Machine-Learning and Radiomics-Based Preoperative Prediction of Ki-67 Expression in Glioma Using MRI Data.

BACKGROUND: Gliomas are the most common primary brain tumours and constitute approximately half of all malignant glioblastomas. Unfortunately, patients diagnosed with malignant glioblastomas typically survive for less than a year. In light of this circumstance, genotyping is an effective means of categorising gliomas. The Ki67 proliferation index, a widely used marker of cellular proliferation in clinical contexts, has demonstrated potential for predicting tumour classification and prognosis. In particular, magnetic resonance imaging (MRI) plays a vital role in the diagnosis of brain tumours. Using MRI to extract glioma-related features and construct a machine learning model offers a viable avenue to classify and predict the level of Ki67 expression. METHODS: This study retrospectively collected MRI data and postoperative immunohistochemical results from 613 glioma patients from the First Affliated Hospital of Nanjing Medical University. Subsequently, we performed registration and skull stripping on the four MRI modalities: T1-weighted (T1), T2-weighted (T2), T1-weighted with contrast enhancement (T1CE), and Fluid Attenuated Inversion Recovery (FLAIR). Each modality's segmentation yielded three distinct tumour regions. Following segmentation, a comprehensive set of features encompassing texture, first-order, and shape attributes were extracted from these delineated regions. Feature selection was conducted using the least absolute shrinkage and selection operator (LASSO) algorithm with subsequent sorting to identify the most important features. These selected features were further analysed using correlation analysis to finalise the selection for machine learning model development. Eight models: logistic regression (LR), naive bayes, decision tree, gradient boosting tree, and support vector classification (SVM), random forest (RF), XGBoost, and LightGBM were used to objectively classify Ki67 expression. RESULTS: In total, 613 patients were enroled in the study, and 24,455 radiomic features were extracted from each patient's MRI. These features were eventually reduced to 36 after LASSO screening, RF importance ranking, and correlation analysis. Among all the tested machine learning models, LR and linear SVM exhibited superior performance. LR achieved the highest area under the curve score of 0.912 ± 0.036, while linear SVM obtained the top accuracy with a score of 0.884 ± 0.031. CONCLUSION: This study introduced a novel approach for classifying Ki67 expression levels using MRI, which has been proven to be highly effective. With the LR model at its core, our method demonstrated its potential in signalling a promising avenue for future research. This innovative approach of predicting Ki67 expression based on MRI features not only enhances our understanding of cell activity but also represents a significant leap forward in brain glioma research. This underscores the potential of integrating machine learning with medical imaging to aid in the diagnosis and prognosis of complex diseases.

A new segmentation algorithm for peripapillary atrophy and optic disk from ultra-widefield Photographs1.

BACKGROUND AND OBJECTIVE: The prevalence of myopia and high myopia is increasing globally, underscoring the growing importance of diagnosing high myopia-related pathologies. While existing image segmentation models, such as U-Net, UNet++, ResU-Net, and TransUNet, have achieved significant success in medical image segmentation, they still face challenges when dealing with ultra-widefield (UWF) fundus images. This study introduces a novel automatic segmentation algorithm for the optic disc and peripapillary atrophy (PPA) based on UWF fundus images, aimed at assisting ophthalmologists in more accurately diagnosing high myopia-related diseases. METHODS: In this study, we developed a segmentation model leveraging a Transformer-based network structure, complemented by atrous convolution and selective boundary aggregation modules, to elevate the accuracy of segmenting the optic disc and PPA in UWF photography. The atrous convolution module adeptly manages multi-scale features, catering to the variances in target sizes and expanding the deep network's receptive field. Concurrently, the incorporation of the selective boundary aggregation module in the skip connections of the model significantly improves the differentiation of boundary information between segmentation targets. Moreover, the comparison of our proposed algorithm with classical segmentation models like U-Net, UNet++, ResU-Net, and TransUNet highlights its considerable advantages in processing UWF photographs. RESULTS: The experimental results show that, compared to the other four models, our algorithm demonstrates substantial improvements in segmenting the optic disc and PPA in UWF photographs. In PPA segmentation, our algorithm improves by 0.8% in Dice, 1.8% in sensitivity, and 1.3% in intersection over union (IOU). In optic disc segmentation, our algorithm improves by 0.3% in Dice, 0.6% in precision, and 0.4% in IOU. CONCLUSION: Our proposed method improves the segmentation accuracy of PPA and optic disks based on UWF photographs, which is valuable for diagnosing high myopia-related diseases in ophthalmology clinics.

Acetyl-CoA synthetase 2 induces pyroptosis and inflammation of renal epithelial tubular cells in sepsis-induced acute kidney injury by upregulating the KLF5/NF-κB pathway.

BACKGROUND: Pyroptosis of the renal tubular epithelial cells (RTECs) and interstitial inflammation are central pathological characteristics of acute kidney injury (AKI). Pyroptosis acts as a pro-inflammatory form of programmed cell death and is mainly dependent on activation of the NLRP3 inflammasome. Previous studies revealed that acetyl-CoA synthetase 2 (ACSS2) promotes inflammation during metabolic stress suggesting that ACSS2 might regulate pyroptosis and inflammatory responses of RTECs in AKI. METHODS AND RESULTS: The expression of ACSS2 was found to be significantly increased in the renal epithelial cells of mice with lipopolysaccharide (LPS)-induced AKI. Pharmacological and genetic strategies demonstrated that ACSS2 regulated NLRP3-mediated caspase-1 activation and pyroptosis through the stimulation of the KLF5/NF-κB pathway in RTECs. The deletion of ACSS2 attenuated renal tubular pathological injury and inflammatory cell infiltration in an LPS-induced mouse model, and ACSS2-deficient mice displayed impaired NLRP3 activation-mediated pyroptosis and decreased IL-1ß production in response to the LPS challenge. In HK-2 cells, ACSS2 deficiency suppressed NLRP3-mediated caspase-1 activation and pyroptosis through the downregulation of the KLF5/NF-κB pathway. The KLF5 inhibitor ML264 suppressed NF-κB activity and NLRP3-mediated caspase-1 activation, thus protecting HK-2 cells from LPS-induced pyroptosis. CONCLUSION: Our results suggested that ACSS2 regulates activation of the NLRP3 inflammasome and pyroptosis by inducing the KLF5/NF-κB pathway in RTECs. These results identified ACSS2 as a potential therapeutic target in AKI.

Air pollution and the burden of long-term care: Evidence from China.

We examine the causal effects of PM2.5 exposure on the burden of long-term care (LTC) by matching a satellite-based PM2.5 (particulate matter smaller than 2.5 micrometers (µm) in diameter) dataset with a nationally representative longitudinal study in China from 2011 to 2018. We find significant adverse effects of PM2.5 exposure-instrumented by thermal inversions-on the LTC burden. A 10 µg/m3 increase in annual PM2.5 exposure increases average monthly hours of LTC and the associated financial costs by 28 h and CNY 452, respectively. The effects are greater for those who had never smoked nor experienced severe PM2.5 pollution (annual average PM2.5  > 35 µg/m3 ) in the previous 5 years. We also find that as PM2.5 increases, chronic diseases, particularly cardiovascular diseases, could lead to a higher likelihood of LTC dependency but reduce the total hours and costs of LTC provision. Finally, we find that PM2.5 reduces the total years of LTC need, suggesting that PM2.5 increases LTC costs by increasing the severity of LTC dependency, rather than the duration of LTC need. Our findings can assist policymakers in planning for LTC provisions and clean air policies.

Mechanism of scutellarin inhibition of astrocyte activation to type A1 after ischemic stroke.

OBJECTIVE: The aim of this study was to investigate the effects of scutellarin on the activation of astrocytes into the A1 type following cerebral ischemia and to explore the underlying mechanism. METHODS: In vivo, a mouse middle cerebral artery wire embolism model was established to observe the regulation of astrocyte activation to A1 type by scutellarin, and the effects on neurological function and brain infarct volume. In vitro, primary astrocytes were cultured to establish an oxygen-glucose deprivation model, and the mRNA and protein expression of C3, a specific marker of A1-type astrocytes pretreated with scutellarin, were examined. The neurons were cultured in vitro to detect the toxic effects of ischemia-hypoxia-activated A1 astrocyte secretion products on neurons, and to observe whether scutellarin could reduce the neurotoxicity of A1 astrocytes. To validate the signaling pathway-related proteins regulated by scutellarin on C3 expression in astrocytes. RESULTS: The results showed that scutellarin treatment reduced the volume of cerebral infarcts and attenuated neurological deficits in mice caused by middle cerebral artery embolism. Immunofluorescence and Western blot showed that treatment with scutellarin down-regulated middle cerebral artery embolism and OGD/R up-regulated A1-type astrocyte marker C3. The secretory products of ischemia-hypoxia-activated A1-type astrocytes were toxic to neurons and induced an increase in neuronal apoptosis, and astrocytes treated with scutellarin reduced the toxic effects on neurons. Further study revealed that scutellarin inhibited the activation of NF-κB signaling pathway and thus inhibited the activation of astrocytes to A1 type.

Development and application of Chinese medical ontology for diabetes mellitus.

OBJECTIVE: To develop a Chinese Diabetes Mellitus Ontology (CDMO) and explore methods for constructing high-quality Chinese biomedical ontologies. MATERIALS AND METHODS: We used various data sources, including Chinese clinical practice guidelines, expert consensus, literature, and hospital information system database schema, to build the CDMO. We combined top-down and bottom-up strategies and integrated text mining and cross-lingual ontology mapping. The ontology was validated by clinical experts and ontology development tools, and its application was validated through clinical decision support and Chinese natural language medical question answering. RESULTS: The current CDMO consists of 3,752 classes, 182 fine-grained object properties with hierarchical relationships, 108 annotation properties, and over 12,000 mappings to other well-known medical ontologies in English. Based on the CDMO and clinical practice guidelines, we developed 200 rules for diabetes diagnosis, treatment, diet, and medication recommendations using the Semantic Web Rule Language. By injecting ontology knowledge, CDMO enhances the performance of the T5 model on a real-world Chinese medical question answering dataset related to diabetes. CONCLUSION: CDMO has fine-grained semantic relationships and extensive annotation information, providing a foundation for medical artificial intelligence applications in Chinese contexts, including the construction of medical knowledge graphs, clinical decision support systems, and automated medical question answering. Furthermore, the development process incorporated natural language processing and cross-lingual ontology mapping to improve the quality of the ontology and improved development efficiency. This workflow offers a methodological reference for the efficient development of other high-quality Chinese as well as non-English medical ontologies.

Applying contrastive pre-training for depression and anxiety risk prediction in type 2 diabetes patients based on heterogeneous electronic health records: a primary healthcare case study.

OBJECTIVE: Due to heterogeneity and limited medical data in primary healthcare services (PHS), assessing the psychological risk of type 2 diabetes mellitus (T2DM) patients in PHS is difficult. Using unsupervised contrastive pre-training, we proposed a deep learning framework named depression and anxiety prediction (DAP) to predict depression and anxiety in T2DM patients. MATERIALS AND METHODS: The DAP model consists of two sub-models. Firstly, the pre-trained model of DAP used unlabeled discharge records of 85 085 T2DM patients from the First Affiliated Hospital of Nanjing Medical University for unsupervised contrastive learning on heterogeneous electronic health records (EHRs). Secondly, the fine-tuned model of DAP used case-control cohorts (17 491 patients) selected from 149 596 T2DM patients' EHRs in the Nanjing Health Information Platform (NHIP). The DAP model was validated in 1028 patients from PHS in NHIP. Evaluation included receiver operating characteristic area under the curve (ROC-AUC) and precision-recall area under the curve (PR-AUC), and decision curve analysis (DCA). RESULTS: The pre-training step allowed the DAP model to converge at a faster rate. The fine-tuned DAP model significantly outperformed the baseline models (logistic regression, extreme gradient boosting, and random forest) with ROC-AUC of 0.91±0.028 and PR-AUC of 0.80±0.067 in 10-fold internal validation, and with ROC-AUC of 0.75 ± 0.045 and PR-AUC of 0.47 ± 0.081 in external validation. The DCA indicate the clinical potential of the DAP model. CONCLUSION: The DAP model effectively predicted post-discharge depression and anxiety in T2DM patients from PHS, reducing data fragmentation and limitations. This study highlights the DAP model's potential for early detection and intervention in depression and anxiety, improving outcomes for diabetes patients.

Risk assessment of pneumothorax in colorectal lung metastases treated by percutaneous thermal ablation: a multicenter retrospective cohort study.

PURPOSE: To evaluate the risk of pneumothorax in the percutaneous image-guided thermal ablation (IGTA) treatment of colorectal lung metastases (CRLM). METHODS: Data regarding patients with CRLM treated with IGTA from five medical institutions in China from 2016 to 2023 were reviewed retrospectively. Pneumothorax and non-pneumothorax were compared using the Student's t -test, χ 2 test and Fisher's exact test. Univariate logistic regression analysis was conducted to identify potential risk factors, followed by multivariate logistic regression analysis to evaluate the predictors of pneumothorax. Interactions between variables were examined and used for model construction. Receiver operating characteristic curves and nomograms were generated to assess the performance of the model. RESULTS: A total of 254 patients with 376 CRLM underwent 299 ablation sessions. The incidence of pneumothorax was 45.5%. The adjusted multivariate logistic regression model, incorporating interaction terms, revealed that tumour number [odds ratio (OR)=8.34 (95% CI: 1.37-50.64)], puncture depth [OR=0.53 (95% CI: 0.31-0.91)], pre-procedure radiotherapy [OR=3.66 (95% CI: 1.17-11.40)], peribronchial tumour [OR=2.32 (95% CI: 1.04-5.15)], and emphysema [OR=56.83 (95% CI: 8.42-383.57)] were significant predictive factors of pneumothorax (all P <0.05). The generated nomogram model demonstrated a significant prediction performance, with an area under the receiver operating characteristic curve of 0.800 (95% CI: 0.751-0.850). CONCLUSIONS: Pre-procedure radiotherapy, tumour number, peribronchial tumour, and emphysema were identified as risk factors for pneumothorax in the treatment of CRLM using percutaneous IGTA. Puncture depth was found to be a protective factor against pneumothorax.

CX3CR1 mediates motor dysfunction in mice through 5-HTR2a.

CX3CR1 knockout could induce motor dysfunction in several neurological disease models mainly through regulating microglia's function. While CX3CR1 was expressed on neurons in a few reports, whether neuronal CX3CR1 could affect the function of neurons and mediate motor dysfunction under physiological conditions is unknown. To elucidate the roles of neuronal CX3CR1 on motor dysfunction, CX3CR1 knockout mice were created. Rotarod test and Open field test found that the CX3CR1-/- mice's motor capacity was reduced. Immunofluorescence staining detected the expression of CX3CR1 in neurons both in vivo and in vitro. Immunohistochemistry and West blot found that knockout of CX3CR1 did not affect the neurons' number in both spinal cord and brain of mice. While inhibiting the function of CX3CR1 by AZD8797 could decrease the expression of 5-Hydroxytryptamine receptor(5-HTR2a), which involved in the regulation of motor function. Further investigation revealed that CX3CR1 regulated the expression of HTR2a through the NF-κB pathway. For the first time, we reported that neuronal CXCR1 mediates motor dysfunction. Our results suggest that modulating CXCR1 activity offers a novel therapeutic strategy for motor dysfunction.

Dissecting molecular mechanisms underlying ferroptosis in human umbilical cord mesenchymal stem cells: Role of cystathionine γ-lyase/hydrogen sulfide pathway.

BACKGROUND: Ferroptosis can induce low retention and engraftment after mesenchymal stem cell (MSC) delivery, which is considered a major challenge to the effectiveness of MSC-based pulmonary arterial hypertension (PAH) therapy. Interestingly, the cystathionine γ-lyase (CSE)/hydrogen sulfide (H2S) pathway may contribute to mediating ferroptosis. However, the influence of the CSE/H2S pathway on ferroptosis in human umbilical cord MSCs (HUCMSCs) remains unclear. AIM: To clarify whether the effect of HUCMSCs on vascular remodelling in PAH mice is affected by CSE/H2S pathway-mediated ferroptosis, and to investigate the functions of the CSE/H2S pathway in ferroptosis in HUCMSCs and the underlying mechanisms. METHODS: Erastin and ferrostatin-1 (Fer-1) were used to induce and inhibit ferroptosis, respectively. HUCMSCs were transfected with a vector to overexpress or inhibit expression of CSE. A PAH mouse model was established using 4-wk-old male BALB/c nude mice under hypoxic conditions, and pulmonary pressure and vascular remodelling were measured. The survival of HUCMSCs after delivery was observed by in vivo bioluminescence imaging. Cell viability, iron accumulation, reactive oxygen species production, cystine uptake, and lipid peroxidation in HUCMSCs were tested. Ferroptosis-related proteins and S-sulfhydrated Kelch-like ECH-associating protein 1 (Keap1) were detected by western blot analysis. RESULTS: In vivo, CSE overexpression improved cell survival after erastin-treated HUCMSC delivery in mice with hypoxia-induced PAH. In vitro, CSE overexpression improved H2S production and ferroptosis-related indexes, such as cell viability, iron level, reactive oxygen species production, cystine uptake, lipid peroxidation, mitochondrial membrane density, and ferroptosis-related protein expression, in erastin-treated HUCMSCs. In contrast, in vivo, CSE inhibition decreased cell survival after Fer-1-treated HUCMSC delivery and aggravated vascular remodelling in PAH mice. In vitro, CSE inhibition decreased H2S levels and restored ferroptosis in Fer-1-treated HUCMSCs. Interestingly, upregulation of the CSE/H2S pathway induced Keap1 S-sulfhydration, which contributed to the inhibition of ferroptosis. CONCLUSION: Regulation of the CSE/H2S pathway in HUCMSCs contributes to the inhibition of ferroptosis and improves the suppressive effect on vascular remodelling in mice with hypoxia-induced PAH. Moreover, the protective effect of the CSE/H2S pathway against ferroptosis in HUCMSCs is mediated via S-sulfhydrated Keap1/nuclear factor erythroid 2-related factor 2 signalling. The present study may provide a novel therapeutic avenue for improving the protective capacity of transplanted MSCs in PAH.

Effects of Pulse Transit Time and Pulse Arrival Time on Cuff-less Blood Pressure Estimation: A Comparison Study with Multiple Experimental Interventions.

Pulse transit time (PTT) has shown a correlation with blood pressure (BP), and it is considered as a potential marker for cuff-less BP estimation. However, pulse arrival time (PAT) including pre-ejection period (PEP) has been utilized more widely because of its convenience to acquisition and calculation. In spite of this, whether PAT can surrogate PTT has been a controversial topic for many years. In this study, we designed an experiment on 55 subjects with multiple interventions, those may cause the changes in BP and PEP. We analyzed the linear and nonlinear correlations between BP and PTT/PAT, and also assessed the performances of PTT-based and PAT-based models on tracking the BP variation. Five typical BP estimation models were used for comparison. We found that PEP could change rapidly in response to the interventions related with physical stress. Although PTT had a better linear correlation with BP, most of the PAT-based models showed more accuracy than PTT-based models in all of the interventions, especially for the calibrated models. It is suggested that PAT has the potential to predict BP, and the inclusion of PEP in the measurement of PAT is necessary.

ATF4 May Be Essential for Adaption of the Ocular Lens to Its Avascular Environment.

The late embryonic mouse lens requires the transcription factor ATF4 for its survival although the underlying mechanisms were unknown. Here, RNAseq analysis revealed that E16.5 Atf4 null mouse lenses downregulate the mRNA levels of lens epithelial markers as well as known markers of late lens fiber cell differentiation. However, a comparison of this list of differentially expressed genes (DEGs) with other known transcriptional regulators of lens development indicated that ATF4 expression is not directly controlled by the previously described lens gene regulatory network. Pathway analysis revealed that the Atf4 DEG list was enriched in numerous genes involved in nutrient transport, amino acid biosynthesis, and tRNA charging. These changes in gene expression likely result in the observed reductions in lens free amino acid and glutathione levels, which would result in the observed low levels of extractable lens protein, finally leading to perinatal lens disintegration. These data demonstrate that ATF4, via its function in the integrated stress response, is likely to play a crucial role in mediating the adaption of the lens to the avascularity needed to maintain lens transparency.

Gastrodin Regulates PI3K/AKT-Sirt3 Signaling Pathway and Proinflammatory Mediators in Activated Microglia.

Activated microglia and their mediated inflammatory responses play an important role in the pathogenesis of hypoxic-ischemic brain damage (HIBD). Therefore, regulating microglia activation is considered a potential therapeutic strategy. The neuroprotective effects of gastrodin were evaluated in HIBD model mice, and in oxygen glucose deprivation (OGD)-treated and lipopolysaccharide (LPS)activated BV-2 microglia cells. The potential molecular mechanism was investigated using western blotting, immunofluorescence labeling, quantitative realtime reverse transcriptase polymerase chain reaction, and flow cytometry. Herein, we found that PI3K/AKT signaling can regulate Sirt3 in activated microglia, but not reciprocally. And gastrodin exerts anti-inflammatory and antiapoptotic effects through the PI3K/AKT-Sirt3 signaling pathway. In addition, gastrodin could promote FOXO3a phosphorylation, and inhibit ROS production in LPSactivated BV-2 microglia. Moreover, the level P-FOXO3a decreased significantly in Sirt3-siRNA group. However, there was no significant change after gastrodin and siRNA combination treatment. Notably, gastrodin might also affect the production of ROS in activated microglia by regulating the level of P-FOXO3a via Sirt3. Together, this study highlighted the neuroprotective role of PI3K/AKT-Sirt3 axis in HIBD, and the anti-inflammatory, anti-apoptotic, and anti-oxidative stress effects of gastrodin on HIBD.

Distribution spectrum and clinical significance of glomerular exostosin (EXT1) deposits in PLA2R-positive membranous nephropathy.

BACKGROUND: The discovery of antigen phospholipase A2 receptor (PLA2R) in 2009 ushered in the antigen-based study of membranous nephropathy. The further putative antigen exostosin 1/2 (EXT1/2) was described in 2019. However, the distribution spectrum of glomerular EXT1 deposits in membranous nephropathy has not been fully elucidated. METHODS: We conducted a retrospective cohort study of biopsy-proven membranous nephropathy patients. Patients with complete baseline data and adequate tissue specimens were included in this study. Tests for glomerular expression of PLA2R and EXT1 and circulating anti-PLA2R antibodies were performed. Clinicopathological and outcome data were reviewed. RESULTS: We included 626 patients, namely, 487 (77.8%) PLA2R-positive patients and 54 (8.6%) EXT1-positive patients; 32 (5.1%) patients were dual-positive for PLA2R and EXT1 (PLA2R + /EXT1 +). A higher percentage of dual-positive patients had low C3 levels (P < 0.001) and were more likely to have autoimmune diseases (P = 0.013) than PLA2R-positive and EXT1-negative (PLA2R + /EXT1-) patients. Kidney biopsy findings revealed that there was a higher percentage of glomerular IgG1, IgG2, IgA, C4, and C1q deposits (P < 0.05), "full-house" staining (P < 0.001), and stronger intensity of C1q staining (P = 0.002) in PLA2R + /EXT1 + patients. Based on Kaplan-Meier analysis, a higher percentage of PLA2R + /EXT1 + patients exhibited partial or complete remission of proteinuria. Furthermore, EXT1-positive expression was a favourable predictor for proteinuria remission, whereas interstitial fibrosis/tubular atrophy was an unfavourable predictor. A complement C3 level < 0.79 g/L was independently associated with EXT1 positivity in PLA2R-positive membranous nephropathy. CONCLUSIONS: We describe a subgroup of PLA2R and EXT1 dual-positive patients. Patients in this subset exhibited more signs of autoimmunity and more frequent clinical remission. In PLA2R-positive membranous nephropathy, a complement C3 level < 0.79 g/L was independently associated with EXT1 positivity, which was a favourable predictor for proteinuria remission.

SUMO1-regulated DBC1 promotes p53-dependent stress-induced apoptosis of lens epithelial cells.

Deleted in breast cancer 1 (DBC1) was initially identified from a homozygously deleted region in human chromosome 8p21. It has been well established that DBC1 plays a dual role during cancer development. Depending on the physiological context, it can promote or inhibit tumorigenesis. Whether it plays a role in lens pathogenesis remains elusive. In the present study, we demonstrated that DBC1 is highly expressed in lens epithelial cells from different vertebrates and in retina pigment epithelial cells as well. Moreover, DBC1 is SUMOylated through SUMO1 conjugation at K591 residue in human and mouse lens epithelial cells. The SUMOylated DBC1 is localized in the nucleus and plays an essential role in promoting stress-induced apoptosis. Silence of DBC1 attenuates oxidative stress-induced apoptosis. In contrast, overexpression of DBC1 enhances oxidative stress-induced apoptosis, and this process depends on p53. Mechanistically, DBC1 interacts with p53 to regulate its phosphorylation status at multiple sites and the SUMOylation of DBC1 enhances its interaction with p53. Together, our results identify that DBC1 is an important regulator mediating stress-induced apoptosis in lens, and thus participates in control of lens cataractogenesis.

Changes of retinal vessel density in low to moderate myopic eyes with orthokeratology evaluated by optical coherence tomography angiography.

AIM: To explore the effect of orthokeratology (OK) fitting on retinal vessel density in low to moderate myopia adolescents by using optical coherence tomography angiography. METHODS: Children aged 10 to 14y with a cycloplegic spherical equivalent refraction of -0.50 diopter (D) to -5.00 D and astigmatism with more than -1.50 D were recruited. The enrolled adolescents were divided into OK group and spectacle group. During regular follow-up, adolescents were measured respectively at pre-wear, 1, 3, and 6mo after treatment. The follow-up included uncorrected distance visual acuity (UDVA), axial length (AL), superficial capillary plexus density (SCPD), deep capillary plexus density (DCPD), central retinal thickness (CRT), foveal avascular zone area (FAZ-A), foveal avascular zone perimeter (FAZ-P) and foveal vessel density in a 300-µm-wide region around foveal avascular zone (FD-300). The collected data were analyzed using statistical methods. RESULTS: By one month, SCPD significantly increased in the fovea and superior retina, and DCPD significantly increased inferiorly in OK group compared to spectacle group (P<0.05). By three months, there were significant increases in SCPD in the fovea and inferior retina, and DCPD in the parafovea, superior, and inferior retina in OK group (P<0.05), while the increase in SCPD and DCPD in the fovea were observed by six months (P<0.05). The FD-300 significantly increased at every follow-up in OK group compared to spectacle group (P<0.05). No significant differences in the CRT, FAZ-A and FAZ-P and FD-300 were observed between two groups (P>0.05). OK group showed a significant improvement in UDVA after wearing OK, compared to spectacle group (P<0.01), while the AL did not show a significant difference between two groups (P>0.05). CONCLUSION: Short-term OK worn can increase local retinal vessel density in adolescents with low-to-moderate myopia.

Identification of m6A methylation-related genes in cerebral ischaemia‒reperfusion of Breviscapus therapy based on bioinformatics methods.

BACKGROUND: Cerebral ischaemia‒reperfusion (I/R) frequently causes late-onset neuronal damage. Breviscapine promotes autophagy in microvascular endothelial cells in I/R and can inhibit oxidative damage and apoptosis. However, the mediation mechanism of breviscapine on neuronal cell death is unclear. METHODS: First, transcriptome sequencing was performed on three groups of mice: the neuronal normal group (Control group), the oxygen-glucose deprivation/ reoxygenation group (OGD/R group) and the breviscapine administration group (Therapy group). Differentially expressed genes (DEGs) between the OGD/R and control groups and between the Therapy and OGD/R groups were obtained by the limma package. N6-methyladenosine (m6A) methylation-related DEGs were selected by Pearson correlation analysis. Then, prediction and confirmation of drug targets were performed by Swiss Target Prediction and UniProt Knowledgebase (UniProtKB) database, and key genes were obtained by Pearson correlation analysis between m6A-related DEGs and drug target genes. Next, gene set enrichment analysis (GSEA) and Ingenuity pathway analysis (IPA) were used to obtain the pathways of key genes. Finally, a circRNA-miRNA‒mRNA network was constructed based on the mRNAs, circRNAs and miRNAs. RESULTS: A total of 2250 DEGs between the OGD/R and control groups and 757 DEGs between the Therapy and OGD/R groups were selected by differential analysis. A total of 7 m6A-related DEGs, including Arl4d, Gm10653, Gm1113, Kcns3, Olfml2a, Stk26 and Tfcp2l1, were obtained by Pearson correlation analysis. Four key genes (Tfcp2l1, Kcns3, Olfml2a and Arl4d) were acquired, and GSEA showed that these key genes significantly participated in DNA repair, e2f targets and the g2m checkpoint. IPA revealed that Tfcp2l1 played a significant role in human embryonic stem cell pluripotency. The circRNA-miRNA‒mRNA network showed that mmu_circ_0001258 regulated Tfcp2l1 by mmu-miR-301b-3p. CONCLUSIONS: In conclusion, four key genes, Tfcp2l1, Kcns3, Olfml2a and Arl4d, significantly associated with the treatment of OGD/R by breviscapine were identified, which provides a theoretical basis for clinical trials.

Deep-learning and conventional radiomics to predict IDH genotyping status based on magnetic resonance imaging data in adult diffuse glioma.

Objectives: In adult diffuse glioma, preoperative detection of isocitrate dehydrogenase (IDH) status helps clinicians develop surgical strategies and evaluate patient prognosis. Here, we aim to identify an optimal machine-learning model for prediction of IDH genotyping by combining deep-learning (DL) signatures and conventional radiomics (CR) features as model predictors. Methods: In this study, a total of 486 patients with adult diffuse gliomas were retrospectively collected from our medical center (n=268) and the public database (TCGA, n=218). All included patients were randomly divided into the training and validation sets by using nested 10-fold cross-validation. A total of 6,736 CR features were extracted from four MRI modalities in each patient, namely T1WI, T1CE, T2WI, and FLAIR. The LASSO algorithm was performed for CR feature selection. In each MRI modality, we applied a CNN+LSTM-based neural network to extract DL features and integrate these features into a DL signature after the fully connected layer with sigmoid activation. Eight classic machine-learning models were analyzed and compared in terms of their prediction performance and stability in IDH genotyping by combining the LASSO-selected CR features and integrated DL signatures as model predictors. In the validation sets, the prediction performance was evaluated by using accuracy and the area under the curve (AUC) of the receiver operating characteristics, while the model stability was analyzed by using the relative standard deviation of the AUC (RSDAUC). Subgroup analyses of DL signatures and CR features were also individually conducted to explore their independent prediction values. Results: Logistic regression (LR) achieved favorable prediction performance (AUC: 0.920 ± 0.043, accuracy: 0.843 ± 0.044), whereas support vector machine with the linear kernel (l-SVM) displayed low prediction performance (AUC: 0.812 ± 0.052, accuracy: 0.821 ± 0.050). With regard to stability, LR also showed high robustness against data perturbation (RSDAUC: 4.7%). Subgroup analyses showed that DL signatures outperformed CR features (DL, AUC: 0.915 ± 0.054, accuracy: 0.835 ± 0.061, RSDAUC: 5.9%; CR, AUC: 0.830 ± 0.066, accuracy: 0.771 ± 0.051, RSDAUC: 8.0%), while DL and DL+CR achieved similar prediction results. Conclusion: In IDH genotyping, LR is a promising machine-learning classification model. Compared with CR features, DL signatures exhibit markedly superior prediction values and discriminative capability.