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Recent years have witnessed growing research on autonomous activity recognition models for use in new settings. However, it lacks comprehensive frameworks for transfer learning, specifically, the ability to deal with partially available data in new settings. To address these, we propose OptiMapper, a novel uninformed cross-subject transfer learning framework. OptiMapper is a combinatorial optimization framework that extracts abstract knowledge across subjects and utilizes it for developing a personalized activity recognition model in new subjects. A novel community-detection-based clustering of unlabeled data is proposed to construct a network of unannotated observations. The clusters are then mapped onto source clusters using a complete bipartite graph model and mapped labels are conditionally fused using a base learner to create a personalized training dataset for target user. We present two instantiations of OptiMapper. The first one is applicable for domains with identical activity labels, and performs a one-to-one bipartite mapping between clusters of source and target. The second instantiation performs optimal many-to-one mapping between source and target clusters. It finds an optimal mapping even when the target dataset does not contain sufficient instances of all activity classes. We formulate this cross-domain mapping as a transportation problem and evaluate our techniques on several datasets.

OBJECTIVE: To determine whether single-cell and plasma proteomic elements of the host’s immune response to surgery accurately identifies patients who develop a Surgical Site Complication (SSC) after major abdominal surgery. SUMMARY BACKGROUND DATA: SSCs may occur in up to 25% of patients undergoing bowel resection, resulting in significant morbidity and economic burden. However, the accurate prediction of SSCs remains clinically challenging. Leveraging high-content proteomic technologies to comprehensively profile patients’ immune response to surgery is a promising approach to identify predictive biological factors of SSCs. METHODS: Forty-one patients undergoing non-cancer bowel resection were prospectively enrolled. Blood samples collected before surgery and on post-operative day one (POD1) were analyzed using a combination of single-cell mass cytometry and plasma proteomics. The primary outcome was the occurrence of an SSC, including surgical site infection, anastomotic leak, or wound dehiscence within 30 days of surgery. RESULTS: A multiomic model integrating the single-cell and plasma proteomic data collected on POD1 accurately differentiated patients with (n=11) and without (n=30) an SSC (AUC = 0.86). Model features included co-regulated pro-inflammatory (e.g. IL-6- and MyD88- signaling responses in myeloid cells) and immunosuppressive (e.g. JAK/STAT signaling responses in M-MDSCs and Tregs) events preceding an SSC. Importantly, analysis of the immunological data obtained before surgery also yielded a model accurately predicting SSCs (AUC = 0.82). CONCLUSIONS: The multiomic analysis of patients’ immune response after surgery and immune state before surgery revealed systemic immune signatures preceding the development of SSCs. Our results suggest that integrating immunological data in perioperative risk assessment paradigms is a plausible strategy to guide individualized clinical care.

The longitudinal assessment of physical function with high temporal resolution at a scalable and objective level in patients recovering from surgery is highly desirable to understand the biological and clinical factors that drive the clinical outcome. However, physical recovery from surgery itself remains poorly defined and the utility of wearable technologies to study recovery after surgery has not been established.Prolonged postoperative recovery is often associated with long-lasting impairment of physical, mental, and social functions. While phenotypical and clinical patient characteristics account for some variation of individual recovery trajectories, biological differences likely play a major role. Specifically, patient-specific immune states have been linked to prolonged physical impairment after surgery. However, current methods of quantifying physical recovery lack patient specificity and objectivity.Here, a combined high-fidelity accelerometry and state-of-the-art deep immune profiling approach was studied in patients undergoing major joint replacement surgery. The aim was to determine whether objective physical parameters derived from accelerometry data can accurately track patient-specific physical recovery profiles (suggestive of a 'clock of postoperative recovery'), compare the performance of derived parameters with benchmark metrics including step count, and link individual recovery profiles with patients' preoperative immune state.The results of our models indicate that patient-specific temporal patterns of physical function can be derived with a precision superior to benchmark metrics. Notably, six distinct domains of physical function and sleep are identified to represent the objective temporal patterns: "activity capacity" and "moderate and overall activity" (declined immediately after surgery); "sleep disruption and sedentary activity" (increased after surgery); "overall sleep", "sleep onset", and "light activity" (no clear changes were observed after surgery). These patterns can be linked to individual patients' preoperative immune state using cross-validated canonical-correlation analysis. Importantly, the pSTAT3 signal activity in M-MDSCs predicted a slower recovery.Accelerometry-based recovery trajectories are scalable and objective outcomes to study patient-specific factors that drive physical recovery.

Fluid intake tracking is crucial in providing interventions that assist individuals to stay hydrated by maintaining an adequate amount of fluid. It also helps to manage calorie intake by accounting for the amount of calorie consumed from beverages. While staying hydrated and controlling calorie intake is critical in both physical wellness and cognitive health, existing technologies do not provide a solution for monitoring both fluid type and fluid volume. To address this limitation, we present the design, implementation, and validation of Liquid Intake Detection System (LIDS) for real-time tracking of fluid intake type and volume. The system devises a sensing module that is composed of ultrasonic, RGB color, temperature, and accelerometer sensors as well as a computational framework for machine-learning-based fluid intake type classification, volume estimation, and bottle-state-recognition. The developed sensing unit is small and light-weight that can be mounted, from inside, on the lid of a drinking bottle. We conduct extensive experiments to collect data in a variety of bottles and environmental settings. The results show that the accuracy of fluid type detection ranges from 74.93% to 94.98% while trying to detect the fluid of an unseen bottle. Our results for volume estimation show that the regression-based volume estimation supports a root-relative-squared-error that ranges from 1.12% to 13.36%.

BACKGROUND: Medication nonadherence is a critical problem with severe implications in individuals at risk for atherosclerotic cardiovascular disease. Many studies have attempted to predict medication adherence in this population, but few, if any, have been effective in prediction, sug-gesting that essential risk factors remain unidentified. OBJECTIVE: This study’s objective was to (1) establish an accurate prediction model of medi-cation adherence in individuals at risk for atherosclerotic cardiovascular disease and (2) identify significant contributing factors to the predictive accuracy of medication adherence. In particular, we aimed to use only the baseline questionnaire data to assess medication adherence prediction feasibility. METHODS: A sample of 40 individuals at risk for atherosclerotic cardiovascular disease was recruited for an eight-week feasibility study. After collecting baseline data, we recorded data from a pillbox that sent events to a cloud-based server. Health measures and medication use events were analyzed using machine learning algorithms to identify variables that best predict medication adherence. RESULTS: Our adherence prediction model, based on only the ten most relevant variables, achieved an average error rate of 12.9%. Medication adherence was closely correlated with being encouraged to play an active role in their treatment, having confidence about what to do in an emergency, knowledge about their medications, and having a special person in their life. CONCLUSIONS: Our results showed the significance of clinical and psychosocial factors for predicting medication adherence in people at risk for atherosclerotic cardiovascular diseases. Clini-cians and researchers can use these factors to stratify individuals to make evidence-based decisions to reduce the risks.

Wearable step counters, also referred to as activity trackers, have been developed for health and activity monitoring, as well as for step tracking. These trackers, however, produce unreliable measurements during slow walking and when walking with assistive devices (i.e., aided walking). To address this challenge, in this article, we introduce, ParaLabel , a filter-based step counting algorithm that is reliable against various walking velocities and intensities. ParaLabel addresses this problem by learning a filter cut-off frequency autonomously in a new domain without the need for collecting sensor data and manually tuning the algorithm parameter for a different velocity and/or pattern of walking. We formulate this problem as a transfer learning problem in which the new filter cut-off frequency is transferred from a bank containing previously fine-tuned parameters from different domain(s). Our extensive analysis using real data collected from 15 participants while wearing an accelerometer sensor on their chest, wrist, or left pocket demonstrates the superiority of ParaLabel to two commercially available trackers worn on the same body location, and state-of-the-art techniques. ParaLabel achieves 96.3% − 99.9% accuracy during walking on a treadmill at three different velocities, 98.2% − 99.9% accuracy during walking with a shopping cart, and 89.3% − 97.3% accuracy while walking with the aid of a walker.

Objective Bolstered by an N-of-1 study design, precision medicine proactively predicts and reverses chronic disease, thereby extending the healthspan. An essential element to this approach is maintaining quality of life despite age. Adult males commonly report a decline in sexual function and libido, which can be triggered by a 1-3% decline in testosterone beginning in their thirties. Our collection, integration, and interpretation of longitudinal data on men revealed symptoms emerging even earlier due to other diagnoses, such as varicocele, depression, or a positive family history of early cardiac disease. In the present study, the efficacy of hormonal optimization to improve sexual function and libido in adult men was investigated. Methods Inclusion criteria for this retrospective chart review were men aged ≥20 years who were active patients for ≥6 months and prescribed hormonal intervention. Clinical encounter notes were reviewed for reported improvement in sexual function and/or libido, which was noted as a binary variable. Exclusion criteria was met if sexual function and libido were never discussed during any encounter. Chi-square tests for independence were performed to assess correlation between improvements in sexual function and Libido. Results 82 patients were eligible for inclusion in this study. Five were never prescribed hormonal intervention and therefore exclude, resulting in a final cohort of 77 patients ranging from 21-90 years (mean=54 years). Patient data was compiled into 6 age groups: 20-29 (8, 10.4%), 30-39 (3, 3.9%), 40-49 (14, 18.2%), 50-59 (29, 37.7%), 60-69 (13, 16.8%), and 70+ (10, 13. 0%). Overall, 84% and 88% of patients reported improvement in sexual function and libido, respectively. Improvements in sexual function and libido were most reported in the 60-69 age group (92% and 93%) and least reported in the 30-39 age group (33% and 67%). Notably, the majority of 11 out of 12 age-outcome groups noted improvement. Improvements in sexual function and libido were significantly correlated, with 71% of all patients reporting improvement in both categories (p < . 001). Discussion Overall, these findings reveal the efficacy of hormonal optimization to improve sexual function and libido in men across age groups. A limitation of this study is sample size, particularly in the 30-39 age group. Repeated analysis with a larger sample would provide deeper insight to this treatment modality. Nevertheless, these results emphasize the importance of the N-of-1 concept, as each patient's baseline hormonal status and physiological response to intervention is unique. Age is just a single variable to consider before hormonal intervention, not the only variable. Importantly, sexual function and libido are among the many aspects that constitute an optimal quality of life. Further investigation is warranted to understand how hormonal optimization impacts other quality of life markers, such as sleep, energy, and cognition, in the adult male population. Presentation: Monday, June 13, 2022 12:30 p.m. - 2:30 p.m.

Stroke is a leading cause of cognitive impairment and dementia, but the mechanisms that underlie post-stroke cognitive decline are not well understood. Stroke produces profound local and systemic immune responses that engage all major innate and adaptive immune compartments. However, whether the systemic immune response to stroke contributes to long-term disability remains ill-defined. We used a single-cell mass cytometry approach to comprehensively and functionally characterize the systemic immune response to stroke in longitudinal blood samples from 24 patients over the course of 1 year and correlated the immune response with changes in cognitive functioning between 90 and 365 days post-stroke. Using elastic net regularized regression modelling, we identified key elements of a robust and prolonged systemic immune response to ischaemic stroke that occurs in three phases: an acute phase (Day 2) characterized by increased signal transducer and activator of transcription 3 (STAT3) signalling responses in innate immune cell types, an intermediate phase (Day 5) characterized by increased cAMP response element-binding protein (CREB) signalling responses in adaptive immune cell types, and a late phase (Day 90) by persistent elevation of neutrophils, and immunoglobulin M(+) (IgM(+)) B cells. By Day 365 there was no detectable difference between these samples and those from an age- and gender-matched patient cohort without stroke. When regressed against the change in the Montreal Cognitive Assessment scores between Days 90 and 365 after stroke, the acute inflammatory phase Elastic Net model correlated with post-stroke cognitive trajectories (r = −0.692, Bonferroni-corrected P = 0.039). The results demonstrate the utility of a deep immune profiling approach with mass cytometry for the identification of clinically relevant immune correlates of long-term cognitive trajectories.

Description, Multiplex single-synapse events from multiple regions of Alzheimer’s disease brain revealed new alterations unique to humans., Synaptic molecular characterization is limited for Alzheimer’s disease (AD). Our newly invented mass cytometry–based method, synaptometry by time of flight (SynTOF), was used to measure 38 antibody probes in approximately 17 million single-synapse events from human brains without pathologic change or with pure AD or Lewy body disease (LBD), nonhuman primates (NHPs), and PS/APP mice. Synaptic molecular integrity in humans and NHP was similar. Although not detected in human synapses, Aβ was in PS/APP mice single-synapse events. Clustering and pattern identification of human synapses showed expected disease-specific differences, like increased hippocampal pathologic tau in AD and reduced caudate dopamine transporter in LBD, and revealed previously unidentified findings including increased hippocampal CD47 and lowered DJ1 in AD and higher ApoE in AD with dementia. Our results were independently supported by multiplex ion beam imaging of intact tissue. This highlights the higher depth and breadth of insight on neurodegenerative diseases obtainable through SynTOF.

Whereas prematurity is a major cause of neonatal mortality, morbidity, and lifelong impairment, the degree of prematurity is usually defined by the gestational age (GA) at delivery rather than by neonatal morbidity. Here we propose a multi-task deep neural network model that simultaneously predicts twelve neonatal morbidities, as the basis for a new data-driven approach to define prematurity. Maternal demographics, medical history, obstetrical complications, and prenatal fetal findings were obtained from linked birth certificates and maternal/infant hospitalization records for 11,594,786 livebirths in California from 1991 to 2012. Overall, our model outperformed traditional models to assess prematurity which are based on GA and/or birthweight (area under the precision-recall curve was 0.326 for our model, 0.229 for GA, and 0.156 for small for GA). These findings highlight the potential of using machine learning techniques to predict multiple prematurity phenotypes and inform clinical decisions to prevent, diagnose and treat neonatal morbidities.

Early identification of pregnant women at risk for preeclampsia (PE) is important, as it will enable targeted interventions ahead of clinical manifestations. The quantitative analyses of plasma proteins feature prominently among molecular approaches used for risk prediction. However, derivation of protein signatures of sufficient predictive power has been challenging. The recent availability of platforms simultaneously assessing over 1000 plasma proteins offers broad examinations of the plasma proteome, which may enable the extraction of proteomic signatures with improved prognostic performance in prenatal care. The primary aim of this study was to examine the generalizability of proteomic signatures predictive of PE in two cohorts of pregnant women whose plasma proteome was interrogated with the same highly multiplexed platform. Establishing generalizability, or lack thereof, is critical to devise strategies facilitating the development of clinically useful predictive tests. A second aim was to examine the generalizability of protein signatures predictive of gestational age (GA) in uncomplicated pregnancies in the same cohorts to contrast physiological and pathological pregnancy outcomes. Serial blood samples were collected during the first, second, and third trimesters in 18 women who developed PE and 18 women with uncomplicated pregnancies (Stanford cohort). The second cohort (Detroit), used for comparative analysis, consisted of 76 women with PE and 90 women with uncomplicated pregnancies. Multivariate analyses were applied to infer predictive and cohort-specific proteomic models, which were then tested in the alternate cohort. Gene ontology (GO) analysis was performed to identify biological processes that were over-represented among top-ranked proteins associated with PE. The model derived in the Stanford cohort was highly significant (p = 3.9E–15) and predictive (AUC = 0.96), but failed validation in the Detroit cohort (p = 9.7E–01, AUC = 0.50). Similarly, the model derived in the Detroit cohort was highly significant (p = 1.0E–21, AUC = 0.73), but failed validation in the Stanford cohort (p = 7.3E–02, AUC = 0.60). By contrast, proteomic models predicting GA were readily validated across the Stanford (p = 1.1E–454, R = 0.92) and Detroit cohorts (p = 1.1.E–92, R = 0.92) indicating that the proteomic assay performed well enough to infer a generalizable model across studied cohorts, which makes it less likely that technical aspects of the assay, including batch effects, accounted for observed differences. Results point to a broader issue relevant for proteomic and other omic discovery studies in patient cohorts suffering from a clinical syndrome, such as PE, driven by heterogeneous pathophysiologies. While novel technologies including highly multiplex proteomic arrays and adapted computational algorithms allow for novel discoveries for a particular study cohort, they may not readily generalize across cohorts. A likely reason is that the prevalence of pathophysiologic processes leading up to the “same” clinical syndrome can be distributed differently in different and smaller-sized cohorts. Signatures derived in individual cohorts may simply capture different facets of the spectrum of pathophysiologic processes driving a syndrome. Our findings have important implications for the design of omic studies of a syndrome like PE. They highlight the need for performing such studies in diverse and well-phenotyped patient populations that are large enough to characterize subsets of patients with shared pathophysiologies to then derive subset-specific signatures of sufficient predictive power.

Preeclampsia is a complex disease of pregnancy whose physiopathology remains unclear and that poses a threat to both mothers and infants. Specific complex changes in women's physiology precede a diagnosis of preeclampsia. Understanding multiple aspects of such a complex changes at different levels of biology, can be enabled by simultaneous application of multiple assays. We developed prediction models for preeclampsia risk by analyzing six omics datasets from a longitudinal cohort of pregnant women. A machine learning-based multiomics model had high accuracy (area under the receiver operating characteristics curve (AUC) of 0.94, 95% confidence intervals (CI): [0.90, 0.99]). A prediction model using only ten urine metabolites provided an accuracy of the whole metabolomic dataset and was validated using an independent cohort of 16 women (AUC = 0.87, 95% CI: [0.76, 0.99]). Integration with clinical variables further improved prediction accuracy of the urine metabolome model (AUC = 0.90, 95% CI: [0.80, 0.99], urine metabolome, validated). We identified several biological pathways to be associated with preeclampsia. The findings derived from models were integrated with immune system cytometry data, confirming known physiological alterations associated with preeclampsia and suggesting novel associations between the immune and proteomic dynamics. While further validation in larger populations is necessary, these encouraging results will serve as a basis for a simple, early diagnostic test for preeclampsia.

This diagnostic/prognostic study describes the use of cell-free transcriptomics, urine metabolomics, and plasma proteomics for identifying the biological measurements associated with preterm birth., Key Points Question What maternal biological modalities are associated with preterm birth (PTB)? Findings In this diagnostic/prognostic study of 81 pregnant women from 5 birth cohorts in low- and middle-income countries, several correlates of preterm birth in urine and blood were found to be associated with PTB. Although cohort-specific signatures were present, a machine learning algorithm was able to generate a model that was capable of predicting PTB across the cohorts. Meaning Results of this study suggest that most PTBs can be predicted using blood and urine samples collected early in the pregnancy, providing opportunities for interventions., Importance Worldwide, preterm birth (PTB) is the single largest cause of deaths in the perinatal and neonatal period and is associated with increased morbidity in young children. The cause of PTB is multifactorial, and the development of generalizable biological models may enable early detection and guide therapeutic studies. Objective To investigate the ability of transcriptomics and proteomics profiling of plasma and metabolomics analysis of urine to identify early biological measurements associated with PTB. Design, Setting, and Participants This diagnostic/prognostic study analyzed plasma and urine samples collected from May 2014 to June 2017 from pregnant women in 5 biorepository cohorts in low- and middle-income countries (LMICs; ie, Matlab, Bangladesh; Lusaka, Zambia; Sylhet, Bangladesh; Karachi, Pakistan; and Pemba, Tanzania). These cohorts were established to study maternal and fetal outcomes and were supported by the Alliance for Maternal and Newborn Health Improvement and the Global Alliance to Prevent Prematurity and Stillbirth biorepositories. Data were analyzed from December 2018 to July 2019. Exposures Blood and urine specimens that were collected early during pregnancy (median sampling time of 13.6 weeks of gestation, according to ultrasonography) were processed, stored, and shipped to the laboratories under uniform protocols. Plasma samples were assayed for targeted measurement of proteins and untargeted cell-free ribonucleic acid profiling; urine samples were assayed for metabolites. Main Outcomes and Measures The PTB phenotype was defined as the delivery of a live infant before completing 37 weeks of gestation. Results Of the 81 pregnant women included in this study, 39 had PTBs (48.1%) and 42 had term pregnancies (51.9%) (mean [SD] age of 24.8 [5.3] years). Univariate analysis demonstrated functional biological differences across the 5 cohorts. A cohort-adjusted machine learning algorithm was applied to each biological data set, and then a higher-level machine learning modeling combined the results into a final integrative model. The integrated model was more accurate, with an area under the receiver operating characteristic curve (AUROC) of 0.83 (95% CI, 0.72-0.91) compared with the models derived for each independent biological modality (transcriptomics AUROC, 0.73 [95% CI, 0.61-0.83]; metabolomics AUROC, 0.59 [95% CI, 0.47-0.72]; and proteomics AUROC, 0.75 [95% CI, 0.64-0.85]). Primary features associated with PTB included an inflammatory module as well as a metabolomic module measured in urine associated with the glutamine and glutamate metabolism and valine, leucine, and isoleucine biosynthesis pathways. Conclusions and Relevance This study found that, in LMICs and high PTB settings, major biological adaptations during term pregnancy follow a generalizable model and the predictive accuracy for PTB was augmented by combining various omics data sets, suggesting that PTB is a condition that manifests within multiple biological systems. These data sets, with machine learning partnerships, may be a key step in developing valuable predictive tests and intervention candidates for preventing PTB.

The dense network of interconnected cellular signaling responses quantifiable in peripheral immune cells provide a wealth of actionable immunological insights. While high-throughput single-cell profiling techniques, including polychromatic flow and mass cytometry, have matured to a point that enables detailed immune profiling of patients in numerous clinical settings, limited cohort size together with the high dimensionality of data increases the possibility of false positive discoveries and model overfitting. We introduce a machine learning platform, the immunological Elastic-Net (iEN), which incorporates immunological knowledge directly into the predictive models. Importantly, the algorithm maintains the exploratory nature of the high-dimensional dataset, allowing for the inclusion of immune features with strong predictive power even if not consistent with prior knowledge. In three independent studies our method demonstrates improved predictive power for clinically-relevant outcomes from mass cytometry data generated from whole blood, as well as a large simulated dataset.

This chapter briefly overviews robust machine-learning solutions for wearable IoT applications. Furthermore, it presents one of the earliest attempts in presenting an autonomous learning framework for wearables. The focus, in particular, is on cases where a new sensor is added to the system and the new (untrained) sensor is worn/used on various body locations. The process of autonomous learning automatically leads to a new collaborative decision-making algorithm. Addressing the problem of expanding pattern-recognition capabilities from a single setting algorithm with a predefined configuration to a dynamic setting where sensors can be added, displaced, and used unobtrusively is challenging. In such cases, successful knowledge transfer is needed to improve the learning performance by avoiding expensive data collection and labeling efforts. In this chapter, a novel and generic approach to transfer learning capabilities of an existing static sensor to a newly added dynamic sensor is described.

While inferring human activities from sensors embedded in mobile devices using machine learning algorithms has been studied, current research relies primarily on sensor data that are collected in controlled settings often with healthy individuals. Currently, there exists a gap in research about how to design activity recognition models based on sensor data collected with chronically-ill individuals and in free-living environments. In this paper, we focus on a situation where free-living activity data are collected continuously, activity vocabulary (i.e., class labels) are not known as a priori, and sensor data are annotated by end-users through an active learning process. By analyzing sensor data collected in a clinical study involving patients with cardiovascular disease, we demonstrate significant challenges that arise while inferring physical activities in uncontrolled environments. In particular, we observe that activity labels that are distinct in syntax can refer to semantically-identical behaviors, resulting in a sparse label space. To construct a meaningful label space, we propose LabelMerger, a framework for restructuring the label space created through active learning in uncontrolled environments in preparation for training activity recognition models. LabelMerger combines the semantic meaning of activity labels with physical attributes of the activities (i.e., domain knowledge) to generate a flexible and meaningful representation of the labels. Specifically, our approach merges labels using both word embedding techniques from the natural language processing domain and activity intensity from the physical activity research. We show that the new representation of the sensor data obtained by LabelMerger results in more accurate activity recognition models compared to the case where original label space is used to learn recognition models.

Chronic periodontitis (ChP) is a prevalent inflammatory disease affecting 46% of the US population. ChP produces a profound local inflammatory response to dysbiotic oral microbiota that leads to destruction of alveolar bone and tooth loss. ChP is also associated with systemic illnesses, including cardiovascular diseases, malignancies, and adverse pregnancy outcomes. However, the mechanisms underlying these adverse health outcomes are poorly understood. In this prospective cohort study, we used a highly multiplex mass cytometry immunoassay to perform an in-depth analysis of the systemic consequences of ChP in patients before ( n = 28) and after ( n = 16) periodontal treatment. A high-dimensional analysis of intracellular signaling networks revealed immune system–wide dysfunctions differentiating patients with ChP from healthy controls. Notably, we observed exaggerated proinflammatory responses to Porphyromonas gingivalis–derived lipopolysaccharide in circulating neutrophils and monocytes from patients with ChP. Simultaneously, natural killer cell responses to inflammatory cytokines were attenuated. Importantly, the immune alterations associated with ChP were no longer detectable 3 wk after periodontal treatment. Our findings demarcate systemic and cell-specific immune dysfunctions in patients with ChP, which can be temporarily reversed by the local treatment of ChP. Future studies in larger cohorts are needed to test the boundaries of generalizability of our results.