to P2_29: Pre-oral, P3_1 to P3_29: Oral, P4_1 to P4_29: Ingestion. N/A refers to not available data including the name and email of each participant, as well as missing data in the questionnaires performed. Missing data were omitted for analysis. RAW_ingestion.xlsx Sheet1: Total ingestion amount of food by each participant in control and GLP-1 Sheet Baseline Char&Withdrawal: Group, Group number, Participant number Control_ingestion, GLP-1_ingestion: Total ingestion amount of food by each participant in control and GLP-1 Ingestion change: GLP-1_ingestion - Control_ingestion. Participants over +30g were excluded for analysis, colored in red. Initial_Weight, GLP1_Weight : Body weight of each participant that was measured initially, and post GLP-1 injection % Weight change: (GLP1_Weight- Initial_Weight)/ Initial_Weight *100. Participants over +5% were excluded for analysis, colored in red. Compliance: Non-compliant 0, Compliant 1. Participants who were non-compliant were excluded for analysis, colored in red. Sex: Male 1, Female 2 Age, Baseline_BMI : Baseline characteristics of participants Excluded from analysis: Colored in red. Figure 2 Figure 2F-N Path: Source Data and Codes\Figure 2\Fig 2F-N NF#: # is number of individual mouse 1. Use only Column 8 & 12. Other columns are not used for analysis * Laser on: Timepoint of start and end of the laser on period or the laser off period Figure 2O-R Path: Source Data and Codes\Figure 2\Fig 2O-R NF#: # is number of individual mouse 1. Column 1: Time of laser on 2. Column 2: Length of laser 3. Column 3: Time of ingestion bout start 4. Column 4: Length of ingestion bout * Cells that contain NaN were removed during analysis (Matlab, rmmissing). #laser_shuffle: # is number of individual mouse 1. Column 1: Trial sequence, 2. Column 2: 1 = real laser, 0 = sham laser #JKEN: # is number of individual mouse 1. Column 2: 1 is laser on, 0 is laser off 2. Column 3: Systematic pulse sending signal to start laser pulse delivery, 1 is system on, 0 is system off Figure 2S-U Path: Source Data and Codes\Figure 2\Fig 2S-U Labels are identical to Figure 2O-R Figure 3 Figure 3D Path: Source Data and Codes\Figure 3\Fig3A-H\Fig.3D.m dod1, dod2 : Non-food-directed locomotion start, Food-directed seeking start md1, md2 : Ingestion Start Data for Pre-Conditioning (Day 1) Path: Source Data and Codes\Figure 3\Fig3A-H\Day0\#ready_csv file 1. #: the individual mouse id Source Data and Codes\Figure 3\Fig3A-H\Day0\#Observer file 1. #: the individual mouse id 2. DO = Food Accessibility / SS = Non-food-directed locomotion start / ES = Ingestion Start / EE = Ingestion End Data for Post-Conditioning (Day 2) Path: Source Data and Codes\Figure 3\Fig3A-H\Day1\#ready_csv file 1. #: the individual mouse id Source Data and Codes\Figure 3\Fig3A-H\Day1\#Observer file 1. #: the individual mouse id 2. DO = Food Accessibility / SS = Food-directed seeking start / ES = Ingestion Start / EE = Ingestion End Figure 3I-L: Data for T-maze test Behavior files 1. # means the individual mouse id 2. W = Neutral Side, R = Red Side, B = Blue Side, E = Condition when mice ate food Ex: EWB: White side to Blue side decision moment, mice ate food after decision, WR: White side to Red side decision moment Ready# files 1. # means the individual mouse id 2. 1st column, time, 2nd column 405nm, 3rd column 465nm 3. T-maze_learn.xlsx, file for Figure 3L Pre-conditioning Path: Source Data and Codes\Figure 3\Fig3I-L\Habituation Post-conditioning Path: Source Data and Codes\Figure 3\Fig3I-L\Test Post-extinction Path: Source Data and Codes\Figure 3\Fig3I-L\Extinction Figure 3M-T Path: \Source Data and Codes\Figure 3\Fig3M-T\Behavior ChoiceProbabilityREV.mat: file that id for discriminated pre-ingestion neurons and ingestion neurons 1. Use only Column 8 & 12. Other columns are not used for analysis * Column 8: Event Time from recording start * Column 12: Behavior Label: i. closeopen = door close to put mice in shelter, food accessibility ii. search = seeking start to food zone in iii. eat = ingestion start to ingestion end iv. fz = food zone in to food zone out v. back = food zone out to before hitass vi. hitass = pushing mouse into shelter Path: ...

Young and older adult vowel categorization responses

https://doi.org/10.5061/dryad.brv15dvh0

On each trial, participants heard a stimulus and clicked a box on the computer screen to indicate whether they heard "SET" or "SAT." Responses of "SET" are coded as 0 and responses of "SAT" are coded as 1. The continuum steps, from 1-7, for duration and spectral quality cues of the stimulus on each trial are named "DurationStep" and "SpectralStep," respectively. Group (young or older adult) and listening condition (quiet or noise) information are provided for each row of the dataset.

UPDATED on October 15 2020 After some mistakes in some of the data were found, we updated this data set. The changes to the data are detailed on Zenodo (http://doi.org/10.5281/zenodo.4061807), and an Erratum has been submitted. This data set under CC-BY license contains time series of total abundance and/or biomass of assemblages of insect, arachnid and Entognatha assemblages (grouped at the family level or higher taxonomic resolution), monitored by standardized means for ten or more years. The data were derived from 165 data sources, representing a total of 1668 sites from 41 countries. The time series for abundance and biomass represent the aggregated number of all individuals of all taxa monitored at each site. The data set consists of four linked tables, representing information on the study level, the plot level, about sampling, and the measured assemblage sizes. all references to the original data sources can be found in the pdf with references, and a Google Earth file (kml) file presents the locations (including metadata) of all datasets. When using (parts of) this data set, please respect the original open access licenses. This data set underlies all analyses performed in the paper 'Meta-analysis reveals declines in terrestrial, but increases in freshwater insect abundances', a meta-analysis of changes in insect assemblage sizes, and is accompanied by a data paper entitled 'InsectChange – a global database of temporal changes in insect and arachnid assemblages'. Consulting the data paper before use is recommended. Tables that can be used to calculate trends of specific taxa and for species richness will be added as they become available. The data set consists of four tables that are linked by the columns 'DataSource_ID'. and 'Plot_ID', and a table with references to original research. In the table 'DataSources', descriptive data is provided at the dataset level: Links are provided to online repositories where the original data can be found, it describes whether the dataset provides data on biomass, abundance or both, the invertebrate group under study, the realm, and describes the location of sampling at different geographic scales (continent to state). This table also contains a reference column. The full reference to the original data is found in the file 'References_to_original_data_sources.pdf'. In the table 'PlotData' more details on each site within each dataset are provided: there is data on the exact location of each plot, whether the plots were experimentally manipulated, and if there was any spatial grouping of sites (column 'Location'). Additionally, this table contains all explanatory variables used for analysis, e.g. climate change variables, land-use variables, protection status. The table 'SampleData' describes the exact source of the data (table X, figure X, etc), the extraction methods, as well as the sampling methods (derived from the original publications). This includes the sampling method, sampling area, sample size, and how the aggregation of samples was done, if reported. Also, any calculations we did on the original data (e.g. reverse log transformations) are detailed here, but more details are provided in the data paper. This table links to the table 'DataSources' by the column 'DataSource_ID'. Note that each datasource may contain multiple entries in the 'SampleData' table if the data were presented in different figures or tables, or if there was any other necessity to split information on sampling details. The table 'InsectAbundanceBiomassData' provides the insect abundance or biomass numbers as analysed in the paper. It contains columns matching to the tables 'DataSources' and 'PlotData', as well as year of sampling, a descriptor of the period within the year of sampling (this was used as a random effect), the unit in which the number is reported (abundance or biomass), and the estimated abundance or biomass. In the column for Number, missing data are included (NA). The years with missing data were added because this was essential for the analysis performed, and retained here because they are easier to remove than to add. Linking the table 'InsectAbundanceBiomassData.csv' with 'PlotData.csv' by column 'Plot_ID', and with 'DataSources.csv' by column 'DataSource_ID' will provide the full dataframe used for all analyses. Detailed explanations of all column headers and terms are available in the ReadMe file, and more details will be available in the forthcoming data paper. WARNING: Because of the disparate sampling methods and various spatial and temporal scales used to collect the original data, this dataset should never be used to test for differences in insect abundance/biomass among locations (i.e. differences in intercept). The data can only be used to study temporal trends, by testing for differences in slopes. The data are standardized within plots to allow the temporal comparison, but not necessarily among plots (even within one dataset).