Substantial evidence demonstrates that falls in older adults are leading causes of fatal and non-fatal injuries and lead to negative impacts on the quality of life in the aging population. Most falls in older fallers result from unrecoverable limb collapse during falling momentum control in the single limb support (SLS) phase. To understand why older adults are more likely to fall than younger adults, we investigated age-related differences in knee extensor eccentric control, lower limb muscle activation patterns, and their relation to balance control. Ten older and ten younger healthy adults were compared during balance control while walking on a compliant surface. There was a positive correlation between knee extensor eccentric work in the perturbed leg and the swinging leg’s speed and margin of stability. In comparison to younger adults, older adults demonstrated (1) less eccentric work, reduced eccentric electromyography burst duration in the perturbed leg, (2) higher postural sway during SLS, and (3) impaired swinging leg balance control. The group-specific muscle synergy showed that older adults had a prominent ankle muscle activation, while younger adults exhibited a more prominent hip muscle activation. These findings provide insight into targeted balance rehabilitation directions to improve postural stability and reduce falls in older adults. While walking over multi-terrain surfaces, such as solid, compliant, rocky, irregular, slippery, and uneven surfaces, older adults demonstrate decreased gait speed, reduced step length, and increased variability in medial–lateral center of mass (CoM) acceleration and trunk roll2. Previous studies over firm, level ground surfaces have found that age-related impairment in neuromuscular control and force-generating capacity in the lower limbs lead to altered muscle activation patterns during walking balance recovery5. For example, neuromuscular control tends to become more simplistic with age, leading to reduced variability and efficiency of muscle synergy options3. In addition, age-related reductions in lower limb force generation leads to altered muscle activation patterns while walking. During the push-off phase of gait, older adults demonstrate greater magnitude of ankle plantar flexor electromyography (EMG) activation compared to younger adults despite having no age-related difference in plantar flexion torque6. This relatively inefficient control of ankle joint forces leads to altered muscle activation patterns, which redistributes push-off force generation to more proximal muscles at the knee and hip joints7. Older adults also demonstrate changes in movement strategies with ineffective inter-joint coordination, such as greater agonist and antagonist coactivation at the ankle and knee joints during downhill walking4, and increased joint kinematic variability at the ankle, knee, and hip joints during walking with lateral balance perturbations8. Nevertheless, although alterations in neuromuscular movement strategies and force-generation capacity have been demonstrated while walking over level surfaces, less is known regarding how these control mechanisms are affected by age while walking over irregular or compliant surfaces.A decrease in the ability to adapt locomotor control to varied surface properties is part of normal aging9. During unexpected slips, for example, a key response mechanism during the single limb support (SLS) phase of gait is eccentric work of the quadriceps to control CoM displacement10. Without adequate eccentric control, either a fall or postural instability in the vertical, anterior–posterior (A-P) and/or mediolateral (M-L) directions will ensue11. Previously identified age-related increases in force-generating errors and larger force variability when producing knee extensor eccentric control12 are important mechanistic considerations that may underlie limb collapse and instability during SLS in older adults. These findings also demonstrate that when standing on a compliant surface, older adults exhibit greater postural sway and more rigid postural behavior compared to younger adults. Therefore, an investigation of age-related changes in neuromuscular eccentric control characteristics at the knee joint and differences in lower limb muscle coordination patterns to secure balance during SLS can reveal key factors underlying increased fall risk in older adults, especially after an encounter with unpredictable ground surface compliance challenges while walking.Surfaces such as muddy ground, grass, sandy soil, or plush carpet place greater demands on reactive locomotor strategies that include critical online task related real-time sensory feedback and integrationThe purpose of this study was to investigate (1) age-related differences in the characteristics of knee extensor eccentric control and muscle coordination patterns of the perturbed leg and swinging (trailing) leg protective step balance control while walking on a compliant (foam) surface and (2) the relationship between perturbed leg eccentric control and postural stability  during the SLS phase and swinging leg protective stepping response. The results of this investigation will provide insights into novel directions for lower limb strengthening exercises to improve dynamic balance control and reduce fall risk in an aging population.13. Participants were included in this study if they (1) were able to walk 10 m without assistive devices and (2) had a “moderate” or higher physical activity level, which meets the following criteria:Ten healthy younger adults  and ten healthy older adults  participated in this study. Physical activity level  was measured using International Physical Activity Questionnaire(a) three or more days of vigorous-intensity activity of at least 20 min per day OR (b) five or more days of moderate-intensity activity and/or walking of at least 30 min per day OR c) five or more days of any combination of walking, moderate-intensity or vigorous intensity activities achieving a minimum total physical activity of at least 600 min/week. Participants’ characteristics are listed in Table 2). All procedures were approved by the Institutional Review Board at the University of Maryland (protocol code: HP-00093655 and dated 1/21/2021) and were in accordance with the Helsinki Declaration of 1975. All participants provided written informed consent prior to study participation.Participants were excluded from this study if they had (1) deficits or disorders that could affect balance control; (2) history of dizziness and imbalance; (3) history of neurological , musculoskeletal, or any other systemic disorders; and (4) Body Mass Index (BMI) within the overweight and obesity range  for each joint  before collecting data15. Thirty-nine reflective markers were placed on the body according to the standard Plug-In-Gait full-body model . A 10-camera motion capture system  was used to record body kinematics.A wireless EMG System  was used for the acquisition of muscle activity signals. Noraxon adhesive pre-gelled Ag/AgCl surface EMG electrodes  were placed bilaterally on the tibialis anterior (TA), medial gastrocnemius (mGas), rectus femoris (RF), biceps femoris (BF), gluteus medius (Gmed), and erector spinae (ES). The positioning of the electrodes was in accordance with SENIAM guidelinesWalking on a compliant (foam) surface, balance challenge was measured at heel strike of gait initiation to standardize the timing of the perturbation and to normalize kinematic responses to the same time period of the gait cycle. Before the start of testing, spatiotemporal gait data  during unperturbed gait were obtained from 3 trials on the GaitRite mat . Step length was used to estimate the starting position for each participant to ensure that heel strike of the leading (perturbed) leg occurred at a center location on the compliant (foam) surface platform. Because we were interested in the naïve response to ground walking balance challenge, we only measured the first exposure to balance challenge on the compliant (foam) surface, which requires active postural balance control during SLS to counteract the unpredictable proprioceptive sensory inputs caused by a foam surface. It should be noted that the participants could anticipate a compliant surface since it was not hidden from them, yet the surface properties  were unknown to the participants.During testing, participants wore a safety harness linked with a rope to an overhead pulley system with little resistance. The length of the rope was adjusted to participants’ height so that they could walk freely. Participants were asked to walk at their normal pace along a 30’ 9.14 m) walkway . The walkway was modular and the first 3’ module was located before a compliant (foam) surface module .16, followed by the application of a 20—450 Hz band-pass filter to extract the frequency range where muscular energy is concentrated18.Pre-processing of sEMG signal: Any DC offset was first eliminated using the “detrend” function in MATLAB. Next, a median filter was applied to the signal to remove noise19. To obtain sEMG envelopes, a 2nd order Butterworth low pass filter with a 20 Hz cutoff frequency was applied as a digital smoothing20.sEMG rectification and linear envelope: sEMG signal values below zero were converted to positive values of the same amplitude to create a full-wave rectified sEMG signalThe raw surface EMG (sEMG) data collected during responses to perturbations underwent the following processing steps:EMG burst duration onset of the RF in the perturbed leg was defined as the time of minimal EMG activity after the instant of knee angle transition from extension (concentric) to flexion (eccentric) following surface drop perturbation (stepping on the compliant surface) start. The end of EMG burst duration was determined as the time of minimum EMG value at the instant of knee angle transition from flexion (eccentric) to extension (concentric) following the highest EMG peak of the eccentric contraction Fig. . EMG pea21. Since we are interested in naïve response during the first exposure  to the balance challenge on the compliant (foam) surface, the synergies were extracted from this single trial. An EMG matrix was constructed where the rows are the perturbed leg muscles, while the columns are the sampled data during the SLS phase.To characterize the muscle activation patterns for balance recovery strategies, we performed muscle synergy analysis using non-negative matrix factorization (NNMF). Prior to NNMF decomposition for muscle synergy extraction, we employed a moving root mean square (RMS) with a window length of 100 samples to create the sEMG envelopesExtraction of muscle synergy: Nonnegative matrix factorization (NNMF), using a multiplicative iterative algorithm, extracted muscle synergies  from the EMG matrix23 NNMF decomposes the EMG signals of a specific muscle activation pattern within a given time period into two distinct components:W: This vector specifies the spatial pattern of the relative activation level of each muscle in the muscle synergy. Each muscle’s contribution is relatively weighted within this spatial structure.C: This scaling coefficient represents the temporal synergy activation. The spatial components are multiplied by a scaling (synergy recruitment) coefficient C. This transformation can be expressed as:where m = the number of muscles, t = the number of time points, n = the number of muscle synergies, e = residual error, and EMGr = reconstructed EMG matrix.24. Therefore, the coefficient (C) specifies how the coordinated muscle activation pattern is modulated over time during the targeted movement period  temporal synergy activation and (2) spatial structure of relative muscle activation levels).The spatial components (W) are fixed time-invariant patterns, whereas the temporal synergy activation coefficient (C) varies over time0 and EMGr, variability accounted for (VAF) was calculated according to the following equation:To evaluate the similarity between EMG25.To determine the optimal number of synergies, we repeated the optimization to extract k (from 1 to the number of EMG sensors) synergies and the associated VAF. Then the smallest k with VAF > 90% was selected26.k-means clustering algorithm (from MATLAB R2022b) categorized the similar groups of muscle synergies across all participants. Then, intra-class correlation coefficient (ICC) was used to examine the internal consistency of all muscle synergies. Common muscle synergies between older and younger adult groups with ICC over 0.75, indicating good reliability, were categorized in the same cluster. To determine age-specific synergies, the ICC value of muscle synergies over 0.9, indicating excellent reliability, were categorized in the same cluster27.The body’s center of mass (CoM) trajectory, knee joint angular displacement and power were calculated using Vicon Nexus 2.12 Software . Kinematic and kinetic data were Butterworth low-pass filtered at 6 Hz and 25 Hz, respectivelyKinematics: To measure protective stepping stability, we used the margins of stability (MoS). MoS is the minimum distance from extrapolated CoM to the posterior boundary of the base of support (BoS) in the A-P direction. The extrapolated CoM was quantified as x + v/ω0, where x and v are the CoM position and instantaneous CoM velocity respectively, and ω0 is angular frequency = Kinetics: Work at the ankle, knee, and hip joints was calculated by the integration of the power-time curve. Knee joint eccentric work in the A-P direction represented the negative work .ork Fig.  resultind) was calculated based on previous studies30, resulting in an effect size of 1.35. In G*Power, a T-test was selected as the test family to assess mean differences between two groups. This was a two-tailed test with 80% power  at the 0.05 alpha level (Type I error rate). Shapiro–Wilk test was performed to test the normality. All EMG, kinetic, kinematic data were normally distributed.A statistical software  was used for all statistical analysis with an established a-priori alpha level of 0.05. For the justification of our sample size, an a priori power analysis was conducted using G*Power. Effect size  was used to determine whether there were any differences between age groups (old and young) on (1) knee extensor eccentric control; knee extensor eccentric work and RF EMG activity , and EMG peak latency from the instant when perturbed leg stepping on the compliant (foam) surface) during SLS, (2) postural sway (SDCoMAccel), (3) MoS of first compensatory step of the trailing leg touchdown following SLS on the compliant (foam) surface and (4) maximum velocity of swinging leg. The Bonferroni test was used for post hoc analyses, pairwise comparisons between two groups, where indicated. Pearson’s correlation (r) was used to examine the correlation between eccentric work in the perturbed leg and swinging leg balance responses: (1) MoS and (2) Maximum velocity of swinging leg for compensatory step).All data are presented as mean ± standard deviation (SD). All participants demonstrated a swinging (trailing) leg protective stepping response in the forward direction without a fall. As we obtained age-specific synergies from each age group, our analysis focused on understanding the composition of age-specific muscle synergies , rather than assessing the relative contributions of each synergy to each muscle. We investigated the relative contributions of individual muscles to age-specific synergies to identify age-specific postural control strategies in terms of muscle activation patterns for regaining postural stability after ground balance challenges.p = 0.027, Fig. p = 0.021, Fig. p = 0.07, Fig. p = 0.158, Fig. Compared to younger adults, older adults demonstrated reduced eccentric knee extensor work  and M-L directions .Compared to younger adults, older adults demonstrated greater postural sway (SDCoMAccel) in the A-P.Compared to younger adults, older adults demonstrated reduced MoS , Fig. p = 0.01, Fig. Across all participants, there were positive correlations between eccentric work in the perturbed leg and (1) RF EMG burst duration Fig. A, (2) Mop = 0.40, Fig. In the early and middle phases of SLS, the number of synergies extracted was 3.05 ± 0.51 and there was no difference between two groups . Within these 4 clusters, we identified common and group-specific muscle synergies between older and younger adults. We observed a cluster showing good reliability  that was consistent across all participants. This particular cluster exhibited predominant mGas muscle activation. Group-specific muscle synergies had excellent reliability . The older adult specific muscle synergy cluster had predominant TA muscle activation, while younger adults specific muscle synergy cluster showed predominant Gmed activation  surface with unpredictable properties (stiffness and damping). We found that knee extensor eccentric control during SLS was positively correlated with the balance control of protective stepping. Compared to younger adults, older adults demonstrated reduced knee extensor eccentric work, EMG burst duration, and showed prominent TA muscle activation (as opposed to prominent Gmed activation in younger adults) in muscle synergy during SLS of the perturbed leg. In addition, decreased swinging (trailing) leg protective step vertical velocity and MoS were observed in older adults.32. A protective step lift-off during SLS, however, increases the body weight load on the perturbed leg and this requires additional negative eccentric force generation to stabilize the SLS phase of the perturbed leg.Loss of walking balance due to unexpected and/or partially expected ground balance challenges such as slip, trip, surface drops from uneven or compliant surfaces can be recovered by a protective stepping reaction33. Most of the negative work, which is needed to absorb the impact from ground perturbation, is created by the knee joint eccentric control, compared to control at the ankle and hip joints35. Therefore, sufficient knee extensor eccentric control is required to decelerate the body’s falling momentum, preventing the risk of limb collapse in the perturbed leg36 and provide postural stability in the SLS phase for the successful protective stepping28.Once protective stepping is initiated in response to a ground balance challenge, knee extensor eccentric control in the perturbed leg during SLS becomes critical as the initial defense against unrecoverable limb collapse38.Indeed, our findings demonstrated a correlation between knee extensor eccentric work of the perturbed leg and balance control during the first protective step touchdown (swinging leg velocity and MoS). In other words, having better eccentric control at the knee joint during SLS leads to a more effective protective step to complete balance recovery. This indicates that insufficient eccentric control during SLS could result in incomplete and/or unstable balance recovery by the first protective step touchdown, and require additional protective steps and/or other types of recovery reactions 39. Force generation at the ankle, knee, and hip joints during dynamic balance reaction is highly associated with falls in older adults40. At the knee joint, the negative eccentric work reduces the risk of falls by absorbing falling momentum during single limb landing and slowing down the body’s forward momentum, thus preventing postural instability during SLS41.Lower extremity muscle strength decreases by 3–4% annually from age 7042, and the steadiness of knee extensor eccentric force generation distinguishes older fallers from older non-fallers43.We found that older adults have reduced knee extensor eccentric work and EMG burst duration compared to younger adults. This observation suggests that the deficiencies in eccentric control at the knee joint may be linked to heightened postural instability in older adults, particularly when they lack access to additional protective balance responses, such as reach-to-grasp movements. Indeed, force generation through knee extensor eccentric contraction is more variable than concentric contraction in older adults44. The outcome of our muscle synergy analysis in this study showed that older adult-specific synergy involves highly activated TA muscle. During the initial phase of SLS, the CoM moves laterally toward the supporting (SLS) leg to prepare for swinging of the opposite leg, therefore, lateral stability is challenged in the frontal plane45. Ankle inversion torque generated by the TA muscle during single leg stance shifts the center of pressure (CoP) laterally46. Given that the CoP should be continuously moved toward the CoM to keep the CoM over the base of support for maintaining balance, the older adults-specific predominant TA activation appears to be one of the movement strategies for lateral balance control at the ankle joint level. At the hip joint level, however, the lateral balance can be more actively controlled by hip abduction when the CoM is shifted laterally. Indeed, the younger adults-specific muscle synergy showed greater activation of the Gmed. Considering reduced hip abduction torque-time production with age47, however, the ankle strategy is shown to be a neuromuscular modification for balance control in older adults. Our findings regarding the work at the ankle and hip joints also suggest that older adults tend to rely on ankle joint control to regain postural stability during the SLS phase, while younger adults more actively engage the hip joint. In the meantime, the activation of the medial gastrocnemius (mGas) was a prominent component of the observed muscle synergy in both older and younger adults. Given that the mGas is a biarticular muscle, with attachments at both the ankle and knee joints, the mGas contributes to generating compressive shear force at the knee joint when the foot is dorsiflexed during the SLS48. The compressive shear force created by the mGas stabilizes the SLS balance by preventing anterior tibial translation and attenuating the valgus loading at the knee joint49.Age-related deterioration in lower extremity muscle strength and function leads to neuromuscular modification in older adults to regain standing balance during SLSWe observed that older adults exhibited significantly greater postural sway in the A-P and M-L directions during the perturbed leg SLS phase. This indicates that even when the perturbed leg is not collapsed, insufficient knee extensor eccentric control results in a weakened and less stable perturbed leg, thereby increasing postural instability, which relates to potential fall risk. In addition, reduced MoS and swinging speed of the trailing leg protective step in older adults demonstrate that insufficient eccentric control during SLS leads to unstable and/or less effective balance recovery by the trailing leg protective (compensatory) step.51 and the aging-related redistribution of mechanical work from distal (the ankle joint) to proximal (the hip joint) during downhill walking are significantly smaller than during uphill walking52. These findings suggest that age-differences may be more prevalent in the ankle and knee, and less prevalent in the hip. Therefore, future comparative analyses of joint work at the ankle and hip should be conducted to better understand whether the hip and knee or knee and ankle or all three joints should be the focus of balance rehabilitation and fall prevention interventions.Our findings highlight the potential importance of knee extensor eccentric control for stabilizing the center of mass during walking balance responses to external ground perturbation. This provides evidence supporting future investigations into eccentric strength rehabilitation training for older adults and older frail adults . In addition, the muscle synergy results further support the inclusion of hip abduction/adduction strengthening rehabilitation training for older adults as a means to improve lateral balance control, providing a more active movement strategy option. Nevertheless, it should be noted that previous studies have demonstrated the relevance of ankle moments for stabilizing the CoMThere are a few limitations of the present study that should be acknowledged. Given our small sample size, the study’s results must be interpreted with caution. Future research with a larger sample size is necessary to assess the entire older population more accurately. In order to characterize muscle synergy patterns for balance recovery strategies, only the first response of each participant  was analyzed as we were interested in capturing the naïve response during the first exposure to the balance challenge on the compliant surface. However, there can be a risk of bias in this method when evaluating a single trial from each subject. Specifically, computing NNMF in just one trial may result in the following limitations: 1) Generalizability (Representability): Results from a single trial can be susceptible to the influence of noise, variability, and atypical behaviors during the reaction motion, potentially rendering them unrepresentative of typical muscle activation patterns. To address within-subject variability in our method, we set the variability accounted for (VAF) threshold to 90, meaning that only the synergies explaining 90% of the data were used in the muscle synergy analysis. Between-subject variability exists in the spatial components of muscle synergies as well. Therefore, we used a statistical clustering method based on the intra-class correlation coefficient (ICC). Muscle synergies were categorized as common synergies if the ICC was greater than 0.75, age-specific synergies if the ICC was greater than 0.9. Through this approach, we mitigated the risk of capturing atypical motion and improved acquisition of representative measures. 2) Muscle Synergy Algorithm Stability: The NNMF algorithm is iterative and sensitive to the initial starting point of each iteration, which may lead to convergence in local minima. Therefore, the repeatability (stability) of NNMF outputs may not be guaranteed. To mitigate this risk and enhance stability, we used 20 random starting points (replicates) in each run of the algorithm. 3) Interpretation: Due to our small sample size, interpretating the muscle activation patterns we observed can be challenging when trying to conclude whether these age-specific muscle synergies apply to the entire population. Therefore, a larger sample size is required to improve the stability of muscle synergy interpretation and better support the identification of age-related movement strategies arising from the intrinsic variations in motor behavior.Finally, to further validate our findings, future studies will involve the inclusion of additional muscles in the upper body and arms, as they contribute to overall postural balance control during the SLS phase. Additionally, it is important to incorporate faller populations in order to broaden the scope of our research.Knee extensor eccentric control during SLS was positively correlated with postural stability while walking on a compliant surface. Reduced knee extensor eccentric control in older adults was associated with increased postural sway and compromised swinging leg balance control in older adults. The muscle synergy analysis demonstrated that older adults exhibit a specific muscle synergy with highly activated TA muscle for postural control at the ankle joint level, while younger adults show prominent Gmed activation for postural control at the hip joint level during the SLS phase of balance response. These findings provide insights into age-related changes in neuromuscular control and movement strategies for balance control, supporting rehabilitation interventions for older adults that focus on improving knee extensor eccentric control and strengthening hip abduction/adduction.Supplementary Information.


Interstitial lung abnormalities (ILA) often represent early fibrotic changes that can portend a progressive fibrotic phenotype. In particular, the fibrotic subtype of ILA is associated with increased mortality and rapid decline in lung function. Understanding the differential gene expression that occurs in the lungs of participants with fibrotic ILA may provide insight into development of a useful biomarker for early detection and therapeutic targets for progressive pulmonary fibrosis.Measures of ILA and gene expression data were available in 213 participants in the Detection of Early Lung Cancer Among Military Personnel (DECAMP1 and DECAMP2) cohorts. ILA was defined using Fleischner Society guidelines and determined by sequential reading of computed tomography (CT) scans. Primary analysis focused on comparing gene expression in ILA with usual interstitial pneumonia (UIP) pattern with those with no ILA.ILA was present in 51 (24%) participants, of which 16 (7%) were subtyped as ILA with a UIP pattern. One gene, pro platelet basic protein (PPBP) and seventeen pathways  were significantly differentially expressed between those with a probable or definite UIP pattern of ILA compared to those without ILA. 16 of these 17 pathways, but no individual gene, met significance when comparing those with ILA to those without ILA.Our study demonstrates that abnormal inflammatory processes are apparent in the bronchial airway gene expression profiles of smokers with and without lung cancer with ILA. Future studies with larger and more diverse populations will be needed to confirm these findings.The online version contains supplementary material available at 10.1186/s12931-023-02536-w. Interstitial lung abnormalities (ILA) are chest computed tomography (CT) findings in persons not known to have interstitial lung disease (ILD) that may represent an early stage of pulmonary fibrosis (PF) . OverlapWhile there are numerous cell types in the lung that likely play important roles in the pathobiology of IPF, growing data suggests that patients with PF have altered patterns of bronchial epithelial cell gene expression . SignifiTo provide an assessment of the genes and pathways associated with ILA, and ILA subtypes we performed bronchial airway epithelial gene expression analyses in patients enrolled in Detection of Early Lung Cancer Among Military Personnel (DECAMP 1 and DECAMP 2) cohorts .The DECAMP Study is a multicenter consortium comprised of 15 military treatment facilities, Veterans Affairs hospitals, and academic centers across the United States. Participants were recruited into one of two study protocols, designated as DECAMP-1 and DECAMP-2 . StudiesAll individuals in the DECAMP study underwent bronchoscopy. Bronchial airway epithelial cells were obtained from brushings of the right mainstem bronchus collected during fiberoptic bronchoscopy with an endoscopic cytobrush . The brushes were immediately placed in 1 mL of RNAprotect Cell Reagent  and kept at − 80 °C until RNA isolation was performed.Total RNA was isolated using the miRNeasy Mini Kit . RNA integrity was assessed by Agilent BioAnalyzer, and RNA purity confirmed using a NanoDrop spectrophotometer. Libraries were generated using the Illumina TruSeq Stranded Total RNA kit and sequenced on the Illumina NextSeq 500 and Illumina HiSeq3000 with 75 base-pair paired-end reads . For data preprocessing, we developed an automatic pipeline using the Nextflow framework. Quality of FASTQ files was assessed with FastQC. Reads were aligned to the human genome with 2-pass STAR and gene-level and isoform-level expression quantified with RSEM. Splice junction saturation, transcript integrity, and biotype distributions were calculated for each sample with RSeQC. DESeq2 or edgeR was used to identify associations between gene expression profiles and clinical variables while controlling for confounding covariates. Genetic variants were called using the Broad Institute’s GATK RNA-seq best-practices workflow. Briefly, duplicates were marked with Picard tools, splitting of intronic reads, realignment around indels, and base quality score recalibration were performed with GATK, and variants were called with Haplotypecaller.DECAMP-1 utilized CT scans collected as part of routine clinical care while DECAMP-2 utilized a standardized protocol for image acquisition and reconstruction. DECAMP-2 scans were collected using low dose helical computed tomography on a minimum 16-slice scanner. The scans were acquired at 2.5 to 5 mm but reconstructed into 1 mm slice thickness using the soft tissue and lung algorithms. Images from all sites were then de-identified and submitted to the American College of Radiology Imaging Network (ACRIN) Core Laboratory for storage.The LIMMA package in R (version 3.4.0) was used to assess differential gene expression (DGE). To do this, raw count matrix of gene expression was initially filtered by counts per million (CPM) such that a gene could only be included if its CPM was greater than 1 in 10% of the total number of patients. DGE analysis was then performed using a pairwise comparison between the ILA phenotypes at a false discovery rate (FDR) of 0.05. The differentially expressed genes (DEG) identified by LIMMA were further analyzed by Enrichr for over-representation analysis. Heatmaps were used to visualize the data and identify unsupervised gene clusters using the “Ward.D2” algorithm. Gene set enrichment analysis (GSEA)  was perfMeasures of ILA were assessed on chest CT scans using a sequential method by readers (including radiologists and pulmonologists) blinded to prior interpretations and participant information and per Fleischner society recommendations . ParticiData are presented as means and standard deviations for continuous measurements and number and percentage for categorical features. P values were calculated using Fisher’s exact test or t test as applicable. All analyses were adjusted for age, gender, cohort, cancer status, smoking status and sequencing method as confounders. Genes and pathways demonstrating a p-value < 0.05 after adjusting for the false discovery rate (FDR) were considered statistically significant.Among the 213 participants with both ILA phenotyping and gene expression data, there were 51 (24%) with ILA and 162 (76%) without ILA  compared to those without ILA n = 162) . At a less stringent threshold, the 122 genes with p < 0.01 . GSEA of the ILA with no UIP group showed 13 pathways that met significance; of which 9 were common to the ones in the ILA with UIP group (hypergeometric test p = 0.003) and 12 in common with the whole ILA group   demonstrated seventeen pathways whose genes were significantly enriched among those with a UIP pattern of ILA when compared to those without ILA (FDR < 0.01) including tumour necrosis factor-alpha (TNF-α) signalling through nuclear factor kappa-B (NF-κB) Table . 16 out This is the first assessment of bronchial epithelial gene expression among participants with ILA and these findings suggest that both the UIP pattern of ILA and ILA may be associated with similarly altered bronchial airway gene expression at the pathway level.In this cohort, the most significantly differentially expressed gene was PPBP; a gene within the C-X-C motif cytokine family, specifically ligand 7. While CXCL7 has been associated with non-small cell lung cancer , it has Genes involved in TNF-α signalling were among the genes most induced in bronchial brushes from individuals with UIP pattern of ILA and in individuals with ILA relative to non-ILA controls. This pathway has similarly been demonstrated to be upregulated in the lungs of patients with pulmonary fibrosis , and it’ILA with no UIP group may represent an intermediate step between the No ILA group and ILA with UIP group based on clinical characteristics and gene expression.There are limitations to the present study. First, our ability to identify significant pathway enrichment among the genes most differentially expressed despite our overall failure to detect differential expression of individual genes suggests that our study is limited by its small sample size. Second, although analyses were adjusted for the presence of malignancy, it is difficult to exclude the possibility that residual differential background gene expression and the potential covariance of lung cancer and ILA could have affected our results. Third, we cannot rule out that performing this analysis on a subset of DECAMP, due to data availability influenced these results. Specifically, the prevalence of ILA in this cohort is larger than previously described and this may be due to selection bias from using a limited dataset of cohort participants with both ILA phenotyping and RNA sequencing data available. The results of the study are also limited to subjects with a known current or former smoking history and may differ from subjects with ILA without a smoking history. For these reasons, replication of these findings in additional cohorts will be important to validate the observed associations and assess generalizability to other populations.There are differentially expressed bronchial epithelial cell genes and pathways associated with ILA among patients in DECAMP. Some of the most differentially expressed genes and pathways have been similarly demonstrated to be upregulated among patients with IPF. Additional studies in larger cohorts are warranted to confirm these findings.Additional file 1: Table S1. Institutional Review Board committee names and project approval numbers for each center for the DECAMP Study.Additional file 2: Figure S1. Study design diagram.Additional file 3: Table S2. List of differentially expressed genes that met p-value < 0.01 among those with ILA as compared to those without ILA.Additional file 4: Table S3. List of differentially expressed genes that met p < 0.01 among those without probable or definite UIP pattern of ILA as compared to those without ILA.Additional file 5: Table S4. List of differentially expressed genes that met p-value < 0.01 among those with Probable or Definite UIP pattern of ILA as compared to those without ILA.Additional file 6: Table S5. List of differentially expressed pathways comparing ILA with no ILA.Additional file 7: Table S6. List of differentially expressed pathways comparing ILA no UIP with no ILA.Additional file 8: Table S7. Cancer breakdown of nodule identified in participants in DECAMP 1 Cohort.


Female scent communication achieves far more than that required to advertise estrus state to males. Female rats are intensely interested in gaining scent information about conspecific females and their receptivity. Further, the proteome of scent marks deposited by female rats and the targeting and uptake of scent information by other females reveal previously unknown layers of complexity in female rat semiochemistry. The intense interest in female scents by both the same and opposite sex will be of broad interest to the large community using this model species for basic biological and biomedical research. Their accessibility makes the rat an ideal model to further investigate the molecular and cognitive mechanisms that underpin complex female social signaling. Rattus norvegicus) to test i) whether females target their deployment of scent information differentially according to their sexual receptivity and the genetic identity of both female and male conspecifics signaling in the local environment and ii) whether females are attracted to gain the same or different information from female scents compared to males. Consistent with targeting of scent information to colony members of similar genetic background, female rats increased scent marking in response to scents from females of the same strain. Females also suppressed scent marking in response to male scent from a genetically foreign strain while sexually receptive. Proteomic analysis of female scent deposits revealed a complex protein profile, contributed from several sources but dominated by clitoral gland secretion. In particular, female scent marks contained a series of clitoral-derived hydrolases and proteolytically truncated major urinary proteins (MUPs). Manipulated blends of clitoral secretion and urine from estrus females were strongly attractive to both sexes, while voided urine alone stimulated no interest. Our study reveals that information about female receptive status is shared between females as well as with males, while clitoral secretions containing a complex set of truncated MUPs and other proteins play a key role in female communication.Chemical communication by females remains poorly understood, with most attention focused on female advertisement of sexual receptivity to males or mother–offspring communication. However, in social species, scents are likely to be important for mediating competition and cooperation between females determining individual reproductive success. Here, we explore chemical signaling by female laboratory rats ( Chemical signals mediate sexual and social communication between conspecifics across a broad range of taxa . As scen37Laboratory rodents have provided key models to unravel the information encoded in complex mammalian scent signals and to understand the neural pathways involved in processing scent information , 13. Lab14Rattus norvegicus) typically live in large colonies in which multiple females share a burrow system and feeding sites while largely tolerating each other but are intolerant of females from neighbor colonies  invest heavily in a family of major urinary proteins (MUPs) that are produced in the liver and excreted at high concentration in urinary scent marks  to address key questions about female scent communication. First, do females target their deployment of scent information differentially according to their sexual receptivity and the genetic identity of both female and male conspecifics signaling in the local environment, consistent with information communicated to both sexes? Second, are females strongly attracted to gain the same or different information from female scents compared to males? To address this, our study integrates behavioral tests to assess the social context in which females deploy scent signals, and the attraction of males and females to female scent marks according to the donor’s receptivity, with detailed molecular characterization of proteins in female scent deposits. This reveals intense female interest in scents from sexually receptive females, while female scent proteins show a surprising level of complexity, contributed from clitoral secretions, urine, and other sources. Information provided by clitoral components is essential for the attraction of both males and other females, but females were attracted only when scents contained a full mixture of information from clitoral and urinary components, using different scent information to males.Here, we use laboratory rats , but a tendency to reduce scent marking if the stimulus was from a same strain male was not significant . By contrast, females in a nonreceptive stage of the estrous cycle were equally likely to deposit scent whether a male stimulus was present or not . This increase in scent marking was mostly a response to urine from females of the same strain . Thus, females were sensitive to the genetic background of female scent donors and were stimulated to signal when the female donor was from a very similar genetic background to themselves but not if they were from a foreign strain.Female urine stimuli increased the likelihood that females deposited scent compared to matched control trials .To understand the molecular basis of female communication, we analyzed the protein content of female scent deposits that were swabbed from glass tiles. In addition to scent marks from laboratory females, we also collected some scent marks deposited on glass tiles by two free-living wild female rats from a nearby farm . Digests of this band using trypsin to produce shorter peptides were analyzed by liquid chromatography–tandem mass spectrometry. This identified unique peptides matching all predicted MUP isoforms, except MUP 15, indicating that female scent marks contain a complex mixture of rat MUPs (Dataset S1). PMF of the 80-kDa band following LysC digestion revealed peptides unique to the enzyme β-glucuronidase . Finally, PMF analysis of a relatively faint 20-kDa band revealed peptides unique to an odorant-binding protein OBP 1f (Uniprot KB Q9QYU9) .First, the scent protein fractions were visualized by SDS-PAGE A. DifferDataset S2). Label-free quantification confirmed the dominance of MUPs, β-glucuronidase (Uniprot KB F1LQQ8), and OBP 1f (Uniprot KB Q9QYU9) .To provide a more complete definition of the total protein content of scent deposits, we conducted a global proteome analysis based on in-solution trypsin digestion of 24 scent deposit samples . A total of 469 proteins were identified at a false discovery rate of 1% and requiring at least two unique peptides identified per protein ( Q9QYU9) B. ProteiIn addition to the dominant proteins, we identified several other members of the lipocalin protein family in female scent samples that might play a role in scent communication, including several lipocalin-like proteins , OBP 2a , OBP 2b (Uniprot KB A0A0G2JZ62), and the rat homolog of feline protein Fel D 4 (Uniprot KB B3EY86).Additionally, scents contained several proteins that are specific to urine, including rat urinary proteins RUP-1, RUP-2, and RUP-3, which previously had been identified in rat urine . These aSI Appendix, Fig. S4). A few scent samples also displayed a larger mass corresponding to the nonphosphorylated form of MUP 1 . Subjects were always unfamiliar with scent donors. Video transcription was carried out blind to the relative position of the two stimuli during each trial, which were marked as left and right. Time spent sniffing and gnawing at each of the mesh caps covering the stimulus tubes was recorded. Results from the behavioral tests are included in Analyses were carried out using R version 4.2.0  with theTo examine attraction to female scent marks, a repeated-measures ANOVA compared the duration of investigation of the female scent stimulus vs. control as a within-trial factor (sniffing through or gnawing the covering mesh), with subject sex, donor sexual receptivity (proestrus/estrus or metestrus/diestrus), and contact with the stimulus (contact or airborne) as between-trial factors . As malePeptide map fingerprint (PMF) was performed as previously described . Protein6 ions with a maximum fill time of 250 ms), and the 10 most intense multiply charged ions (z ≥ 2) were sequentially isolated and fragmented by high-energy collisional dissociation at 30% standardized collision energy. Fragments ions were detected at 35,000 resolution (AGC set to 1e5 ions with a maximum fill time of 100 ms). Dynamic exclusion was set as 20 s.Scent marks, urine, or clitoral gland extracts were diluted in 25 mM ammonium bicarbonate according to protein complexity and concentration. Protein concentration was measured using the Bradford method . ProteinR. norvegicus in UniProt . Trypsin was selected as the specific enzyme allowing one missed cleavage. Carbamidomethylation of cysteine residues was set as fixed modification and methionine oxidation as variable modification, allowing a mass tolerance of 10 ppm for precursors and 0.01 Da for fragment ions. To accept an identification, we set FDR<1% at the peptide level and required at least two unique peptides identified with a MASCOT score higher than 20. Label-free quantification was performed using Progenesis QI (Waters). Since protein concentration and sample complexity differed greatly among samples, we set “normalization off” in Progenesis QI and calculated the proportional abundance of proteins. For the Mascot search, only the top five spectra were exported. Evidence for MUP truncation was derived using PEAKS v8 (Bioinformatics solutions Inc.). Proteomics data were visualized using R (v.4.2.0) (PXD039325 and 10.6019/PXD039325.Proteome Discoverer version 1.4 (Thermo Scientific) was used to generate peak lists from raw data using default parameters. Mascot version 2.8 was used as a search engine to identify peptides and proteins. For identifications, we search our data against a database containing all entries for v.4.2.0) . The masv.4.2.0)  partner g for 10 min. All analyses were performed on a Synapt G2 mass spectrometer (Waters Corporation), fitted with an Atmospheric Pressure Ionization (API) source. Samples were desalted and concentrated on a C4 reverse phase trap (Thermo Scientific), and protein was eluted at a flow rate of 10 μL/min using three repeated 0 to 100% acetonitrile gradients. Data were collected between 800 and 3,500 Th (m/z), processed, and transformed to a neutral average mass using MaxEnt 1 . The instrument was calibrated with 250 fmol of myoglobin (16.7 kDa) from equine heart (Sigma-Aldrich).Samples were diluted in 0.1% (v/v) formic acid and centrifuged at 13,000Appendix 01 (PDF)Click here for additional data file.Dataset S01 (XLSX)Click here for additional data file.Dataset S02 (XLSX)Click here for additional data file.Dataset S03 (XLSX)Click here for additional data file.Dataset S04 (XLSX)Click here for additional data file.


Identification of treatment-specific predictors of drug therapies for bipolar disorder (BD) is important because only about half of individuals respond to any specific medication. However, medication response in pediatric BD is variable and not well predicted by clinical characteristics.n = 71) or lithium (n = 50). Participants completed structural magnetic resonance imaging (MRI) at baseline before treatment and 1 week after treatment initiation, and brain morphometric features were extracted for each individual based on MRI scans. Positive antimanic treatment response at week 6 was defined as an over 50% reduction of Young Mania Rating Scale scores from baseline. Two-stage deep learning prediction model was established to distinguish responders and non-responders based on different feature sets.A total of 121 youth with early course BD (acute manic/mixed episode) were prospectively recruited and randomized to 6 weeks of double-blind treatment with quetiapine (p < 0.05). Combining brain morphometry at baseline and week 1 allows prediction with the highest balanced accuracy (quetiapine: 83.2% and lithium: 83.5%). Predictions in the quetiapine and lithium group were found to be driven by different morphometric patterns.Pre-treatment morphometry and morphometric changes occurring during the first week can both independently predict treatment outcome of quetiapine and lithium with balanced accuracy over 75% (all These findings demonstrate that pre-treatment morphometric measures and acute brain morphometric changes can serve as medication response predictors in pediatric BD. Brain morphometric features may provide promising biomarkers for developing biologically-informed treatment outcome prediction and patient stratification tools for BD treatment development. Bipolar disorder (BD) frequently emerges during adolescence  baseline image data could significantly predict medication response in bipolar youth at the individual level; and (ii) change in morphometric features from baseline to 1 week after treatment initiation would predict treatment response, and the longitudinal joint model combining baseline data and week 1 structural changes data would predict medication response with highest accuracy. Finally, considering that the drug mechanisms of lithium and quetiapine are different  .This study was approved by the University of Cincinnati and the Cincinnati Children's Hospital Medical Center Institutional Review Boards. Youth with bipolar I disorder were recruited from the Cincinnati Children's Hospital Medical Center, the University of Cincinnati, and the local community. Diagnosis of bipolar I disorder was confirmed using the Washington University in St. Louis Kiddie Schedule of Affective Disorders and Schizophrenia administered by raters with demonstrated inter-rater reliability  within the age range of 10–18 years old; (2) experiencing a manic or mixed episode; (3) having a baseline YMRS score ⩾20; (4) within 2 years from onset of BD; (5) having no prior psychiatric hospitalizations for mania; (6) having no treatment history with therapeutic doses of antipsychotics or mood stabilizers for over 3 months, and no psychotropic medication during the week prior to baseline scans. Participants were excluded if they had (1) a contraindication to MRI scanning; (2) an IQ <70; (3) a positive pregnancy test; (4) a history of a major systemic or neurological illness, or an episode of loss of consciousness >10 min; (5) any lifetime DSM-IV-TR substance use disorder (nicotine dependence was permitted); or (6) a lifetime DSM-IV-TR diagnosis of a pervasive developmental disorder or post-traumatic stress disorder.v. manic episode). Quetiapine was initiated at 100 mg qhs and lithium carbonate was initiated at 30 mg/kg (maximum starting dose of 600 mg twice daily). Patients were also given placebo capsules for the medication to which they were not assigned. Quetiapine was titrated to a target dose of 400–600 mg/day based on tolerability and response. Lithium was titrated to a serum level of 1.0–1.2 mEq/L. Treatment was administered in a double-dummy, double-blind manner, with an unblinded study psychiatrist monitoring trough lithium levels and making dose adjustments independent from treating psychiatrist and clinical raters. Blinded clinical tolerability dose adjustment recommendations took precedent over un-blinded double dummy dose adjustment recommendations. There were no significant changes of the treatment methods or outcomes after trial commencement. We used the YMRS changes to assess antimanic treatment response at week 6, and treatment responder was defined as exhibiting ⩾50% reduction in YMRS scores from baseline  to double-blind treatment with lithium or quetiapine and evaluated clinically weekly for 6 weeks. The randomization schedule was stratified by presence/absence of attention deficit and hyperactivity disorder, presence/absence of psychosis, and the mood state  protocol, optimized for the 4T Varian scanner . Acquired T1-weighted images were inspected by two experienced neuroradiologists who made decisions about excessive motion artifact for scan exclusion. No observable scanning artifacts or gross brain abnormalities were identified in any participant included in the following analyses.http://surfer.nmr.mgh.harvard.edu/) to obtain unbiased estimates of morphometric measures, including surface area, cortical thickness, and subcortical volumes . Each participant was thus represented by a 150-dimensional feature vector consisting of morphometric features across the whole brain. Specifically, we included the surface area and cortical thickness of 68 cortical regions labeled in the Desikan/Killiany Atlas  and the remaining nine parts for training (i.e. training set). To avoid information leakage, we performed the non-linear dimensionality reduction  only based on the training set, and the testing set was only used to evaluate performance and never used for tuning or training our models. The model performance was determined by the balanced accuracy, sensitivity, specificity, and area under receiver operating characteristic curve (AUC). We independently reported the model performance in quetiapine and lithium medication group. All the machine learning analyses were programmed using Python language, where the neural network was implemented in the Pytorch library  for drug-specific model training and optimization, and the other group to evaluate the predictive utility of the model for treatment outcome to the other drug therapy. To maintain consistency with preceding analyses, model transferability test was also implemented in the identical two-stage prediction pipeline.Given morphometric features with the highest predictive power may differ in different medication groups which can provide useful neurobiological implications, we sought to identify these potentially discriminating patterns. Detailed calculation of feature contribution is presented in online Supplementary Materials. Top 10 features with greatest contributions are reported for different models and medication groups.t test, while differences in sex and number of responders were assessed using χ2 test. The p value of differences in parental socioeconomic status was calculated by Mann–Whitney U test given the non-normality, and the statistical test for differences in longitudinal clinical scale scores was two-way repeated measures ANOVA.Group comparisons of demographic and clinical characteristics were performed using SPSS software [version 23 (IBM SPSS Statistics)]. The significance level of prediction model was evaluated using permutation test  and eight participants in the quetiapine treatment group (n = 79) were lost to follow-up. Ultimately, 71 participants treated with quetiapine and 50 participants treated with lithium who had completed all follow-up structural MRI examinations and clinical assessments were finally included in the analysis.This prospective randomized clinical trial recruited 149 youth with BD. Data from nine participants were excluded due to failure to finish baseline structural MRI scan or excessive head motion. Eleven participants in the lithium treatment group (p > 0.05). No significant time × treatment group interaction effects in YMRS scores were observed. For the group effect, we found no significant between-group differences in YMRS scores at any follow-up timepoint (p > 0.05). For the time effect, the YMRS scores were significantly decreased from baseline to week 6 in both quetiapine and lithium groups (p < 0.001).The demographic and clinical characteristics are listed in p < 0.001) for the quetiapine treatment group. For the lithium treatment group, baseline morphometric measures achieved balanced accuracy of 75.8% . When using 1-week brain morphometric changes as predictors, balanced accuracies in the quetiapine and lithium treatment group were 76.5%  and 78.9% , respectively. Combining brain morphometric features at baseline and week 1 allowed for the highest prediction performance. For the quetiapine treatment group, the balanced accuracy was 83.2% . For the lithium treatment group, the balanced accuracy was 83.5% . All the evaluation metrics for model performance were listed in The prediction pipeline in the current study was shown in p > 0.05; vice versa . Using week 1 structural change data, both models showed poor transferability below chance level . When considering both baseline and week 1 morphometric data together, both models transferred around chance level .All models failed to reach comparable performance in the transferability test when prediction models developed for one drug were applied to the other drug treated group  in models predicting 6-week treatment response modestly improved the accuracy (83.2% for the quetiapine group and 83.5% for the lithium group). Therefore, combining baseline and follow-up data within a single model may provide a promising direction for maximizing early prediction of treatment outcome in individuals with BD. While these findings require replication in an independent sample to confirm predictive utility of the models developed, they offer a promising preliminary step toward the development of clinically useful MRI-based biomarkers for guiding optimal and flexible treatment planning for individuals in the early course of BD.Given these differences in prediction performance, we sought to identify which morphometric features provide the greatest contribution to treatment outcome. At baseline, cortical thickness features were better predictors of lithium response, while cortical surface area and subcortical volumes were more prominent as features predicting response to quetiapine. In contrast, when considering morphometric changes at week 1, the opposite pattern was observed with changes in cortical surface area and subcortical volume measures being more prominent in lithium response prediction, and changes in cortical thickness measures being more predictive of quetiapine response. According to the radial unit hypothesis and the supra-granular layer expansion hypothesis, cortical thickness is determined by the number and size of cells within a cortical column and surrounding neuropil, while surface area is less dynamic being primarily driven by the number of cortical columns established during brain maturation Rakic, . This suFor the regionality of the most discriminative brain morphometric patterns, at baseline, the most discriminative regions for quetiapine were mainly located in temporal and subcortical regions, while frontal and parietal regions predicted treatment outcome better in lithium group. When considering morphometric changes at week 1, the regions contributing most to prediction mainly comprised frontal and temporal regions for both quetiapine and lithium. Previous research had reported disruption in fronto-temporal neural circuitry in remitted patients with BD (Robinson et al., In addition to these general findings, our transferability test for the two treatments was also informative. The results showed that all models failed to reach comparable performance in the transferability test, indicating that the contributing features of the predictive models differed between treatment groups, both for baseline data and for treatment-related change at week 1. While predictive models for the two treatments had similar success, the quite limited transferability of drug-specific models suggests modest level of shared predictors, so that drug class-specific predictive models appear to be needed for treatment outcome prediction. Different changes in morphometric features at week 1 are perhaps not surprising given the markedly different pharmacology of the two study drugs, though the nature of the differences offers insight into regional differences in early drug impact on brain that are relevant to their therapeutic efficacy.Following recent recommendations on overcoming methodological issues that can lead to inflated results (Arbabshirani, Plis, Sui, & Calhoun, Of note, there are several limitations in our current study. First, the models described in this paper will require replication in an independent dataset before any application in clinical decision making. Second, each patient in our study received either quetiapine or lithium monotherapy. Thus, our study can only predict response of each drug and identify predictors of response in two independent treatment groups. Information about prediction of individual-level preferential response to quetiapine or lithium cannot be provided, which should be interpreted with caution. Third, our study was not powered to examine the relationship between MRI features and outcome separately in different age groups. Fourth, the utility of predictive models is generalizable within drug class and remains to be evaluated. Fifth, additional outstanding questions include the ability of other imaging modalities, such as DTI and resting-state fMRI, to inform and supplement prediction models. In addition, it would be helpful to further evaluate the utility of our models for predicting treatment outcomes in adults and in individuals with a long history of illness and drug therapy.In conclusion, the present study demonstrates that brain morphometric features may predict treatment response for two widely used medications for the treatment of youth with BD. Brain morphometric features provided similar overall prediction performance for both lithium and quetiapine therapies, though the features used to achieve that prediction varied between the two medications. In addition, our findings indicate that brain morphometric changes occurring after a single week of medication exposure are predictive of treatment outcome. These findings provide insight into brain morphometric features associated with the treatment outcome prediction in bipolar youth treated with quetiapine and lithium, and support the potential use of neuroanatomical scans as biomarkers for the optimization of treatment or personalized medication approach which offers potential for reducing the risk of a failed medication trial detectable only following a full course of treatment.


What are the psychometric properties of a Substance Use Symptom Checklist used routinely in primary care among patients reporting high-risk cannabis and/or other drug use?In this cross-sectional study of 23 304 positive screens for daily cannabis or any other drug use, the 11-item checklist provided scaled, unidimensional information on the presence and severity of substance use disorder. The checklist performed well across patient age, sex, race, and ethnicity.The findings of this study support the use of the checklist in primary care as a tool to aid clinicians in eliciting patient symptoms, identifying a spectrum of substance use disorder severity, and clinical decision-making based on diagnostic criteria. This cross-sectional study evaluates the psychometric properties of the Substance Use Symptom Checklist to assess substance use disorder in a primary care setting among adult patients reporting daily cannabis and/or other drug use. Substance use disorders (SUDs) are underrecognized in primary care, where structured clinical interviews are often infeasible. A brief, standardized substance use symptom checklist could help clinicians assess SUD.To evaluate the psychometric properties of the Substance Use Symptom Checklist (hereafter symptom checklist) used in primary care among patients reporting daily cannabis use and/or other drug use as part of population-based screening and assessment.This cross-sectional study was conducted among adult primary care patients who completed the symptom checklist during routine care between March 1, 2015, and March 1, 2020, at an integrated health care system. Data analysis was conducted from June 1, 2021, to May 1, 2022.Diagnostic and Statistical Manual for Mental Disorders (Fifth Edition) (DSM-5). Item response theory (IRT) analyses tested whether the symptom checklist was unidimensional and reflected a continuum of SUD severity and evaluated item characteristics (discrimination and severity). Differential item functioning analyses examined whether the symptom checklist performed similarly across age, sex, race, and ethnicity. Analyses were stratified by cannabis and/or other drug use.The symptom checklist included 11 items corresponding to SUD criteria in the DSM-5 SUD. For all cannabis and drug subsamples, IRT models supported the unidimensionality of the symptom checklist, and all items discriminated between higher and lower levels of SUD severity. Differential item functioning was observed for some items across sociodemographic subgroups but did not result in meaningful change (<1 point difference) in the overall score (0-11).A total of 23 304 screens were included . Overall, 16 140 patients reported daily cannabis use only, 4791 patients reported other drug use only, and 2373 patients reported both daily cannabis and other drug use. Among patients with daily cannabis use only, other drug use only, or both daily cannabis and other drug use, 4242 (26.3%), 1446 (30.2%), and 1229 (51.8%), respectively, endorsed 2 or more items on the symptom checklist, consistent with In this cross-sectional study, a symptom checklist, administered to primary care patients who reported daily cannabis and/or other drug use during routine screening, discriminated SUD severity as expected and performed well across subgroups. Findings support the clinical utility of the symptom checklist for standardized and more complete SUD symptom assessment to help clinicians make diagnostic and treatment decisions in primary care. While these brief screens have been validated for SUDs generally,16 they do not provide information on DSM-5 SUD symptoms. Clinicians need to assess consequences or symptoms resulting from substance use to identify the presence and severity of an SUD. However, clinicians may lack time and training to do so consistently. Brief, standardized assessments offer clinicians a way to assess SUDs and are preferable in general medical settings to full diagnostic interviews,17 which are time-consuming (10-45 minutes) and have specific training and supervision requirements.18 Brief, standardized assessments balance patient and clinician burden while offering a way to help clinicians diagnose SUDs and assess patient treatment needs.20 While brief SUD assessments have been validated in research and specialty settings,22 their performance when used in general medical settings, with responses documented in electronic health records (EHRs), has not been psychometrically evaluated.Brief, validated substance use screens, recommended in primary care by the US Preventive Services Task Force,28 This effort provided a unique opportunity to evaluate the clinical application of the Substance Use Symptom Checklist using data collected as part of routine care. Although clinically informative and practical for use in primary care to engage patients in discussions of problems they are experiencing due to cannabis or other drug use,27 its psychometric performance as a tool to assess SUD severity is untested. The objective of this study was to evaluate the psychometric properties of the Substance Use Symptom Checklist  when used routinely among primary care patients reporting daily cannabis use, other drug use, or both to support its use in general medical settings.As part of behavioral health integration, Kaiser Permanente Washington began routinely screening patients for past-year cannabis and other drug use followed by Substance Use Symptom Checklist assessment when patients reported daily cannabis or any other drug use on screens.STROBE) checklist.This cross-sectional study used EHR data from Kaiser Permanente Washington. Kaiser Permanente Washington is a large integrated health care system providing primary care across 32 clinics in Washington state. This study received approval, waivers of consent, and HIPAA authorization to use existing EHR data from the Kaiser Permanente Washington Health Research Institute institutional review board. Researchers followed the Strengthening the Reporting of Observational Studies in Epidemiology  were asked to complete a 7-item behavioral health screen   and enter responses into the EHR. The decision to use “daily or almost daily” as the threshold for a positive cannabis screen came directly from the health system—based on who might be most likely to have an SUD as well as health system capacity to respond to positive screens—and is consistent with a validation study demonstrating that a threshold of daily cannabis use had high specificity.DSM-5 criteria4 to elicit SUD symptoms in primary care. It was developed in partnership with frontline clinicians and implemented with behavioral health screening as a tool to facilitate conversations between clinicians and their patients who reported high-risk substance use.5 Approximately 73% to 78% of patients who reported high-risk substance use completed the symptom checklist.28The symptom checklist  were included if they had at least 1 primary care visit from March 1, 2015, to March 1, 2020, and completed 1 or more eligible symptom checklists. Symptom checklists (N = 42 650) were eligible if prompted by daily cannabis use and/or any other drug use screens (39 135 [91.8%]) and completed on the same day as screening (32 872 [84.0%]). We excluded incomplete symptom checklists (1667 [5.1%]) and those linked to a virtual encounter due to differences in workflow (199 [0.6%]). Each symptom checklist fell into 1 of 3 mutually exclusive subsamples, those triggered by (1) daily cannabis use only (n = 22 497); (2) other drug use only (n = 5706); or (3) both daily cannabis and other drug use (n = 2803). However, patients could have completed more than 1 symptom checklist over the study period and thereby be included in more than 1 symptom checklist subsample or in 1 subsample multiple times. Within each symptom checklist subsample, we randomly selected 1 symptom checklist per patient .DSM-5 symptom criteria on the symptom checklist. The symptom checklist had a past-year timeframe, consistent with diagnostic standards.4 The symptom checklist was scored by summing symptom criteria counts (0-11).31DSM-5 considers 2 to 3 criteria mild; 4 to 5, moderate; and 6 to 11, severe SUD.Patients self-reported the presence or absence of 11 32 Participants reporting other or unknown race or unknown ethnicity were included in descriptive tables and full sample analyses but excluded from race- and ethnicity-stratified analyses.Demographic characteristics collected from patients and documented in the EHR by the health system were used to approximate patients’ lived identities and experiences. This included age , sex  listed on legal forms of identification, self-identified race , and ethnicity (Hispanic or non-Hispanic).33 A 2-parameter logistic IRT model using maximum likelihood was fit with the mirt package in R version 4.2.3   in each of the symptom checklist subsamples. IRT aims to model the relationships that an observable set of measures  have with an unobservable or latent trait .35 Using a confirmatory approach, we examined the fit of unidimensional IRT models by comparing model fit indices  to standard cutoffs for acceptable fit: comparative fit index (CFI) greater than 0.95, root mean square error of approximation (RMSEA) less than 0.05, and standardized root mean square residual (SRMSR) less than 0.05.37 Standardized factor loadings (λ) characterized the relationship between each item and latent SUD severity38; loadings  with absolute value of 0.40 or greater were considered substantive.39 We examined residual correlation between items after fitting the unidimensional model to identify potential item clusters violating the IRT assumption of local independence and may indicate an unmodeled second factor. If correlations were small ,40 a unidimensional model was considered appropriate.41IRT assumes that the latent variable (SUD severity) exists along a unidimensional continuum. This unidimensionality is consistent with current diagnosing standards characterizing SUD as a single brain disorder with varying severity.a) and severity (b) to characterize the association between how patients responded to items on the symptom checklist and their latent SUD severity. Discrimination characterized how well an item differentiated patients with higher vs lower SUD severity; values could range from −∞ to ∞ 42 with higher values indicative of better discrimination. Severity  characterized the value along the continuum of latent SUD severity at which an item best discriminated, which was also the level of latent SUD severity needed for patients to have a 50% chance of endorsing the item; values could range from −∞ to ∞ , with higher values endorsed at higher levels of SUD severity.41 IRT parameters were plotted graphically as item characteristic curves  to illustrate how the probability of endorsing each of the 11 items (y-axis) was associated with one’s latent SUD severity (x-axis).42IRT models used 2 parameters, discrimination  within each symptom checklist subsample, we used a likelihood ratio test to statistically test for DIF by comparing a more complex IRT model in which item parameters could vary by demographic subgroup to a simpler model that constrained item parameters to be the same for subgroups  could occur if the probabilities of endorsing specific symptom checklist items were influenced by a patient’s broader lived experiences rather than their latent level of SUD severity.43 Because SUD severity was determined by the number of SUD criteria (0-11), knowing whether DIF led to differences in criteria count may be more clinically meaningful than knowing whether DIF was present for individual items.44 We examined differences in the total expected number of criteria (0-11) endorsed by patients with the same level of latent SUD severity to understand whether the test as a whole performed differently across demographic subgroups. We also examined differences in CFIs between models with and without correction for DIF for each demographic subgroup . Symptom checklist subsamples included 16 140 patients who reported daily cannabis use only, 4791 patients who reported other drug use only, and 2373 patients who reported both daily cannabis and other drug use . All 3 s4 For patients with daily cannabis use only, prevalence of each SUD criterion varied from 4.4% (705 patients) for the item neglect roles to 21.9% (3536 patients) for the item tolerance. For patients with other drug use only, they ranged from 11.9% (570 patients) for the item hazardous use to 25.2% (1207 patients) for the item physical or psychological problems. For patients with daily cannabis use and other drug use, they ranged from 18.7% (444 patients) for the item neglect roles to 41.5% (985 patients) for the item tolerance , 1446 (30.2%), and 1229 (51.8%), respectively, reported symptoms consistent with SUD .4 For paolerance .Unidimensional factor models demonstrated excellent fit to the symptom checklist’s 11 items  , indicat33  with the probability of endorsing each item increasing as SUD severity increased. While discrimination was higher than typicalSeveral items had significant DIF by age and sex for all 3 symptom checklist subsamples. Additionally, there was significant DIF by race for patients who reported daily cannabis use only and by ethnicity for patients who reported other drug use only  to a single SUD represented by a continuum of severity from 2 to 11 symptoms (DSM-5).31 Our findings build on this work to support use of the symptom checklist as a tool for assessing the spectrum of DSM-5 SUD in primary care. While beyond the scope of this study, exploratory psychometric studies71 could improve on DSM-5 conceptualization of SUD.To our knowledge, this is the first study to evaluate a Substance Use symptom checklist of 31 particularly for patients who use cannabis only. This may reflect physiological adaptation to regular but not necessarily harmful use, and in the absence of 2 additional criteria, experts consider tolerance less likely to indicate SUD.31 Consistent with a previous study evaluating the psychometric performance of an Alcohol Symptom Checklist used in routine care,44 more discriminating items included neglect roles, time spent, and activities given up, which may reflect life domains that are important to patients.SUD symptoms represent negative consequences of use, which can be used in clinical conversations to help patients identify reasons for reducing substance use. Consistent with prior studies, tolerance was less discriminating than other criteria,25 Implementation of symptom checklists for patients who self-report daily cannabis and/or other drug use, is affordable,27 feasible,26 and acceptable to patients in primary care.25 The ability to measure SUD symptoms can help with SUD identification, symptom management, and treatment planning for patients.72 Study findings support the symptom checklist’s construct validity73 as a scaled measure of SUD severity. The strong psychometric performance identified may help clinicians feel confident in measurement-based tools to support SUD identification and care in general medical settings where they are underrecognized and undertreated.9 A lack of clinically meaningful difference between expected total scores on the symptom checklist for patients of different ages, sexes, races, and ethnicities supports its use in diverse patient populations. Future studies are needed to evaluate test-retest reliability of the symptom checklist and discriminative validity compared with a confidential interview comparison standard for SUD.Measurement is essential for improving care for patients using cannabis and other drugs.74 and findings may not generalize to other patient populations, health systems, and settings. Despite these limitations, the use of data collected as part of routine care is an important and novel strength that lends external validity to findings.The use of routinely collected assessment data documented in EHRs is a unique strength of this study; it also introduced important limitations. Symptom checklists were only routinely administered to those reporting “daily or almost daily” cannabis or any other drug use. Nevertheless, we identified a spectrum of SUDs in this patient population. For patients who reported daily cannabis and other drug use, it was unclear which substance predominantly contributed to symptoms. Similarly, among patients who reported other drug use, it was unknown what class of drugs  contributed to symptoms. However, for the symptom checklist to be practical in primary care, it was not possible to use different checklists for specific substances. Some patients may have underestimated or underreported substance use on behavioral health screens and/or symptoms on symptom checklists, although many patients were willing to report substance use and multiple SUD symptoms. Dichotomous endorsement of symptoms may not reflect the reality of how patients experience an SUD, and the symptom checklist did not assess the frequency of criteria; clinicians must use clinical judgment and confirm symptoms are recurrent to diagnose SUD. Nevertheless, the symptom checklist can prompt a patient-clinician conversation leading to diagnosis of specific SUDs. Additionally, this study was conducted in a predominantly non-Hispanic, White patient population insured by an integrated health care system in a US state where adult cannabis use is legal. Analyses of DIF by race may have been underpowered,This cross-sectional study supports the Substance Use Symptom Checklist as a measure of SUD severity when used routinely in primary care. Specifically, the 11-item symptom checklist provided scaled, unidimensional information to help clinicians assess SUD symptoms and severity among patients with high-risk cannabis and other drug use. The symptom checklist was brief, feasible to administer in primary care, and performed equitably across age, sex, race, and ethnicity. These findings support the clinical utility of the symptom checklist as a tool for primary care.


This cohort study examines whether Medicaid waivers were associated with a reduced risk of Medicaid disenrollment among autistic adolescents who are transitioning to adulthood. The study followed the 3 and (3) were aged 14 to 26 years during the study period.We extracted data from Medicaid for the period 2008 to 2016 from 47 states and Washington, DC . Individuals were included if they: (1) had 12 or more consecutive months of Medicaid enrollment, (2) had at least 1 inpatient or at least 2 other claims with an autism spectrum disorder (ASD) diagnosis code,1 The exposure was a time-varying categorical measure of waiver availability constructed as residence in a state with an active ASD-specific 1915(c) waiver (hereinafter ASD-specific), a non–ASD-specific 1915(c) waiver (hereinafter other), or no waiver. The exposure was interacted with age in a given month to assess variation in the waiver availability–disenrollment association by age. Beneficiary characteristics served as covariates, along with state of residence and calendar year. Characteristics were compared between continuously enrolled and disenrolled individuals using χ2 tests with a 2-sided α of .05. Risk of disenrollment was assessed using marginal predicted probabilities as generated from an adjusted person-month discrete-time proportional hazards model. Additional details are provided in the eMethods in We constructed longitudinal analytic data sets at the person-month level, combining individual-month–level information on Medicaid eligibility and service use with state-level information on Medicaid programs and procedures. The binary outcome was first disenrollment while enrolled in Medicaid.This study included 133 955 autistic individuals: 14 739 who had disenrolled in Medicaid and 119 216 who were continuously enrolled; the mean (SD) age for the total cohort was 16.5 (3.4) years. A higher proportion of disenrolled vs continuously enrolled individuals were Medicaid-eligible due to poverty ; a lower proportion were Medicaid-eligible because of disability  . A smallP < .001)   was simi5 and may reduce inpatient and long-term care services and expenses.6 States without waivers available for autistic young adults should consider implementing or extending existing waivers to maintain insurance coverage and subsequently improve health outcomes. Interestingly, ASD-specific waivers were not associated with greater decreases in disenrollment compared with waivers for other diagnoses or functional eligibility criteria, suggesting that ASD-specific waivers may not mitigate disenrollment. Our findings suggest that requirements for remaining continuously enrolled in Medicaid when poverty-eligible may be challenging, as small changes in income may result in disenrollment.Residence in a state with Medicaid waivers was associated with a 6-fold or greater decrease in probability of disenrollment among autistic adolescents entering adulthood. These findings are important for state policy, as being insured is associated with fewer unmet health care needs2Study limitations include the potential for misclassification due to incomplete or missing claims data. Policy makers should consider bolstering continuity of waiver availability to keep autistic youths insured and connected to necessary health care services or explore other eligibility mechanisms for Medicaid.


Using this platform, we exposed primary human airway smooth muscle cell-derived microtissues to a panel of six inflammatory cytokines present in the asthmatic niche, identifying TGF-β1 and IL-13 as inducers of a hypercontractile phenotype. RNAseq analysis further demonstrated enrichment of contractile and remodeling-relevant pathways in TGF-β1 and IL-13 treated tissues as well as pathways generally associated with asthma. Screening of 78 kinase inhibitors on TGF-β1 treated tissues suggests that inhibition of protein kinase C and mTOR/Akt signaling can prevent this hypercontractile phenotype from emerging, while direct inhibition of myosin light chain kinase does not. Taken together, these data establish a disease-relevant 3D tissue model for the asthmatic airway, which combines niche specific inflammatory cues and complex mechanical readouts that can be utilized in drug discovery efforts.Asthma is often characterized by tissue-level mechanical phenotypes that include remodeling of the airway and an increase in airway tightening, driven by the underlying smooth muscle. Existing therapies only provide symptom relief and do not improve the baseline narrowing of the airway or halt progression of the disease. To investigate such targeted therapeutics, there is a need for models that can recapitulate the 3D environment present in this tissue, provide phenotypic readouts of contractility, and be easily integrated into existing assay plate designs and laboratory automation used in drug discovery campaigns. To address this, we have developed DEFLCT, a high-throughput plate insert that can be paired with standard labware to easily generate high quantities of microscale tissues  Chronic asthma is characterized by permanent structural changes to the airways known as airway remodeling, which consists of modifications to epithelial cells, smooth muscle, and fibroblasts, as well as changes in the extracellular matrix (ECM) composition.Contractility in airway smooth muscle is largely governed by the dynamics of actin and myosin filaments within the cytoskeleton.in vitro modeling can allow for an assay design that replicates the core phenotypic readouts found in vivo, such as BSMC contractile force generation, and allows for systematic testing of notable inflammatory signals found in the niche. Previous in vitro BSMC studies have utilized single-cell contraction assays, which demonstrate that cells derived from asthmatic patients are more contractile than those from healthy patientsin vitro and be easily integrated with existing automation systems and high-content imagers.in vitro models for modeling contractility, such as simple gel contraction assaysTo address this, complex Elastomer-based cantilever strain gauges have emerged as a powerful means to model gross multi-cellular contractile forces in 3D at both the macro and microscales.To address these challenges, we developed a 96-well device enabling functional linear contractility of tissues (DEFLCT) for multiplexed disease modeling of microtissue structures comprised of contractile cells. This system is simple to construct in high volumes, is fully compatible with off the shelf assay plates, and leverages hanging construction for reproducible tissue casting. We then utilized this platform to expose airway smooth muscle cell microtissues to a panel of inflammatory cytokines to induce smooth muscle shortening via increased contractility. Finally, we validated the shift toward an asthmatic disease phenotype through RNAseq, which revealed enrichment of pathways in the remodeled tissues that are associated with asthma, airway remodeling, and contractility.μN/μm and demonstrated symmetrical, linear deflections in response to applied force [supplementary material Figs. 2(b) and 2(c)].To achieve sufficient assay throughput for parallelized multi-stimuli screening of contractility, we developed a 96-well format cantilever array to culture airway smooth muscle 3D microtissues. The device functions as a plate insert hanging down into the individual wells, which allows for the use of standard ANSI/SLAS (Society of Biomolecular Screening) dimensioned labware and existing high content laboratory instruments .Kinase inhibitor libraries were purchased from Enzo Life Sciences (Screen-Well BML-2832-0100) and diluted to a stock concentration of 1 mM in DMSO. A full table of library contents, structures, and targets is included in supplementary material Table S9. Stocks were then further diluted in starvation cell culture media and then immediately transferred to 96w plates containing media and TGF-β1 as described above such that the final compound concentration was 1 All experiments were performed using cells from four different donors obtained from Lonza and n biological replicates as indicated in each figure where appropriate. Bar graphs are represented as mean ± SEM, excepting kinase screen results, which is represented by median ± M.A.D. Statistical analyses were performed using GraphPad Prism9, the threshold for significance level is set at P < 0.05. Screen hits were determined by a reduction in contractility greater than 3× M.A.D. of TGF-β1 treated controls. A detailed information on analyses is indicated in the figure legends.See the supplementary material for gene expression data tables  generated from RNAseq analysis of TGF-β1 and IL-13 treated microtissues.