CHCHD2 P14L, found in amyotrophic lateral sclerosis, exhibits cytoplasmic mislocalization and alters Ca2+ homeostasis.

CHCHD2 and CHCHD10, linked to Parkinson's disease and amyotrophic lateral sclerosis-frontotemporal dementia (ALS), respectively, are mitochondrial intermembrane proteins that form a heterodimer. This study aimed to investigate the impact of the CHCHD2 P14L variant, implicated in ALS, on mitochondrial function and its subsequent effects on cellular homeostasis. The missense variant of CHCHD2, P14L, found in a cohort of patients with ALS, mislocalized CHCHD2 to the cytoplasm, leaving CHCHD10 in the mitochondria. Drosophila lacking the CHCHD2 ortholog exhibited mitochondrial degeneration. In contrast, human CHCHD2 P14L, but not wild-type human CHCHD2, failed to suppress this degeneration, suggesting that P14L is a pathogenic variant. The mitochondrial Ca2+ buffering capacity was reduced in Drosophila neurons expressing human CHCHD2 P14L. The altered Ca2+-buffering phenotype was also observed in cultured human neuroblastoma SH-SY5Y cells expressing CHCHD2 P14L. In these cells, transient elevation of cytoplasmic Ca2+ facilitated the activation of calpain and caspase-3, accompanied by the processing and insolubilization of TDP-43. These observations suggest that CHCHD2 P14L causes abnormal Ca2+ dynamics and TDP-43 aggregation, reflecting the pathophysiology of ALS.

[Clinical characteristics and genetic analysis of a child with Char syndrome caused by TFAP2B gene variant].

OBJECTIVE: To explore the clinical features and genetic etiology of a child with Char syndrome. METHODS: A child who was presented at the Department of Child Health, Henan Children's Hospital in February 2022 was selected as the study subject. Clinical data of the child was collected, and peripheral blood samples of the child and her parents were collected for the extraction of genomic DNA. Whole exome sequencing was carried out, and candidate variants were verified by Sanger sequencing and bioinformatic analysis. RESULTS: The child had mainly manifested facial dysmorphism, patent ductus arteriosus, growth retardation, curving of fifth fingers and middle toes. Whole exome sequencing revealed that she has harbored a heterozygous c.944A>C (p.Glu315Ala) variant of the TFAP2B gene, which was verified to be de novo by Sanger sequencing. Based on the guidelines from the American College of Medical Genetics and Genomics (ACMG), the variant was rated to be likely pathogenic (PM1+PM2_Supporting+PM6+PP3). CONCLUSION: The heterozygous c.944A>C (p.Glu315Ala) variant of the TFAP2B gene probably underlay the Char syndrome in this child. Above finding has expanded the mutational and phenotypic spectra of the TFAP2B gene, which has facilitated early identification and diagnosis of Char syndrome.

Utilizing multimodal radiomics technology from cervical MRI for diagnosis of cervical spinal cord injury and spinal cord concussion.

The primary aim of this study is to assess the viability of employing multimodal radiomics techniques for distinguishing between cervical spinal cord injury and spinal cord concussion in cervical magnetic resonance imaging. This is a multicenter study involving 288 patients from a major medical center as the training group, and 75 patients from two other medical centers as the testing group. Data regarding the presence of spinal cord injury symptoms and their recovery status within 72 h were documented. These patients underwent sagittal T1-weighted and T2-weighted imaging using cervical magnetic resonance imaging. Radiomics techniques are used to help diagnose whether these patients have cervical spinal cord injury or spinal cord concussion. 1197 radiomics features were extracted for each modality of each patient. The accuracy of T1 modal in testing group is 0.773, AUC is 0.799. The accuracy of T2 modal in testing group is 0.707, AUC is 0.813. The accuracy of T1 + T2 modal in testing group is 0.800, AUC is 0.840. Our research indicates that multimodal radiomics techniques utilizing cervical magnetic resonance imaging can effectively diagnose the presence of cervical spinal cord injury or spinal cord concussion.

The Utility of Long-Read Sequencing in Diagnosing Genetic Autosomal Recessive Parkinson's Disease: a genetic screening study.

Background: Mutations within the genes PRKN and PINK1 are the leading cause of early onset autosomal recessive Parkinson's disease (PD). However, the genetic cause of most early-onset PD (EOPD) cases still remains unresolved. Long-read sequencing has successfully identified many pathogenic structural variants that cause disease, but this technology has not been widely applied to PD. We recently identified the genetic cause of EOPD in a pair of monozygotic twins by uncovering a complex structural variant that spans over 7 Mb, utilizing Oxford Nanopore Technologies (ONT) long-read sequencing. In this study, we aimed to expand on this and assess whether a second variant could be detected with ONT long-read sequencing in other unresolved EOPD cases reported to carry one heterozygous variant in PRKN or PINK1. Methods: ONT long-read sequencing was performed on patients with one reported PRKN/PINK1 pathogenic variant. EOPD patients with an age at onset younger than 50 were included in this study. As a positive control, we also included EOPD patients who had already been identified to carry two known PRKN pathogenic variants. Initial genetic testing was performed using either short-read targeted panel sequencing for single nucleotide variants and multiplex ligation-dependent probe amplification (MLPA) for copy number variants. Results: 48 patients were included in this study (PRKN "one-variant" n = 24, PINK1 "one-variant" n = 12, PRKN "two-variants" n = 12). Using ONT long-read sequencing, we detected a second pathogenic variant in six PRKN "one-variant" patients (26%, 6/23) but none in the PINK1 "one-variant" patients (0%, 0/12). Long-read sequencing identified one case with a complex inversion, two instances of structural variant overlap, and three cases of duplication. In addition, in the positive control PRKN "two-variants" group, we were able to identify both pathogenic variants in PRKN in all the patients (100%, 12/12). Conclusions: This data highlights that ONT long-read sequencing is a powerful tool to identify a pathogenic structural variant at the PRKN locus that is often missed by conventional methods. Therefore, for cases where conventional methods fail to detect a second variant for EOPD, long-read sequencing should be considered as an alternative and complementary approach.

Knowledge, Attitude and Practice of Radiologists Regarding Artificial Intelligence in Medical Imaging.

Purpose: This study aimed to investigate the knowledge, attitudes, and practice (KAP) of radiologists regarding artificial intelligence (AI) in medical imaging in the southeast of China. Methods: This cross-sectional study was conducted among radiologists in the Jiangsu, Zhejiang, and Fujian regions from October to December 2022. A self-administered questionnaire was used to collect demographic data and assess the KAP of participants towards AI in medical imaging. A structural equation model (SEM) was used to analyze the relationships between KAP. Results: The study included 452 valid questionnaires. The mean knowledge score was 9.01±4.87, the attitude score was 48.96±4.90, and 75.22% of participants actively engaged in AI-related practices. Having a master's degree or above (OR=1.877, P=0.024), 5-10 years of radiology experience (OR=3.481, P=0.010), AI diagnosis-related training (OR=2.915, P<0.001), and engaging in AI diagnosis-related research (OR=3.178, P<0.001) were associated with sufficient knowledge. Participants with a junior college degree (OR=2.139, P=0.028), 5-10 years of radiology experience (OR=2.462, P=0.047), and AI diagnosis-related training (OR=2.264, P<0.001) were associated with a positive attitude. Higher knowledge scores (OR=5.240, P<0.001), an associate senior professional title (OR=4.267, P=0.026), 5-10 years of radiology experience (OR=0.344, P=0.044), utilizing AI diagnosis (OR=3.643, P=0.001), and engaging in AI diagnosis-related research (OR=6.382, P<0.001) were associated with proactive practice. The SEM showed that knowledge had a direct effect on attitude (ß=0.481, P<0.001) and practice (ß=0.412, P<0.001), and attitude had a direct effect on practice (ß=0.135, P<0.001). Conclusion: Radiologists in southeastern China hold a favorable outlook on AI-assisted medical imaging, showing solid understanding and enthusiasm for its adoption, despite half lacking relevant training. There is a need for more AI diagnosis-related training, an efficient standardized AI database for medical imaging, and active promotion of AI-assisted imaging in clinical practice. Further research with larger sample sizes and more regions is necessary.

Finding influential nodes in complex networks based on Kullback-Leibler model within the neighborhood.

As a research hot topic in the field of network security, the implementation of machine learning, such as federated learning, involves information interactions among a large number of distributed network devices. If we regard these distributed network devices and connection relationships as a complex network, we can identify the influential nodes to find the crucial points for optimizing the imbalance of the reliability of devices in federated learning system. This paper will analyze the advantages and disadvantages of existing algorithms for identifying influential nodes in complex networks, and propose a method from the perspective of information dissemination for finding influential nodes based on Kullback-Leibler divergence model within the neighborhood (KLN). Firstly, the KLN algorithm removes a node to simulate the scenario of node failure in the information dissemination process. Secondly, KLN evaluates the loss of information entropy within the neighborhood after node removal by establishing the KL divergence model. Finally, it assesses the damage influence of the removed node by integrating the network attributes and KL divergence model, thus achieving the evaluation of node importance. To validate the performance of KLN, this paper conducts an analysis and comparison of its results with those of 11 other algorithms on 10 networks, using SIR model as a reference. Additionally, a case study was undertaken on a real epidemic propagation network, leading to the proposal of management and control strategies for daily protection based on the influential nodes. The experimental results indicate that KLN effectively evaluates the importance of the removed node using KL model within the neighborhood, and demonstrate better accuracy and applicability across networks of different scales.

Clinical characteristics and pathophysiological properties of newly discovered LRRK2 variants associated with Parkinson's disease.

Leucine-rich repeat kinase 2 (LRRK2) is the most common gene responsible for familial Parkinson's disease (PD). The gene product of LRRK2 contains multiple protein domains, including armadillo repeat, ankyrin repeat, leucine-rich repeat (LRR), Ras-of-complex (ROC), C-terminal of ROC (COR), kinase, and WD40 domains. In this study, we performed genetic screening of LRRK2 in our PD cohort, detecting sixteen LRRK2 rare variants. Among them, we selected seven variants that are likely to be familial and characterized them in terms of LRRK2 protein function, along with clinical information and one pathological analysis. The seven variants were S1120P and N1221K in the LRR domain; I1339M, S1403R, and V1447M in the ROC domain; and I1658F and D1873H in the COR domain. The kinase activity of the LRRK2 variants N1221K, S1403R, V1447M, and I1658F toward Rab10, a well-known phosphorylation substrate, was higher than that of wild-type LRRK2. LRRK2 D1873H showed enhanced self-association activity, whereas LRRK2 S1403R and D1873H showed reduced microtubule-binding activity. Pathological analysis of a patient with the LRRK2 V1447M variant was also performed, which revealed Lewy pathology in the brainstem. No functional alterations in terms of kinase activity, self-association activity, and microtubule-binding activity were detected in LRRK2 S1120P and I1339M variants. However, the patient with PD carrying LRRK2 S1120P variant also had a heterozygous Glucosylceramidase beta 1 (GBA1) L444P variant. In conclusion, we characterized seven LRRK2 variants potentially associated with PD. Five of the seven variants in different LRRK2 domains exhibited altered properties in kinase activity, self-association, and microtubule-binding activity, suggesting that each domain variant may contribute to disease progression in different ways.

[Risk factors for embolism in children with refractory Mycoplasmapneumoniae pneumonia and construction of a nomogram model for prediction of embolism]. / éš¾æ²»æ€§è‚ºç‚Žæ”¯åŽŸä½“è‚ºç‚Žæ‚£å„¿åˆå¹¶æ “å¡žçš„å±é™©å› ç´ åˆ†æžåŠåˆ—çº¿å›¾é¢„æµ‹æ¨¡åž‹çš„æž„å»º.

OBJECTIVES: To study the risk factors for embolism in children with refractory Mycoplasma pneumoniae pneumonia (RMPP) and to construct a nomogram model for prediction of embolism. METHODS: This retrospective study included 175 children diagnosed with RMPP at Children's Hospital Affiliated toZhengzhou University from January 2019 to October 2023. They were divided into two groups based on the presence of embolism: the embolism group (n=62) and the non-embolism group (n=113). Multivariate logistic regression analysis was used to screen for risk factors of embolism in children with RMPP, and the R software was applied to construct the nomogram model for prediction of embolism. RESULTS: Multivariate logistic regression analysis indicated that higher levels of D-dimer, interleukin-6 (IL-6) and neutrophil to lymphocyte ratio (NLR), lung necrosis, and pleural effusion were risk factors for embolism in children with RMPP (P<0.05). The area under the curve of the nomogram model for prediction of embolism constructed based on the aforementioned risk factors was 0.912 (95%CI: 0.871-0.952, P<0.05). The Hosmer-Lemeshow goodness-of-fit test showed that the model had a good fit with the actual situation (P<0.05). Calibration and decision curve analysis indicated that the model had high predictive efficacy and clinical applicability. CONCLUSIONS: Higher levels of D-dimer, IL-6 and NLR, lung necrosis, and pleural effusion are risk factors for embolism in children with RMPP. The nomogram model based on these risk factors has high clinical value for predicting embolism in children with RMPP.

Enhancing Dental Material Performance: Tung Oil-Infused Polyurea Microcapsule Coatings for Self-Healing and Antimicrobial Applications.

Within the realm of dental material innovation, this study pioneers the incorporation of tung oil into polyurea coatings, setting a new precedent for enhancing self-healing functionality and durability. Originating from an ancient practice, tung oil is distinguished by its outstanding water resistance and microbial barrier efficacy. By synergizing it with polyurea, we developed coatings that unite mechanical strength with biological compatibility. The study notably quantifies self-healing efficiency, highlighting the coatings' exceptional capacity to mend physical damages and thwart microbial incursions. Findings confirm that tung oil markedly enhances the self-repair capabilities of polyurea, leading to improved wear resistance and the inhibition of microbial growth, particularly against Streptococcus mutans, a principal dental caries pathogen. These advancements not only signify a leap forward in dental material science but also suggest a potential redefinition of dental restorative practices aimed at prolonging the lifespan of restorations and optimizing patient outcomes. Although this study lays a substantial foundation for the utilization of natural oils in the development of medical-grade materials, it also identifies the critical need for comprehensive cytotoxicity assays. Such evaluations are essential to thoroughly assess the biocompatibility and the safety profile of these innovative materials for clinical application. Future research will concentrate on this aspect, ensuring that the safety and efficacy of the materials align with clinical expectations for dental restorations.

Free water in gray matter linked to gut microbiota changes with decreased butyrate producers in Alzheimer's disease and mild cognitive impairment.

Neuroinflammation contributes to the pathology and progression of Alzheimer's disease (AD), and it can be observed even with mild cognitive impairment (MCI), a prodromal phase of AD. Free water (FW) imaging estimates the extracellular water content and has been used to study neuroinflammation across several neurological diseases including AD. Recently, the role of gut microbiota has been implicated in the pathogenesis of AD. The relationship between FW imaging and gut microbiota was examined in patients with AD and MCI. Fifty-six participants underwent neuropsychological assessments, FW imaging, and gut microbiota analysis targeting the bacterial 16S rRNA gene. They were categorized into the cognitively normal control (NC) (n = 19), MCI (n = 19), and AD (n = 18) groups according to the neuropsychological assessments. The correlations of FW values, neuropsychological assessment scores, and the relative abundance of gut microbiota were analyzed. FW was higher in several white matter tracts and in gray matter regions, predominantly the frontal, temporal, limbic and paralimbic regions in the AD/MCI group than in the NC group. In the AD/MCI group, higher FW values in the temporal (superior temporal and temporal pole), limbic and paralimbic (insula, hippocampus and amygdala) regions were the most associated with worse neuropsychological assessment scores. In the AD/MCI group, FW values in these regions were negatively correlated with the relative abundances of butyrate-producing genera Anaerostipes, Lachnospiraceae UCG-004, and [Ruminococcus] gnavus group, which showed a significant decreasing trend in the order of the NC, MCI, and AD groups. The present study showed that increased FW in the gray matter regions related to cognitive impairment was associated with low abundances of butyrate producers in the AD/MCI group. These findings suggest an association between neuroinflammation and decreased levels of the short-chain fatty acid butyrate that is one of the major gut microbial metabolites having a potentially beneficial role in brain homeostasis.

Entrepreneurial mental health in the wake of COVID-19 in China with an emphasis on attention deficit hyperactivity disorder (ADHD) and dyslexia analysis.

The COVID-19 pandemic has precipitated a global mental health crisis, with a particularly pronounced impact on the entrepreneurial sector. This paper presents a comparative analysis of mental health challenges among entrepreneurs in China during the pandemic, with a specific focus on attention deficit hyperactivity disorder (ADHD) and Dyslexia. The study assesses the prevalence of ADHD and dyslexia symptoms among established and emerging entrepreneurs in China, finding notable occurrences within this group. The research also examines the self-care practices of these entrepreneurs, shedding light on their approaches during the pandemic period. The findings highlight a complex interplay between mental health issues and entrepreneurial activities, suggesting that certain ADHD and dyslexia traits may offer unexpected benefits in the entrepreneurial realm. These insights are critical for developing supportive frameworks that leverage the strengths of neurodiverse entrepreneurs while mitigating associated challenges, especially in a post-pandemic economic landscape. The study concludes with policy and practice recommendations to bolster the wellbeing and resilience of entrepreneurs facing the multifaceted impacts of the pandemic.

The value of CT-based radiomics in predicting hemorrhagic transformation in acute ischemic stroke patients without recanalization therapy.

Objective: The aim of this study is to investigate the clinical value of radiomics based on non-enhanced head CT in the prediction of hemorrhage transformation in acute ischemic stroke (AIS). Materials and methods: A total of 140 patients diagnosed with AIS from January 2015 to August 2022 were enrolled. Radiomic features from infarcted areas on non-enhanced CT images were extracted using ITK-SNAP. The max-relevance and min-redundancy (mRMR) and the least absolute shrinkage and selection operator (LASSO) were used to select features. The radiomics signature was then constructed by multiple logistic regressions. The clinicoradiomics nomogram was constructed by combining radiomics signature and clinical characteristics. All predictive models were constructed in the training group, and these were verified in the validation group. All models were evaluated with the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). Results: Of the 140 patients, 59 experienced hemorrhagic transformation, while 81 remained stable. The radiomics signature was constructed by 10 radiomics features. The clinicoradiomics nomogram was constructed by combining radiomics signature and atrial fibrillation. The area under the ROC curve (AUCs) of the clinical model, radiomics signature, and clinicoradiomics nomogram for predicting hemorrhagic transformation in the training group were 0.64, 0.86, and 0.86, respectively. The AUCs of the clinical model, radiomics signature, and clinicoradiomics nomogram for predicting hemorrhagic transformation in the validation group were 0.63, 0.90, and 0.90, respectively. The DCA curves showed that the radiomics signature performed well as well as the clinicoradiomics nomogram. The DCA curve showed that the clinical application value of the radiomics signature is similar to that of the clinicoradiomics nomogram. Conclusion: The radiomics signature, constructed without incorporating clinical characteristics, can independently and effectively predict hemorrhagic transformation in AIS patients.

Nomogram model for predicting invasive placenta in patients with placenta previa: integrating MRI findings and clinical characteristics.

This study aims to validate a nomogram model that predicts invasive placenta in patients with placenta previa, utilizing MRI findings and clinical characteristics. A retrospective analysis was conducted on a training cohort of 269 patients from the Second Affiliated Hospital of Fujian Medical University and a validation cohort of 41 patients from Quanzhou Children's Hospital. Patients were classified into noninvasive and invasive placenta groups based on pathological reports and intraoperative findings. Three clinical characteristics and eight MRI signs were collected and analyzed to identify risk factors and develop the nomogram model. The mode's performance was evaluated in terms of its discrimination, calibration, and clinical utility. Independent risk factors incorporated into the nomogram included the number of previous cesarean sections ≥ 2 (odds ratio [OR] 3.32; 95% confidence interval [CI] 1.28-8.59), type-II placental bulge (OR 17.54; 95% CI 3.53-87.17), placenta covering the scar (OR 2.92; CI 1.23-6.96), and placental protrusion sign (OR 4.01; CI 1.06-15.18). The area under the curve (AUC) was 0.908 for the training cohort and 0.803 for external validation. The study successfully developed a highly accurate nomogram model for predicting invasive placenta in placenta previa cases, based on MRI signs and clinical characteristics.

Two transcription factors, AcREM14 and AcC3H1, enhance the resistance of kiwifruit Actinidiachinensis var. chinensis to Pseudomonas syringae pv. actinidiae.

Kiwifruit bacterial canker is a global disease caused by Pseudomonas syringae pv. actinidiae (Psa), which poses a major threat to kiwifruit production worldwide. Despite the economic importance of Actinidia chinensis var. chinensis, only a few resistant varieties have been identified to date. In this study, we screened 44 kiwifruit F1 hybrid lines derived from a cross between two A. chinensis var. chinensis lines and identified two offspring with distinct resistance to Psa: resistant offspring RH12 and susceptible offspring SH14. To identify traits associated with resistance, we performed a comparative transcriptomic analysis of these two lines. We identified several highly differentially expressed genes (DEGs) associated with flavonoid synthesis, pathogen interactions, and hormone signaling pathways, which play essential roles in disease resistance. Additionally, using weighted gene co-expression network analysis, we identified six core transcription factors. Moreover, qRT-PCR results demonstrated the high expression of AcC3H1 and AcREM14 in Psa-induced highly resistant hybrid lines. Ultimately, Overexpression of AcC3H1 and AcREM14 in kiwifruit enhanced disease resistance, and this was associated with upregulation of enzymatic activity and gene expression in the salicylic acid (SA) signaling pathway. Our study elucidates a molecular mechanism underlying disease resistance in kiwifruit and contributes to the advancement of research on kiwifruit breeding.

Research on automatic recognition radiomics algorithm for early sacroiliac arthritis based on sacroiliac MRI imaging.

OBJECTIVE: To create an automated machine learning model using sacroiliac joint MRI imaging for early sacroiliac arthritis detection, aiming to enhance diagnostic accuracy. METHODS: We conducted a retrospective analysis involving 71 patients with early sacroiliac arthritis and 85 patients with normal sacroiliac joint MRI scans. Transverse T1WI and T2WI sequences were collected and subjected to radiomics analysis by two physicians. Patients were randomly divided into training and test groups at a 7:3 ratio. Initially, we extracted the region of interest on the sacroiliac joint surface using ITK-SNAP 3.6.0 software and extracted radiomic features. We retained features with an Intraclass Correlation Coefficient > 0.80, followed by filtering using max-relevance and min-redundancy (mRMR) and LASSO algorithms to establish an automatic identification model for sacroiliac joint surface injury. Receiver operating characteristic (ROC) curves were plotted, and the area under the ROC curve (AUC) was calculated. Model performance was assessed by accuracy, sensitivity, and specificity. RESULTS: We evaluated model performance, achieving an AUC of 0.943 for the SVM-T1WI training group, with accuracy, sensitivity, and specificity values of 0.878, 0.836, and 0.943, respectively. The SVM-T1WI test group exhibited an AUC of 0.875, with corresponding accuracy, sensitivity, and specificity values of 0.909, 0.929, and 0.875, respectively. For the SVM-T2WI training group, the AUC was 0.975, with accuracy, sensitivity, and specificity values of 0.933, 0.889, and 0.750. The SVM-T2WI test group produced an AUC of 0.902, with accuracy, sensitivity, and specificity values of 0.864, 0.889, and 0.800. In the SVM-bimodal training group, we achieved an AUC of 0.974, with accuracy, sensitivity, and specificity values of 0.921, 0.889, and 0.971, respectively. The SVM-bimodal test group exhibited an AUC of 0.964, with accuracy, sensitivity, and specificity values of 0.955, 1.000, and 0.875, respectively. CONCLUSION: The radiomics-based detection model demonstrates excellent automatic identification performance for early sacroiliitis.

Single-cell transcriptional gene signature analysis identifies IL-17 signaling pathway as the key pathway in sepsis.

Sepsis is a multiple dysregulated systemic inflammatory response with high mortality and leads to public concern. This study was designed to identify possible critical pathways associated with sepsis clinical severity and outcome, which offer potential biomarkers and therapeutic targets for sepsis diagnosis and treatment. Single-cell transcriptome profiles of human peripheral blood mononuclear (PBMC) in the healthy control population and sepsis patients were downloaded from the sepsis database GSE167363 and performed quality control before subsequent analysis. The bulk-RNA sequencing of blood samples in the sepsis-associated databases GSE100159 and GSE133822 was also used to confirm the association between critical pathways and sepsis pathology after processing raw data. We found there was a total of 18 distinct clusters in PBMC of sepsis, which was identified by the t-SNE and UMAP dimension reduction analysis. Meanwhile, the main cell types including B, NK, T, and monocyte cells were identified via the cell maker website and the "Single R" package cell-type annotation analysis. Subsequently, GO and KEGG enrichment analysis of differential expression genes in each cluster found that DEGs between healthy control and sepsis patients were significantly enriched in the IL-17 signaling pathway in monocyte, NK, and T cells. Finally, GSE100159 and GSE133822 confirmed IL-17 signaling pathway-associated genes including IL-17R, TRAF6, RELB, TRAF5, CEBPB, JUNB, CXCL1, CXCL3, CXCL8, CXCR1, and CXCR2 were significantly up-regulated in sepsis blood samples compared with the age-matched healthy control population. Taken together, we concluded that the IL-17 signaling pathway serves as a significant potential mechanism of sepsis and provides a promising therapeutic target for sepsis treatment. This research will further deepen our understanding of sepsis development.

Computer-Aided Diagnosis of Laryngeal Cancer Based on Deep Learning with Laryngoscopic Images.

Laryngeal cancer poses a significant global health burden, with late-stage diagnoses contributing to reduced survival rates. This study explores the application of deep convolutional neural networks (DCNNs), specifically the Densenet201 architecture, in the computer-aided diagnosis of laryngeal cancer using laryngoscopic images. Our dataset comprised images from two medical centers, including benign and malignant cases, and was divided into training, internal validation, and external validation groups. We compared the performance of Densenet201 with other commonly used DCNN models and clinical assessments by experienced clinicians. Densenet201 exhibited outstanding performance, with an accuracy of 98.5% in the training cohort, 92.0% in the internal validation cohort, and 86.3% in the external validation cohort. The area under the curve (AUC) values consistently exceeded 92%, signifying robust discriminatory ability. Remarkably, Densenet201 achieved high sensitivity (98.9%) and specificity (98.2%) in the training cohort, ensuring accurate detection of both positive and negative cases. In contrast, other DCNN models displayed varying degrees of performance degradation in the external validation cohort, indicating the superiority of Densenet201. Moreover, Densenet201's performance was comparable to that of an experienced clinician (Clinician A) and outperformed another clinician (Clinician B), particularly in the external validation cohort. Statistical analysis, including the DeLong test, confirmed the significance of these performance differences. Our study demonstrates that Densenet201 is a highly accurate and reliable tool for the computer-aided diagnosis of laryngeal cancer based on laryngoscopic images. The findings underscore the potential of deep learning as a complementary tool for clinicians and the importance of incorporating advanced technology in improving diagnostic accuracy and patient care in laryngeal cancer diagnosis. Future work will involve expanding the dataset and further optimizing the deep learning model.

A fusion of VGG-16 and ViT models for improving bone tumor classification in computed tomography.

Background and Objective: Bone tumors present significant challenges in orthopedic medicine due to variations in clinical treatment approaches for different tumor types, which includes benign, malignant, and intermediate cases. Convolutional Neural Networks (CNNs) have emerged as prominent models for tumor classification. However, their limited perception ability hinders the acquisition of global structural information, potentially affecting classification accuracy. To address this limitation, we propose an optimized deep learning algorithm for precise classification of diverse bone tumors. Materials and Methods: Our dataset comprises 786 computed tomography (CT) images of bone tumors, featuring sections from two distinct bone species, namely the tibia and femur. Sourced from The Second Affiliated Hospital of Fujian Medical University, the dataset was meticulously preprocessed with noise reduction techniques. We introduce a novel fusion model, VGG16-ViT, leveraging the advantages of the VGG-16 network and the Vision Transformer (ViT) model. Specifically, we select 27 features from the third layer of VGG-16 and input them into the Vision Transformer encoder for comprehensive training. Furthermore, we evaluate the impact of secondary migration using CT images from Xiangya Hospital for validation. Results: The proposed fusion model demonstrates notable improvements in classification performance. It effectively reduces the training time while achieving an impressive classification accuracy rate of 97.6%, marking a significant enhancement of 8% in sensitivity and specificity optimization. Furthermore, the investigation into secondary migration's effects on experimental outcomes across the three models reveals its potential to enhance system performance. Conclusion: Our novel VGG-16 and Vision Transformer joint network exhibits robust classification performance on bone tumor datasets. The integration of these models enables precise and efficient classification, accommodating the diverse characteristics of different bone tumor types. This advancement holds great significance for the early detection and prognosis of bone tumor patients in the future.