Regulation of matrix reloading by tumor endothelial marker 1 protects against abdominal aortic aneurysm.

Tumor endothelial marker 1 (TEM1), an activated mesenchymal cell marker, is implicated in tissue remodeling and repair. Herein, we investigated the role and therapeutic implications of TEM1 in abdominal aortic aneurysm (AAA), a potentially life-threatening aortic disease characterized by vascular inflammation and matrix turnover. Characterization of human AAA revealed increased TEM1 expression derived mainly from medial vascular smooth muscle cells (VSMCs) and adventitial fibroblasts. Bioinformatics analysis demonstrated the association between TEM1-expressing VSMCs and fibroblasts and collagen gene expression. Consistently, collagen content and TEM1 expressed by VSMCs and fibroblasts were increased during CaCl2-induced AAA formation in mice. TEM1 silencing in VSMCs and fibroblasts inhibited transforming growth factor-ß1-induced phenotypic change, SMAD2 phosphorylation, and COL1A1 gene expression. Also, Tem1 deficiency reduced collagen synthesis and exacerbated CaCl2-induced AAA formation in mice without disturbing elastin destruction and inflammatory responses. In contrast, rTEM1 promoted phenotypic change and COL1A1 gene expression through SMAD2 phosphorylation in VSMCs and fibroblasts. Treatment with rTEM1 enhanced collagen synthesis, attenuated elastin fragmentation, and inhibited CaCl2-induced and angiotensin II-infused AAA formation. In summary, TEM1 in resident stromal cells regulates collagen synthesis to counteract aortic wall failure during AAA formation. Matrix integrity restored by rTEM1 treatment may hold therapeutic potential against AAA.

Prognostic Value of Coronary Microvascular Dysfunction Assessed by Coronary Angiography-Derived Index of Microcirculatory Resistance in Patients With ST-Segment Elevation Myocardial Infarction.

BACKGROUND: CaIMR is proposed as a novel angiographic index designed to assess microcirculation without the need for pressure wires or hyperemic agents. We aimed to investigate the impact of caIMR on predicting clinical outcomes in STEMI patients. METHODS: One hundred and forty patients with STEMI who received PCI in Putuo Hospital of Shanghai from October 2021 to September 2022 were categorized into CMD and non-CMD groups according to the caIMR value. The baseline information, patient-related examinations, and the occurrence of MACE at the 12-month follow-up were collected to investigate risk factors in patients with STEMI. RESULTS: We divided 140 patients with STEMI enrolled into two groups according to caIMR results, including 61 patients diagnosed with CMD and 79 patients diagnosed with non-CMD. A total of 21 MACE occurred during the 1 year of follow-up. Compared with non-CMD group, patients with CMD showed a significantly higher risk of MACE. A multivariate Cox regression model was conducted for the patients, and it was found thatcaIMR was a significant predictor of prognosis in STEMI patients (HR: 8.921). Patients with CMD were divided into culprit vascular CMD and non-culprit vascular CMD, and the result found that culprit vascular CMD was associated with the incidence of MACE (OR: 4.75) and heart failure (OR: 7.50). CONCLUSION: CaIMR is a strong predictor of clinical outcomes and can provide an objective risk stratification for patients with STEMI. There is a strong correlation among leukocyte index, the use of furosemide, Killips classification, and clinical outcomes.

Development and evaluation of real-time quantitative PCR assays for detection of Phellinus noxius causing brown root rot disease.

Brown root rot disease (BRRD) is a highly destructive tree disease. Early diagnosis of BRRD has been challenging because the first symptoms and signs are often observed after extensive tissue colonization. Existing molecular detection methods, all based on the internal transcribed spacer (ITS) region, were developed without testing against global Phellinus noxius isolates, other wood decay fungi, or host plant tissues. This study developed SYBR Green real-time quantitative PCR (qPCR) assays for P. noxius. The primer pair Pn_ITS_F/Pn_ITS_R targets the ITS, and the primer pair Pn_NLR_F/Pn_NLR_R targets a P. noxius-unique group of homologous genes identified through a comparative genomics analysis. The homologous genes belong to the nucleotide-binding-oligomerization-domain-like receptor (NLR) superfamily. The new primer pairs and a previous primer pair G1F/G1R were optimized for qPCR conditions and tested for specificity using 61 global P. noxius isolates, five other Phellinus species, and 22 non-Phellinus wood decay fungal species. While all three primer pairs could detect as little as 100 fg (about 2.99 copies) of P. noxius genomic DNA, G1F/G1R had the highest specificity and Pn_NLR_F/Pn_NLR_R had the highest efficiency. To avoid false positives, the cutoff Cq values were determined as 34 for G1F/G1R, 29 for Pn_ITS_F/Pn_ITS_R, and 32 for Pn_NLR_F/Pn_NLR_R. We further validated these qPCR assays using Ficus benjamina seedlings artificially inoculated with P. noxius, six tree species naturally infected by P. noxius, rhizosphere soil, and bulk soil. The newly developed qPCR assays provide sensitive detection and quantification of P. noxius, which is useful for long-term monitoring of BRRD status.

Development of a modified 3D region proposal network for lung nodule detection in computed tomography scans: a secondary analysis of lung nodule datasets.

BACKGROUND: Low-dose computed tomography (LDCT) has been shown useful in early lung cancer detection. This study aimed to develop a novel deep learning model for detecting pulmonary nodules on chest LDCT images. METHODS: In this secondary analysis, three lung nodule datasets, including Lung Nodule Analysis 2016 (LUNA16), Lung Nodule Received Operation (LNOP), and Lung Nodule in Health Examination (LNHE), were used to train and test deep learning models. The 3D region proposal network (RPN) was modified via a series of pruning experiments for better predictive performance. The performance of each modified deep leaning model was evaluated based on sensitivity and competition performance metric (CPM). Furthermore, the performance of the modified 3D RPN trained on three datasets was evaluated by 10-fold cross validation. Temporal validation was conducted to assess the reliability of the modified 3D RPN for detecting lung nodules. RESULTS: The results of pruning experiments indicated that the modified 3D RPN composed of the Cross Stage Partial Network (CSPNet) approach to Residual Network (ResNet) Xt (CSP-ResNeXt) module, feature pyramid network (FPN), nearest anchor method, and post-processing masking, had the optimal predictive performance with a CPM of 92.2%. The modified 3D RPN trained on the LUNA16 dataset had the highest CPM (90.1%), followed by the LNOP dataset (CPM: 74.1%) and the LNHE dataset (CPM: 70.2%). When the modified 3D RPN trained and tested on the same datasets, the sensitivities were 94.6%, 84.8%, and 79.7% for LUNA16, LNOP, and LNHE, respectively. The temporal validation analysis revealed that the modified 3D RPN tested on LNOP test set achieved a CPM of 71.6% and a sensitivity of 85.7%, and the modified 3D RPN tested on LNHE test set had a CPM of 71.7% and a sensitivity of 83.5%. CONCLUSION: A modified 3D RPN for detecting lung nodules on LDCT scans was designed and validated, which may serve as a computer-aided diagnosis system to facilitate lung nodule detection and lung cancer diagnosis.

A modified 3D RPN for detecting lung nodules on CT images that exhibited greater sensitivity and CPM than did several previously reported CAD detection models was established.

Early changes in skin surface temperature predict body temperature increases in patients with fever: A pilot study.

OBJECTIVE: To investigate the correlation between body temperature and skin surface temperature in intensive care unit patients and to identify specific indicators of skin surface temperature for early fever detection. RESEARCH METHODOLOGY/DESIGN: This pilot study was a prospective, observational investigation conducted at National Cheng Kung University Hospital in Tainan, Taiwan. A total of 54 patients admitted to the Surgical Intensive Care Unit of a tertiary hospital between April and August 2020 were included. Patients utilized the wearable device HEARThremoTM to continuously monitor skin surface temperature and heart rate. Analysis of Variance was applied to identify the association of skin surface temperature with different body temperature groups. The comparison between skin surface temperature and fever over eight time intervals was studied using a generalized estimating equation. RESULTS: In 34 patients (63 %) with a fever (≥38 °C), skin surface temperature increased (P < 0.001) when body temperature increased. The maximum skin surface temperature was significantly associated with fever 180-210 min before the fever events occurred (OR: 2.22, 95 % CI: 1.30-3.80). The mean skin surface temperature was associated with fever 120-150 min before the fever events (OR: 8.70, 95 % CI: 2.08-36.36). CONCLUSIONS: Skin surface temperature can be an important early predictive sign before the onset of fever. Continuous temperature monitoring can detect fever early and initiate treatment in advance. This study serves as a preliminary exploration in this area, laying the groundwork for future comprehensive research. IMPLICATIONS FOR CLINICAL PRACTICE: Continuous monitoring of skin surface temperature empowers nurses to swiftly detect fever, transcending conventional methods. This proactive approach allows for the early identification of physiological abnormalities, facilitating the prompt initiation of further physical assessments and relevant examinations for early treatment commencement.

Explainable Learning-Based Timeout Optimization for Accurate and Efficient Elephant Flow Prediction in SDNs.

Accurately and efficiently predicting elephant flows (elephants) is crucial for optimizing network performance and resource utilization. Current prediction approaches for software-defined networks (SDNs) typically rely on complete traffic and statistics moving from switches to controllers. This leads to an extra control channel bandwidth occupation and network delay. To address this issue, this paper proposes a prediction strategy based on incomplete traffic that is sampled by the timeouts for the installation or reactivation of flow entries. The strategy involves assigning a very short hard timeout (Tinitial) to flow entries and then increasing it at a rate of r until flows are identified as elephants or out of their lifespans. Predicted elephants are switched to an idle timeout of 5 s. Logistic regression is used to model elephants based on a complete dataset. Bayesian optimization is then used to tune the trained model Tinitial and r over the incomplete dataset. The process of feature selection, model learning, and optimization is explained. An extensive evaluation shows that the proposed approach can achieve over 90% generalization accuracy over 7 different datasets, including campus, backbone, and the Internet of Things (IoT). Elephants can be correctly predicted for about half of their lifetime. The proposed approach can significantly reduce the controller-switch interaction in campus and IoT networks, although packet completion approaches may need to be applied in networks with a short mean packet inter-arrival time.

Sex difference in the association between excessive daytime sleepiness and health-related quality of life in community-dwelling older adults: The Yilan study, Taiwan.

OBJECTIVES: The association between excessive daytime sleepiness and health-related quality of life among older adults and at-risk individuals remains unclear. This study examined relationships between excessive daytime sleepiness and unfavorable health-related quality of life and explored the moderating effect of sex. STUDY DESIGN: This was a community-based study of adults aged 65 years or more. Excessive daytime sleepiness was defined as a score exceeding 10 on the Epworth Sleepiness Scale. Multiple logistic regression analyses were used to examine the relationships between excessive daytime sleepiness and health-related quality of life. The moderating effect of sex was examined by testing interaction terms. MAIN OUTCOME MEASURES: Health-related quality of life was measured using the Short Form 12 Health Survey, which includes a physical component summary and a mental component summary. Unfavorable health-related quality of life was defined as the lowest tertile of the scores for both components. RESULTS: In total, 3788 individuals participated. After controlling for covariates, older adults with excessive daytime sleepiness did not have an unfavorable physical component summary but were more likely to have an unfavorable mental component summary (odds ratio 1.96; 95 % confidence interval 1.47-2.61). When stratified by sex, excessive daytime sleepiness was associated with a poor physical component summary in men (odds ratio 1.77, 95 % confidence interval 1.00-3.13) but not in women. CONCLUSIONS: Excessive daytime sleepiness was associated with a poor mental component summary in both sexes; however, the association with a poor physical component summary was specific to men.

TEM1/endosialin/CD248 promotes pathologic scarring and TGF-ß activity through its receptor stability in dermal fibroblasts.

BACKGROUND: Pathologic scars, including keloids and hypertrophic scars, represent a common form of exaggerated cutaneous scarring that is difficult to prevent or treat effectively. Additionally, the pathobiology of pathologic scars remains poorly understood. We aim at investigating the impact of TEM1 (also known as endosialin or CD248), which is a glycosylated type I transmembrane protein, on development of pathologic scars. METHODS: To investigate the expression of TEM1, we utilized immunofluorescence staining, Western blotting, and single-cell RNA-sequencing (scRNA-seq) techniques. We conducted in vitro cell culture experiments and an in vivo stretch-induced scar mouse model to study the involvement of TEM1 in TGF-ß-mediated responses in pathologic scars. RESULTS: The levels of the protein TEM1 are elevated in both hypertrophic scars and keloids in comparison to normal skin. A re-analysis of scRNA-seq datasets reveals that a major profibrotic subpopulation of keloid and hypertrophic scar fibroblasts greatly expresses TEM1, with expression increasing during fibroblast activation. TEM1 promotes activation, proliferation, and ECM production in human dermal fibroblasts by enhancing TGF-ß1 signaling through binding with and stabilizing TGF-ß receptors. Global deletion of Tem1 markedly reduces the amount of ECM synthesis and inflammation in a scar in a mouse model of stretch-induced pathologic scarring. The intralesional administration of ontuxizumab, a humanized IgG monoclonal antibody targeting TEM1, significantly decreased both the size and collagen density of keloids. CONCLUSIONS: Our data indicate that TEM1 plays a role in pathologic scarring, with its synergistic effect on the TGF-ß signaling contributing to dermal fibroblast activation. Targeting TEM1 may represent a novel therapeutic approach in reducing the morbidity of pathologic scars.

Auto-suppression of Tet dioxygenases protects the mouse oocyte genome from oxidative demethylation.

DNA cytosine methylation plays a vital role in repressing retrotransposons, and such derepression is linked with developmental failure, tumorigenesis and aging. DNA methylation patterns are formed by precisely regulated actions of DNA methylation writers (DNA methyltransferases) and erasers (TET, ten-eleven translocation dioxygenases). However, the mechanisms underlying target-specific oxidation of 5mC by TET dioxygenases remain largely unexplored. Here we show that a large low-complexity domain (LCD), located in the catalytic part of Tet enzymes, negatively regulates the dioxygenase activity. Recombinant Tet3 lacking LCD is shown to be hyperactive in converting 5mC into oxidized species in vitro. Endogenous expression of the hyperactive Tet3 mutant in mouse oocytes results in genome-wide 5mC oxidation. Notably, the occurrence of aberrant 5mC oxidation correlates with a consequent loss of the repressive histone mark H3K9me3 at ERVK retrotransposons. The erosion of both 5mC and H3K9me3 causes ERVK derepression along with upregulation of their neighboring genes, potentially leading to the impairment of oocyte development. These findings suggest that Tet dioxygenases use an intrinsic auto-regulatory mechanism to tightly regulate their enzymatic activity, thus achieving spatiotemporal specificity of methylome reprogramming, and highlight the importance of methylome integrity for development.

Combined model integrating deep learning, radiomics, and clinical data to classify lung nodules at chest CT.

OBJECTIVES: The study aimed to develop a combined model that integrates deep learning (DL), radiomics, and clinical data to classify lung nodules into benign or malignant categories, and to further classify lung nodules into different pathological subtypes and Lung Imaging Reporting and Data System (Lung-RADS) scores. MATERIALS AND METHODS: The proposed model was trained, validated, and tested using three datasets: one public dataset, the Lung Nodule Analysis 2016 (LUNA16) Grand challenge dataset (n = 1004), and two private datasets, the Lung Nodule Received Operation (LNOP) dataset (n = 1027) and the Lung Nodule in Health Examination (LNHE) dataset (n = 1525). The proposed model used a stacked ensemble model by employing a machine learning (ML) approach with an AutoGluon-Tabular classifier. The input variables were modified 3D convolutional neural network (CNN) features, radiomics features, and clinical features. Three classification tasks were performed: Task 1: Classification of lung nodules into benign or malignant in the LUNA16 dataset; Task 2: Classification of lung nodules into different pathological subtypes; and Task 3: Classification of Lung-RADS score. Classification performance was determined based on accuracy, recall, precision, and F1-score. Ten-fold cross-validation was applied to each task. RESULTS: The proposed model achieved high accuracy in classifying lung nodules into benign or malignant categories in LUNA 16 with an accuracy of 92.8%, as well as in classifying lung nodules into different pathological subtypes with an F1-score of 75.5% and Lung-RADS scores with an F1-score of 80.4%. CONCLUSION: Our proposed model provides an accurate classification of lung nodules based on the benign/malignant, different pathological subtypes, and Lung-RADS system.

Aucubin inhibits the proliferation of human hepatocellular carcinoma cells line HepG2 / 基础医学与临床

Objective To investigate the effects of aucubin(AU)on the proliferation,apoptosis,and cell cycle of human liver cancer cells line HepG2 and its mechanism of action.Methods HepG2 cells were cultured in vitro,CCK-8 method was applied to screen the optimal dosage concentration of AU.HepG2 cells were randomly grouped into a control group,an AU 12.5 mg/L group(AU L group),an AU 62.5 mg/L group(AU H group),and an AU H+Akt pathway agonist(SC79)group(AU H+SC79 group).The cell proliferation was observed in each group;5-Ethynyl-2′-deoxyuridine(EDU)method was applied to detect cell proliferation;Flow cytometry was applied to detect cell apoptosis and cell cycle;Western blot was applied to detect the expression levels of phosphorylated pro-tein kinase B(p-Akt),Akt,p-MDM2,MDM2,p-p53,and p53 proteins.Results AU concentrations of 12.5 and 62.5 mg/L were selected for subsequent experiments.Compared with 0 mg/L AU,the proliferation of 12.5 and 62.5 mg/L AU cells was obviously reduced(P＜0.05);Compared with the control group,the number of suspended and exfoliated cells in the AU L and AU H groups gradually increased.Cells shrunk and became round.The propor-tion of G0/G1 phase cells,the proportion of EDU positive staining cells increased and the expression level of p-Akt/Akt and p-MDM2/MDM2 proteins decreased.The proportions of S and G2/M phase cells,the rate of cell apoptosis,and the expression level of p-p53/p53 protein all increased(P＜0.05).Compared with the AU H group,the above changes in the AU H+SC79 group were recovered(P＜0.05).After AU treatment,the tumor vol-ume and weight of transplanted nude mice decreased.Conclusions AU may inhibit the proliferation of liver cancer cells,induce cell cycle arrest and apoptosis by regulating the Akt/MDM2/p53 signaling pathway.

Establishment and assessment of a postoperative delirium risk scale for elderly patients undergoing hip and knee replacement / 临床麻醉学杂志

Objective To construct a risk assessment scale for postoperative delirium(POD)in elderly patients undergoing hip and knee joint replacement and evaluate the effect.Methods A total of 474 elderly patients undergoing hip and knee arthroplasty from March 2021 to May 2022 were collected as the training set,and a total of 153 the homogeneous patients from January 2022 to May 2022 were collected as the validation set.The patients were divided into two groups based on whether or not POD occurred:non-POD group and POD group.Risk factors of POD in the training set were analyzed by univariate analysis and multifactorial logistic regression.The consistency of the model was evaluated by Homser-Lemeshow goodness of fit test.The postoperative delirium risk assessment scale was established after the selected variables as-signed value according to OR value,and the predictive efficacy of the scale was evaluated by receiver oper-ating characteristic(ROC)curve.The patients in the training set and the validation set were divided into two groups according to the cut-off value:high-risk and low-risk.The incidence rate of POD with different risk stratification was calculated and the applicability of the risk assessment scale was evaluated.Results Fifty-eight patients(12.2%)with POD in the training set,and nineteen patients(12.4%)with POD in the validation set.Multifactor logistic regression showed that age≥85 years,ASA physical status Ⅲ or Ⅳ,the mini-mental state examination(MMSE)score≤24 points,preoperative sleep disorder,comorbid neu-rological disorders,use of general anesthesia,and non-use of dexmedetomidine were independent risk factors of POD.The POD risk assessment scale was then published based the seven risk factors.The ROC curve showed that the area under the curve(AUC)for this scale to predict the risk of POD was 0.956(95%CI 0.937-0.975),and the risk stratification was performed with a cut-off value of 44.5 points,which divided the patients into low-risk and high-risk.Compared with low-risk,the incidence rate of POD in high-risk patients group was significantly increased(P＜0.001).Conclusion A risk assessment scale based on the seven risk factors:age≥85 years,ASA physical status Ⅲ or Ⅳ,MMSE score≤24 points,preoperative sleep disorder,combined neurological disease,use of general anesthetic modality,and non-use of dexmedetomidine,can effectively identify elderly patients undergoing hip and knee replacement who are at high risk of developing POD.

Do We Need to Administer Fludrocortisone in Addition to Hydrocortisone in Adult Patients With Septic Shock? An Updated Systematic Review With Bayesian Network Meta-Analysis of Randomized Controlled Trials and an Observational Study With Target Trial Emulation.

OBJECTIVES: This systematic review and Bayesian network meta-analysis evaluated the efficacy and safety of hydrocortisone combined with fludrocortisone or hydrocortisone alone, compared with placebo in adult patients with septic shock. DATA SOURCES: By extending a prior Cochrane review, databases, including PubMed, Embase, the Cochrane Library, and ClinicalTrials.gov , along with other relevant websites, were searched until August 31, 2023. STUDY SELECTION: Randomized controlled trials (RCTs) and observational studies using target trial emulation were included. DATA EXTRACTION: The primary outcome was short-term mortality with an emphasis on 28- or 30-day mortality as the main measure and in-hospital or ICU mortality as the nearest surrogate of this measure. Three of the most common adverse events, namely, gastroduodenal bleeding, superinfection, and hyperglycemia, were also considered. DATA SYNTHESIS: A total of 19 studies involving 95,841 patients were included. Hydrocortisone plus fludrocortisone showed the lowest short-term mortality versus placebo (odds ratio [OR]: 0.79; 95% credible interval [CrI], 0.64-0.99; number needed to treat [NNT]: 21, range: 12-500; low certainty of evidence) in terms of informative priors. The surface under the cumulative ranking curve values for hydrocortisone plus fludrocortisone, hydrocortisone alone, and placebo were 0.9469, 0.4542, and 0.0989, respectively. Consistent results were observed in RCTs alone and those using a daily 200-mg dose of hydrocortisone. Although gastroduodenal bleeding or superinfection showed no clear increase, hyperglycemia risk increased. The ORs were 0.53 for placebo versus hydrocortisone plus fludrocortisone and 0.64 for placebo versus hydrocortisone alone, with very low certainty of evidence. CONCLUSIONS: In adults with septic shock, hydrocortisone plus fludrocortisone improved short-term survival with minimal adverse events compared with hydrocortisone alone or placebo. However, these findings are not definitive due to the limited certainty of evidence and wide NNT range. Additional large-scale, placebo-controlled RCTs are needed to provide conclusive evidence.

Clinical heterogeneity and cluster analysis of multiple system atrophy / 中华老年医学杂志

Objective:To examine the clinical subtypes of patients with multisystem atrophy(MSA)that may indicate the prognosis of patients.Additionally, we aim to compare the ability to perform daily activities among patients of each subtype using cluster analysis.Methods:The retrospective analysis included demographic data, clinical symptoms and signs, scale scores, and ancillary examinations of 94 patients diagnosed with multisystem atrophy at Xuanwu Hospital of Capital Medical University.The study aimed to analyze the clinical characteristics of each subtype obtained through clustering.Additionally, a comparison was made between patients with traditional motor subtypes and those with new subtypes in terms of activities of daily living.The study consisted of 94 MSA patients, with an average age of 61 years and a female representation of 51.1%.Using the data collected on the continuum, a full linkage hierarchical cluster analysis was performed to classify MSA patients into four clinical subtypes: gait disorder(17 cases, 18.1%), malignant tonic hyperkinetic with premature haircut(25 cases, 26.6%), intermediate(43 cases, 45.7%), and autonomic benign type(9 cases, 9.6%).Each subtype exhibited various clinical motor and non-motor symptoms, including UPDRS-Ⅲ( χ2=27.90, P<0.001), gait disturbance( χ2=33.23, P<0.001), MoCA( χ2=10.98, P=0.012), HAMA( χ2=12.14, P=0.007), HAMD( χ2=13.62, P=0.003), smell score( χ2=10.16, P=0.017), postural hypotension( χ2=14.59, P=0.028), and a statistically significant difference in the ability to perform daily living score( χ2=25.35, P<0.001).No statistically significant differences in non-motor symptoms and activities of daily living abilities were observed between the cerebellar and Parkinsonian types of traditional motor typing( P>0.05). Conclusions:The hierarchical clustering analysis conducted in this study reveals that the clinical phenotype of MSA provides a more accurate reflection of patients' clinical characteristics and their impact on quality of life compared to the traditional motor phenotype.Additionally, it may help predict variations in the underlying pathological impairment and the rate of disease progression.These findings offer a foundation for precise diagnostic interventions in patients with MSA.

Personalized Federated Learning Algorithm with Adaptive Clustering for Non-IID IoT Data Incorporating Multi-Task Learning and Neural Network Model Characteristics.

The proliferation of IoT devices has led to an unprecedented integration of machine learning techniques, raising concerns about data privacy. To address these concerns, federated learning has been introduced. However, practical implementations face challenges, including communication costs, data and device heterogeneity, and privacy security. This paper proposes an innovative approach within the context of federated learning, introducing a personalized joint learning algorithm for Non-IID IoT data. This algorithm incorporates multi-task learning principles and leverages neural network model characteristics. To overcome data heterogeneity, we present a novel clustering algorithm designed specifically for federated learning. Unlike conventional methods that require a predetermined number of clusters, our approach utilizes automatic clustering, eliminating the need for fixed cluster specifications. Extensive experimentation demonstrates the exceptional performance of the proposed algorithm, particularly in scenarios with specific client distributions. By significantly improving the accuracy of trained models, our approach not only addresses data heterogeneity but also strengthens privacy preservation in federated learning. In conclusion, we offer a robust solution to the practical challenges of federated learning in IoT environments. By combining personalized joint learning, automatic clustering, and neural network model characteristics, we facilitate more effective and privacy-conscious machine learning in Non-IID IoT data settings.

Soluble CD93 lectin-like domain sequesters HMGB1 to ameliorate inflammatory diseases.

Rationale: CD93, a C-type lectin-like transmembrane glycoprotein, can be shed in a soluble form (sCD93) upon inflammatory stimuli. sCD93 effectively enhances apoptotic cell clearance and has been proposed as an inflammatory disease biomarker. The function of sCD93 involved directly in inflammation remains to be determined. Herein, we attempted to examine the hypothesis that sCD93 might sequester proinflammatory high-mobility group box 1 protein (HMGB1), exerting anti-inflammatory properties. Methods: Different forms of soluble recombinant human CD93 (rCD93) were prepared by a mammalian protein expression system. rCD93-HMGB1 interaction was assessed using co-immunoprecipitation and solid-phase binding assays. Effects of soluble rCD93 were evaluated in HMGB1-induced macrophage and vascular smooth muscle cells (VSMC) activation and receptor activator of nuclear factor-κB ligand (RANKL)-induced osteoclastogenesis, CaCl2-induced and angiotensin II-infused abdominal aortic aneurysm (AAA) formation and ovariectomized-induced osteoporosis in mice. Results: Protein binding studies revealed that soluble rCD93, via the lectin-like domain (D1), can bind to HMGB1 and intercept HMGB1-receptor interaction. Soluble rCD93 containing D1 inhibited HMGB1-induced proinflammatory cytokine production and intracellular mitogen-activated protein kinase (MAPK)/nuclear factor (NF)-κB activation in macrophages and VSMCs, thereby attenuating CaCl2-induced and angiotensin II-infused AAA models. During osteoclastogenesis, RANKL stimulated HMGB1 secretion that promoted RANKL-induced osteoclastogenesis in return. Soluble rCD93 containing D1 impeded RANKL-induced osteoclastogenic marker gene expression and intracellular MAPK/NF-κB signaling, thereby mitigating ovariectomized-induced osteoporosis. Conclusion: These findings demonstrate the therapeutic potential of soluble recombinant CD93 containing D1 in inflammatory diseases. Our study highlights a novel anti-inflammatory mechanism, i.e., sequestration of HMGB1, through which sCD93 prevents HMGB1-receptor interaction on effector cells and alleviates inflammation.

Indoor CO2 monitoring in a surgical intensive care unit under visitation restrictions during the COVID-19 pandemic.

Background: Indoor CO2 concentration is an important metric of indoor air quality (IAQ). The dynamic temporal pattern of CO2 levels in intensive care units (ICUs), where healthcare providers experience high cognitive load and occupant numbers are frequently changing, has not been comprehensively characterized. Objective: We attempted to describe the dynamic change in CO2 levels in the ICU using an Internet of Things-based (IoT-based) monitoring system. Specifically, given that the COVID-19 pandemic makes hospital visitation restrictions necessary worldwide, this study aimed to appraise the impact of visitation restrictions on CO2 levels in the ICU. Methods: Since February 2020, an IoT-based intelligent indoor environment monitoring system has been implemented in a 24-bed university hospital ICU, which is symmetrically divided into areas A and B. One sensor was placed at the workstation of each area for continuous monitoring. The data of CO2 and other pollutants (e.g., PM2.5) measured under standard and restricted visitation policies during the COVID-19 pandemic were retrieved for analysis. Additionally, the CO2 levels were compared between workdays and non-working days and between areas A and B. Results: The median CO2 level (interquartile range [IQR]) was 616 (524-682) ppm, and only 979 (0.34%) data points obtained in area A during standard visitation were ≥ 1,000 ppm. The CO2 concentrations were significantly lower during restricted visitation (median [IQR]: 576 [556-596] ppm) than during standard visitation (628 [602-663] ppm; p < 0.001). The PM2.5 concentrations were significantly lower during restricted visitation (median [IQR]: 1 [0-1] µg/m3) than during standard visitation (2 [1-3] µg/m3; p < 0.001). The daily CO2 and PM2.5 levels were relatively low at night and elevated as the occupant number increased during clinical handover and visitation. The CO2 concentrations were significantly higher in area A (median [IQR]: 681 [653-712] ppm) than in area B (524 [504-547] ppm; p < 0.001). The CO2 concentrations were significantly lower on non-working days (median [IQR]: 606 [587-671] ppm) than on workdays (583 [573-600] ppm; p < 0.001). Conclusion: Our study suggests that visitation restrictions during the COVID-19 pandemic may affect CO2 levels in the ICU. Implantation of the IoT-based IAQ sensing network system may facilitate the monitoring of indoor CO2 levels.

A Trusted Reputation Management Scheme for Cross-Chain Transactions.

Blockchain has become a well-known, secured, decentralized datastore in many domains, including medical, industrial, and especially the financial field. However, to meet the requirements of different fields, platforms that are built on blockchain technology must provide functions and characteristics with a wide variety of options. Although they may share similar technology at the fundamental level, the differences among them make data or transaction exchange challenging. Cross-chain transactions have become a commonly utilized function, while at the same time, some have pointed out its security loopholes. It is evident that a secure transaction scheme is desperately needed. However, what about those nodes that do not behave? It is clear that not only a secure transaction scheme is necessary, but also a system that can gradually eliminate malicious players is of dire need. At the same time, integrating different blockchain systems can be difficult due to their independent architectures, and cross-chain transactions can be at risk if malicious attackers try to control the nodes in the cross-chain system. In this paper, we propose a dynamic reputation management scheme based on the past transaction behaviors of nodes. These behaviors serve as the basis for evaluating a node's reputation to support the decision on malicious behavior and enable the system to intercept it in a timely manner. Furthermore, to establish a reputation index with high precision and flexibility, we integrate Particle Swarm Optimization (PSO) into our proposed scheme. This allows our system to meet the needs of a wide variety of blockchain platforms. Overall, the article highlights the importance of securing cross-chain transactions and proposes a method to prevent misbehavior by evaluating and managing node reputation.