Lepidoptera are used as a model for the study of insect olfactory proteins. Among them, odorant degrading enzymes (ODEs), that degrade odorant molecules to maintain the sensitivity of antennae, have received less attention. In particular, antennal esterases  are crucial for intraspecific communication in Lepidoptera. Currently, transcriptomic and genomic studies have provided AEs in several species. However, efforts in gene annotation, classification, and functional assignment are still lacking. Therefore, we propose to combine evidence at evolutionary, structural, and functional level to update ODEs as well as key information into an easier classification, particularly of AEs. Finally, the kinetic parameters for putative inhibition of ODEs are discussed in terms of its role in future integrated pest management (IPM) strategies. In insects, the detection and processing of chemical cues through olfaction is crucial for successful mating, avoidance of harmful compounds, and location of either oviposition sites or food sources . For insThese volatile chemicals are called semiochemicals and mediate interactions between organisms of the same species  and different species . These chemicals are recognized through a systematic cascade of events that occur in chemosensory organs named sensilla, which can be found in maxillary palps, legs, and mainly covering the antennae . ChemoreOne question that arises is the final destination of these chemicals after receptor activation: if these compounds are not degraded, then they could accumulate in the peripheral space interfering with the olfactory system in insects . Indeed,Antheraea polyphemus -6,11-hexadecadienyl acetate  has an estimated half-life of 15 ms suggesting that ApolPDE is a pheromonal deactivator  from the sensillar lumen of the Giant silk moth, lyphemus . Interesctivator . Ishida echanism . Accordihttps://www.pherobase.com/), the main focus of this review is the structure and evolutionary traits of antennal esterases (AEs) in Lepidoptera, which are responsible for the degradation of acetate ester-type pheromone components.Despite the increasing amount of reported ODEs, no evolutionary analyses have been performed on these proteins among Lepidoptera. Furthermore, the structural features of the enzymes that could explain their selectivity have not yet been studied. Considering the diversity of acetate esters reported as sex pheromone compounds  to attach a function through further functional studies. We propose specific guidelines that might help to clarify whether an ODE can be classified into a pheromone degrading enzyme (PDE) or not. The last step includes directed studies for this type of enzymes due to their participation in the degradation of pheromones, which in turn have a role in the behavior of organisms of the same species. In this way, new alternative and less costly pest management strategies could be implemented.The olfactory system of insects has evolved to such an extent that it can process hundreds of compounds with different chemical structures from the environment to produce a change in behavior. Particularly, Lepidoptera emerged from a basal linage called non-Ditrysia to a new linage called Ditrysia since the Mesozoic era (over 100 million years); the species-specific pheromone components have similarly evolved. The sex pheromone that is generally emitted by females is crucial to attract a conspecific partner and achieve reproductive success. It is therefore not surprising that there are considerable structural differences in the blends of sex pheromones . We adviEriocrania semipurpurella, is an example of non-Ditrysia moths and uses -6-nonen-2-ol and -6-nonen-2-ol as sex pheromone -10,12-hexadecadien-1-ol (bombykol) – the sex pheromone of the silk moth Bombyx mori -3,8,11-tetradecatrienyl acetate and -3,8-tetradecadienyl acetate] have been tested in greenhouses to control this insect (Spodoptera frugiperda against the inhibitor (Z)-9-tetradecenyl TFMK (Z9-14:TFMK) through electroantennography assay. This inhibitor is analogous to the main pheromone component (Z)-9-tetradecenyl acetate (Z9-14:Ac) of S. frugiperda, and it can significantly reduce the antennal response from 2.51 ± 0.37 mV to 1.10 ± 0.24 mV. On the other hand, Spodoptera littoralis and Sesamia nonagrioides males in wind tunnel assays using TFMKs.Most mating disruption techniques used today for controlling and monitoring moth pests are based on these chemicals . For inss insect . Some stThis background suggests that ODEs have an important role in the degradation of these pheromones. Consequently, one question that arises is if ODEs have evolved in order to degrade a wide range of these chemical cues or are limited to degrade a particular sex pheromone group for olfactory purposes. In this sense, transcriptomic analyses have provided the profile of ODEs that several moths could use for olfaction purposes. Acetates are the main sex pheromone components in Lepidoptera, and dozens of esterases in antennae have been reported as summarized in CXE) genes have been identified in the rice leaffolder, Cnaphalocrocis medinalis, through its antennal transcriptome  and quantitative (RT-qPCR) experiments. For example, 18 carboxylesterase  to respond to an environmental alteration with a change in its morphology, physiology, behavior, or life history . These eGenetic drift and natural selection can also contribute to the divergence of new enzymatic functions . In suppS. littoralis  as enzymes with neuro/developmental functions; (II) intracellular enzymes with dietary detoxification functions, and (III) secreted enzymes that use hormones or pheromones as substrates . The laterfamily . Their ferfamily . First, erfamily . Thus, terfamily . The firlyphemus ; more re. litura , the ligtvittana , and the ipsilon  among ot ipsilon .CXE genes have been localized in different tissues of several moth species according to RT-PCR and RT-qPCR experiments. On the other hand, native polyacrylamide gel electrophoresis (Native-PAGE) has been used to study the enzymatic activity of some esterases  were significantly expressed in different tissues, such as pheromone glands, thoraxes, abdomens, and antennae. However, SinfCXE10 was expressed specifically in the antennae; therefore, the authors propose that this gene could be involved in pheromone degradation particularly in (Z)-11-hexadecenyl acetate. Although CXEs are present in olfactory organs , it seems that their pheromone-degrading function is more related to sex-biased expression. S. littoralis (SlCXE7) was restricted to antennae rather than other tissues through in situ hybridization (ISH) technique. This was more significantly expressed in males than in females according to RT-qPCR. Depending on its sensillar location such as pheromone-sensitive sensilla , SlCXE7 could play a role in pheromone signal termination as well as degrading odorant background noise.To date, sterases . The expS. exigua (SexiCXE4), highly expressed in antennae and proboscises but had no sex-biased expression. SexiCXE4 presented a higher preference (70-fold) to plant volatiles [(Z)-3-hexenyl acetate and hexyl acetate] than pheromone compounds -9,12-tetradecenyl acetate and (Z)-9-tetradecenyl acetate via in vitro functional assays. This suggests a role as a general ODE (GODE). Likewise, two CXEs genes (EoblCXE7 and EoblCXE13) in the tea geometrid moth Ectropis obliqua were localized in pheromone-related sensilla (Str I) as well as sensilla basiconica (Sba) and gustatory sensilla styloconica (Sst) using fluorescence ISH (FISH) technique -3,6,9-octadecatriene and 6,7-epoxy--3,9-octadecadiene] as sex pheromone  inhibitors such as organophosphorus compounds used as insecticides or toxic carbamates applied as pesticides . For insOn the other hand, three and two conserved disulfide bridges play an important role giving structural stability in OBPs and CSPs, respectively . As a thA. polyphemus CXE as a PDE (ApolPDE), structural investigations revealed that these enzymes share common features among moths. For instance, they have a conserved pentapeptide “G-X-S-X-G” (X represent any amino acid) motif that is characteristic of esterases , SinfCXE13 from S. inferens, and SlitCXE13 from S. litura. No crystallized CXEs structures have been published yet for Lepidoptera; however, three crystallized structures in Diptera with the access code 4FNM : Only EsemCXE6 was in the same clade as ApolPDE1, SinfCXE13, and SlitCXE13. Here, is possible to visualize the α-helices , β-sheets (yellow arrows), and loops (green smooth ropes) in all modeled structures -11-hexadecenyl TFMK was efficient in displacing the sex pheromone by binding to the recombinant PBP from Mamestra brassicae (MbraPBP1) through functional assays. Likewise, TFMKs can interact with ODEs inside the sensilla where it may form stable hydrates acting as transition-state analogues of pheromones. In brief, the inhibitory activity of TFMKs is due to a stable hemiacetal of tetrahedral geometry that is formed between the conserve serine residues of the antennal esterases with the highly electrophilic carbonyl -11-hexadecenyl TFMK (Z11-16:TFMK) and 3-octylthio-1,1,1-trifluoropropan-2-one (OTFP) in mice, and the results showed low toxicity (LD50 1 g/kg after the 6th day of the assay). Overall, an accurate identification and characterization of these enzymes could provide the basis for the development of putative inhibitory compounds that will be an alternative to IPM.The olfactory system is critical for reproductive success in many insect species. Some of these are of great economic importance in agriculture and forestry fields such as moths from the Tortricidae and Pyralidae family. Fortunately, the semiochemical compounds involved in host recognition, mating, or defense behaviors are being used to manage insect pests through environmentally friendly approaches, e.g., mating disruption and mass trapping. However, globalization has facilitated the dissemination of insect species throughout the world. How do insects adapt to these new environments? More specifically, how their olfactory system responds to these new conditions?To answer these questions, it is important to understand the molecular basis and mechanisms involved in insect olfaction where proteins are the main players. Comprehensive studies have been performed for ORs, OBPs, and CSPs; ODEs have garnered less attention. These enzymes are novel targets for the use of species-specific chemicals in IPM. Therefore, we propose that an improved approach to classify a certain ODE into PDE would be crucial based on a highly specific sex pheromone communication. The evidence presented in this work suggests that a limited number of ODEs would actually be antennal- and pheromone-specific. Therefore, gene expression through RT-qPCR should consider comparing between sexes in antennae and then in the rest of tissues.S. inferens and S. littura) found that a monophyletic clade (proposed as PDE clade) is present in moths. With this in mind, further ODEs that fall within this clade could be considered for characterization as putative PDEs. Finally, heterologous expression of the selected ODE(s) with purification and kinetic assays would be crucial for such task.Phylogenetic analyses could help to filter ODEs close to already characterized PDEs . Particularly, some studies  were shown to visualize their conformation and the residues of the active site, which resulted in conserved function. With the above issue addressed, the identification and characterization of these enzymes could provide the basis for the development of putative inhibitory compounds  that will be a complement to IMP strategies.E. semipurpurella.All the information performed in this work is of a public nature. We created the figures and the data generated in relation to the transcripts identified from the antennal transcriptome of RG wrote all sections. JM developed the tables and figures. HV and AM conceived the idea for the review article. AQ supervised the project. All authors contributed to the article and approved the submitted version.E. semipurpurella.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. All the information performed in this work is of a public nature. We created the figures and the data generated in relation to the transcripts identified from the antennal transcriptome of


Inherited cardiomyopathies are among the major causes of heart failure and associated with significant mortality and morbidity. Currently, over 70 genes have been linked to the etiology of various forms of cardiomyopathy, some of which are X-linked. Due to the lack of appropriate cell and animal models, it has been difficult to model these X-linked cardiomyopathies. With the advancement of induced pluripotent stem cell (iPSC) technology, the ability to generate iPSC lines from patients with X-linked cardiomyopathy has facilitated in vitro modelling and drug testing for the condition. Nonetheless, due to the mosaicism of the X-chromosome inactivation, disease phenotypes of X-linked cardiomyopathy in heterozygous females are also usually more heterogeneous, with a broad spectrum of presentation. Recent advancements in iPSC procedures have enabled the isolation of cells with different lyonisation to generate isogenic disease and control cell lines. In this review, we will summarise the current strategies and examples of using an iPSC-based model to study different types of X-linked cardiomyopathy. The potential application of isogenic iPSC lines derived from a female patient with heterozygous Danon disease and drug screening will be demonstrated by our preliminary data. The limitations of an iPSC-derived cardiomyocyte-based platform will also be addressed. Cardiomyopathy is a highly heterogeneous myocardial disease that may contribute to the development of heart failure. In general, cardiomyopathies can be inherited or acquired. To date, more than 70 genes have been identified that are associated with various forms of cardiomyopathies, some of which  are X-liMost stem cells are of embryonic origin or derived from adult tissues with the ability to proliferate . These stem cells differentiate into a myriad of cell types but do not undergo a de-differentiation process . InducedLAMP2 mutation [Since iPSCs are human in origin and pluripotent, they have enabled the development of patient-specific cell models to recapitulate the pathophysiology of human diseases . This famutation ; this alAn iPSC-based model has multiple advantages compared to the traditional cell lines. The traditional primary cell lines were difficult to establish from patients, especially for cell types that were difficult to access clinically. For instance, there are limited scenarios where human adult cardiomyocytes can be isolated, so most cultured cardiomyocytes have been isolated from neonatal cardiomyocytes . NonetheIn the early years, a disease iPSC line would be paired with a control cell line derived from an individual without the disease. Nonetheless, with the advancement of gene-editing techniques, iPSC models can now be genetically manipulated to study the disease phenotype . The devThe unique features of iPSCs provide many potential opportunities in disease modelling. An iPSC-based system allows for the development of a 3D platform to form self-organised organoids with intensive cell–cell interactions. These iPSC-derived organoids form primary tissue-like clumps that contain multiple cell types similar to their natural human counterpart . This haThe fundamental principle of iPSC production is to express the reprogramming Yamanaka factors  in cells in order to drive the transcriptome into the pluripotent stage. Theoretically, iPSCs can be reprogrammed from any somatic cells with a complete genome. Although fibroblasts derived from skin biopsy have been widely used to generate iPSCs, collection is invasive and painful; thus, various alternatives are now available, including peripheral blood mononuclear cells (PBMC) from a blood sample and renal epithelial cells from urine ,46,47,48The methods of delivering the factors into the somatic cells dictate the outcomes of the iPSCs. The first method involves the use of a retrovirus to transduce fibroblasts, but viral integration has been a huge issue . IntegraThe iPSC was traditionally cultured on serum-dependent feeder layers comprised of either mouse or human fibroblasts . NonetheLAMP2 allele, and used it to model Danon disease [Refering to X-linked disorders, for male patients with only one X-chromosome in the genome, the iPSC line can usually be routinely generated and the cell lines produced will contain the mutation. Nevertheless, if the patient is a heterozygous female, due to the mosaic patten of X-chromosome inactivation, the generation of iPSCs with a specific active allele is not guaranteed. Based on our observation that the X-chromosome inactivation status remains unchanged during reprogramming, one should consider separating cells (skin fibroblasts or PBMCs) with a different X-chromosome inactivation status prior to transduction of the reprogramming factors. This can be achieved by diluting the cells to single cells and then checking the expression of the wildtype or mutant allele of interest using immunostaining or mRNA-based methods. . Using t disease .Differentiation protocols of established iPSC lines to cardiomyocytes have progressed from early differentiation of only 8–22% efficiency to current methods that achieve a high yield of cardiac troponin-T positive cells ,53. EarlTM PSC Cardiomyocyte Differentiation Kit from Thermo Fisher Scientific with a differentiation efficiency of 30–90%, depending on the genetic background of the iPSCs. For wildtype iPSCs, we consistently yield over 80% of cTNT positive cells  analysis is another means of investigating the electrophysiology of cardiomyocytes. It enables the investigation of the mutations that induce conduction system defects . By cultLAMP2, can indirectly alter intracellular calcium handling [During excitation–contraction coupling, the release and reuptake of sarcomeric calcium relays the electric signalling and the consequent contraction of cardiomyocytes. Failure in the regulation or handling of intracellular calcium may significantly compromise cardiac function or lead to cardiomyocyte death . Again, handling ; thus, thandling .FLNA). Contractile function can be evaluated by different methods. The IonOptix cardiomyocyte contraction system allows for a high-speed recording (1000 hz) of the shortening and re-lengthening of a single cardiomyocyte  is an X-linked condition affecting 19.8 males in every 100,000 and resulting in progressive muscle wasting . It is cmutation . The DMDe domain . The defe domain ,135.Most patients with DMD will ultimately develop dilated cardiomyopathy (DCM), one of the main causes of DMD-associated mortality . Indeed,Exon 50, Δ Exons 45–52, c.5899C>T, and more [Several iPSC-based models have been established to recapitulate the pathophysiology of DMD-associated DCM, including calcium handling changes, impairment of myofibrils, and membrane fragility. After repeated stretch–contraction cycles, it was observed that the cell morphology was altered due to rupture of the sarcolemma. Compared with patient-derived tissues, iPSC models permit investigation of the early-stage pathogenesis of DMD and the different gene mutations associated with DMD-associated DCM, including Δ and more ,120,140.2+ concentration, especially after mechanical stretching [The in vivo model can recapitulate the disease phenotypes and provide a more synoptic picture of the pathophysiology. The in vitro cellular models show the molecular changes observed in the tissues of the DMD patient, including the DMD signature dystrophin deficiency and the downstream changes, such as increased oxidative stress, impaired autophagy, and immunoproteasome dysregulation ,141,142.retching . Utilisiretching .ICa,L density. This explains the arrhythmic pattern observed in DMD patients [The molecular pathogenic profile can also be linked to the cardiac cardiomyopathic and electrophysiologic phenotype observed in affected patients. Patients with DMD-associated cardiomyopathy frequently suffer supraventricular and ventricular arrhythmias . The dispatients .exons 49–50, a proteasome inhibitor was applied to rescue the immunoproteasome dysregulation observed in iPSC-derived cardiomyocytes [exons 45–52, a membrane sealant Polaxamer 188 was able to restore the normal phenotype upon understanding how the damaged membrane could lead to dysregulated mechanosensitive channels [DMD phenotype. Human artificial chromosomes (HACs) have been shown to restore the dystrophin expression level and the normal phenotype [After identifying the pathophysiological changes, novel drug targets can be identified to alleviate the disease phenotype. In a study with Δ myocytes . In anotchannels ,144. Furhenotype .GLA site and results in a defect or deficiency in the production of the alpha-galactosidase A enzyme (a-GAL A). a-GAL A is a lysosomal enzyme responsible for the removal of terminal alpha-galactosyl groups in glycolipids, particularly globotriaosylceramide (Gb3). Deficiency leads to the consequent accumulation of glycolipids in various tissues [Fabry disease is an X-linked multi-system lysosomal storage disorder with an incidence of around 0.04 to 0.25 per 10,000 males, and a higher incidence for the later-onset variant ,146,147. tissues . Progres tissues . Some pa tissues . Other c tissues . There i tissues .GLA-IVS4–919-G>A mutation with a significantly reduced a-GAL A protein level in the resultant cardiomyocytes  [MECP2 gene encodes for methyl-CpG binding protein 2, which is abundantly expressed in the brain as an epigenetic modulator to control gene expression through DNA methylation [CDKL5), Forkhead box protein G1 (FOXG1), myocyte-specific enhancer factor 2C (MEF2C), and transcription factor 4 (TCF4) genes [Rett syndrome is a monogenic X-linked dominant neurological disorder affecting 0.44 to 0.72 per 10,000 females aged 2 to 18 years, with similar rates across different races and ethnicities ,161,162.e (Xq28) . The MEChylation . A minor4) genes .Patients with Rett syndrome are at an increased risk of life-threatening cardiac arrhythmia with prolongation of QT corrected interval and alternations of ventricular repolarisation with the possibility of sudden unexpected cardiac death . Other cCDKL5 mutation exhibited a decrease in axon outgrowth, dendritic morphogenesis, and synapse formation in diseased neurons [Patient fibroblast-derived iPSCs have been used for disease modelling and drug testing in Rett syndrome. Rett-iPSCs from female patients retained the ability to undergo X-inactivation and differentiation into neurons. Compared with their wildtype controls, these diseased iPSC-generated neurons were smaller in size, and had fewer glutamatergic synapses, reduced spine density, smaller soma size, altered calcium signalling, and electrophysiological defects ,162. Dis neurons .Medhya Rasayana herbs have also been shown to reverse neurological disorders when tested in iPSC models [A patient-derived iPSC model can also be used to screen the effects of candidate drugs by observation of the reversal of Rett disease phenotypes. IGF-1 and gentamicin administration have been found to be effective in the partial reversal of Rett-like symptoms in a cell culture, similar to results in a previously described mouse model . AdminisC models .LAMP2 gene that encodes lysosomal-associated membrane protein 2 (LAMP2). Deficiency in LAMP2 protein production leads to the failed fusion of autophagosomes and lysosomes, with the consequent accumulation of glycogen in muscle cells [Danon disease is an X-linked dominant lethal multi-systemic condition that affects 1–4% of patients with hypertrophic cardiomyopathy . It is cle cells . Male Dale cells ,171. In le cells .To this end, several Danon disease patient-specific iPSC-based models have been established. Using this model, we have previously demonstrated that the accumulation of autophagy materials inside the affected cardiomyocytes is mainly due to the failure to clear the autophagic cargo, rather than over-activation of the autophagy signalling pathway .LAMP2 mutant [In addition, increased mitochondrial oxidative stress, impaired mitophagy flux, and increased apoptosis have also been observed in iPSC-derived cardiomyocytes carrying the 2 mutant . This re2 mutant . Treatme2 mutant . The Dan2 mutant ,176.LAMP2 mutation (c.247C>T) using the CRISPR-cas9system [2+-ATPase 2, and ryanodine receptor 2 (RYR2) [Danon disease iPSC-derived cardiomyocytes also exhibit diseased cardiac phenotypes, demonstrating an impaired contractile function similar to hypertrophic cardiomyopathy. It was shown that the contractility was decreased , and thes9system . The LAM2 (RYR2) . The Dan2 (RYR2) .LAMP2 gene will rescue the diseased phenotype in the heterozygous female Danon disease patient. To prove this concept, we have created isogenic iPSCs from female Danon disease patients. By reactivating the wildtype LAMP2 allele by demethylation, we were able to restore LAMP2 protein production and improve the autophagic functions in the affected cells [As the disease is due to phenotypic demonstration of the diseased allele, activating the previously silenced normal X-chromosome with the wildtype ed cells .LAMP2 allele on the silenced X-chromosome was treated with a drug that could reactivate the silenced X-chromosome, cells started to produce a functional LAMP2 protein that could be detected by a simple immunoassay (As mentioned before, isogenic iPSC-pairs with distinctive phenotypes can now be generated from a heterozygous female carrying one X-linked wildtype allele and one X-linked mutant allele. Making use of such a system, we have established an isogenic-iPSC-based platform to screen chemicals that can activate the silenced X-chromosome. In brief, as demonstrated previously , when thunoassay . Using tCardiomyocytes derived from patient-specific iPSCs offer an exclusive and convenient means to model disease phenotypes that are associated with X-linked mutations. Nonetheless, the functional immaturity of the iPSC-derived cardiomyocytes remains an important issue to be addressed. As demonstrated earlier by our group, compared with human ESC-derived cardiomyocytes, human iPSC-derived cardiomyocytes usually exhibit a reduced calcium handling ability and sarcoplasmic reticulum function . This is


Background: There has been limited evaluation of medication adherence, healthcare resource utilization (HCRU), and healthcare costs over time in patients with osteoarthritis (OA), and stratification by pain severity level has not been reported. Assessing such longitudinal changes may be useful to patients and healthcare providers for tracking disease progression, informing treatment options, and employing strategies to optimize patient outcomes. Objectives: To characterize treatment patterns, HCRU, and costs over time in patients with moderate to severe (MTS) OA pain in the United States. Methods: We conducted a retrospective claims analysis, using IBM® MarketScan® databases, from 2013-2018. Eligible patients were aged ≥45 years with ≥12 months pre-index (baseline) and ≥24 months (follow-up) of continuous enrollment; index date was defined as a physician diagnosis of hip or knee OA. An algorithm was employed to identify MTS OA pain patients, who were propensity score matched with patients having non-MTS OA pain. Data were summarized using descriptive statistics and univariate analyses. Results: After propensity score matching, the overall OA pain cohorts consisted of 186 374 patients each: 61% were female, mean age was 63 years, and two-thirds (65.6%) were of working age (45-65 years). Sleep-related conditions, anxiety, and depression were significantly higher in the MTS OA pain cohort vs non-MTS (P<0.001). At baseline and 12- and 24-month follow-ups, receipt of prescription pain medications, HCRU, and direct medical costs were significantly higher in the MTS OA pain cohort . Medication adherence was significantly higher in the MTS OA pain cohort for all medication classes except analgesics/antipyretics, which were significantly lower vs the non-MTS OA pain cohort . Conclusions: The burden of MTS OA pain is substantial, with patterns that show increasing medication use, HCRU, and costs vs non-MTS OA pain patients over time. Understanding the heterogeneity within the OA population may allow us to further appreciate the true burden of illness for patients in pain. Osteoarthritis (OA) is a chronic joint disease involving the cartilage and surrounding tissues that most commonly affects the hands, hips, and knees.Numerous guidelines have been published regarding the nonpharmacological and pharmacological management of OA. The 2019 American College of Rheumatology guidelines for management of OA emphasize optimizing benefit-risk outcomes for each patient, and recommend a multimodal approach based on efficacy, safety, tolerability, patient preference, and appropriateness of pharmacologic treatments.Regardless of published treatment guidelines and available pharmacologic options for the symptomatic treatment of OA, the current standard of care may be inadequate since patients often continue to experience unsatisfactory pain levels despite treatment.The economic impact of OA has been shown to include both direct medical costs and indirect costs associated with patient treatment expenses as well as lost productivity.Longitudinal studies of OA-related treatment patterns have been   reported.The aim of this study was to examine clinical comorbidities and changes in treatment patterns, medication adherence, HCRU, and healthcare costs over 12- and 24-month follow-up periods in patients with moderate to severe (MTS) OA pain, compared with a matched cohort of patients with non-MTS OA pain using a large, geographically dispersed patient population.Data for this retrospective cohort study were from the IBM® MarketScan® Commercial Claims and Encounters and Medicare Supplemental and Coordination of Benefits claims databasesSupplemental Table 1). Patients <45 years were excluded due to the smaller number of patients in this age cohort and the different presentation of OA in these younger population . Patients with a diagnosis of neoplasm-related pain (ICD-9 338.3 or ICD-10 G89.3) were excluded. Patients with OA were required to have at least 3 years of continuous pharmaceutical and medical benefit enrollment, including 12 months pre-index (baseline) and 24 months post-index (follow-up).This analysis included data from patients aged ≥45 years who had a diagnosis of hip OA, knee OA, or unspecified OA, plus a diagnosis of pain in the hip or knee within 3 months after the first OA diagnosis within the study period (index date), per International Statistical Classification of Diseases and Related Health Problems (ICD) diagnostic codes  within the 12-month post-index period; (3) prescriptions for ≥2 different topical or oral NSAIDs within 3 months post-index; (4) prescriptions for ≥2 different opioids within 3 months post-index; or (5) an emergency room (ER) visit for hip and/or knee OA within 12 months post-index, followed by a primary care physician visit within 14 days of the ER visit. This definition came from a case-selection algorithm that was based on a targeted review of the published literature, treatment guidelines, professional organizations, and expert opinion.Patients were classified as having MTS OA pain if they met at least one of the following criteria: (1) a visit to a rheumatology, orthopedic, or pain management specialist within 3 months post-index; (2) a surgical or nonsurgical invasive procedure relating to OA treatment   1:1 with the non-MTS OA pain cohort on baseline age; gender; health plan type; region; obesity; anxiety; depression; Charlson Comorbidity Index (CCI)Medication was defined at the class level using NDC codes from the claims, and medication adherence was defined as proportion of days covered (PDC) and was calculated as the number of days covered in the period by a prescription/drug class divided by the number of days in the period.t tests and chi-square tests for continuous and categorical variables, respectively. Comparisons from baseline in medication use at 12 and 24 months were conducted using chi-square tests to generate P values with patient-level data. Statistical significance was defined as P<0.05. All analyses were conducted using Statistical Analysis System® version 9.4 or higher .Data were summarized for PSM cohorts using descriptive statistics, including means with standard differences for continuous variables, and counts and proportions for categorical variables. Differences in demographic and clinical characteristics were evaluated using Supplemental Figure 1). Among these patients, 546 254 met the inclusion criteria, of whom 342 019 (62.6%) were identified as having MTS OA pain and 204 235 (37.3%) were identified having non-MTS OA pain. PSM resulted in 186 374 patients in each cohort and, as shown in From January 2013 to December 2018, there were 91 019 047 patients enrolled in the IBM® MarketScan® Commercial Claims and Encounters and MDCR claims databases (P<0.0001), and the distribution of scores, which was significantly different between the cohorts (P<0.0001), indicated a trend toward a greater comorbidity burden in the MTS OA pain cohort (P<0.0001). However, a significantly lower proportion of patients with MTS OA pain had cerebrovascular disease than patients with non-MTS OA pain . The MTS OA pain cohort had a significantly higher prevalence of prespecified comorbidities overall compared with the non-MTS OA pain cohort , including sleep-related conditions, obesity, depression, and anxiety .The mean CCI score was significantly higher in the MTS OA pain cohort compared with non-MTS OA pain . Furthermore, all evaluated medications  were prescribed to significantly higher proportions of patients with MTS OA pain vs non-MTS OA pain at baseline. Of these medications, the most prescribed in the MTS vs non-MTS OA pain cohorts were nontramadol opioids (41.2% vs 28.6%), NSAIDs/COX-2 inhibitors (39.5% vs 32.3%), antidepressants (28.3% vs 21.7%), benzodiazepines (23.3% vs 17.9%), and anticonvulsants (21.9% vs 16.8) , except for antidepressants . In contrast, the MTS OA pain cohort had significantly lower antidepressant use (21.2%) at 24 months compared with the non-MTS cohort (26.0%) (P<0.0001).Examination of changes from baseline to the 24-month follow-up revealed generally similar patterns for patients with MTS OA pain as that at 12 months, although not all differences between cohorts were statistically significant. Significantly higher changes in the MTS pain cohort were observed, in decreasing order, for IA corticosteroid/HA injections, tramadol, analgesics/antipyretics, anticonvulsants, nontramadol opioids, and anxiolytics/sedatives/hypnotics , except for analgesics/antipyretics, in which patients with MTS OA pain had significantly lower PDC compared with patients with non-MTS OA pain . The largest differences in PDC between the 2 cohorts were observed for non-tramadol opioids: 37% in the MTS cohort vs 7% in the non-MTS cohort at 12 months and 34% vs 7%, respectively, at 24 months (both P<0.001).Medication adherence, as measured by PDC, was highest at the 12- and 24-month follow-ups for antidepressants in both OA pain cohorts (78%-84%), followed by anticonvulsants (58%-68%) and anxiolytics/sedatives/hypnotics (33%-62%) . At bothP<0.0001; P<0.0001). Length of hospital stay was significantly longer at 12 and 24 months for the MTS cohort compared with the non-MTS cohort, as were hospital readmissions (both P<0.0001). Hospital readmissions increased in the MTS cohort from 1236 patients (0.7%) at baseline to 2509 (1.4%) and 4262 (2.3%) patients at the 12- and 24-month follow-ups, respectively. Increases were also observed for the non-MTS OA pain cohort, although to a lesser extent (P<0.0001). In patients with MTS OA pain, the mean (SD) of filled prescriptions increased from 28.7 (26.8) at baseline to 32.2 (28.2) at 12 months and to 63.9 (55.6) at 24 months. A similar pattern of increase over time was observed for patients with non-MTS OA pain but to a lesser extent than patients with MTS OA pain  visits for baseline and 12- and 24-month follow-up periods  (P<0.0001). The total costs in the MTS OA pain cohort were higher than non-MTS OA pain by 19% at baseline, 64% at 12 months, and 53% at 24 months.The patients with MTS OA pain had significantly higher mean total healthcare costs overall and for the individual cost components of outpatient, inpatient, and pharmacy categories compared with patients with non-MTS OA at baseline and the 12- and 24-month follow-ups  . Total mAcross all time periods and cohorts, total costs were primarily driven by outpatient costs. At baseline, outpatient costs accounted for the majority of costs (55%) for both OA pain cohorts, as shown in For both the 12- and 24-month follow-up periods, outpatient costs continued to be the main driver of total healthcare costs in both cohorts, although the proportion relative to total costs decreased over time in patients with MTS OA pain but increased in the non-MTS cohort. Furthermore, inpatient costs, relative to total costs, increased over time in patients with MTS OA pain, whereas pharmacy costs decreased . There wThis study contributes important longitudinal information on the clinical and economic burden of OA, by pain severity level, spanning 24 months of follow-up post-OA diagnosis. It relies on a unique algorithm that allows healthcare researchers to identify patients with more severe OA pain while utilizing administrative claims data to track resource utilization and costs. Mean age in the PSM cohorts was 63 years, with approximately two-thirds of patients of working age and enrolled in commercial plans. Patients with MTS OA pain in our study had a significantly greater comorbidity burden compared with patients with non-MTS pain, including sleep disturbance, insomnia, depression, and anxiety, not unlike what has been reported in other OA studies using cross-sectional survey data in patients with increasing pain severity or pain interference with activities (PIA).P<0.0001), which is approximately half the proportions in the current study, but may not be unexpected as their study was based on patient self-report rather than administrative claims. In their study, non-tramadol opioids and NSAIDs were the most prevalent classes prescribed among MTS OA pain respondents (18% each cohort). Nalamachu et alPrescription pain medication use for OA showed a similar pattern to comorbidities, with significantly increased use of OA medications in patients with MTS OA pain relative to patients with non-MTS OA pain. Although the high prescribing of OA pain medications at baseline may be related to treatment of conditions other than OA, it is also possible that the patient was being treated for OA in the absence of an OA diagnosis during the baseline period. In patients with MTS OA pain, baseline prescribing was highest for nontramadol opioids (41.2%) followed by NSAIDs/ COX-2 inhibitors (39.5%). The use of NSAIDs is in line with current treatment guidelines for OA. The high use of non- tramadol opioids contrasts with the conditional recommendation to start tramadol if an opioid is necessary, and only choose non-tramadol opioids when all other treatment options have failed.A unique aspect of this study is that it provides insights into the current real-world prescribing of pain medications for MTS OA by following patients for 2 years. Significantly greater pain medication use was observed for patients with MTS OA pain compared with non-MTS at 12 months for all pain medication types, except antidepressants. This trend was also observed at 24 months in which all medication types except NSAID/COX-2 inhibitors, benzodiazepines, and muscle relaxants were significantly greater in the MTS OA pain cohort. Although 2 studies reported on the change in OA pain medications over time, neither stratified patients by OA pain severity.Our study showed significantly greater adherence, measured by PDC, to prescription OA pain medications in patients with MTS OA pain vs non-MTS for all medication types. Although PDC at the 12- and 24-month follow-ups was highest for antidepressants, anticonvulsants, and anxiolytics/sedatives/hypnotics in both OA cohorts, a PDC ≥80%, defined as the threshold for being considered adherent, was only observed with antidepressants across both OA pain severity cohorts. Using PDC as a proxy for adherence, this is not surprising given antidepressants are also used for symptoms beyond OA pain where high adherence is often observed. Pain medications, on the other hand, are often used as needed, and it may be possible that the lower PDCs observed in this study represent numerically lower yet appropriate rates of adherence. PDC is a useful measure in that, regardless of as-needed or scheduled use, it informs the level of utilization of a particular medication or class of medications. No studies of medication adherence were identified in OA, although several studies reported suboptimal treatment adherence in patients with other rheumatic diseases.P<0.01). Lastly, Wei et alHCRU was significantly greater for patients with MTS OA pain compared with non-MTS for inpatient hospitalizations, readmissions, outpatient, ER, and rehabilitation and PT visits. Somewhat similar findings were observed by Schepman et al,The economic burden of OA, especially in patients with MTS OA pain, is substantial. Our findings showed significantly higher costs in the MTS OA pain cohort at baseline and at both 12- and 24-month follow-ups compared with the non-MTS cohort, with increasing costs for each of the cohorts over time. In 2 published studies, higher healthcare costs were significantly associated with increasing PIAIn the current study, 63% of the patients with OA were categorized as having MTS OA pain. This proportion is similar to the 65% reported by Schepman et alSeveral study limitations should be considered. Although providing a wealth of clinical and economic information, claims data are limited in that medication use cannot be attributed to a specific disease, so pain medications prescribed within 30 days of index may not have been for OA. Claims data also do not capture information on patients change of healthcare insurance or if they have multiple insurance plans at the same time. Some prescription pain medications commonly used to treat OA also treat a number of other illnesses including depression, anxiety, and epilepsy. Given that patients with OA, especially those with more advanced disease, have a high comorbidity burden, these medications could be prescribed for other underlying conditions. Despite the strengths of our approach , there is an overlap of certain variables included in the definition of the cohorts and the outcomes analyzed. However, as the goal was to describe overall utilization, this study used a narrow timeline to define the variables included in the definition of the cohorts the length in time for the outcomes. Claims data also do not capture information on nonpharmacologic therapies and over-the-counter medications used to treat OA pain, nor are indirect costs, such as those related to lost productivity at work, absenteeism, and caregiver burden, captured. Therefore, the total costs reported in our population are likely underestimated. Although the sample size in our study was robust, patients were required to be continuously enrolled for 3 years in a health plan. Such patients may differ from other plan members; hence, generalizability beyond this commercially insured population may be limited. These results are not representative of all patients with OA. This study included only commercial insured patients with private or Medicare plus supplemental insurance that were seeking medical care. Generalizability may have been impacted when we applied PSM, as very healthy and very severe patients would have been excluded from the final cohorts. Claims data, however, are a rich source of information and, when used alongside other methodologies, expand our understanding of the clinical and economic impact of patients with MTS OA pain.The clinical and economic burden observed in this MTS OA population suggests a high unmet need that may require development of novel therapies or strategies to treat OA pain and improved healthcare strategies to manage disease progression. Patients with OA do not represent a homogeneous population, and focusing on population subtypes such as pain severity will allow a more targeted and efficient approach to patient management. Further research is warranted to examine the factors contributing to the substantial burden in this more severe OA pain population.The study was sponsored by Pfizer and Eli Lilly and Company. The findings of this manuscript have been presented at The International Osteoporosis Foundation (IOF) and the European Society for Clinical and Economic Aspects of Osteoporosis and Osteoarthritis (ESCEO) 2020 Virtual World Congress (WCO-IOF-ESCEO); August 20-22, 2020, and at PAINWeek 2020 Virtual Meeting; September 11-13, 2020. The following authors are employees of Pfizer with stock and/or stock options: PS, ST, CB, DM, and BE. RLR is an employee and stockholder of Eli Lilly and Company. RNH received consulting fees associated with this study from Pfizer and Eli Lilly and Company. The authors report no conflicts of interest in this work.


The COVID-19 pandemic triggered a seismic shift in education to web-based learning. With nearly 20 million students enrolled in colleges across the United States, the long-simmering mental health crisis in college students was likely further exacerbated by the pandemic.This study leveraged mobile health  technology and sought to (1) characterize self-reported outcomes of physical, mental, and social health by COVID-19 status; (2) assess physical activity through consumer-grade wearable sensors (Fitbit); and (3) identify risk factors associated with COVID-19 positivity in a population of college students prior to release of the vaccine., while categorical measures were summarized as n (%) values. Formal comparisons were made on the basis of COVID-19 status. The multivariate model was determined by entering all statistically significant variables (P<.05) in univariable associations at once and then removing one variable at a time through backward selection until the optimal model was obtained.After completing a baseline assessment  of demographics, mental, and social health constructs through the Roadmap 2.0 app, participants were instructed to use the app freely, wear the Fitbit, and complete subsequent assessments at T1, T2, and T3, followed by a COVID-19 assessment of history and timing of COVID-19 testing and diagnosis (T4: ~14 days after T3). Continuous measures were described using mean (SD) valuesP=.52). Our analyses revealed significant associations of COVID-19 positivity with the use of marijuana and alcohol  and with lower belief in public health measures (P=.003). In addition, graduate students were less likely and those with ≥20 roommates were more likely to report a COVID-19 diagnosis (P=.009).During the fall 2020 semester, 1997 participants consented, enrolled, and met criteria for data analyses. There was a high prevalence of anxiety, as assessed by the State Trait Anxiety Index, with moderate and severe levels in 465 (24%) and 970 (49%) students, respectively. Approximately one-third of students reported having a mental health disorder . The average daily steps recorded in this student population was approximately 6500 . Neither reported mental health nor step count were significant based on COVID-19 status (Mental health problems were common in this student population. Several factors, including substance use, were associated with the risk of COVID-19. These data highlight important areas for further attention, such as prioritizing innovative strategies that address health and well-being, considering the potential long-term effects of COVID-19 on college students.ClinicalTrials.gov NCT04766788; https://clinicaltrials.gov/ct2/show/NCT04766788RR2-10.2196/29561 As SARS-CoV-2 spread throughout the United States and worldwide , the COV. The Roadmap platform was initially developed to provide support to patients and their family caregivers in health care delivery  because of its accessibility and scalability ). All study activities were conducted remotely with no in-person contact, and all study materials were mailed to participants’ residences through a US shipping company.The study protocol has been previously published with more details . BrieflyThe recruitment period was between September 2020 and December 2020. While paper flyers and postings were distributed throughout the campus buildings, the primary mode of recruitment was by the “Targeted Email and Data Service,” coordinated by the U-M Registrar’s Office, with IRBMED approval .Interested participants who contacted the study team by telephone or email received additional study information . Following confirmation of university student status, the research coordinator emailed the informed consent through the SignNow platform, and the participant signed the document electronically .The study procedures are outlined in The Fitbit was mailed to the participants’ homes. They were instructed to use it continuously (at least ~40 hours/week) to measure their physical activity, heart rate, and sleep during the monitoring period. The Fitbit automatically generated accelerometer-based summary data  that were based on “activity counts” collected over the course of the day. We assessed participant compliance in wearing the Fitbit by identifying when heart rate data were present through the Roadmap platform using the Fitbit API . We measParticipants were instructed to download the Roadmap 2.0  and FitbAll self-reported physical, mental, and social health data were collected using Roadmap 2.0, which used Qualtrics , a web-based research tool that enables researchers to create study-specific websites for administering study surveys and storing participant data. The data were associated via a unique study participant ID and did not contain any identifying information. Data were stored in the cloud and regularly downloaded and saved on Health Insurance Portability and Accountability Act–compliant and password-protected university servers. Participants were instructed to complete surveys at baseline . A list of the survey questionnaires is provided in the Research Protocol . PsychomP<.05.For the descriptive statistics, continuous measures were described using mean (SD) values, while categorical measures were summarized as n (%) values. These data were analyzed using SAS software (SAS Institute). Formal comparisons were made on the basis of COVID-19 status  with Cronbach α levels  set at P<.05) in univariable associations at once and then removing one variable at a time by backward selection until the optimal model was obtained .Logistic regression models were fit in two stages. First, univariate associations of student demographics and characteristics, mental health, self-reported substance use and social health measures were assessed by COVID-19 status. Second, the multivariate associations of student demographics and characteristics, mental health, self-reported substance use, and social health measures were assessed by COVID-19 status. The multivariate model was developed by entering all statistically significant variables  curves were plotted for candidate models: Model 1 included only demographic variables; Model 2 included demographic and mental health measures; Model 3 included demographic, mental health measures, and self-reported substance use; and Model 4 (Full Model) included all the variables in Models 1 through 3 plus all other significant characteristics and social health variables from univariate associations; as well as Model 5 , which was selected through backward stepwise regression from Model 4. Model 5 provided the minimal Akaike Information Criterion. The area under the ROC curve (AUC) represented the prediction accuracy of the current model. When we constructed our models, we observed an increase in accuracy as more variables were added from Model 1 to Model 4. Importantly, even though Model 5 included fewer variables than the Model 4, we did not observe a significant loss in prediction accuracy. Thus, Model 5 was selected as the final multivariate model owing to its simplicity. The univariate and multivariate logistic regression were analyzed using R (version 4.1.1). Figures and graphs were generated with GraphPad Prism . Ethical approval for this study was obtained by the U-M Medical School Institutional Review Board (IRBMED), and the study was registered on ClinicalTrials.gov (NCT04766788).The majority of students consented and enrolled in the study during the months of October and November 2020 , which cIn this population, 178 (8.9%) students reported a positive COVID-19 diagnosis (COVID-19 positivity), which occurred either before or during the study period . These individuals were more likely to be non-Asian, non–multi-racial, domestic undergraduate students, living with ≥20 housemates, or owning iPhone devices.The most common COVID-19 symptoms reported by students included body aches , loss of smell , chills , and cough . Additionally, the most common clusters of associated dyadic symptoms were chills and body aches (Cluster 1: n=59) and loss of taste (ageusia) and anosmia (Cluster 2: n=49). The most common triad of symptoms were fever, chills, and body aches (Cluster 3: n=40). Not surprisingly, all respiratory symptoms  were associated with each other . HoweverA high prevalence of anxiety, as assessed by the State Trait Anxiety Index (STAI), was reported at moderate  and severe  levels in this student population . These fr=–0.22) as well as anxiety and flourishing (r=–0.71). There was also a significant positive correlation between compassion and flourishing (r=0.22). There was no relationship between compassion and adherence to public health COVID-19 measures , while the numbers of students who reported any marijuana use, vaping, and alcohol use were 847 (42.6%), 431 (21.6%), and 1600 (80.4%), respectively, which were all associated with COVID-19 positivity . MoreoveP=.04). P=.52).In addition to completing longitudinal survey measures, students also provided continuous physiological data by wearing the Fitbit device throughout the study period. The average wear time of the device was 14.5 hours (in a 24-hour day), 7.4 hours during daytime (between 8 AM and 8 PM), and 7.1 hours during nighttime (between 8 PM and 8 AM). As shown in P<.001). Model 5 with an AUC of 85% is available in In the final multivariate model of student demographics and characteristics, including physical, mental, and social health variables, individuals who reported marijuana or alcohol use and lived with a greater number of housemates (≥20) were at increased risk of COVID-19 positivity. However, being a graduate student and being an individual who aligned with public health measures were associated with COVID-19 negativity . GraduatA major finding in this study indicates concerns of adverse mental health symptoms reported by college students, confirming data from other recent studies -34. WhenOur study did not find a difference in mental health data reported by COVID-19 status, whereas substance use was significantly associated with COVID-19 positivity. It is possible that in some students who reported mental health problems, their coping strategies may have included substance use behaviors , which tend to be social activities occurring in groups . Indirectly, this may have accounted for increased COVID-19 risk owing to less vigilant safety practices. Alternatively, among other students who reported mental health problems, those problems may be associated with (or be due to) isolation, thereby decreasing their COVID-19 risk. It is possible this competing process canceled out any significant total effect of mental health problems by COVID-19 status.This study leveraged mHealth technology to characterize the demographics and physical, mental, and social health of college students during a global pandemic. During a unique period in history where all in-person research activities were halted, the mHealth platform facilitated this type of data collection. The findings herein were self-reported by students prior to the availability of COVID-19 vaccines nationwide. Approximately 9% of students who participated in this study reported COVID-19 positivity. Across the nation, there were over 30 million cumulative reported positive COVID-19 cases by April 01, 2021 , which wIn our sample of college students, the most common symptoms were body aches, anosmia, chills, and cough. Interestingly, in a large meta-analysis of 9 countries and 24,410 adults, the most commonly reported symptoms were fever (78%) and cough (58%) . HoweverWe found that second-year graduate students, Asians and multi-racial students, and international students were significantly less likely to report COVID-19 positivity. There was also an association between an increased number of roommates and an increased risk of COVID-19 positivity. Graduate students in our sample lived with significantly fewer people, presumably decreasing their risk of COVID-19. Marijuana and alcohol consumption were significant risk factors for COVID-19. Additionally, students who agreed or believed in public health measures were less likely to report COVID-19 positivity.Not surprisingly, we observed a relatively modest decrease in Fitbit compliance over the study duration. This was likely due to decreased engagement with both the Fitbit device and the study over time. Despite the ease of use of consumer-grade wearable sensors, “wearables abandonment” is a well-documented issue . NonetheThe average daily step count observed in this study population was 6474 (SD 3371), which was a relatively low level of physical activity . The NetWe interpret the findings herein within the context of several limitations. Owing to the single-institution design, our findings are not generalizable outside of our student cohort. In the fall 2020 semester, the undergraduate U-M student population was represented by the following racial and ethnic categories: White , Asian ; international ; Hispanic or Latinx ; not indicated ; 2 or more ; Black or African American ; American Indian/Alaska Native ; and Native Hawaiian/Other Pacific Islander . While our cohort recruited similar proportions of racial or ethnic categories that are reflective of the U-M student demographics, there was a greater percentage of Asian students. In addition, females were much more likely to participate in our study than males (68% vs 31%), despite a roughly equal percentage of gender types attending the U-M .Second, data attrition resulting from wearable abandonment or digital fatigue may have underestimated the student population’s physical activity based on their daily step count. Future work involving emerging adults should consider types of compensation models to incentivize engagement  as well Despite these limitations, our study had robust recruitment during a period wherein face-to-face research activities were largely halted, indicating the feasibility and merit of conducting a longitudinal study of this nature during a global pandemic. Mobile health technology enabled the team to conduct a virtual and contactless study from recruitment, informed consent, enrollment, onboarding, and multiparameter data collection, including self-report measures and physiological data. The study design aligned with student preferences regarding their ease of use of technology, such as the use of Fitbits and smartphones.In summary, our results provide initial data supporting the use of an mHealth platform during a global pandemic, while in-person activities were significantly altered. The most significant factors associated with the risk of COVID-19 positivity in this population included student demographics , behavioral factors , and beliefs in public health measures. Soberingly, a substantial proportion of this student population was facing a mental health problem and substance use was common. Over the course of the COVID-19 pandemic, students’ educational opportunities have been abruptly disrupted, which may have long-term, unintended consequences. Thus, attention to the current student mental health crisis is imperative with an urgent need to develop novel and timely interventions that address student health and well-being .


State-level reopenings in late spring 2020 facilitated the resurgence of severe acute respiratory syndrome coronavirus 2 transmission. Here, we analyze age-structured case, hospitalization, and death time series from three states—Rhode Island, Massachusetts, and Pennsylvania—that had successful reopenings in May 2020 without summer waves of infection. Using 11 daily data streams, we show that from spring to summer, the epidemic shifted from an older to a younger age profile and that elderly individuals were less able to reduce contacts during the lockdown period when compared to younger individuals. Clinical case management improved from spring to summer, resulting in fewer critical care admissions and lower infection fatality rate. Attack rate estimates through 31 August 2020 are 6.2%  of the total population infected for Rhode Island, 6.7%  in Massachusetts, and 2.7%  in Pennsylvania. COVID-19 clinical outcomes improved over the first 6 months, but infection fatality rates were initially above 1.5%. The coronavirus SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), the cause of coronavirus disease 2019 (COVID-19), has led to more than 750,000 deaths across the United States in just 20 months of transmission. During the initial wave in spring 2020, a critical question in managing the U.S. COVID-19 epidemic was whether regional reopenings of social and economic activity would result in rebounds of cases and hospitalizations . The statistical inference described here—on attack rates, underreporting, and changing age profiles—can guide more granular real-time decision-making and public health messaging than data streams alone.Here, we analyze the age-structured case, hospitalization, and death time series from three states—Rhode Island (RI), Massachusetts (MA), and Pennsylvania (PA)—that, during summer 2020, did not experience substantial epidemic rebounds when compared to March/April levels. We evaluate 11 clinical data streams reported by the respective state health departments in a Bayesian inference framework built on an ordinary differential equation (ODE) age-structured epidemic model that includes compartments  for hospitalization, critical care, and mechanical ventilation. We infer parameters on surveillance, transmission, and clinical characteristics of the first epidemic wave in RI, MA, and PA. We describe the patterns of persistently low transmission in these three states throughout 31 August, compare these low-transmission scenarios to changes in human mobility metrics, and evaluate changes in age structure and clinical outcomes. We evaluate the impact of the spring epidemic on elderly populations in these three states, and we compare infection fatality rates (IFRs) to published estimates from other parts of the world. Preliminary analyses resulting from this work were regularly posted at t describing person-to-person contact (mixing) rates and the probability of virus transmission per unit contact. From the 5 to 15 March period to 1 April, population mixing rates dropped by 65.2%  in RI, by 79.8%  in MA, and by 95.4%  in PA  in PA . During t can be compared to mobility metrics , in general agreement with studies being released during this time (see Discussion).Our RI attack rate can be validated with a 2.2% late-April attack-rate estimate obtained from a household serosurvey  where ac is the mixing rate for age group a; see equations (section S6). Use of the Belgian CoMix study’s contact rates was evaluated ; these data streams are separated in RI/PA but not MA. Our model allows for inference of mortality outside hospital settings, with the outside-hospital sCFR estimated at between 20 and 35% for the ≥80 group and 5 and 20% for the 70 to 79 age group . This alThis is among the first studies to evaluate multiple simultaneous clinical data streams with an epidemic transmission model. The analysis of concurrent data streams is necessary to describe certain important but unreported characteristics of regional SARS-CoV-2 epidemics; these include underreporting of cases, changing age patterns of infection, changing patterns of clinical progression, and an understanding of mortality rates outside hospital settings. The inclusion of multiple age-structured data streams on death and hospitalization allows for statistical estimation of symptomatic case underreporting—a quantity that is generally resistant to robust estimation especially in public health reporting systems that (i) mix active and passive surveillance, (ii) mix multiple diagnostic tests and testing visits, and (iii) have not made estimates of their catchment areas. With an estimate of symptomatic case underreporting , we can estimate the population-level SARS-CoV-2 attack rate in each state by summing the reported symptomatics, the unreported symptomatics, and an externally estimated number of asymptomatics. One month later, an attack rate estimate can be validated by comparing to results from a seroprevalence survey. Four seroprevalence estimates available for RI, MA, and PA show no major inconsistencies with our results. It is important to remember that SARS-CoV-2 serosurveys can be subject to biases depending on approaches to recruitment , the time since the original infection (antibody titers wane over time), and the specific test used  that would influence both IFR and attack-rate estimates is that both case and hospitalization data were underreported. A 1% symptomatic attack rate in PA over the first 6 months of the pandemic is lower than expected, but the underreporting fraction estimate (ρ = 98.9%) in our analysis is likely wrong if hospitalization numbers were also underreported. The reporting rate ρ determines the attack-rate estimates; if ρ is overestimated, then the IFRs presented here would also be overestimated. This may be the reason that our estimated fatality rates for PA appear to be unreasonably high and that the PA attack-rate estimate through 31 August 2020 is so low.The sCFRs inferred for RI, PA, and MA (estimates range from 3.2 and 4.4%) are in the higher ranges of previously reported estimates . Although most studies have converged on an age-adjusted “60% symptomatic” number, age-specific estimates come with less certainty and differences in diagnostic tests and testing protocols have resulted in substantial variation in these estimates (fig. S1).The data streams we present here do not allow us to evaluate the degree to which the epidemic runs through specific subpopulations  that are more vulnerable, susceptible, or transmit more easily. To measure variability in transmission and susceptibility from state-level data, we suggest including these common data types into the same databases/datasheets currently maintained by all state DOHs as part of routine COVID reporting: (i) contact counts and positivity rates from contact tracing efforts, (ii) positive/negative case counts and inclusion criteria from asymptomatic random screening programs (We cannot exclude the possibility that our reporting rate estimate (ρ) is incorrectly estimated because of model misspecification or data integrity problems. In addition, symptomatic reporting is likely to vary by age  reporting tools from the Centers from Disease Control and Prevention that would allow all states to consistently report the same high-quality data types. The rationale for this systems upgrade would be to advance our surveillance systems to those of countries such as New Zealand, Hong Kong, Singapore, Vietnam, Taiwan, and South Korea that successfully controlled epidemic waves and introductions of SARS-CoV-2.Eleven data streams were assembled from three state DOH websites and data dashboards: (I) cumulative confirmed cases, (II) cumulative confirmed cases by age, (III) cumulative hospitalized cases, (IV) cumulative hospitalized cases by age, (V) number of patients currently hospitalized, (VI) number of patients in ICU currently, (VII) number of patients on mechanical ventilation currently, (VIII) cumulative deaths, (IX) cumulative deaths by age, (X) cumulative hospital deaths, and (XI) cumulative hospital discharges. Data streams VI and XI were missing in PA, and X and XI were missing in MA. Cumulative hospitalizations (data streams III and IV) in MA and PA were reported as a subset of symptomatic cases (via follow-up case investigations) and were excluded from the analysis. Reporting started on 27 February (RI), 1 March (MA), and 6 March (PA) and datasets used in this analysis comprise about 180 days of data through 6 September. Age-specific counts often summed up to be less than the corresponding total daily counts of new symptomatic cases, new hospitalizations, or new deaths. This was common because of lack of age reporting in some proportion of cases. We assumed missing age-structured data to be missing completely at random when their sum was less than the total count.The first set of mobility data is provided by the COVID-19 Mobility Network . When clinical parameters were inferred, their median estimates were typically close to observed values in hospital or surveillance datasets. No data were available in RI, MA, or PA to infer the asymptomatic fraction for each age group, and these were obtained from cohort analyses available at the time (see section S1.7) and the inferred age-specific asymptomatic fractions in Davies et al. . Dependence across data streams is assumed to be captured by the ODE system. Total data streams, summed over all age classes, were viewed as the sum of independent negative binomial random variables and are as such negative binomial random variables themselves, with mean given by the sum of the age-specific means. When both age-structured and total data are available, we assume that any missing age-structured data are missing completely at random and approximate the joint likelihood of the total and age-structured counts by ignoring overdispersion and assuming that, conditioned on the total data, the age-structured counts are multinomially distributed with probabilities proportional to the age-structured ODE means. Data on current hospitalizations, as well as current numbers in ICUs, and current intubated individuals were modeled using reported weekly totals. The total number of intubated individuals, individuals in ICUs (but not intubated), and hospitalized individuals (not in ICUs) was each modeled as independent Gaussian random variables with means equal to the corresponding totals predicted by the ODE system and with unknown variances. Additional details on the likelihood framework can be found in section S3.t, describing person-to-person contact mixing, is constructed via a cubic B-spline, with a random walk prior  on the B-spline coefficients to penalize overfitting. In RI and PA, the symptomatic reporting rate ρ is constructed as an I-spline with a similar prior; in MA, it is assumed to be constant across time. Additional parameters found within the ODE system, including length of hospital stay and proportion of cases needing hospitalization within each age class, are given uniform priors with bounds determined by expert judgement, while negative binomial dispersion parameters are given weakly informative exponential priors and Gaussian variance parameters are given conjugate inverse-gamma priors. Given these priors and the previously defined likelihood, we constructed a Markov chain Monte Carlo algorithm to sample from the posterior distribution of model parameters. Block updates for parameters were obtained using a random walk Metropolis-Hastings algorithm with an adaptive proposal distribution (https://github.com/bonilab/public-covid19model.We chose a Bayesian approach to inference, allowing for appropriate penalization of time-varying parameters and a combination of strongly and weakly informative priors for parameters relating to clinical progression of disease. The composite rate parameter, β


With the prevalence of cardiovascular diseases increasing worldwide, early prediction and accurate assessment of heart failure (HF) risk are crucial to meet the clinical demand.Our study objective was to develop machine learning (ML) models based on real-world electronic health records to predict 1-year in-hospital mortality, use of positive inotropic agents, and 1-year all-cause readmission rate.For this single-center study, we recruited patients with newly diagnosed HF hospitalized between December 2010 and August 2018 at the First Affiliated Hospital of Dalian Medical University . The models were constructed for a population set (90:10 split of data set into training and test sets) using 79 variables during the first hospitalization. Logistic regression, support vector machine, artificial neural network, random forest, and extreme gradient boosting models were investigated for outcome predictions.9/L).Of the 13,602 patients with HF enrolled in the study, 537 (3.95%) died within 1 year and 2779 patients (20.43%) had a history of use of positive inotropic agents. ML algorithms improved the performance of predictive models for 1-year in-hospital mortality (areas under the curve [AUCs] 0.92-1.00), use of positive inotropic medication (AUCs 0.85-0.96), and 1-year readmission rates (AUCs 0.63-0.96). A decision tree of mortality risk was created and stratified by single variables at levels of high-sensitivity cardiac troponin I (<0.068 μg/L), followed by percentage of lymphocytes (<14.688%) and neutrophil count (4.870×10ML techniques based on a large scale of clinical variables can improve outcome predictions for patients with HF. The mortality decision tree may contribute to guiding better clinical risk assessment and decision making. Heart failure (HF) syndrome is a life-threatening chronic disorder with a global prevalence that has been rising consistently over recent decades because of population aging, shifts in disease spectrums, and improved survival rates among patients with various cardiovascular diseases ,2. HF isA prediction model was developed as an important risk assessment tool and used in various health care areas over the past decades. It has been recognized to facilitate early identification of patients at disease or event risk and enables effective interventions for those who might benefit most from identifying specific risk factors ,7. PreviBesides this, predictive models have been constructed in various health care domains with a certain degree of success by automated mining of EHRs, combined with machine learning (ML) approaches , specifiIn our study, we explored the use of the traditional method—logistic regression (LR)—and the four novel ML approaches—support vector machine (SVM), artificial neural network (ANN), random forest (RF), and extreme gradient boosting (XGBoost)—to predict prognostic outcomes for subjects with HF in real-world settings. We demonstrated the development of EHR-based models to predict the 1-year in-hospital mortality, use of positive inotropic agents, and all-cause readmissions in a single year.We collected the EHR data from hospitalized patients diagnosed with HF at the First Affiliated Hospital of Dalian Medical University  over 7 years between December 2010 and August 2018. All patients with HF were diagnosed and treated according to institutional guidelines. Because the diagnostic terminologies of EHRs have been structured and normalized according to the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10 ), newly The platform incorporates comprehensive and detailed data on patients’ routine health care. Common and cardiovascular-specific variables were structured and normalized by natural language processing, ML techniques, and well-defined logical rules. Considering the large scale of EHR data and the advantages of ML techniques, candidate variables with known clinical significance and inaccessible through traditional medical records were collected in our study. We excluded variables with missing values greater than 20%. Finally, a total of 79 variables related to the first hospitalization were extracted from a big data intelligence platform. The features were as follows: demographics (age and sex), personal history (smoking and drinking), history (comorbidities and surgery), etiology, vital signs, routine laboratory examinations, interventions, and medication use on admission . RepeateWe established models using 5 algorithms separately to predict the primary outcome: all-cause in-hospital mortality within 1 year. The secondary outcomes were (1) use of positive inotropic agents for patients with HF and (2) all-cause readmission. Mortality was defined as a clear record of death of an inpatient within 1 year of hospitalization. The following commonly used positive inotropic agents in clinical practice were selected for our study: dopamine, dobutamine, milrinone, levosimendan, and deslanoside. Readmission was defined as any patient with an interval of more than 1 day from the last discharge until the next admission; no readmitted patients died within 1 year of hospitalization. Therefore, enrolled patients were labeled as “deceased” or “survivors.”The synthetic minority oversampling technique was first employed to address the imbalance in the data set, with a ratio of 1:1 between deceased patients and survivors. It is common for a minority to be oversampled by deriving new, “synthetic” samples to alleviate imbalance .The hold-out method was used to divide the data set into a training/validation data set (90%) and a hold-out test set (10%). Five approaches were explored with 10-fold cross-validation for building models and adjusting parameters: LR, RF, SVM, ANN, and XGBoost. The hold-out test set was used to evaluate the best-performing models created with the training set. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was chosen as the primary evaluation metric for our models, including accuracy, precision, and recall. The Brier score (range of 0 to 1)—the average squared error between the predicted and the actual value—was also commonly represented as a “calibration” for overall measurement. Shapley additive explanation (SHAP) values were used to evaluate feature importance.LR is the most basic dichotomous linear method of model selection that makes classification decisions. LR is superior in measuring the probability between “0” and “1” based on the relationships of binary classifications in continuous or categorical variables . We usedRF is an algorithm that integrates multiple decision tree classifiers. Each node of the decision tree represents a predictive variable to separate the outcome classes by setting the optimal threshold. The importance of features can also be obtained with the sum of weights of the classifier’s nodes . The RF As a dichotomous supervised algorithm, SVM can be used in high-dimensional feature space. The best hyperplane can be achieved using a kernel-based function for separating two classes at maximum intervals .A multilayer perceptron (MLP) classifier was implemented using the sklearn.neural_network module to develop models . MLP is XGBoost is one of the boosting methods; this algorithm aims to integrate weak classifiers into a single robust classifier in an iterative fashion . This alt tests or chi-square tests were applied for group comparisons. P values <.05 were considered statistically significant.All ML algorithms were performed using the scikit-learn (version 0.21.1) package in Python , and statistical analysis was conducted using an open-source Scipy (version 1.3.0) database from Python (version 3.6.3). All categorical data were presented as percentages. All continuous data performing a normal distribution were presented as mean (SD); otherwise, they were expressed as median (IQR). Student P<.001). Concerning the etiology of HF, the number of patients with cardiomyopathy and cardiac arrhythmia was found to be different between the deceased and survivor groups (both P<.001). There was a significant difference in the vital signs (heart rate and blood pressure) of patients with HF between the 2 groups . In-hospital medication use  was higher among subjects who survived in the hospital (P<.001).A total of 13,602 hospitalized patients with newly diagnosed HF were enrolled in this study, of whom 3.95% (n=537) died in hospital within 1 year. The usage rate of positive inotropic agents was 20.43% (n=2779). The overall all-cause readmission rates of 30 days, 60 days, and 1 year for patients with HF were 4.83%, 14.77%, and 21.16%, respectively . The eligible population’s baseline characteristics were compared between 2 groups according to survival status . PatientPredictive models for 1-year in-hospital mortality risk assessment were conducted using 5 algorithms. P value based on whether it favored judgment in survival. As shown in Furthermore, we explored the importance of the features that affect mortality prediction by applying RF and XGBoost approaches , with thInterestingly, BNP, international normalized ratio (INR), pulse rate, hs-cTnI, and AST were the top 5 markers predicting use of positive inotropic agents by the RF approach. BNP was also identified as a critical marker for forecasting positive inotropic agent use in the XGBoost model, followed by basophil counts, pulse rate, INR, and hs-cTnI C and 3D.The discrimination of different models for 1-year all-cause readmissions represented by AUCs is shown in Of the 79 variables analyzed, the presence of diabetes was identified as the most important marker for prediction of 1-year all-cause readmission using the SVM method, whereas basophil count was a significant predictor of readmission using the XGBoost algorithm. There appeared to be a certain discrepancy in the ranking of other features derived by the two methods E and 3F.9/L. Subsequently, the patients with HF were stratified by these branch points: high risk ; low risk ; intermediate risk 1 ; and intermediate risk 2 .The patients were divided into subgroups of high-, intermediate-, and low-risk in-hospital mortality according to the cutoff value of various variables obtained using the XGBoost algorithm . Of the In our study, the EHR data–driven prognostic models based on 5 different ML algorithms were developed for predicting 1-year in-hospital mortality, use of positive inotropic agents, and all-cause readmissions within 1 year for patients with HF in a single Chinese class A tertiary comprehensive hospital. These ML models produced better predictive values for prognostic outcomes by growing interpretability than traditional linear methods. Besides, the novel ML techniques could take advantage of a large scale of complex, high-dimensional variables to widen the scope of HF predictive indicators concerning prognostic outcomes.An integrated EHR system comprises various data resources, including patients’ demographic data, diagnostic information, laboratory test results, and prescriptions. However, many experts revealed that the availability of EHR-derived data is a prerequisite for promoting real-world studies ,32. In oSeveral traditional prognostic models have been established to estimate mortality in hospitalized patients with HF. Our results are consistent with the previous studies of 1-year mortality prediction while demonstrating a better predictive capability. Our study, which predicted outcomes for 13,602 patients with HF using 79 predictive variables, indicated that the ML approaches are superior to the EFFECT method (AUC 0.77) in predicting mortality, with an average AUC of 0.81 . The claReadmission rate is a common index used to assess the quality of health care services for patient populations. Currently, most hospitals and institutions still implement traditional readmission risk models and certain variables to infer readmission probability . Like prPositive inotropic agents are a kind of drug that can increase myocardial contractility and cardiac output and are often used to treat patients with HF. Among the drugs included in our study, dopamine and dobutamine have mainly inotropic effects, whereas milrinone, lanatoside C, and levosimendan have extra vasodilation functions . HoweverThe study’s strength was that we also developed a stratified risk assessment tool for the prediction of 1-year in-hospital mortality using the XGBoost algorithm. Far more features—including hs-cTnl, percentage of lymphocytes, percentage of neutrophils, cholinesterase, urea, respiratory rate, and AST—were identified for the first time using an ML approach for mortality prediction. Neutrophilia was reported to be associated with an increased incidence of acute decompensated HF (ADHF) in patients with acute myocardial infarction, and lymphopenia is related to poor prognosis in patients with HF ,47. ResuThere are several limitations to our study that are worth mentioning. First, this was an in-hospital outcome prediction study based on retrospective use of EHR-derived data. Although our models’ performances were considerable on their own, the predictive power could be further adjusted and compared with the established reference tools. Second, the number of patients who died was small compared with the number of surviving subjects; although rich in terms of clinical variables, the imbalance problem remained. Third, validation in an external cohort was not done in the present study but is planned for subsequent analysis. Fourth, different phenotypes of patients with HF should be taken into account in further model developments.EHR-driven models, using novel ML algorithms, were developed to predict 1-year in-hospital mortality, use of positive inotropic agents, and 1-year all-cause readmission in patients hospitalized with HF. The discrimination and performance of our models also outperformed the existing tools constructed using traditional techniques. Besides, identifying a greater range of variables can further improve decisions regarding risk assessment for patients with HF.


The group housing of animals supports species-specific behavior and avoids stress induction. Therefore, novel housing systems need to be established that provide sufficient space and meet the hygiene criteria for laboratory animals. Here, we describe the successful implementation of an improved housing system for single and group housing of laboratory rabbits under high hygiene environmental conditions. This system includes modular and mobile floor pens of different size and shape. Rabbits housed in this system showed species-specific behavior that depended on the circadian rhythm—a prerequisite for interpretable animal experiments. We propose that this housing system will be of great benefit for the well-being of laboratory rabbits used in biomedical science.The major responsibility of researchers and laboratory animal facilities is to ensure animal well-being during the time of acclimatization, experiments, and recovery. In this context, animal housing conditions are of utmost importance. Here, we implemented a mobile and modular floor-pen housing system for laboratory rabbits that combines rabbits’ natural behavioral requirements and the high hygiene standards needed in biomedical science. Twelve female New Zealand White (NZW) rabbits were single- or group-housed for 12 months in mobile and modular floor-pens. Their general health status was evaluated at the end of the experimental setup. Further, we performed behavioral analysis of six additional NZW females group-housed for eight weeks in pens of two different sizes. We show that our improved housing concept supported species-specific behavioral patterns. Taken together, our housing system provides an optimal setup for rabbits in animal facilities that combines strict requirements for animal experiments with animal welfare. Oryctolagus cuniculus). Therefore, the adequate group housing of male and female rabbits has attracted extensive attention over the past decades [The concept of refinement, reduction, and replacement, also called the 3R strategy, is an intrinsic part of the EU Directive 2010/63 to protect all non-human vertebrates used for scientific purposes . Traditi decades ,6,7,8.It has been recommended that the housing of laboratory animals should ensure sufficient space to permit species-typical locomotion, explorative behavior, and social interactions. In this context, direct social interaction, or at least social non-contact enrichment, is required for animals’ well-being . Thus, sImportantly, group-housing strategies have to meet the criteria for the specific-pathogen-free (SPF) housing of laboratory animals. These standardized environmental housing conditions should support the animals’ natural, circadian-driven behavior. The circadian rhythm has an impact on a broad range of physiological, circadian-controlled parameters, especially behavior, that might impact the outcome of research experiments .Thus, it is important to establish new group-housing systems that are applicable for laboratory animals. In our study, we aimed at implementing and evaluating a modular and mobile floor-pen housing system for rabbits under routine laboratory settings. Therefore, we tested NZW rabbits for their health status and species-specific behavior. We report that our housing system allows for species-specific behavior that depends on the circadian cycle. Moreover, our system is applicable for SPF conditions in animal facilities and provides the possibility to flexibly adjust size restrictions according to, for example, population size or official rules. Therefore, the modular floor pen system represents a refinement of animal housing and an alternative to conventional single or pair cage-housing systems.Eighteen outbred New Zealand White (NZW) female rabbits (Crl:KBL), aged between 1 to 4 years and body weights between 1.8 to 5.5 kg, were purchased from Charles River laboratories  and housed under barrier conditions in a specific-pathogen-free (SPF) animal facility.2O2). Pens were completely exchanged once per week. Hygiene monitoring was performed every six months based on the recommendations of the Federation of European Laboratory Animal Science Associations (FELASA) working group [Room temperature and relative humidity were 18–20 °C and 45–55%, respectively. The light cycle was adjusted to 12 h light and 12 h dark periods. Room air was exchanged 15 times per hour and filtered via a high-efficiency particulate air (HEPA) filter system. All rabbits had free access to water  and food  as well as irradiated and autoclaved hay . Soiled bedding  was removed each day and partially replaced when necessary. Pens were sanitized  and decontaminated  for rabbits ranging from 3 to 5 kg (minimum: 4200–5400 cm2)) [2O2 ) [2O2 D,E.2O2) to ensure thorough decontamination. Notably, the SPF status of the colony was stable during the study, indicating adequate and appropriate decontamination measures for the floor-pen elements. Each module can be rotated by 90° or 180°. This assembly strategy provides different and complex environmental setups that stimulate the explorative behavior of rabbits. Furthermore, single pens can be easily connected to a larger area , three black backplanes (108 cm) see D,E, and ger area B,C. By ager area A,C.2.We provided all enrichment items as described for the standard pen  rabbits (weight: 2.8 to 5.5 kg), were randomly allocated in either three groups of at least two animals  or five individuals (housed in single pens next to each other) and monitored for one year. For all experiments, we compared a group of seven animals housed in three pens to a group of five animals in six pens. To determine the impact of housing on their behavior under routine lab work conditions, we chose rabbits that were used for antibody production and were handled once every four weeks for subcutaneous injections and/or bleedings over a longer period of time. In addition, we examined housing-related health problems, such as bone deformation or paw lesions, at the end of the experiments using gross necropsy in all twelve female NZW rabbits. Testing the group-housing effect on the efficacy of antibody production was not included in the study since this has been previously reported [2) and a smaller area . The rabbit ethogram  rabbits (weight: 1.8–2.1 to 2.8–3.6 kg) that were not included in the health study. We decided to use six animals for the behavioral analyses because group sizes ranging from four to eight animals have been recommended for group housing in animal facilities . To addrethogram  was adapp-values of non-parametric data. One-way repeated measure ANOVA was used to identify significant differences between the two trials  tested. Two-way repeated measure ANOVA was used to identify statistically significant behavioral differences among different time points and trials tested. p < 0.05 was considered statistically significant if not stated otherwise.Parametric data are presented as mean ± standard error of the mean (SEM) or as median and interquartile range (IQR) for non-parametric data. Statistics were calculated using the software GraphPad Prism . The Kolmogorov–Smirnov test was used to test for Gaussian distribution. The Kruskal–Wallis test followed by Dunn’s multiple comparison was used to calculate We tested our mobile and modular floor-pen housing system (named: mobile-R-pen) under specific-pathogen-free housing conditions in two different experimental setups. First, we evaluated the health status in twelve female New Zealand White rabbits over a period of 12 months while they were housed in the mobile-R-pen. Second, we evaluated the species-specific circadian behavior of an additional six female New Zealand White rabbits for a total of 8 weeks. Furthermore, we evaluated the animals’ behavior after size restriction in two trials with different sizes of the enclosure.Health study: During the health study, rabbits were single- or group-housed on loose wood chip bedding material. Wood chip bedding was used to reduce bone deformations or paw injuries A,B. AnimBehavioral study: The behavioral repertoire of six young adult female NZW rabbits was evaluated in two floor-pen setups differing in the size of the enclosure. We analyzed rabbits’ behavior during an 8-week period based on the ethogram depicted in Furthermore, rabbits showed a broad range of species-specific behavioral patterns in the 6-pen and the 3-pen trials . While tNext, to investigate whether the mobile-R-pen allows the performance of circadian behavioral repertoires, we counted rabbit-specific behavior at four different time points between 7:30 a.m. and 12:30 p.m. during 4 weeks of housing in the 6-pen/3-pen trials A,B. ImpoIn this study, we implemented a novel, mobile housing system (mobile-R-pen) for rabbits that provides spatial flexibility. Our analyses revealed species-specific behavioral patterns of group-housed female NZW rabbits in a larger enclosure as well as in size-restricted compartments. Size restriction led to no general alteration of NZW rabbits. We observed that hiding was significantly decreased with size restriction. Moreover, fighting was increased in 6-pen versus 3-pen trials, indicating enhancing species-specific behavior that accompanies hierarchy establishment. In line with this notion is the finding that size restriction is a stressor that impacts social structure establishment in female NZW rabbits . The timMoreover, we identified fighting events, but no injuries, which has been observed after re-grouping of rabbits in an unfamiliar pen by others . FurtherOryctolagus cuniculus even under standardized conditions in a vivarium. In line with recent observations [Next, we identified a circadian behavioral pattern of group-housed rabbits, independent of the size of the enclosure. Our data mirror the natural behavior of rvations , we idenrvations . Even inrvations . In thisThis system is applicable in animal facilities where high hygiene standards are important. In contrast to commercially available floor-pen systems, the mobile-R-pen concept allows a maximum of flexibility in the interior design of a vivarium without the need for a stable connection to the floor or wall of the room. Mobile-R-pens can be easily moved or transferred (with or without rabbits) for experiments or for decontamination and cleaning. This is a major advantage of the housing system that contributes to the minimization of hygiene risk and disease spread. The solid ground of the pen allows the use of autoclavable bedding materials normally used in most mouse/rat animal facilities. This soiled bedding material can be easily removed when necessary and allows the monitoring of excretion from outside of the pen. The health status of the rabbits was not negatively affected by single or group housing and contact with soiled bedding materials, which is in line with previous reports . It is aIn conclusion, easy handling and disassembly of pens ensure sufficient decontamination as an important measure for SPF housing conditions of rabbit colonies. Our improved mobile-R-pen housing concept supports species-specific, circadian behavioral repertoire, which is of great benefit for the well-being of single- and group-housed laboratory rabbits and provides an easy refinement concept according to animal welfare recommendations for rabbits used in research.