Performance of preferentially expressed antigen in melanoma (PRAME) immunohistochemistry for metastatic melanoma in cytology specimens.

BACKGROUND: Preferentially expressed antigen in melanoma (PRAME) has been introduced as a new melanoma marker and potential target for immunotherapy. While PRAME immunohistochemistry (IHC) is well documented in surgical pathology, similar data in cytology are limited. Metastatic melanoma is frequently diagnosed via cytology samples in which IHC plays an important role. We aimed to accordingly evaluate the performance of PRAME IHC in diagnosing metastatic melanoma in cytology samples relative to other commonly used melanoma markers. MATERIALS AND METHODS: The study included 156 archival cytology cases, of which 93 were melanoma cases and 63 nonmelanoma cases (controls). All cases underwent PRAME IHC staining on cell blocks. Nuclear staining of PRAME was evaluated using a quantitative and qualitative scale. Other melanocytic IHC stain results (SOX10, S-100, Melan-A, and HMB45) were also documented. RESULTS: PRAME was detected in tumor cells in 86% of melanoma cases, which was significantly lower than SOX10 (100%) (p < .01), and similar to HMB45 (84%) and Melan-A (82%). S-100 had the lowest sensitivity of 71%. In comparison to other types of melanomas, spindle cell melanoma exhibited higher negativity for PRAME IHC (4/10 = 40%). PRAME was also expressed in some nonmelanocytic malignancies including carcinoma (5/22 = 23%), sarcoma (5/15 = 33%), and hematologic malignancies (1/9 = 11%). Overall, PRAME showed a sensitivity of 86%, specificity of 82%, positive predictive value of 70%, and negative predictive value of 92% for metastatic melanoma. CONCLUSIONS: PRAME is a useful marker for the diagnosis of melanoma in cytology material, but it is less sensitive than SOX10. PRAME is also expressed in other nonmelanocytic tumors which limits its specificity.

Enhancing the spatial resolution of time-of-flight based non-line-of-sight imaging via instrument response function deconvolution.

Non-line-of-sight (NLOS) imaging retrieves the hidden scenes by utilizing the signals indirectly reflected by the relay wall. Benefiting from the picosecond-level timing accuracy, time-correlated single photon counting (TCSPC) based NLOS imaging can achieve theoretical spatial resolutions up to millimeter level. However, in practical applications, the total temporal resolution (also known as total time jitter, TTJ) of most current TCSPC systems exceeds hundreds of picoseconds due to the combined effects of multiple electronic devices, which restricts the underlying spatial resolution of NLOS imaging. In this paper, an instrument response function deconvolution (IRF-DC) method is proposed to overcome the constraints of a TCSPC system's TTJ on the spatial resolution of NLOS imaging. Specifically, we model the transient measurements as Poisson convolution process with the normalized IRF as convolution kernel, and solve the inverse problem with iterative deconvolution algorithm, which significantly improves the spatial resolution of NLOS imaging after reconstruction. Numerical simulations show that the IRF-DC facilitates light-cone transform and frequency-wavenumber migration solver to achieve successful reconstruction even when the system's TTJ reaches 1200 ps, which is equivalent to what was previously possible when TTJ was about 200 ps. In addition, the IRF-DC produces satisfactory reconstruction outcomes when the signal-to-noise ratio (SNR) is low. Furthermore, the effectiveness of the proposed method has also been experimentally verified. The proposed IRF-DC method is highly applicable and efficient, which may promote the development of high-resolution NLOS imaging.

High accuracy ranging for space debris with spaceborne single photon Lidar.

The increasing risk posed by space debris highlights the need for accurate localization techniques. Spaceborne single photon Lidar (SSPL) offers a promising solution, overcoming the limitations of traditional ground-based systems by providing expansive coverage and superior maneuverability without being hindered by weather, time, or geographic constraints. This study introduces a novel approach leveraging non-parametric Bayesian inference and the Dirichlet process mixture model (DPMM) to accurately determine the distance of space debris in low Earth orbit (LEO), where debris exhibits nonlinear, high dynamic motion characteristics. By integrating extended Kalman filtering (EKF) for range gating, our method captures the temporal distribution of reflected photons, employing Markov chain Monte Carlo (MCMC) for iterative solutions. Experimental outcomes demonstrate our method's superior accuracy over conventional statistical techniques, establishing a clear correlation between radial absolute velocity and ranging error, thus significantly enhancing monostatic space debris localization.

Changing digital and telecytology practices post COVID-19 comparing ASC survey results from 2016 to 2023.

INTRODUCTION: During the COVID-19 pandemic, the need for digital pathology tools became more urgent. However, there needs to be more knowledge of the use in cytology. We aimed to evaluate current digital cytology practices and attitudes and compare the results with a pre-COVID-19 American Society of Cytopathology (ASC) survey. MATERIALS AND METHODS: Fourteen survey questions assessing current attitudes toward digital cytology were developed from a 2016 ASC Digital Pathology Survey. Ten new survey questions were also created to evaluate telecytology use. The survey was e-mailed to ASC members over 6 weeks in 2023. RESULTS: A total of 123 individuals responded (116 in 2016). Attitudes toward digital cytology were unchanged; most participants stated digital cytology is beneficial (87% 2023 versus 90% 2016). The percentage of individuals using digital cytology was unchanged (56% in 2016 and 2023). However, telecytology for rapid onsite assessment (ROSE) is now considered the best application (55% 2023 versus 31% 2016). Forty-three institutions reported using digital and telecytology tools; 40% made implementations after 2020; most did not feel that COVID-19 affected digital cytology (56%). Telecytology for ROSE is the most common application now (78%) compared with education (30%) in 2016. Limitations for implementing digital imaging in cytology included inability to focus (38%) and expense (33%). CONCLUSIONS: General attitudes toward digital tools by the cytology community have essentially remained the same between 2016 and now. However, telecytology for ROSE is increasingly being used, which supports a need for validation and competency guidelines.

Quantitative Imaging for Predicting Hematoma Expansion in Intracerebral Hemorrhage: A Multimodel Comparison.

BACKGROUND: Several studies report that radiomics provides additional information for predicting hematoma expansion in intracerebral hemorrhage (ICH). However, the comparison of diagnostic performance of radiomics for predicting revised hematoma expansion (RHE) remains unclear. METHODS: The cohort comprised 312 consecutive patients with ICH. A total of 1106 radiomics features from seven categories were extracted using Python software. Support vector machines achieved the best performance in both the training and validation datasets. Clinical factors models were constructed to predict RHE. Receiver operating characteristic curve analysis was used to assess the abilities of non-contrast computed tomography (NCCT) signs, radiomics features, and combined models to predict RHE. RESULTS: We finally selected the top 21 features for predicting RHE. After univariate analysis, 4 clinical factors and 5 NCCT signs were selected for inclusion in the prediction models. In the training and validation dataset, radiomics features had a higher predictive value for RHE (AUC = 0.83) than a single NCCT sign and expansion-prone hematoma. The combined prediction model including radiomics features, clinical factors, and NCCT signs achieved higher predictive performances for RHE (AUC = 0.88) than other combined models. CONCLUSIONS: NCCT radiomics features have a good degree of discrimination for predicting RHE in ICH patients. Combined prediction models that include quantitative imaging significantly improve the prediction of RHE, which may assist in the risk stratification of ICH patients for anti-expansion treatments.

Food Insecurity Prevalence and Risk Factors at a Large Academic Medical Center in Michigan.

Importance: Health care systems are increasingly adopting methods to screen for and integrate food insecurity and other social risk factors into electronic health records. However, there remain knowledge gaps regarding the cumulative burden of food insecurity in large clinical settings, which patients are most at risk, and the extent to which patients are interested in social assistance through their health care system. Objective: To evaluate the 5-year prevalence and associated risk factors of food insecurity among adult primary care patients, and to examine factors associated with patients' interest in social assistance among those with food insecurity. Design, Setting, and Participants: This cross-sectional analysis of a retrospective cohort study took place at a tertiary care academic medical center (encompassing 20 primary care clinics) in Michigan. Participants included adult patients who completed screening for social risk factors between August 1, 2017, and August 1, 2022. Data analysis was performed from November 2022 to June 2023. Exposure: Food insecurity was assessed using the Hunger Vital Sign. Main Outcomes and Measures: The primary outcome was patients' interest in social assistance, and associated factors were examined using multivariate logistic regression models, adjusting for patients' demographic and health characteristics. Results: Over the 5-year period, 106 087 adult primary care patients (mean [SD] age, 52.9 [17.9] years; 61 343 women [57.8%]) completed the standardized social risk factors questionnaire and were included in the analysis. The overall prevalence of food insecurity was 4.2% (4498 patients), with monthly trends ranging from 1.5% (70 positive screens) in August 2018 to 5.0% (193 positive screens) in June 2022. Food insecurity was significantly higher among patients who were younger, female, non-Hispanic Black or Hispanic, unmarried or unpartnered, and with public health insurance. Food insecurity was significantly associated with a higher cumulative burden of social needs, including social isolation, medical care insecurity, medication nonadherence, housing instability, and lack of transportation. Only 20.6% of patients with food insecurity (927 patients) expressed interest in social assistance. Factors associated with interest in social assistance including being non-Hispanic Black, unmarried or unpartnered, a current smoker, and having a higher burden of other social needs. Conclusions and Relevance: In this retrospective cohort study, the overall prevalence of food insecurity was 4.2%, of whom approximately 1 in 5 patients with food insecurity expressed interest in assistance. This study highlights ongoing challenges in ensuring all patients complete routine social determinants of health screening and gaps in patients' interest in assistance for food insecurity and other social needs through their health care system.

Mechanical Performance of Copper-Nanocluster-Polymer Nanolattices.

A type of copper-nanocluster-polymer composites is reported and showcased that their 3D nanolattices exhibit a superior combination of high strength, toughness, deformability, resilience, and damage-tolerance. Notably, the strength and toughness of ultralight copper-nanocluster-polymer nanolattices in some cases surpass current best performers, including alumina, nickel, and other ceramic or metallic lattices at low densities. Additionally, copper-nanocluster-polymer nanolattices are super-resilient, crack-resistant, and one-step printed under ambient condition which can be easily integrated into sophisticated microsystems as highly effective internal protectors. The findings suggest that, unlike traditional nanocomposites, the laser-induced interface and the high fraction of ultrasmall Cu15 nanoclusters as crosslinking junctions contribute to the marked nonlinear elasticity of copper-nanocluster-polymer network, which synergizes with the lattice-topology effect and culminates in the exceptional mechanical performance.

Subgraph Isomorphic Decision Tree to Predict Radical Thermochemistry with Bounded Uncertainty Estimation.

Detailed chemical kinetic models offer valuable mechanistic insights into industrial applications. Automatic generation of reliable kinetic models requires fast and accurate radical thermochemistry estimation. Kineticists often prefer hydrogen bond increment (HBI) corrections from a closed-shell molecule to the corresponding radical for their interpretability, physical meaning, and facilitation of error cancellation as a relative quantity. Tree estimators, used due to limited data, currently rely on expert knowledge and manual construction, posing challenges in maintenance and improvement. In this work, we extend the subgraph isomorphic decision tree (SIDT) algorithm originally developed for rate estimation to estimate HBI corrections. We introduce a physics-aware splitting criterion, explore a bounded weighted uncertainty estimation method, and evaluate aleatoric uncertainty-based and model variance reduction-based prepruning methods. Moreover, we compile a data set of thermochemical parameters for 2210 radicals involving C, O, N, and H based on quantum chemical calculations from recently published works. We leverage the collected data set to train the SIDT model. Compared to existing empirical tree estimators, the SIDT model (1) offers an automatic approach to generating and extending the tree estimator for thermochemistry, (2) has better accuracy and R2, (3) provides significantly more realistic uncertainty estimates, and (4) has a tree structure much more advantageous in descent speed. Overall, the SIDT estimator marks a great leap in kinetic modeling, offering more precise, reliable, and scalable predictions for radical thermochemistry.

Prevotella copri and microbiota members mediate the beneficial effects of a therapeutic food for malnutrition.

Microbiota-directed complementary food (MDCF) formulations have been designed to repair the gut communities of malnourished children. A randomized controlled trial demonstrated that one formulation, MDCF-2, improved weight gain in malnourished Bangladeshi children compared to a more calorically dense standard nutritional intervention. Metagenome-assembled genomes from study participants revealed a correlation between ponderal growth and expression of MDCF-2 glycan utilization pathways by Prevotella copri strains. To test this correlation, here we use gnotobiotic mice colonized with defined consortia of age- and ponderal growth-associated gut bacterial strains, with or without P. copri isolates closely matching the metagenome-assembled genomes. Combining gut metagenomics and metatranscriptomics with host single-nucleus RNA sequencing and gut metabolomic analyses, we identify a key role of P. copri in metabolizing MDCF-2 glycans and uncover its interactions with other microbes including Bifidobacterium infantis. P. copri-containing consortia mediated weight gain and modulated energy metabolism within intestinal epithelial cells. Our results reveal structure-function relationships between MDCF-2 and members of the gut microbiota of malnourished children with potential implications for future therapies.

Statistical methods for chemical mixtures: a roadmap for practitioners.

Quantitative characterization of the health impacts associated with exposure to chemical mixtures has received considerable attention in current environmental and epidemiological studies. With many existing statistical methods and emerging approaches, it is important for practitioners to understand when each method is best suited for their inferential goals. In this study, we conduct a review and comparison of 11 analytical methods available for use in mixtures research, through extensive simulation studies for continuous and binary outcomes. These methods fall in three different classes: identifying important components of a mixture, identifying interactions and creating a summary score for risk stratification and prediction. We carry out an illustrative data analysis in the PROTECT birth cohort from Puerto Rico. Most importantly we develop an integrated package "CompMix" that provides a platform for mixtures analysis where the practitioner can implement a pipeline for several types of mixtures analysis. Our simulation results suggest that the choice of methods depends on the goal of analysis and there is no clear winner across the board. For selection of important toxicants in the mixture and for identifying interactions, Elastic net by Zou et al. (Enet), Lasso for Hierarchical Interactions by Bien et al (HierNet), Selection of nonlinear interactions by a forward stepwise algorithm by Narisetty et al. (SNIF) have the most stable performance across simulation settings. Additionally, the predictive performance of the Super Learner ensembling method by Van de Laan et al. and HierNet are found to be superior to the rest of the methods. For overall summary or a cumulative measure, we find that using the Super Learner to combine multiple Environmental Risk Scores can lead to improved risk stratification properties. We have developed an R package "CompMix: A comprehensive toolkit for environmental mixtures analysis", allowing users to implement a variety of tasks under different settings and compare the findings. In summary, our study offers guidelines for selecting appropriate statistical methods for addressing specific scientific questions related to mixtures research. We identify critical gaps where new and better methods are needed.

Plant-based diets, mediating biomarkers, and mortality risk among adults with diabetes or prediabetes.

Background: A healthy eating pattern characterized by a higher intake of healthy plant foods has been associated with a lower risk of premature mortality, but whether this applies to individuals with varying glycemic status remains unclear. Methods: This study included 4621 participants with diabetes and 8061 participants with prediabetes from the US National Health and Nutrition Examination Survey (2007-2016). Using the dietary data assessed by two 24 h dietary recalls, a healthful plant-based diet index (hPDI) and an unhealthful plant-based diet index (uPDI) were created based on 15 food groups and were assessed for their relationships with mortality risk. Results: Over a median follow-up of 7.2 years, there were 1021 deaths in diabetes and 896 deaths in prediabetes. A higher hPDI (highest vs. lowest quartile) was associated with a 41% (HR = 0.59, 95% CI: 0.49-0.72; P-trend < 0.001) lower risk of all-cause mortality in diabetes and a 31% (HR = 0.69, 95% CI: 0.55-0.85; P-trend < 0.001) lower risk in prediabetes. A higher uPDI was associated with an 88% (HR = 1.88, 95% CI: 1.55-2.28; P-trend < 0.001) higher risk of mortality in diabetes and a 63% (HR = 1.63, 95% CI: 1.33-1.99; P-trend < 0.001) higher risk in prediabetes. Mediation analysis suggested that C-reactive protein and γ-glutamine transaminase explained 6.0% to 10.9% of the relationships between hPDI or uPDI and all-cause mortality among participants with diabetes. Conclusions: For adults with diabetes as well as those with prediabetes, adhering to a plant-based diet rich in healthier plant foods is associated with a lower mortality risk, whereas a diet that incorporates less healthy plant foods is associated with a higher mortality risk.

Revealing Novel Genes Related to Parkinson's Disease Pathogenesis and Establishing an associated Model.

Parkinson's disease (PD) represents a multifaceted neurological disorder whose genetic underpinnings warrant comprehensive investigation. This study focuses on identifying genes integral to PD pathogenesis and evaluating their diagnostic potential. Initially, we screened for differentially expressed genes (DEGs) between PD and control brain tissues within a dataset comprising larger number of specimens. Subsequently, these DEGs were subjected to weighted gene co-expression network analysis (WGCNA) to discern relevant gene modules. Notably, the yellow module exhibited a significant correlation with PD pathogenesis. Hence, we conducted a detailed examination of the yellow module genes using a cytoscope-based approach to construct a protein-protein interaction (PPI) network, which facilitated the identification of central hub genes implicated in PD pathogenesis. Employing two machine learning techniques, including XGBoost and LASSO algorithms, along with logistic regression analysis, we refined our search to three pertinent hub genes: FOXO3, HIST2H2BE, and HDAC1, all of which demonstrated a substantial association with PD pathogenesis. To corroborate our findings, we analyzed two PD blood datasets and clinical plasma samples, confirming the elevated expression levels of these genes in PD patients. The association of the genes with PD, as reflected by the area under the curve (AUC) values for FOXO3, HIST2H2BE, and HDAC1, were moderate for each gene. Collectively, this research substantiates the heightened expression of FOXO3, HIST2H2BE, and HDAC1 in both PD brain and blood samples, underscoring their pivotal contribution to the pathogenesis of PD.

Comparison of Two Methods for Weaning from Nasal Continuous Positive Airway Pressure via the Cyclic Use of High-Flow Nasal Cannula or Room Air in Preterm Infants.

Nasal continuous positive airway pressure (NCPAP) is extensively used for preterm infants experiencing respiratory distress syndrome (RDS). Weaning from NCPAP includes direct weaning or gradually extending room air exposure. However, a high-flow nasal cannula (HFNC) is an alternative weaning method. Therefore, this study evaluated the clinical outcomes of HFNC and progressively increasing room air duration as weaning strategies. This study enrolled 46 preterm infants with RDS receiving NCPAP support who underwent the cyclic use of NCPAP and HFNC weaning protocol as the HFNC group; a retrospective analysis included 87 preterm infants weaned from NCPAP by gradually extending room air duration as the room air group. Differences in clinical conditions, complications, and short-term outcomes between the weaning methods were compared. The mean post-menstrual age at initiating NCPAP weaning was lower in the room air group than in the HFNC group (mean ± SD, 35.2 ± 2.3 weeks vs. 33.2 ± 2.5 weeks, p < 0.001). Hospital stay duration and total respiratory therapy days were longer in the HFNC group (96 ± 38 days and 80 ± 37 days, respectively) than in the room air group (78 ± 28 days and 56 ± 25 days, respectively), with p-values of 0.006 and <0.001. In conclusion, employing HFNC for weaning from NCPAP resulted in longer hospital admissions and respiratory therapy days than the room air method. However, further studies with a larger sample size are warranted for a more comprehensive evaluation, given the limited number of enrolled patients.

Machine learning models for screening clinically significant nephrolithiasis in overweight and obese populations.

PURPOSES: Our aim is to build and evaluate models to screen for clinically significant nephrolithiasis in overweight and obesity populations using machine learning (ML) methodologies and simple health checkup clinical and urine parameters easily obtained in clinics. METHODS: We developed ML models to screen for clinically significant nephrolithiasis (kidney stone > 2 mm) in overweight and obese populations (body mass index, BMI ≥ 25 kg/m2) using gender, age, BMI, gout, diabetes mellitus, estimated glomerular filtration rate, bacteriuria, urine pH, urine red blood cell counts, and urine specific gravity. The data were collected from hospitals in Kaohsiung, Taiwan between 2012 and 2021. RESULTS: Of the 2928 subjects we enrolled, 1148 (39.21%) had clinically significant nephrolithiasis and 1780 (60.79%) did not. The testing dataset consisted of data collected from 574 subjects, 235 (40.94%) with clinically significant nephrolithiasis and 339 (59.06%) without. One model had a testing area under curve of 0.965 (95% CI, 0.9506-0.9794), a sensitivity of 0.860 (95% CI, 0.8152-0.9040), a specificity of 0.947 (95% CI, 0.9230-0.9708), a positive predictive value of 0.918 (95% CI, 0.8820-0.9544), and negative predictive value of 0.907 (95% CI, 0.8756-0.9371). CONCLUSION: This ML-based model was found able to effectively distinguish the overweight and obese subjects with clinically significant nephrolithiasis from those without. We believe that such a model can serve as an easily accessible and reliable screening tool for nephrolithiasis in overweight and obesity populations and make possible early intervention such as lifestyle modifications and medication for prevention stone complications.

Balancing the Ion/Electron Transport of Graphite Anodes by a La-Doped TiNb2O7 Functional Coating for Fast-Charging Li-Ion Batteries.

A functional coating layer (FCL) is widely applied in fast-charging lithium-ion batteries to improve the sluggish Li+ transport kinetics of traditional graphite anodes. However, blindly increasing the Li+ conductivity for FCL reduces the overall electron conductivity of the anodes. Herein, we decoupled the effect of La-doping on TiNb2O7 (TNO) in terms of the phase evolution, Li+/electron transport, and lithiation behavior, and then proposed a promising La0.1TNO FCL with balanced Li+/electron transport for a fast-charging graphite anode. By optimizing the doping concentration of La, more holes are introduced into the TNO as electron carriers without causing lattice distortion, thus maintaining the fast Li+ diffusion channel in TNO. As a result, the graphite with La0.1TNO FCL delivers an excellent capacity of 220.2 mAh g-1 (96.3% retention) after 450 cycles at 3 C, nearly twice that of the unmodified one.

Developing a practical neurodevelopmental prediction model for targeting high-risk very preterm infants during visit after NICU: a retrospective national longitudinal cohort study.

BACKGROUND: Follow-up visits for very preterm infants (VPI) after hospital discharge is crucial for their neurodevelopmental trajectories, but ensuring their attendance before 12 months corrected age (CA) remains a challenge. Current prediction models focus on future outcomes at discharge, but post-discharge data may enhance predictions of neurodevelopmental trajectories due to brain plasticity. Few studies in this field have utilized machine learning models to achieve this potential benefit with transparency, explainability, and transportability. METHODS: We developed four prediction models for cognitive or motor function at 24 months CA separately at each follow-up visits, two for the 6-month and two for the 12-month CA visits, using hospitalized and follow-up data of VPI from the Taiwan Premature Infant Follow-up Network from 2010 to 2017. Regression models were employed at 6 months CA, defined as a decline in The Bayley Scales of Infant Development 3rd edition (BSIDIII) composite score > 1 SD between 6- and 24-month CA. The delay models were developed at 12 months CA, defined as a BSIDIII composite score < 85 at 24 months CA. We used an evolutionary-derived machine learning method (EL-NDI) to develop models and compared them to those built by lasso regression, random forest, and support vector machine. RESULTS: One thousand two hundred forty-four VPI were in the developmental set and the two validation cohorts had 763 and 1347 VPI, respectively. EL-NDI used only 4-10 variables, while the others required 29 or more variables to achieve similar performance. For models at 6 months CA, the area under the receiver operating curve (AUC) of EL-NDI were 0.76-0.81(95% CI, 0.73-0.83) for cognitive regress with 4 variables and 0.79-0.83 (95% CI, 0.76-0.86) for motor regress with 4 variables. For models at 12 months CA, the AUC of EL-NDI were 0.75-0.78 (95% CI, 0.72-0.82) for cognitive delay with 10 variables and 0.73-0.82 (95% CI, 0.72-0.85) for motor delay with 4 variables. CONCLUSIONS: Our EL-NDI demonstrated good performance using simpler, transparent, explainable models for clinical purpose. Implementing these models for VPI during follow-up visits may facilitate more informed discussions between parents and physicians and identify high-risk infants more effectively for early intervention.

ADORA3: A Key Player in the Pathogenesis of Intracranial Aneurysms and a Potential Diagnostic Biomarker.

BACKGROUND: The effects of genes on the development of intracranial aneurysms (IAs) remain to be elucidated, and reliable blood biomarkers for diagnosing IAs are yet to be established. This study aimed to identify genes associated with IAs pathogenesis and explore their diagnostic value by analyzing IAs datasets, conducting vascular smooth muscle cells (VSMC) experiments, and performing blood detection. METHODS: IAs datasets were collected and the differentially expressed genes were analyzed. The selected genes were validated in external datasets. Autophagy was induced in VSMC and the effect of selected genes was determined. The diagnostic value of selected gene on the IAs were explored using area under curve (AUC) analysis using IAs plasma samples. RESULTS: Analysis of 61 samples (32 controls and 29 IAs tissues) revealed a significant increase in expression of ADORA3 compared with normal tissues using empirical Bayes methods of "limma" package; this was further validated by two external datasets. Additionally, induction of autophagy in VSMC lead to upregulation of ADORA3. Conversely, silencing ADORA3 suppressed VSMC proliferation and autophagy. Furthermore, analysis of an IAs blood sample dataset and clinical plasma samples demonstrated increased ADORA3 expression in patients with IA compared with normal subjects. The diagnostic value of blood ADORA3 expression in IAs was moderate when analyzing clinical samples (AUC: 0.756). Combining ADORA3 with IL2RB or CCR7 further enhanced the diagnostic ability for IAs, with the AUC value over 0.83. CONCLUSIONS: High expression of ADORA3 is associated with IAs pathogenesis, likely through its promotion of VSMC autophagy. Furthermore, blood ADORA3 levels have the potential to serve as an auxiliary diagnostic biomarker for IAs.

Immunotherapy-resistant acute lymphoblastic leukemia cells exhibit reduced CD19 and CD22 expression and BTK pathway dependency.

While therapies targeting CD19 by antibodies, chimeric antigen receptor T cells (CAR-T), and T cell engagers have improved the response rates in B cell malignancies, the emergence of resistant cell populations with low CD19 expression can lead to relapsed disease. We developed an in vitro model of adaptive resistance facilitated by chronic exposure of leukemia cells to a CD19 immunotoxin. Single-cell RNA-Seq (scRNA-Seq) showed an increase in transcriptionally distinct CD19lo populations among resistant cells. Mass cytometry demonstrated that CD22 was also decreased in these CD19lo-resistant cells. An assay for transposase-accessible chromatin with sequencing (ATAC-Seq) showed decreased chromatin accessibility at promoters of both CD19 and CD22 in the resistant cell populations. Combined loss of both CD19 and CD22 antigens was validated in samples from pediatric and young adult patients with B cell acute lymphoblastic leukemia (B-ALL) that relapsed after CD19 CAR-T-targeted therapy. Functionally, resistant cells were characterized by slower growth and lower basal levels of MEK activation. CD19lo resistant cells exhibited preserved B cell receptor signaling and were more sensitive to both Bruton's tyrosine kinase (BTK) and MEK inhibition. These data demonstrate that resistance to CD19 immunotherapies can result in decreased expression of both CD19 and CD22 and can result in dependency on BTK pathways.

Myoelectric, Myo-Oxygenation, and Myotonometry Changes during Robot-Assisted Bilateral Arm Exercises with Varying Resistances.

Robot-assisted bilateral arm training has demonstrated its effectiveness in improving motor function in individuals post-stroke, showing significant enhancements with increased repetitions. However, prolonged training sessions may lead to both mental and muscle fatigue. We conducted two types of robot-assisted bimanual wrist exercises on 16 healthy adults, separated by one week: long-duration, low-resistance workouts and short-duration, high-resistance exercises. Various measures, including surface electromyograms, near-infrared spectroscopy, heart rate, and the Borg Rating of Perceived Exertion scale, were employed to assess fatigue levels and the impacts of exercise intensity. High-resistance exercise resulted in a more pronounced decline in electromyogram median frequency and recruited a greater amount of hemoglobin, indicating increased muscle fatigue and a higher metabolic demand to cope with the intensified workload. Additionally, high-resistance exercise led to increased sympathetic activation and a greater sense of exertion. Conversely, engaging in low-resistance exercises proved beneficial for reducing post-exercise muscle stiffness and enhancing muscle elasticity. Choosing a low-resistance setting for robot-assisted wrist movements offers advantages by alleviating mental and physiological loads. The reduced training intensity can be further optimized by enabling extended exercise periods while maintaining an approximate dosage compared to high-resistance exercises.

Interferon gamma induces higher neutrophil extracellular traps leading to tumor-killing activity in microsatellite stable colorectal cancer.

Many colorectal cancer (CRC) patients do not respond to immune checkpoint blockade (ICB) therapy, highlighting the urgent need to understand tumor resistance mechanisms. Recently, the link between the IFNγ signaling pathway integrity and ICB resistance in the CRC tumor microenvironment has been revealed. The immunosuppressive microenvironment poses a significant challenge to antitumor immunity in CRC development. Tumor-associated neutrophils (TANs) found in tumor tissues exhibit an immunosuppressive phenotype and are associated with CRC patient prognosis. Neutrophil extracellular traps (NETs), DNA meshes containing cytotoxic enzymes released into the extracellular space, may be promising therapeutic targets in cancer. This study showed increased NETs in tumor tissues and peripheral neutrophils of high levels of microsatellite instability (MSI-H) CRC patients compared to microsatellite stable (MSS) CRC patients. IFNγ response genes were enriched in MSI-H CRC patients compared to MSS CRC patients. Co-culturing neutrophils with MSI-H CRC cell lines induced more NET formation and higher cellular apoptosis than MSS CRC cell lines. IFNγ treatment induced more NET formation and apoptosis in MSS CRC cell lines. Using subcutaneous or orthotopic CT-26 (MSS)-tumor-bearing mice models, IFNγ reduced tumor size and enhanced PD-1 antibody-induced tumor-killing activity, accompanied by upregulated NETs and cellular apoptosis. These findings suggest IFNγ could be a therapeutic strategy for MSS CRC.