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Temporary cardiac pacemakers used in periods of need during surgical recovery involve percutaneous leads and externalized hardware that carry risks of infection, constrain patient mobility and may damage the heart during lead removal. Here we report a leadless, battery-free, fully implantable cardiac pacemaker for postoperative control of cardiac rate and rhythm that undergoes complete dissolution and clearance by natural biological processes after a defined operating timeframe. We show that these devices provide effective pacing of hearts of various sizes in mouse, rat, rabbit, canine and human cardiac models, with tailored geometries and operation timescales, powered by wireless energy transfer. This approach overcomes key disadvantages of traditional temporary pacing devices and may serve as the basis for the next generation of postoperative temporary pacing technology. A biodegradable pacemaker without external leads improves the safety of temporary cardiac pacing.

BackgroundImmune checkpoint inhibitors (ICIs), including anti-PD-1 therapy, have limited efficacy in patients with microsatellite stable (MSS) colorectal cancer (CRC). Interleukin 17A (IL-17A) activity leads to a protumor microenvironment, dependent on its ability to induce the production of inflammatory mediators, mobilize myeloid cells and reshape the tumor environment. In the present study, we aimed to investigate the role of IL-17A in resistance to antitumor immunity and to explore the feasibility of anti-IL-17A combined with anti-PD-1 therapy in MSS CRC murine models.MethodsThe expression of programmed cell death-ligand 1 (PD-L1) and its regulation by miR-15b-5p were investigated in MSS CRC cell lines and tissues. The effects of miR-15b-5p on tumorigenesis and anti-PD-1 treatment sensitivity were verified both in vitro and in colitis-associated cancer (CAC) and APCmin/+ murine models. In vivo efficacy and mechanistic studies were conducted using antibodies targeting IL-17A and PD-1 in mice bearing subcutaneous CT26 and MC38 tumors.ResultsEvaluation of clinical pathological specimens confirmed that PD-L1 mRNA levels are associated with CD8+ T cell infiltration and better prognosis. miR-15b-5p was found to downregulate the expression of PD-L1 at the protein level, inhibit tumorigenesis and enhance anti-PD-1 sensitivity in CAC and APCmin/+ CRC models. IL-17A led to high PD-L1 expression in CRC cells through regulating the P65/NRF1/miR-15b-5p axis. Combined IL-17A and PD-1 blockade had efficacy in CT26 and MC38 tumors, with more cytotoxic T lymphocytes cells and fewer myeloid-derived suppressor cells in tumors.ConclusionsIL-17A increases PD-L1 expression through the p65/NRF1/miR-15b-5p axis and promotes resistance to anti-PD-1 therapy. Blocking IL-17A improved the efficacy of anti-PD-1 therapy in MSS CRC murine models. IL-17A might serve as a therapeutic target to sensitize patients with MSS CRC to ICI therapy.

Hundreds of rodent gait studies have been published over the past two decades, according to a PubMed search. Treadmill gait data, for example from the DigiGait system, generates over 30 + spatial and temporal measures. Despite this multi-dimensional data, all but a handful of the published literature on rodent gait has conducted univariate analysis that reveals limited information on the relationships that are characteristic of different gait states. This study conducted rigorous multivariate analysis in the form of sequential feature selection and factor analysis on gait data from a variety of gait deviations (due to injury i.e. peripheral nerve transection and transplantation, disease i.e. IUGR and hyperoxia, and age-related changes) and used machine learning to train a classifier to distinguish among and score different gait states. Treadmill gait data (DigiGait) of three different types of gait deviations were collected. Data were collected from B6 mice using the DigiGait system, with gait measurements taken at standardized treadmill speeds of 10, 17, and 24 cm/s over a period of 3–4 s per observation. Each mouse underwent at least two trials at each speed. Data were collected on B6 mice that were healthy and had various types of gait deficit due to: (a) a peripheral nerve injury model with increasing degrees of damage to the neuromusculoskeletal sequence of gait i.e. nerve transection, total hind limb transplantation, (b) a central nerve injury model of increasing degrees of damage to the motor regions responsible for gait i.e. IUGR, IUGR + hyperoxia, and (c) gait changes due to increasing age. Multivariate factor analysis (using MATLAB’s factoran) and forward feature selection (with ten-fold cross-validation) were conducted to identify those features and factors most descriptive of each gait state for comparison. Various machine learning classifier models were trained with ten-fold cross-validation and evaluated (e.g. random forest, regression, discriminant analysis, support vector machine, and ensemble) in a 70 − 30 training-testing split for their accuracy, precision, recall, and F-score. The highest performing model was used to score each type of gait for direct comparison on a scale of -0.5 to 0.5. The score distributions were plotted on a histogram for direct comparisons of score populations among various gait states. Multivariate feature selection revealed that not all 30 + features were relevant to describing the gait states. Plotting misclassification error (MCE) as a function of number of features included revealed that there was a critical number of features (~ 16) that minimized MCE (0.17 via univariate feature selection vs. 0.12 via multivariate feature selection). Incorporating more than 16 features led MCE to increase linearly indicating overfitting. Relationships among the identified features were understood via factor analysis. The factor analysis results were consistent with the biological differences between the groups (e.g. total hind limb transplantation was distinguishable via features descriptive of the positioning of the paw in relation to the body while nerve transection injury alone was distinguishable via features descriptive of changes to fine motor movements). Across all gait states, there was significant conservation of features and factors. This suggests certain relationships may be fundamental to rodent gait analysis regardless of the gait pathology in question. The highest performing machine learning classifier model (ensemble) was able to distinguish between gait deficits with high performance (F-score, recall, precision, and accuracy all > 0.90). This included the ability to distinguish between peripheral vs. central gait deficit, between individual types of peripheral deficit, between individual types of central deficit, and between younger vs. older animals. Using the classifier to score individual animals and plot the scores by group revealed score distributions that were consistent with biological phenomena. For example, the multivariate gait score trends as a result of increasing central nerve injury were consistent with the trends of white matter volume loss in relevant motor regions of the brain as measured via MRI. Finally, the degrees of separation between multivariate gait scores were consistent with the degree of biological difference between gaits (e.g. central injury had greater separation from healthy vs. peripheral injury; older and younger animals had more moderate, yet still statistically significant, separation in scores vs. any of the injury / disease states did with each other). In conclusion, this study establishes a new methodology to quantify and evaluate gait deviations across a variety of different models. Its novelty is in using multivariate statistics to describe the features and factors that characterize gait states due to injury, disease, and age for use in machine learning model training. This includes statistically describing the differences in gait between diseases with vastly different etiologies of gait deficits (peripheral vs. central). In doing so the methodology’s novelty includes accounting for relationships between groupings of features in model training; something that traditional univariate analysis is unable to do. It used multivariate statistics and machine learning to reveal gait as a quantifiable, preclinical biomarker of injury, disease, and age. It collapsed a multi-dimensional biological phenomena (gait) into a single score by encoding revealed biological relationships allowing for direct, quantifiable comparisons of function as it pertains to ambulation. It revealed how these multivariate gait scores can visualize biologically consistent separation and combined effects. Finally, we demonstrate the application of this methodology to already published univariate study that is representative of the hundreds of univariate treadmill gait analysis published over the last two decades. Thereby, opening the door to a new class of multivariate gait analyses that provides greater insight and value than the current state-of-the art.

Background Although the TNM staging system plays a critical role in guiding adjuvant chemotherapy for colorectal cancer (CRC), its precision for risk stratification in stage II and III CRC patients with proficient DNA mismatch repair (pMMR) remains limited. Therefore, precise predictive models and research on postoperative treatments are crucial for enhancing patient survival and improving quality of life. Methods This retrospective study analyzed 1051 pMMR CRC patients who underwent radical resection and were randomly assigned to training (n = 736) and validation (n = 315) groups. Immunohistochemistry and hematoxylin and eosin staining were utilized to evaluate regulatory-Immunoscore (RIS), tertiary lymphoid structures (TLS), and tumor budding (TB). The Tumor Aggression-Defense Index (TADI) was derived through a multi-factor COX regression model. Subgroup analysis demonstrated potential of TADI in guiding personalized adjuvant therapy for stage II and III CRC. Results Univariate and multivariate Cox analysis indicated that TADI was an independent prognostic indicator. Among stage II CRC, chemotherapy was significantly correlated with improved recurrence times in individuals with intermediate (95% CI 0.19–0.59, P < 0.001) and high (95% CI 0.36–0.95, P = 0.031) TADI. In stage III CRC receiving adjuvant chemotherapy, a duration of 3 months or longer was notably associated with a prolonged time to recurrence in those with high TADI (95% CI 0.40–0.98, P = 0.041) compared to durations of less than 3 months. Conclusion The TADI serves as an effective parameter for predicting the survival outcomes of stage I-III pMMR CRC patients and guiding precision treatment strategies.

Animal models of nerve injury are important for studying nerve injury and repair, particularly for interventions that cannot be studied in humans. However, the vast majority of gait analysis in animals has been limited to univariate analysis even though gait data is highly multi-dimensional. As a result, little is known about how various spatiotemporal components of the gait relate to each other in the context of peripheral nerve injury and trauma. We hypothesize that a multivariate characterization of gait will reveal relationships among spatiotemporal components of gait with biological relevance to peripheral nerve injury and trauma. We further hypothesize that legitimate relationships among said components will allow for more accurate classification among distinct gait phenotypes than if attempted with univariate analysis alone. DigiGait data was collected of mice across groups representing increasing degrees of damage to the neuromusculoskeletal sequence of gait; that is (a) healthy controls, (b) nerve damage only via total nerve transection + reconnection of the femoral and sciatic nerves, and (c) nerve, muscle, and bone damage via total hind-limb transplantation. Multivariate relationships among the 30+ spatiotemporal measures were evaluated using exploratory factor analysis and forward feature selection to identify the features and latent factors that best described gait phenotypes. The identified features were then used to train classifier models and compared to a model trained with features identified using only univariate analysis. 10-15 features relevant to describing gait in the context of increasing degrees of traumatic peripheral nerve injury were identified. Factor analysis uncovered relationships among the identified features and enabled the extrapolation of a set of latent factors that further described the distinct gait phenotypes. The latent factors tied to biological differences among the groups (e.g. alterations to the anatomical configuration of the limb due to transplantation or aberrant fine motor function due to peripheral nerve injury). Models trained using the identified features generated values that could be used to distinguish among pathophysiological states with high statistical significance (p < .001) and accuracy (>80%) as compared to univariate analysis alone. This is the first performance evaluation of a multivariate approach to gait analysis and the first demonstration of superior performance as compared to univariate gait analysis in animals. It is also the first study to use multivariate statistics to characterize and distinguish among different gradations of gait deficit in animals. This study contributes a comprehensive, multivariate characterization pipeline for application in the study of any pathologies in which gait is a quantitative translational outcome metric.

Vascularized Composite Allotransplantation (VCA) is an important therapeutic option for patients that incur debilitating injuries to the face or limbs. The complexity and immunogenicity of tissue types within VCA grafts pose unique challenges and necessitate the use of intensive immunosuppression; however, graft rejection remains a challenge in VCA. Deep proteomic profiling and high dimensional analysis with cytometry time of flight were used to define the cell types and effector mechanisms elicited by VCA in BALB/c (H-2Kd) > C57BL/6 (H-2Kb) limb recipients. Spleen and cervical draining lymph nodes were collected post-transplant days 1, 3, 5, and 7 (n =4-6 mice/group/day). We identified dynamic, coordinated signatures in T cell and monocyte populations associated with VCA allograft rejection. In comparison to syngeneic transplant recipients, allogeneic recipients exhibited significant alterations in the immune cell populations within secondary lymphoid tissues. These changes included very early expansion of double-negative TCRβ T cells, including IL-17A-producing γδ T cells, and patrolling monocytes. Subsequently, CD8+CD62L+ T cells and CD8+ effector/effector memory T cells (Teff/Tem), Ly6C CCR2 CX3CR1 classical monocytes, CD4+ Teff/Tem, and CD8+CD25 CCR7 Teff/Tem were increased by day 5. CD8+CD25 CCR7 Teff/Tem with the highest expression of IFN-γ, perforin, and granzyme B were enriched by day 7. High dimensional proteomic analysis reveals multiple innate and Teff/Tem subsets in acute rejection following VCA. In particular, IL-17A-producing γδ T cells and classical monocytes may be particularly important in initiating the alloimmune response in VCA recipients.

Combination therapy with immune checkpoint inhibitors (ICIs) may benefit approximately 10-20% of microsatellite-stable colorectal cancer (MSS-CRC) patients. However, there is a lack of optimal biomarkers. This study aims to understand the predictive value of epigenetic-related gene mutations in ICIs therapy in MSS-CRC patients. We analyzed DNA sequences and gene expression profiles from The Cancer Genome Atlas (TCGA) to examine their immunological features. The Harbin Medical University Cancer Hospital (HMUCH) clinical cohort of MSS-CRC patients was used to validate the efficacy of ICIs in patients with epigenetic-related gene mutations (Epigenetic_Mut). In TCGA, 18.35% of MSS-CRC patients (78/425) had epigenetic-related gene mutations. The Epigenetic_Mut group had a higher tumor mutation burden (TMB) and frameshift mutation (FS_mut) rates. In all MSS-CRC samples, Epigenetic_Mut was elevated in the immune subtype (CMS1) and had a strong correlation with immunological features. Epigenetic_Mut was also associated with favorable clinical outcomes in MSS-CRC patients receiving anti-PD-1-based therapy from the HMUCH cohort. Using immunohistochemistry and flow cytometry, we demonstrated that Epigenetic_Mut samples were associated with increased anti-tumor immune cells both in tumor tissues and peripheral blood. MSS-CRC patients with epigenetic regulation impairment exhibit an immunologically active environment and may be more susceptible to treatment strategies based on ICIs.

BackgroundAlthough, immune checkpoint inhibitors (ICIs) have been widely applied in the therapy of malignant tumors, the efficacy and safety of ICIs in patients with tumors and pre-existing CAD, especially chronic coronary syndromes (CCS) or their risk factors (CRF), is not well identified.MethodsThis was a nationwide multicenter observational study that enrolled participants who diagnosed with solid tumors and received ICIs therapy. The main efficacy indicators were progression-free survival (PFS) and overall survival (OS), followed by objective response rate (ORR) and disease control rate (DCR). Safety was assessed by describing treatment-related adverse events (TRAEs) during ICIs therapy evaluated by the Common Terminology Criteria for Adverse Events 5.0 (CTCAE 5.0).ResultsIn the current research, we retrospectively analyzed the data of 551 patients diagnosed with solid tumors and received ICIs therapy, and these patients were divided into CCS/CRF group and non-CCS/CRF group. Patients with CCS/CRF had more favorable PFS and OS than patients without CCS/CRF (P < 0.001) and the pre-existing CCS/CRF was a protective factor for survival. The ORR (51.8% vs. 39.1%) and DCR (95.8% vs. 89.2%) were higher in CCS/CRF group than in non-CCS/CRF group (P = 0.003, P = 0.006). In this study, there was no significant difference in treatment-related adverse events (TRAEs), including immune-related adverse events (irAEs), between the two groups.ConclusionsWe concluded that ICIs appear to have better efficacy in malignant solid tumor patients with pre-existing CCS/CRF and are not accompanied by more serious irAEs.

To investigate the prevalence, clinical characteristics, and prognosis of thrombocytopenia (TP) in Chinese patients with systemic lupus erythematosus (SLE). The study was conducted based on the Chinese SLE Treatment and Research group (CSTAR) registry. Thrombocytopenia was defined as the platelet count<100,000/mm3 at enrollment. Severe thrombocytopenia was defined as the platelet count<50,000/mm3. The prevalence of SLE-related TP, the associations of thrombocytopenia with demographic data, organ involvements, laboratory findings, disease activity, damage, and mortality were investigated. Of 2104 patients with SLE, 342 patients (16.3%) were diagnosed with thrombocytopenia. The prevalence of neuropsychiatric SLE, vasculitis, myositis, nephritis, mucocutaneous lesions, pleuritis, fever, leukocytopenia and hypocomplementemia were significantly higher in patients with thrombocytopenia (p<0.05). SLE disease activity index (SLEDAI) was significantly higher in patients with thrombocytopenia (p<0.05). Multivariate analysis showed that leukocytopenia (OR = 2.644), lupus nephritis (OR = 1.539), hypocomplementemia (OR = 1.497) and elevated SLEDAI (OR = 1.318) were independently associated with thrombocytopenia (p<0.05). Long disease duration (OR = 1.006) was an independent risk factor of severe thrombocytopenia, while anti-rRNP (OR = 0.208) was an independent protective factor of severe thrombocytopenia (p<0.05). Long disease duration was an independent risk factor of mortality in patients with thrombocytopenia (RR = 1.006). The 6-year survival of patients with thrombocytopenia was significantly lower than patients without thrombocytopenia (88.2% vs. 95.5%). Thrombocytopenia was a common manifestation of SLE and was associated with leukocytopenia, nephritis and severe disease activity. Severe thrombocytopenia tended to occur in long-term and relatively inactive SLE. Patients with SLE-related thrombocytopenia has a decreased long-term survival rate. Long disease duration was an independent risk factor of mortality in patients with thrombocytopenia.

ObjectiveAnimal models of nerve injury are important for studying nerve injury and repair, particularly for interventions that cannot be studied in humans. However, the vast majority of gait analysis in animals has been limited to univariate analysis even though gait data is highly multi-dimensional. As a result, little is known about how various spatiotemporal components of the gait relate to each other in the context of peripheral nerve injury and trauma. We hypothesize that a multivariate characterization of gait will reveal relationships among spatiotemporal components of gait with biological relevance to peripheral nerve injury and trauma. We further hypothesize that legitimate relationships among said components will allow for more accurate classification among distinct gait phenotypes than if attempted with univariate analysis alone.MethodsDigiGait data was collected of mice across groups representing increasing degrees of damage to the neuromusculoskeletal sequence of gait; that is (a) healthy controls, (b) nerve damage only via total nerve transection + reconnection of the femoral and sciatic nerves, and (c) nerve, muscle, and bone damage via total hind-limb transplantation. Multivariate relationships among the 30+ spatiotemporal measures were evaluated using exploratory factor analysis and forward feature selection to identify the features and latent factors that best described gait phenotypes. The identified features were then used to train classifier models and compared to a model trained with features identified using only univariate analysis.Results10-15 features relevant to describing gait in the context of increasing degrees of traumatic peripheral nerve injury were identified. Factor analysis uncovered relationships among the identified features and enabled the extrapolation of a set of latent factors that further described the distinct gait phenotypes. The latent factors tied to biological differences among the groups (e.g. alterations to the anatomical configuration of the limb due to transplantation or aberrant fine motor function due to peripheral nerve injury). Models trained using the identified features generated values that could be used to distinguish among pathophysiological states with high statistical significance (p < .001) and accuracy (>80%) as compared to univariate analysis alone.ConclusionThis is the first performance evaluation of a multivariate approach to gait analysis and the first demonstration of superior performance as compared to univariate gait analysis in animals. It is also the first study to use multivariate statistics to characterize and distinguish among different gradations of gait deficit in animals. This study contributes a comprehensive, multivariate characterization pipeline for application in the study of any pathologies in which gait is a quantitative translational outcome metric.