3D magnetic flower-spherical Fe2O3-NiO derived from NiFe-layered double hydroxides for the efficient removal of Bensulfuron methyl: Morphological control and bimetallic synergy.

Three-dimensional (3D) magnetic flower-spherical Fe2O3-NiO derived from NiFe-layered double hydroxides (NiFe-LDHs) was fabricated through urea hydrothermal and calcination methods. The as-prepared materials were applied to activate PMS to degrade one of herbicide named Bensulfuron methyl (BSM). Fe2O3-NiO-1 demonstrated the highest catalytic activity and the lowest ions leaching by comparing the performance of LDHs and derivative bimetallic oxide synthesized by co-precipitation method, urea hydrothermal method and direct calcination method. Based on the results of SEM, BET and CV, the high catalytic activity of Fe2O3-NiO-1 originated from 3D morphology, lager specific area and pore size and faster electron transfer capability. The factors influencing the degradation performance were investigated and 0.1 g·L-1 Fe2O3-NiO could effectively activate PMS (1 mmol·L-1) to completely remove 10 mg·L-1 BSM within 30 min at pH 7.0. In Fe2O3-NiO/PMS system, OH, SO4- and 1O2 were produced and contributed to the BSM removal according to the results of EPR and quenching experiments. In order to expand its application range, Fe2O3-NiO/PMS system was used to degrade aniline (AN), sulfamethoxazole (SMZ), phenacetin (PNT), bisphenol A (BPA) and 2,4,6-triclofen (2,4,6-TCP) and the results showed the degradation efficiency could reach 90 % or more. Additionally, the application of catalysts in different actual water samples and the ability of reuse were tested. Based on the strategies of bimetallic synergy and morphology control, Fe-based bimetallic oxides with 3D morphology were developed in this study, which could effectively enhance the catalytic activity and inhibit the dissolution of metal ions, providing the design ideas for the construction of efficient catalysts and the removal of complex organic pollutants.

Deep learning with noisy labels in medical prediction problems: a scoping review.

OBJECTIVES: Medical research faces substantial challenges from noisy labels attributed to factors like inter-expert variability and machine-extracted labels. Despite this, the adoption of label noise management remains limited, and label noise is largely ignored. To this end, there is a critical need to conduct a scoping review focusing on the problem space. This scoping review aims to comprehensively review label noise management in deep learning-based medical prediction problems, which includes label noise detection, label noise handling, and evaluation. Research involving label uncertainty is also included. METHODS: Our scoping review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched 4 databases, including PubMed, IEEE Xplore, Google Scholar, and Semantic Scholar. Our search terms include "noisy label AND medical/healthcare/clinical," "uncertainty AND medical/healthcare/clinical," and "noise AND medical/healthcare/clinical." RESULTS: A total of 60 papers met inclusion criteria between 2016 and 2023. A series of practical questions in medical research are investigated. These include the sources of label noise, the impact of label noise, the detection of label noise, label noise handling techniques, and their evaluation. Categorization of both label noise detection methods and handling techniques are provided. DISCUSSION: From a methodological perspective, we observe that the medical community has been up to date with the broader deep-learning community, given that most techniques have been evaluated on medical data. We recommend considering label noise as a standard element in medical research, even if it is not dedicated to handling noisy labels. Initial experiments can start with easy-to-implement methods, such as noise-robust loss functions, weighting, and curriculum learning.

One-step growth of Cu-doped carbon dots in amino-modified carbon nanotube-modified electrodes for sensitive electrochemical detection of BPA.

Copper-doped carbon dots and aminated carbon nanotubes (Cu-CDs/NH2-CNTs) nanocomposites were synthesized by a one-step growth method, and the composites were characterized for their performance. An electrochemical sensor for sensitive detection of bisphenol A (BPA) was developed for using Cu-CDs/NH2-CNTs nanocomposites modified with glassy carbon electrodes (GCE). The sensor exhibited an excellent electrochemical response to BPA in 0.2 M PBS (pH 7.0) under optimally selected conditions. The linear range of the sensor for BPA detection was 0.5-160 µM, and the detection limit (S/N = 3) was 0.13 µM. Moreover, the sensor has good interference immunity, stability and reproducibility. In addition, the feasibility of the practical application of the sensor was demonstrated by the detection of BPA in bottled drinking water and Liu Yang River water.

Bone marrow stromal cells dictate lanosterol biosynthesis and ferroptosis of multiple myeloma.

Ferroptosis has been demonstrated a promising way to counteract chemoresistance of multiple myeloma (MM), however, roles and mechanism of bone marrow stromal cells (BMSCs) in regulating ferroptosis of MM cells remain elusive. Here, we uncovered that MM cells were more susceptible to ferroptotic induction under the interaction of BMSCs using in vitro and in vivo models. Mechanistically, BMSCs elevated the iron level in MM cells, thereby activating the steroid biosynthesis pathway, especially the production of lanosterol, a major source of reactive oxygen species (ROS) in MM cells. We discovered that direct coupling of CD40 ligand and CD40 receptor constituted the key signaling pathway governing lanosterol biosynthesis, and disruption of CD40/CD40L interaction using an anti-CD40 neutralizing antibody or conditional depletion of Cd40l in BMSCs successfully eliminated the iron level and lanosterol production of MM cells localized in the Vk*MYC Vk12653 or NSG mouse models. Our study deciphers the mechanism of BMSCs dictating ferroptosis of MM cells and highlights the therapeutic potential of non-apoptosis strategies for managing refractory or relapsed MM patients.

Tolerogenic dendritic cells alleviate collagen-induced arthritis by regulating T-cell differentiation and inhibiting NLRP3-mediated apoptosis.

OBJECTIVE: Tolerogenic dendritic cells (tolDCs) have emerged as a potential treatment for rheumatoid arthritis (RA). However, the detailed mechanism requires further investigation. In this study, we aimed to explore the effects of tolDCs on T-cell differentiation and NLRP3-mediated pyroptosis in a collagen-induced arthritis (CIA) rat model. METHODS: TolDCs were induced using NF-κB ODN decoy. The efficacy of tolDCs intervention in alleviating arthritis symptoms was evaluated in CIA rats. Flow cytometry was employed to analyze CD4+ T-cell subpopulations, while scanning electron microscopy was utilized to observe pyroptosis morphology. Immunohistochemistry was used to assess the expression of pyroptosis-associated proteins. RESULTS: TolDCs intervention significantly reduced joint inflammation and damage in CIA rats. Moreover, it successfully restored the balance of Th1/Th2 cells as well as the balance of Treg/Th17 cells. Furthermore, tolDCs intervention effectively suppressed NLRP3-mediated pyroptosis in the synovium, decreasing the release of IL-1ß and IL-18. CONCLUSION: Our findings underscore the efficacy of tolDCs in attenuating CIA progression through modulation of CD4+ T-cell subpopulations and inhibition of NLRP3-mediated pyroptosis.

Association Between Peripheral Retinal Defocus and Myopia by Multispectral Refraction Topography in Chinese Children.

Objective: To investigate the association between the peripheral refractive errors of the fundus in different regions and moderate and high myopia. Methods: In this case-control study, 320 children and adolescents aged 6 to 18 years were recruited. Peripheral refractive errors were measured using multispectral retinal refractive topography (MRT). Spherical equivalent (SE) and cylinder errors were classified into low, moderate, and high categories based on the magnitude range. Logistic regression was performed to test the factors associated with myopia. Results: There were 152 participants with low myopia and 168 participants with moderate and high myopia included in the current study. Participants with moderate and high myopia were most likely to be older, with larger axial length (AL), lower SE, less time to watch electronic devices on the weekend, a higher difference between central refractive error and paracentral refractive error from the superior side of the retina (RDV-S), but a smaller difference between the central refractive error and paracentral refractive error from the inferior side of the retina (RDV-I) than those with low myopia (all P <0.05). After logistic analysis, female sex (odds ratio [OR] = 4.14; 95% confidence interval [CI] = 2.16-7.97, P <0.001), AL (OR = 6.88, 95% CI = 4.33-10.93, P <0.001), and RDV-I (OR = 0.52, 95% CI = 0.32-0.86, P = 0.010) were independent factors for moderate and high myopia. Conclusion: Our study demonstrated that the retina peripheral refraction of the eyes (RDV-I) was associated with moderate and high myopia, and RDV-S was only associated with high myopia.

Parallel Alternating Iterative Optimization for Cardiac Magnetic Resonance Image Blind Super-Resolution.

Cardiac magnetic resonance imaging (CMRI) super-resolution (SR) reconstruction technology can enhance the resolution and quality of CMRI, providing experts with clearer and more accurate information about cardiac structure and function. This technology aids in the rapid and accurate diagnosis of cardiac abnormalities and the development of personalized treatment plans. In the processing of CMRI, existing bicubic degradation-based SR methods often suffer from performance degradation, resulting in blurred SR images. To address the aforementioned problem, we present a parallel alternating iterative optimization for CMRI image blind SR method (PAIBSR). Specifically, we propose a parallel alternating iterative optimization strategy, which employs dynamically corrected blur kernels and dynamically extracted intermediate low-resolution features as prior knowledge for both the blind SR process and the blur kernel correction process. Meanwhile, we propose a blur kernel update module composed of a blur kernel extractor and a low-resolution kernel extractor to correct the blur kernel. Furthermore, we propose an enhanced spatial feature transformation residual block, leveraging the corrected blur kernel as prior knowledge for the blind SR process. Through extensive experiments conducted on synthetic datasets, we have validated the superiority of PAIBSR method. It outperforms state-of-the-art SR methods in terms of performance and produces visually pleasing results.

Trace metal elements: a bridge between host and intestinal microorganisms.

Trace metal elements, such as iron, copper, manganese, and zinc, are essential nutrients for biological processes. Although their intake demand is low, they play a crucial role in cell homeostasis as the cofactors of various enzymes. Symbiotic intestinal microorganisms compete with their host for the use of trace metal elements. Moreover, the metabolic processes of trace metal elements in the host and microorganisms affect the organism's health. Supplementation or the lack of trace metal elements in the host can change the intestinal microbial community structure and function. Functional changes in symbiotic microorganisms can affect the host's metabolism of trace metal elements. In this review, we discuss the absorption and transport processes of trace metal elements in the host and symbiotic microorganisms and the effects of dynamic changes in the levels of trace metal elements on the intestinal microbial community structure. We also highlight the participation of trace metal elements as enzyme cofactors in the host immune process. Our findings indicate that the host uses metal nutrition immunity or metal poisoning to resist pathogens and improve immunity.

CXCL10 Recruitment of γδ T Cells into the Hypoxic Bone Marrow Environment Leads to IL17 Expression and Multiple Myeloma Progression.

In multiple myeloma (MM), bone marrow stromal cells (BMSC) shape a unique niche within the bone marrow, promoting T-cell dysfunction and driving MM progression; however, the precise underlying mechanisms remain elusive. Here, we show that BMSC-mediated reprogramming of MM cells led to heightened production of CXCL10. CXCL10 orchestrated the recruitment of γδ T cells into the bone marrow, and this was observed in both the Vk*MYC and 5TGM1 mouse models of MM, as well as in patients experiencing refractory or relapsed MM. Furthermore, the dysfunctional γδ T cells in the MM bone marrow niche exhibited increased PD-1 expression and IL17 production. In the Vk*MYC mouse model, MM-associated bone lesions and mortality were markedly alleviated in Tcrd-/- mice, and MM disease progression could be rescued in these mice upon transplantation of γδ T cells expanded from wild-type mice, but not from Il17-/- mice. Mechanistically, the hypoxic microenvironment prevailing in the MM bone marrow niche stimulated the expression of steroid receptor coactivator 3 (SRC-3) in γδ T cells, which in turn interacted with the transcriptional factor RORγt, promoting Il17 transcription. Pharmacologic inhibition of SRC-3 utilizing SI-2 effectively suppressed Il17A expression in γδ T cells, leading to alleviation of MM progression in the murine models and enhancing the anti-multiple myeloma efficacy of bortezomib. Our results illuminated the bone marrow microenvironment's involvement in provoking γδ T-cell dysfunction throughout MM progression and suggest SRC-3 inhibition as a promising strategy to enhance the effectiveness of immunotherapies targeting γδ T cells.

A flexible quasi-likelihood model for microbiome abundance count data.

In this article, we present a flexible model for microbiome count data. We consider a quasi-likelihood framework, in which we do not make any assumptions on the distribution of the microbiome count except that its variance is an unknown but smooth function of the mean. By comparing our model to the negative binomial generalized linear model (GLM) and Poisson GLM in simulation studies, we show that our flexible quasi-likelihood method yields valid inferential results. Using a real microbiome study, we demonstrate the utility of our method by examining the relationship between adenomas and microbiota. We also provide an R package "fql" for the application of our method.

Establishment of a prognostic risk prediction modelfor non-small cell lung cancer patients with brainmetastases: a retrospective study.

Background: Patients with non-small cell lung cancer (NSCLC) who develop brain metastases (BM) have a poor prognosis. This study aimed to construct a clinical prediction model to determine the overall survival (OS) of NSCLC patients with BM. Methods: A total of 300 NSCLC patients with BM at the Yunnan Cancer Centre were retrospectively analysed. The prediction model was constructed using the least absolute shrinkage and selection operator-Cox regression. The bootstrap sampling method was employed for internal validation. The performance of our prediction model was compared using recursive partitioning analysis (RPA), graded prognostic assessment (GPA), the update of the graded prognostic assessment for lung cancer using molecular markers (Lung-molGPA), the basic score for BM (BSBM), and tumour-lymph node-metastasis (TNM) staging. Results: The prediction models comprising 15 predictors were constructed. The area under the curve (AUC) values for the 1-year, 3-year, and 5-year time-dependent receiver operating characteristic (curves) were 0.746 (0.678-0.814), 0.819 (0.761-0.877), and 0.865 (0.774-0.957), respectively. The bootstrap-corrected AUC values and Brier scores for the prediction model were 0.811 (0.638-0.950) and 0.123 (0.066-0.188), respectively. The time-dependent C-index indicated that our model exhibited significantly greater discrimination compared with RPA, GPA, Lung-molGPA, BSBM, and TNM staging. Similarly, the decision curve analysis demonstrated that our model displayed the widest range of thresholds and yielded the highest net benefit. Furthermore, the net reclassification improvement and integrated discrimination improvement analyses confirmed the enhanced predictive power of our prediction model. Finally, the risk subgroups identified by our prognostic model exhibited superior differentiation of patients' OS. Conclusion: The clinical prediction model constructed by us shows promise in predicting OS for NSCLC patients with BM. Its predictability is superior compared with RPA, GPA, Lung-molGPA, BSBM, and TNM staging.

Targeting small GTPases: emerging grasps on previously untamable targets, pioneered by KRAS.

Small GTPases including Ras, Rho, Rab, Arf, and Ran are omnipresent molecular switches in regulating key cellular functions. Their dysregulation is a therapeutic target for tumors, neurodegeneration, cardiomyopathies, and infection. However, small GTPases have been historically recognized as "undruggable". Targeting KRAS, one of the most frequently mutated oncogenes, has only come into reality in the last decade due to the development of breakthrough strategies such as fragment-based screening, covalent ligands, macromolecule inhibitors, and PROTACs. Two KRASG12C covalent inhibitors have obtained accelerated approval for treating KRASG12C mutant lung cancer, and allele-specific hotspot mutations on G12D/S/R have been demonstrated as viable targets. New methods of targeting KRAS are quickly evolving, including transcription, immunogenic neoepitopes, and combinatory targeting with immunotherapy. Nevertheless, the vast majority of small GTPases and hotspot mutations remain elusive, and clinical resistance to G12C inhibitors poses new challenges. In this article, we summarize diversified biological functions, shared structural properties, and complex regulatory mechanisms of small GTPases and their relationships with human diseases. Furthermore, we review the status of drug discovery for targeting small GTPases and the most recent strategic progress focused on targeting KRAS. The discovery of new regulatory mechanisms and development of targeting approaches will together promote drug discovery for small GTPases.

Association of MicroRNA-10b Expression and P16 with Pathological Features in Various Stages of Cervical Precancerous Lesions.

Cervical cancer is the fourth most prevalent cancer for females with 14,100 new cases each year globally. Efficient screening and intervention at the precancerous stage is the key point to the prevention and treatment of cervical cancer. However, no widely recognized biomarkers have been discovered yet. We investigated the expression of miR-10b in cervical cells and its correlation with clinicopathological features in different pathological grades of cervical precancerous lesions. The expression of miR-10b in cervical cytology samples from 20 cases of LSIL, 22 cases of HSIL, 18 cases of early-stage cervical cancer, and 20 cases of cervicitis controls were assessed using qPCR. From the same cervical cytology samples, the human papillomavirus (HPV) load was assessed using semi-PCR and the lesion size, and gland involvement levels from the same subjects were assessed during the cervical examination. The correlation between miR-10b expression and different pathological grades of cervical lesions was analyzed. We also calculated the correlation between HPV load, lesion size, gland involvement, P16 expression, and different pathological grades. The expression of miR-10b exhibited a step-decreasing manner from cervicitis control (4.23(4.00,4.71)) to LSIL (2.67(2.52,2.90)), HSIL (1.49(1.30,1.80)) and cervical cancer group (0.65(0.55,0.80)). There is a significant difference (P<0.001) between cervicitis and HSIL, cervicitis and cervical cancer, ISIL and HSIL, as well as ISIL and cervical cancer but not between the cervicitis group and the LSIL group. In addition, more severe pathological grades were correlated with a bigger rate of gland involvement (P＜0.001). We also found that different pathological grades were correlated with the intensity of P16 expression (P=0.001), and the intensity of P16 expression is positively correlated with different pathological grades (P<0.05). Repressed expression of miR-10b is related to the progression of cervical precancerous lesions. Increased gland involvement rate and increased intensity of P16 expression are risk factors for developing cervical cancers. Our result showed that miR-10b may be a potential biomarker for the screening and ranking of cervical precancerous lesions.

Deacetylation induced nuclear condensation of HP1γ promotes multiple myeloma drug resistance.

Acquired chemoresistance to proteasome inhibitors is a major obstacle in managing multiple myeloma but key regulators and underlying mechanisms still remain to be explored. We find that high level of HP1γ is associated with low acetylation modification in the bortezomib-resistant myeloma cells using SILAC-based acetyl-proteomics assay, and higher HP1γ level is positively correlated with poorer outcomes in the clinic. Mechanistically, elevated HDAC1 in the bortezomib-resistant myeloma cells deacetylates HP1γ at lysine 5 and consequently alleviates the ubiquitin-mediated protein degradation, as well as the aberrant DNA repair capacity. HP1γ interacts with the MDC1 to induce DNA repair, and simultaneously the deacetylation modification and the interaction with MDC1 enhance the nuclear condensation of HP1γ protein and the chromatin accessibility of its target genes governing sensitivity to proteasome inhibitors, such as CD40, FOS and JUN. Thus, targeting HP1γ stability by using HDAC1 inhibitor re-sensitizes bortezomib-resistant myeloma cells to proteasome inhibitors treatment in vitro and in vivo. Our findings elucidate a previously unrecognized role of HP1γ in inducing drug resistance to proteasome inhibitors of myeloma cells and suggest that targeting HP1γ may be efficacious for overcoming drug resistance in refractory or relapsed multiple myeloma patients.

All-trans retinoic acid improves NSD2-mediated RARα phase separation and efficacy of anti-CD38 CAR T-cell therapy in multiple myeloma.

BACKGROUND: Immunotherapies targeting CD38 have demonstrated salient efficacy in relapsed/refractory multiple myeloma (MM). However, loss of CD38 antigen and outgrowth of CD38 negative plasma cells have emerged as a major obstacle in clinics. All-trans retinoic acid (ATRA) has been reported to upregulate CD38 expression, but the mechanism and adaptive genetic background remain unexplored. METHODS: The efficacy of ATRA in upregulating CD38 expression in MM cells is evaluated by flow cytometry. The interaction between NSD2 and the RARα is analyzed by immunoprecipitation, and the nuclear condensation of RARα is evaluated under laser confocal microscope. A graft model of MM is established in NOD.Cg-PrkdcscidIl2rgtm1Wjl /SzJ mice, and the tumor burden is assessed by in vivo fluorescence imaging. RESULTS: We report that ATRA upregulates MM cells CD38 in a non-linear manner, which is t(4;14) translocation dependent, and t(4;14) translocation-induced NSD2 shows positive correlation with ATRA-induced level of, but not with basal level of CD38 expression. Mechanistically, NSD2 interacts with the ATRA receptor, RARα, and protects it from degradation. Meanwhile, NSD2 enhances the nuclear condensation of RARα and modifies the histone H3 dimethylation at lysine 36 on CD38 promoter. Knockdown of NSD2 attenuates the sensitization of MM against ATRA induced CD38 upregulation. Translationally, ATRA is prone to augment the efficacy of anti-CD38 CAR T cells in NSD2high MM cells in vitro and in vivo. CONCLUSION: This study elucidates a mechanism of ATRA in regulating CD38 expression and expands the clinical potential of ATRA in improving immunotherapies against CD38 in patients with MM.Cite Now.

Individualized Biliary Reconstruction Techniques in Liver Transplantation: Five Years' Experience of a Single Institution.

BACKGROUND: To summarize the experience of individualized biliary reconstruction techniques in deceased donor liver transplantation and explore potential risk factors for biliary stricture. METHODS: We retrospectively collected medical records of 489 patients undergoing deceased donor liver transplantation at our center between January 2016 and August 2020. According to anatomical and pathological conditions of donor and recipient biliary ducts, patients' biliary reconstruction methods were divided into six types. We summarized the experience of six different reconstruction methods and analyzed the biliary complications' rate and risk factors after liver transplantation. RESULTS: Among 489 cases of biliary reconstruction methods during liver transplantation, there were 206 cases of type I, 98 cases of type II, 96 cases of type III, 39 cases of type IV, 34 cases of type V, and 16 cases of type VI. Biliary tract anastomotic complications occurred in 41 cases (8.4%), including 35 cases with biliary stricture (7.2%), 9 cases with biliary leakage (1.8%), 19 cases with biliary stones (3.9%), 1 case with biliary bleeding (0.2%), and 2 cases with biliary infection (0.4%). One of 41 patients died of biliary tract bleeding and one died of biliary infection. Thirty-six patients significantly improved after treatment, and 3 patients received secondary transplantation. Compared with patients without biliary stricture, a higher warm ischemic time was observed in patients with non-anastomotic stricture and more leakage of bile in patients with an anastomotic stricture. CONCLUSION: The individualized biliary reconstruction methods are safe and feasible to decrease perioperative anastomotic biliary complications. Biliary leakage may contribute to anastomotic biliary stricture and cold ischemia time to non-anastomotic biliary stricture.

Design, Preparation and Properties of Polyurethane Dispersions via Prepolymer Method.

A waterborne polyurethane dispersion for foamed synthetic leather base was designed and prepared using prepolymer method. There are many variables in the emulsification and chain-extension process of waterborne polyurethane (WPUR) dispersions prepared by prepolymer method. This work thoroughly evaluated the impacts of the steps of adding emulsified water, the temperature of the prepolymer and emulsified water, and concentration of ammonia water on WPUR dispersions by investigating the particle sizes/distributions and the mechanical stability. Changes in the temperature of the prepolymer and emulsified water, the concentration of ammonia water, and the step of adding emulsified water showed great impacts on the appearance and particle size of dispersions. Decreasing the temperature of the prepolymer and emulsified water and increasing the dilution ration of H2O to ethylenediamine (EDA) led to safe emulsification and dispersions with good appearance and narrow particle size distributions can be prepared. Surprising results were obtained by adding emulsified water in two steps, WPUR dispersions with a small particle size, narrow particle distribution and excellent tensile properties can be obtained. The optimized WPUR1 was applied to prepare water-based synthetic leather base after mechanical foaming, and the base presented the desired high performance, such as high folding resistance and peel strength.

Comparison of State-of-the-Art Neural Network Survival Models with the Pooled Cohort Equations for Cardiovascular Disease Risk Prediction.

BACKGROUND: The Pooled Cohort Equations (PCEs) are race- and sex-specific Cox proportional hazards (PH)-based models used for 10-year atherosclerotic cardiovascular disease (ASCVD) risk prediction with acceptable discrimination. In recent years, neural network models have gained increasing popularity with their success in image recognition and text classification. Various survival neural network models have been proposed by combining survival analysis and neural network architecture to take advantage of the strengths from both. However, the performance of these survival neural network models compared to each other and to PCEs in ASCVD prediction is unknown. METHODS: In this study, we used 6 cohorts from the Lifetime Risk Pooling Project (with 5 cohorts as training/internal validation and one cohort as external validation) and compared the performance of the PCEs in 10-year ASCVD risk prediction with an all two-way interactions Cox PH model (Cox PH-TWI) and three state-of-the-art neural network survival models including Nnet-survival, Deepsurv, and Cox-nnet. For all the models, we used the same 7 covariates as used in the PCEs. We fitted each of the aforementioned models in white females, white males, black females, and black males, respectively. We evaluated models' internal and external discrimination power and calibration. RESULTS: The training/internal validation sample comprised 23216 individuals. The average age at baseline was 57.8 years old (SD = 9.6); 16% developed ASCVD during average follow-up of 10.50 (SD = 3.02) years. Based on 10 × 10 cross-validation, the method that had the highest C-statistics was Deepsurv (0.7371) for white males, Deepsurv and Cox PH-TWI (0.7972) for white females, PCE (0.6981) for black males, and Deepsurv (0.7886) for black females. In the external validation dataset, Deepsurv (0.7032), Cox-nnet (0.7282), PCE (0.6811), and Deepsurv (0.7316) had the highest C-statistics for white male, white female, black male, and black female population, respectively. Calibration plots showed that in 10 × 10 validation, all models had good calibration in all race and sex groups. In external validation, all models overestimated the risk for 10-year ASCVD. CONCLUSIONS: We demonstrated the use of the state-of-the-art neural network survival models in ASCVD risk prediction. Neural network survival models had similar if not superior discrimination and calibration compared to PCEs.

Truncation mutations in MYRF underlie primary angle closure glaucoma.

Mutations in myelin regulatory factor (MYRF), a gene mapped to 11q12-q13.3, are responsible for autosomal dominant high hyperopia and seem to be associated with angle closure glaucoma, which is one of the leading causes of irreversible blindness worldwide. Whether there is a causal link from the MYRF mutations to the pathogenesis of primary angle-closure glaucoma (PACG) remains unclear at this time. Six truncation mutations, including five novel and one previously reported, in MYRF are identified in seven new probands with hyperopia, of whom all six adults have glaucoma, further confirming the association of MYRF mutations with PACG. Immunofluorescence microscopy demonstrates enriched expression of MYRF in the ciliary body and ganglion cell layer in humans and mice. Myrfmut/+ mice have elevated IOP and fewer ganglion cells along with thinner retinal nerve fiber layer with ganglion cell layer than wild-type. Transcriptome sequencing of Myrfmut/+ retinas shows downregulation of Dnmt3a, a gene previously associated with PACG. Co-immunoprecipitation demonstrates a physical association of DNMT3A with MYRF. DNA methylation sequencing identifies several glaucoma-related cell events in Myrfmut/+ retinas. The interaction between MYRF and DNMT3A underlies MYRF-associated PACG and provides clues for pursuing further investigation into the pathogenesis of PACG and therapeutic target.

Prognostic value of lactate dehydrogenase in non-small cell lung cancer patients with brain metastases: a retrospective cohort study.

Background: At present, although there are some known molecular markers for the prognosis of non-small cell lung cancer (NSCLC) brain metastases, but there are still shortcomings in sensitivity and specificity. Lactate dehydrogenase (LDH) is one of the key enzymes involved in malignancy vital glycolytic pathway. Elevated serum LDH levels are reported significantly associated with a poor prognosis in various malignancies. However, there is currently no consensus regarding the prognostic value of LDH in NSCLC patients with brain metastases. Methods: We retrospectively analyzed 224 patients diagnosed with lung cancer brain metastases between January 2006 and June 2020 after excluding patients meeting combined with other malignancies and inaccurate clinical information. The LDH cutoff values were obtained using a restricted cubic spline (RCS) model, and the patients were divided into two groups according to the optimal cut-off value (180 U/L). 107 patients with LDH ≤180 (47.77%) and 117 patients with LDH >180 (52.23%) were identified. Univariate and multivariate logistic regression analyses were performed to identify the risk factors. The overall survival (OS) time was defined as the time from the first diagnosis of brain metastases to the last follow-up or death. Of the included patients, 147 survived and 77 died. The Kaplan-Meier method was used to illustrate the OS difference between the two groups. Finally, sensitivity analysis was employed to evaluate the robustness of the results. Results: The OS rate was significantly lower in the high LDH group versus the low LDH group (P=0.009). The median survival times of the high and low LDH groups were approximately 16 and 33 months, respectively. Multivariate analysis showed that high LDH was associated with a significantly worse OS [adjusted hazard ratio (aHR), 1.567; 95% confidence interval (CI): 1.058 to 2.32, P=0.025] with adjustment for covariables that P<0.05 in univariate analysis. Sensitivity analysis indicated that the results of this study are robust, despite potential unmeasured confounders. Conclusions: High level of serum LDH indicates poor prognosis for patients with NSCLC brain metastases. This finding may provide useful prognostic information for patients and clinicians to choose more aggressive treatment strategies.