Identifying the risk factors of ICU-acquired fungal infections: clinical evidence from using machine learning.

Background: Fungal infections are associated with high morbidity and mortality in the intensive care unit (ICU), but their diagnosis is difficult. In this study, machine learning was applied to design and define the predictive model of ICU-acquired fungi (ICU-AF) in the early stage of fungal infections using Random Forest. Objectives: This study aimed to provide evidence for the early warning and management of fungal infections. Methods: We analyzed the data of patients with culture-positive fungi during their admission to seven ICUs of the First Affiliated Hospital of Chongqing Medical University from January 1, 2015, to December 31, 2019. Patients whose first culture was positive for fungi longer than 48 h after ICU admission were included in the ICU-AF cohort. A predictive model of ICU-AF was obtained using the Least Absolute Shrinkage and Selection Operator and machine learning, and the relationship between the features within the model and the disease severity and mortality of patients was analyzed. Finally, the relationships between the ICU-AF model, antifungal therapy and empirical antifungal therapy were analyzed. Results: A total of 1,434 cases were included finally. We used lasso dimensionality reduction for all features and selected six features with importance ≥0.05 in the optimal model, namely, times of arterial catheter, enteral nutrition, corticosteroids, broadspectrum antibiotics, urinary catheter, and invasive mechanical ventilation. The area under the curve of the model for predicting ICU-AF was 0.981 in the test set, with a sensitivity of 0.960 and specificity of 0.990. The times of arterial catheter (p = 0.011, OR = 1.057, 95% CI = 1.053-1.104) and invasive mechanical ventilation (p = 0.007, OR = 1.056, 95%CI = 1.015-1.098) were independent risk factors for antifungal therapy in ICU-AF. The times of arterial catheter (p = 0.004, OR = 1.098, 95%CI = 0.855-0.970) were an independent risk factor for empirical antifungal therapy. Conclusion: The most important risk factors for ICU-AF are the six time-related features of clinical parameters (arterial catheter, enteral nutrition, corticosteroids, broadspectrum antibiotics, urinary catheter, and invasive mechanical ventilation), which provide early warning for the occurrence of fungal infection. Furthermore, this model can help ICU physicians to assess whether empiric antifungal therapy should be administered to ICU patients who are susceptible to fungal infections.

Apple E3 ligase MdPUB23 mediates ubiquitin-dependent degradation of MdABI5 to delay ABA-triggered leaf senescence.

ABSCISIC ACID-INSENSITIVE5 (ABI5) is a core regulatory factor that mediates the ABA signaling response and leaf senescence. However, the molecular mechanism underlying the synergistic regulation of leaf senescence by ABI5 with interacting partners and the homeostasis of ABI5 in the ABA signaling response remain to be further investigated. In this study, we found that the accelerated effect of MdABI5 on leaf senescence is partly dependent on MdbHLH93, an activator of leaf senescence in apple. MdABI5 directly interacted with MdbHLH93 and improved the transcriptional activation of the senescence-associated gene MdSAG18 by MdbHLH93. MdPUB23, a U-box E3 ubiquitin ligase, physically interacted with MdABI5 and delayed ABA-triggered leaf senescence. Genetic and biochemical analyses suggest that MdPUB23 inhibited MdABI5-promoted leaf premature senescence by targeting MdABI5 for ubiquitin-dependent degradation. In conclusion, our results verify that MdABI5 accelerates leaf senescence through the MdABI5-MdbHLH93-MdSAG18 regulatory module, and MdPUB23 is responsible for the dynamic regulation of ABA-triggered leaf senescence by modulating the homeostasis of MdABI5.

Arranged cohabitation among Chinese Muslims.

While modern family-related ideas and behaviors have become more widely accepted in contemporary China, Chinese Muslim minorities continue to hold on to traditional religious practices. Surprisingly, data from our survey conducted in Gansu province in China's northwestern borderlands reveal that Muslims of the Hui and Dongxiang ethnicities reported much higher rates of cohabitation experience than the secular majority Han. Based on follow-up qualitative interviews, we found the answer to lie in the interplay between the highly interventionist Chinese state and the robust cultural resilience of local Islamic communities. While the state maintains a high minimum legal age of marriage, the early marriage norm remains strong in Chinese Muslim communities, where religion constitutes an alternative and often more powerful source of legitimacy-at least in the private sphere of life. Using the 2000 census data, we further show that women in almost all 10 Muslim ethnic groups have higher percentages of underage births and premarital births than Han women, both nationally and in the northwest where most Chinese Muslims live. As the once-outlawed behavior of cohabitation became more socially acceptable during the reform and opening-up era, young Muslim Chinese often found themselves in "arranged cohabitations" as de facto marriages formed at younger-than-legal ages. In doing so, Chinese Muslim communities have reinvented the meaning of cohabitation. Rather than liberal intimate relationship based on individual autonomy, cohabitation has served as a coping strategy by which Islamic patriarchs circumvent the Chinese state's aggressive regulations aimed at "modernizing" the Muslim family.

HOTAIR/miR-1277-5p/FBN2 signaling axis is involved in recurrent spontaneous abortion by regulating the growth, migration, and invasion of HTR-8/SVneo cells.

OBJECTIVE: This study aimed to explore the specific pathways by which HOX transcript antisense intergenic RNA (HOTAIR) contributes to the pathogenesis of unexplained recurrent spontaneous abortion (URSA). METHODS: Real-time quantitative PCR (RT-qPCR) was employed to assess the differential expression levels of HOTAIR in chorionic villi tissues from URSA patients and women with voluntarily terminated pregnancies. HTR-8/SVneo served as a cellular model. Knockdown and overexpression of HOTAIR in the cells were achieved through siRNA transfection and pcDNA3.1 transfection, respectively. Cell viability, migration, and invasion were evaluated using cell counting kit-8 (CCK-8), scratch, and Transwell assays, respectively. The interaction among the HOTAIR/miR-1277-5p/fibrillin 2 (FBN2) axis was predicted through bioinformatics analysis and confirmed through in vitro experiments. Furthermore, the regulatory effects of the HOTAIR/miR-1277-5p/FBN2 signaling axis on cellular behaviors were validated in HTR-8/SVneo cells. RESULTS: We found that HOTAIR was downregulated in chorionic villi tissues from URSA patients. Overexpression of HOTAIR significantly enhanced the viability, migration, and invasion of HTR-8/SVneo cells, while knockdown of HOTAIR had the opposite effects. We further confirmed the regulatory effect of the HOTAIR/miR-1277-5p/FBN2 signaling axis in URSA. Specifically, HOTAIR and FBN2 were found to reduce the risk of URSA by enhancing cell viability, migration, and invasion, whereas miR-1277-5p exerted the opposite effects. CONCLUSION: HOTAIR promotes URSA development by targeting inhibition of miR-1277-5p/FBN2 axis.

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.

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.

From genomics to metabolomics: Deciphering sanguinarine biosynthesis in Dicranostigma leptopodum.

Dicranostigma leptopodum (Maxim) Fedde (DLF) is a renowned medicinal plant in China, known to be rich in alkaloids. However, the unavailability of a reference genome has impeded investigation into its plant metabolism and genetic breeding potential. Here we present a high-quality chromosomal-level genome assembly for DLF, derived using a combination of Nanopore long-read sequencing, Illumina short-read sequencing and Hi-C technologies. Our assembly genome spans a size of 621.81 Mb with an impressive contig N50 of 93.04 Mb. We show that the species-specific whole-genome duplication (WGD) of DLF and Papaver somniferum corresponded to two rounds of WGDs of Papaver setigerum. Furthermore, we integrated comprehensive homology searching, gene family analyses and construction of a gene-to-metabolite network. These efforts led to the discovery of co-expressed transcription factors, including NAC and bZIP, alongside sanguinarine (SAN) pathway genes CYP719 (CFS and SPS). Notably, we identified P6H as a promising gene for enhancing SAN production. By providing the first reference genome for Dicranostigma, our study confirms the genomic underpinning of SAN biosynthesis and establishes a foundation for advancing functional genomic research on Papaveraceae species. Our findings underscore the pivotal role of high-quality genome assemblies in elucidating genetic variations underlying the evolutionary origin of secondary metabolites.

Impact of moist wound dressing on wound healing time: A meta-analysis.

Among the assortment of available dressings aimed at promoting wound healing, moist dressings have gained significant popularity because of their ability to create an optimal environment for wound recovery. This meta-analysis seeks to compare the effects of moist dressing versus gauze dressing on wound healing time. A comprehensive literature search was conducted, encompassing publications up until April 1, 2023, across multiple databases including PubMed, Web of Science, China National Knowledge Infrastructure (CNKI), and Cochrane Library. Stringent criteria were used to determine study inclusion and evaluate methodological quality. Statistical analyses were performed utilizing Stata 17.0. A total of 13 articles, encompassing 866 participants, were included in the analysis. The findings indicate that moist dressing surpasses gauze dressing in terms of wound healing time (standard mean difference [SMD] -2.50, 95% confidence interval [CI] -3.35 to -1.66, p < 0.01; I2 = 97.24%), wound site infection rate (odds ratio [OR] 0.30, 95% CI 0.17 to 0.54, p < 0.01; I2 = 39.91%), dressing change times (SMD -3.65, 95% CI -5.34 to -1.97, p < 0.01; I2 = 96.48%), and cost (SMD -2.66, 95% CI -4.24 to -1.09, p < 0.01; I2 = 94.90%). Subgroup analyses revealed possible variations in wound healing time based on wound types and regions. This study underscores the significant advantages associated with the use of moist dressings, including expedited wound healing, reduced infection rates, decreased frequency of dressing changes, and lower overall treatment costs.

Quantitative study of peripapillary retinal nerve fiber layer thickness and peripapillary vessel density in patients with different stages of Parkinson's disease.

AIM: To observe the changes in the thickness of peripapillary retinal nerve fiber layer (pRNFL) and peripapillary vessel density (pVD) in patients with different stages of Parkinson's disease (PD). METHODS: Totally 47 patients (47 eyes) with primary PD were divided into the mild group and the moderate-to-severe group according to Hoehn & Yahr (H&Y) stage. Among them, there were 27 cases (27 eyes) in mild group and 20 cases (20 eyes) in moderate-to-severe group. And 20 cases (20 eyes) who were included in the control group were healthy people who came to our hospital for health screening at the same time. All participants underwent optical coherence tomography angiography (OCTA) examinations. The pRNFL thickness, total vessel density (tVD) and capillary vessel density (cVD) of the optic disc in average, superior half, inferior half, superior nasal (SN), nasal superior (NS), nasal inferior (NI), inferior nasal (IN), inferior temporal (IT), temporal inferior (TI), temporal superior (TS), and superior temporal (ST) were measured. One-way ANOVA was used to compare the differences of optic disc parameters among the three groups, and Pearson and Spearman correlations were used to analyze the correlation between pRNFL, pVD and the disease duration, H&Y stage and UPDRS-III score in patients with PD, respectively. RESULTS: There were significant differences in pRNFL thickness in average, superior half, inferior half, SN, NS, IN, IT and ST quadrants among the three groups (P<0.05). In PD group, the pRNFL thickness in average, superior half, inferior half, NS and IT quadrants were negatively correlated with H&Y stage and UPDRS-III score, respectively (P<0.05). There were statistically significant differences in the cVD of whole image, inferior half, NI and TS quadrants, the tVD of the whole image, inferior half, and peripapillary among the three groups (P<0.05). In PD group, the tVD of whole image and the cVD of NI and TS quadrants were negatively correlated with the H&Y stage, respectively (P<0.05); the cVD of TS quadrant was negatively correlated with UPDRS-III score (P<0.05). CONCLUSION: The thickness of pRNFL in PD patients is significantly decreased, and it is negatively correlated with H&Y stage and UPDRS-III score. With the increase of the severity of the disease, the pVD parameters in PD patients increase at first in the mild group, and then decrease in the moderate-to-severe group, and negatively correlate with H&Y stage and UPDRS-III score.

RSU-Net: U-net based on residual and self-attention mechanism in the segmentation of cardiac magnetic resonance images.

BACKGROUND: Automated segmentation techniques for cardiac magnetic resonance imaging (MRI) are beneficial for evaluating cardiac functional parameters in clinical diagnosis. However, due to the characteristics of unclear image boundaries and anisotropic resolution anisotropy produced by cardiac magnetic resonance imaging technology, most of the existing methods still have the problems of intra-class uncertainty and inter-class uncertainty. However, due to the irregularity of the anatomical shape of the heart and the inhomogeneity of tissue density, the boundaries of its anatomical structures become uncertain and discontinuous. Therefore, fast and accurate segmentation of cardiac tissue remains a challenging problem in medical image processing. METHODOLOGY: We collected cardiac MRI data from 195 patients as training set and 35patients from different medical centers as external validation set. Our research proposed a U-net network architecture with residual connections and a self-attentive mechanism (Residual Self-Attention U-net, RSU-Net). The network relies on the classic U-net network, adopts the U-shaped symmetric architecture of the encoding and decoding mode, improves the convolution module in the network, introduces skip connections, and improves the network's capacity for feature extraction. Then for solving locality defects of ordinary convolutional networks. To achieve a global receptive field, a self-attention mechanism is introduced at the bottom of the model. The loss function employs a combination of Cross Entropy Loss and Dice Loss to jointly guide network training, resulting in more stable network training. RESULTS: In our study, we employ the Hausdorff distance (HD) and the Dice similarity coefficient (DSC) as metrics for assessing segmentation outcomes. Comparsion was made with the segmentation frameworks of other papers, and the comparison results prove that our RSU-Net network performs better and can make accurate segmentation of the heart. New ideas for scientific research. CONCLUSION: Our proposed RSU-Net network combines the advantages of residual connections and self-attention. This paper uses the residual links to facilitate the training of the network. In this paper, a self-attention mechanism is introduced, and a bottom self-attention block (BSA Block) is used to aggregate global information. Self-attention aggregates global information, and has achieved good segmentation results on the cardiac segmentation dataset. It facilitates the diagnosis of cardiovascular patients in the future.

[Comparative analysis of two assaysin detection of sperm DNA fragmentation index, flow cytometry and AI-based fluorescence microscopy, based on AO staining: A multicentre study].

OBJECTIVE: To study the correlation, consistency, and variations between two assays of DNA fragmentation index based on acridine orange (AO) staining via AI-based fluorescence microscopy(AI-DFI), and flow cytometry (FCM-DFI) across multiple centers. METHODS: We selected 421 male patients from Nanjing Drum Tower hospital ( Hospital G) (226 cases), Eastern Theatre General Hospital (Hospital J) (89 cases) and Jiangsu Province Hospital (Hospital S) (106 cases) . Semen samples from each patient were analyzed for routine semen parameters and for DFI using both AI fluorescence microscopy and flow cytometry. We studied the two methods' stability as well as the correlation, consistency, and variation between the two methods' results in various centers. RESULTS: The two replicate studies' results of AI-DFI and the three centers' FCM-DFI for linear regression analysis indicated strong stability (R2>0.9).Overall(Group A), the AI-DFI results demonstrated good correlation and consistency with the FCM-DFI results of three centers (r>0.85；ICC>0.9).The semen specimens were categorized into two groups: normal specimen group (group B) and abnormal specimen group (group C) (including asthenozoospermia, oligospermia, and semen samples with high impurities).Group C's results showed a decline in correlation and consistency when compared to group A and group B, whereas group B's results showed a little rise in correlation and consistency when compared to group A. Although the consistency and correlation between the results of the two DFI testing methods in the three centers were good, there was still a significant difference between Groups A and C (P<0.05), and in Group B there was a significant difference between the two DFI testing methods only in Hospital G (p＝0.02), with no significant difference in Hospitals J and S (P> 0.05). CONCLUSION: The two detection methods exhibit good stability and correlation. However, significant differences are observed in the DFI detection methods in samples with abnormal semen parameters and high complexity. The main reason for these significant differences may lie in the variations in detection principles. Each detection method has its own advantages, allowing clinical or research settings to choose between them based on laboratory conditions or specific requirements.

Research progress of tight junction between retinal pigment epithelium cells and its disruption in the pathogenesis of age-related macular degeneration / 国际眼科杂志(Guoji Yanke Zazhi)

Age-related macular degeneration(AMD)is a common eye disease causing irreversible visual impairment in the elderly. The tight junction(TJ)between retinal pigment epithelium cells(RPECs)is an important structural unit of the outer blood retinal barrier(oBRB). The TJ is defective in the pathogenesis of AMD, which in turn promotes the destruction of oBRB and accelerates the occurrence and progression of AMD. In this paper, the roles of TJ and TJ protein in maintaining oBRB function, TJ protein abnormality and oBRB destruction in the pathogenesis of AMD were reviewed, aiming to provide new ideas for the treatment of AMD.

Cardiac MRI segmentation of the atria based on UU-NET.

Background and objective: In today's society, people's work pressure, coupled with irregular diet, lack of exercise and other bad lifestyle, resulting in frequent cardiovascular diseases. Medical imaging has made great progress in modern society, among which the role of MRI in cardiovascular field is self-evident. Based on this research background, how to process cardiac MRI quickly and accurately by computer has been extensively discussed. By comparing and analyzing several traditional image segmentation and deep learning image segmentation, this paper proposes the left and right atria segmentation algorithm of cardiac MRI based on UU-NET network. Methods: In this paper, an atrial segmentation algorithm for cardiac MRI images in UU-NET network is proposed. Firstly, U-shaped upper and lower sampling modules are constructed by using residual theory, which are used as encoders and decoders of the model. Then, the modules are interconnected to form multiple paths from input to output to increase the information transmission capacity of the model. Results: The segmentation method based on UU-NET network has achieved good results proposed in this paper, compared with the current mainstream image segmentation algorithm results have been improved to a certain extent. Through the analysis of the experimental results, the image segmentation algorithm based on UU-NET network on the data set, its performance in the verification set and online set is higher than other grid models. The DSC in the verification set is 96.7%, and the DSC in the online set is 96.7%, which is nearly one percentage point higher than the deconvolution neural network model. The hausdorff distance (HD) is 1.2 mm. Compared with other deep learning models, it is significantly improved (about 3 mm error is reduced), and the time is 0.4 min. Conclusion: The segmentation algorithm based on UU-NET improves the segmentation accuracy obviously compared with other segmentation models. Our technique will be able to help diagnose and treat cardiac complications.

RU-Net: An improved U-Net placenta segmentation network based on ResNet.

BACKGROUND: In recent years, with the increase of late puerperium, cesarean section and induced abortion, the incidence of placenta accreta has been on the rise. It has become one of the common clinical diseases in obstetrics and gynecology. In clinical practice, accurate segmentation of placental tissue is the basis for identifying placental accreta and assessing the degree of accreta. By analyzing the placenta and its surrounding tissues and organs, it is expected to realize automatic computer segmentation of placental adhesion, implantation, and penetration and help clinicians in prenatal planning and preparation. METHODOLOGY: We propose an improved U-Net framework: RU-Net. The direct mapping structure of ResNet was added to the original contraction path and expansion path of U-Net. The feature information of the image was restored to a greater extent through the residual structure to improve the segmentation accuracy of the image. RESULTS: Through testing on the collected placenta dataset, it is found that our proposed RU-Net network achieves 0.9547 and 1.32% on the Dice coefficient and RVD index, respectively. We also compared with the segmentation frameworks of other papers, and the comparison results show that our RU-Net network has better performance and can accurately segment the placenta. CONCLUSION: Our proposed RU-Net network addresses issues such as network degradation of the original U-Net network. Good segmentation results have been achieved on the placenta dataset, which will be of great significance for pregnant women's prenatal planning and preparation in the future.

Automated meniscus segmentation and tear detection of knee MRI with a 3D mask-RCNN.

BACKGROUND: The diagnostic results of magnetic resonance imaging (MRI) are essential references for arthroscopy as an invasive procedure. A deviation between medical imaging diagnosis and arthroscopy results may cause irreversible damage to patients and lead to excessive medical treatment. To improve the accurate diagnosis of meniscus injury, it is urgent to develop auxiliary diagnosis algorithms to improve the accuracy of radiological diagnosis. PURPOSE: This study aims to present a fully automatic 3D deep convolutional neural network (DCNN) for meniscus segmentation and detects arthroscopically proven meniscus tears. MATERIALS AND METHODS: Our institution retrospectively included 533 patients with 546 knees who underwent knee magnetic resonance imaging (MRI) and knee arthroscopy. Sagittal proton density-weighted (PDW) images in MRI of 382 knees were regarded as a training set to train our 3D-Mask RCNN. The remaining data from 164 knees were used to validate the trained network as a test set. The masks were hand-drawn by an experienced radiologist, and the reference standard is arthroscopic surgical reports. The performance statistics included Dice accuracy, sensitivity, specificity, FROC, receiver operating characteristic (ROC) curve analysis, and bootstrap test statistics. The segmentation performance was compared with a 3D-Unet, and the detection performance was compared with radiological evaluation by two experienced musculoskeletal radiologists without knowledge of the arthroscopic surgical diagnosis. RESULTS: Our model produced strong Dice coefficients for sagittal PDW of 0.924, 0.95 sensitivity with 0.823 FPs/knee. 3D-Unet produced a Dice coefficient for sagittal PDW of 0.891, 0.95 sensitivity with 1.355 FPs/knee. The difference in the areas under 3D-Mask-RCNN FROC and 3D-Unet FROC was statistically significant (p = 0.0011) by bootstrap test. Our model detection performance achieved an area under the curve (AUC) value, accuracy, and sensitivity of 0.907, 0.924, 0.941, and 0.785, respectively. Based on the radiological evaluations, the AUC value, accuracy, sensitivity, and specificity were 0.834, 0.835, 0.889, and 0.754, respectively. The difference in the areas between 3D-Mask-RCNN ROC and radiological evaluation ROC was statistically significant (p = 0.0009) by bootstrap test. 3D Mask RCNN significantly outperformed the 3D-Unet and radiological evaluation demonstrated by these results. CONCLUSIONS: 3D-Mask RCNN has demonstrated efficacy and precision for meniscus segmentation and tear detection in knee MRI, which can assist radiologists in improving the accuracy and efficiency of diagnosis. It can also provide effective diagnostic indicators for orthopedic surgeons before arthroscopic surgery and further promote precise treatment.

Breast MRI Segmentation and Ki-67 High- and Low-Expression Prediction Algorithm Based on Deep Learning.

Results: The DSC, PPV, and sensitivity of our combined model are 0.94, 0.93, and 0.94, respectively, with better segmentation performance. And we compare with the segmentation frameworks of other papers and find that our combined model can make accurate segmentation of breast tumors. Conclusion: Our method can adapt to the variability of breast tumors and segment breast tumors accurately and efficiently. In the future, it can be widely used in clinical practice, so as to help the clinic better formulate a reasonable diagnosis and treatment plan for breast cancer patients.

An Immune-Related Prognostic Risk Model in Colon Cancer by Bioinformatics Analysis.

Colon cancer is one of the leading malignancies with poor prognosis worldwide. Immune cell infiltration has a potential prognostic value for colon cancer. This study aimed to establish an immune-related prognostic risk model for colon cancer by bioinformatics analysis. A total of 1670 differentially expressed genes (DEGs), including 177 immune-related genes, were identified from The Cancer Genome Atlas (TCGA) dataset. A prognostic risk model was constructed based on six critical immune-related genes (C-X-C motif chemokine ligand 1 (CXCL1), epiregulin (EREG), C-C motif chemokine ligand 24 (CCL24), fatty acid binding protein 4 (FABP4), tropomyosin 2 (TPM2), and semaphorin 3G (SEMA3G)). This model was validated using the microarray dataset GSE35982. In addition, Cox regression analysis showed that age and clinical stage were correlated with prognostic risk scores. Kaplan-Meier survival analysis showed that high risk scores correlated with low survival probabilities in patients with colon cancer. Downregulated TPM2, FABP4, and SEMA3G levels were positively associated with the activated mast cells, monocytes, and macrophages M2. Upregulated CXCL1 and EREG were positively correlated with macrophages M1 and activated T cells CD4 memory, respectively. Based on these results, we can conclude that the proposed prognostic risk model presents promising novel signatures for the diagnosis and prognosis prediction of colon cancer. This model may provide therapeutic benefits for the development of immunotherapy for colon cancer.

Breast MRI Tumor Automatic Segmentation and Triple-Negative Breast Cancer Discrimination Algorithm Based on Deep Learning.

Background: Breast cancer is a kind of cancer that starts in the epithelial tissue of the breast. Breast cancer has been on the rise in recent years, with a younger generation developing the disease. Magnetic resonance imaging (MRI) plays an important role in breast tumor detection and treatment planning in today's clinical practice. As manual segmentation grows more time-consuming and the observed topic becomes more diversified, automated segmentation becomes more appealing. Methodology. For MRI breast tumor segmentation, we propose a CNN-SVM network. The labels from the trained convolutional neural network are output using a support vector machine in this technique. During the testing phase, the convolutional neural network's labeled output, as well as the test grayscale picture, is passed to the SVM classifier for accurate segmentation. Results: We tested on the collected breast tumor dataset and found that our proposed combined CNN-SVM network achieved 0.93, 0.95, and 0.92 on DSC coefficient, PPV, and sensitivity index, respectively. We also compare with the segmentation frameworks of other papers, and the comparison results prove that our CNN-SVM network performs better and can accurately segment breast tumors. Conclusion: Our proposed CNN-SVM combined network achieves good segmentation results on the breast tumor dataset. The method can adapt to the differences in breast tumors and segment breast tumors accurately and efficiently. It is of great significance for identifying triple-negative breast cancer in the future.

Reliability and validity of the Chinese version of the kiddie-schedule for affective disorders and schizophrenia-present and lifetime version DSM-5 (K-SADS-PL-C DSM-5).

BACKGROUND: As the Diagnostic and Statistical Manual of Mental Disorders fifth version (DSM-5) was published, the Kiddie-Schedule for Affective Disorders and Schizophrenia-Present and Lifetime version (K-SADS-PL) was modified to adapt the new version (K-SADS-PL DSM-5). We translated it to Chinese (K-SADS-PL-C DSM-5) and described its reliability and validity. METHODS: A total of 154 groups of 6 to 18-year-old children and their guardians were included. Trained interviewers interviewed subjects using the K-SADS-PL-C DSM-5. Interrater reliability was assessed by audio recording. Parent-reported scales, like child behavior checklist (CBCL), the Chinese version of Swan-son Nolan and Pelham, version IV scale-parent form (SNAP-IV), social responsiveness scale (SRS-1), and children-reported scales like depression self-rating scale for children (DSRSC) and the screen for child anxiety related emotional disorders (SCARED) were used to examine the validity of depressive disorder, ADHD, ASD, and ODD. RESULTS: The K-SADS-PL-C DSM-5 had fair to excellent interrater (0.537-1.000) and test-retest (0.468-0.885) reliability of affective disorder and neurodevelopment disorder. The convergent validity of affective disorder and neurodevelopment disorder was good, and their divergent validity was acceptable. LIMITATIONS: i) Clinical questionnaires were insensitive in classifying disorders and had limitations in derived diagnoses. ii) Samples only came from clinical environment, iii) covered limited disease species, and iv) were small. CONCLUSION: The K-SADS-PL-C DSM-5 can support reliable and valid diagnoses for children with affect, neurodevelopmental, and behavioral disorders in China.

MdMYB10 affects nitrogen uptake and reallocation by regulating the nitrate transporter MdNRT2.4-1 in the red flesh apple.

Nitrate is the major nitrogen sources for higher plants. In addition to serving not only as a nutrient, it is also a signaling molecule that regulates plant growth and development. Although membrane-bound nitrate transporter/peptide transporters (NRT/PTR) have been extensively studied and shown to regulate nitrate uptake and movement, little is known about how these factors are regulated by the external nitrogen environment. Red flesh apple, the coloration of which is determined by the transcription factor MdMYB10, had higher nitrate uptake efficiency than non-red flesh apple. Nitrate assimilation and utilization were increased in red flesh apple cultivar, and comparative transcriptome analysis showed that the expression of genes encoding the NRT2s was increased in red flesh apple. In vitro and in vivo experiments showed that MdMYB10 directly bound to the MdNRT2.4-1 promoter to transcriptionally activate its expression, resulting in enhanced nitrate uptake. MdMYB10 also controlled nitrate reallocation from old leaves to new leaves through MdNRT2.4-1. Overall, our findings provide novel insights into the mechanism by which MdMYB10 controls nitrate uptake and reallocation in apple, which facilitates adaptation to low nitrogen environment.