IBPGNET: lung adenocarcinoma recurrence prediction based on neural network interpretability.

Lung adenocarcinoma (LUAD) is the most common histologic subtype of lung cancer. Early-stage patients have a 30-50% probability of metastatic recurrence after surgical treatment. Here, we propose a new computational framework, Interpretable Biological Pathway Graph Neural Networks (IBPGNET), based on pathway hierarchy relationships to predict LUAD recurrence and explore the internal regulatory mechanisms of LUAD. IBPGNET can integrate different omics data efficiently and provide global interpretability. In addition, our experimental results show that IBPGNET outperforms other classification methods in 5-fold cross-validation. IBPGNET identified PSMC1 and PSMD11 as genes associated with LUAD recurrence, and their expression levels were significantly higher in LUAD cells than in normal cells. The knockdown of PSMC1 and PSMD11 in LUAD cells increased their sensitivity to afatinib and decreased cell migration, invasion and proliferation. In addition, the cells showed significantly lower EGFR expression, indicating that PSMC1 and PSMD11 may mediate therapeutic sensitivity through EGFR expression.

DeepKEGG: a multi-omics data integration framework with biological insights for cancer recurrence prediction and biomarker discovery.

Deep learning-based multi-omics data integration methods have the capability to reveal the mechanisms of cancer development, discover cancer biomarkers and identify pathogenic targets. However, current methods ignore the potential correlations between samples in integrating multi-omics data. In addition, providing accurate biological explanations still poses significant challenges due to the complexity of deep learning models. Therefore, there is an urgent need for a deep learning-based multi-omics integration method to explore the potential correlations between samples and provide model interpretability. Herein, we propose a novel interpretable multi-omics data integration method (DeepKEGG) for cancer recurrence prediction and biomarker discovery. In DeepKEGG, a biological hierarchical module is designed for local connections of neuron nodes and model interpretability based on the biological relationship between genes/miRNAs and pathways. In addition, a pathway self-attention module is constructed to explore the correlation between different samples and generate the potential pathway feature representation for enhancing the prediction performance of the model. Lastly, an attribution-based feature importance calculation method is utilized to discover biomarkers related to cancer recurrence and provide a biological interpretation of the model. Experimental results demonstrate that DeepKEGG outperforms other state-of-the-art methods in 5-fold cross validation. Furthermore, case studies also indicate that DeepKEGG serves as an effective tool for biomarker discovery. The code is available at https://github.com/lanbiolab/DeepKEGG.

MHCLMDA: multihypergraph contrastive learning for miRNA-disease association prediction.

The correct prediction of disease-associated miRNAs plays an essential role in disease prevention and treatment. Current computational methods to predict disease-associated miRNAs construct different miRNA views and disease views based on various miRNA properties and disease properties and then integrate the multiviews to predict the relationship between miRNAs and diseases. However, most existing methods ignore the information interaction among the views and the consistency of miRNA features (disease features) across multiple views. This study proposes a computational method based on multiple hypergraph contrastive learning (MHCLMDA) to predict miRNA-disease associations. MHCLMDA first constructs multiple miRNA hypergraphs and disease hypergraphs based on various miRNA similarities and disease similarities and performs hypergraph convolution on each hypergraph to capture higher order interactions between nodes, followed by hypergraph contrastive learning to learn the consistent miRNA feature representation and disease feature representation under different views. Then, a variational auto-encoder is employed to extract the miRNA and disease features in known miRNA-disease association relationships. Finally, MHCLMDA fuses the miRNA and disease features from different views to predict miRNA-disease associations. The parameters of the model are optimized in an end-to-end way. We applied MHCLMDA to the prediction of human miRNA-disease association. The experimental results show that our method performs better than several other state-of-the-art methods in terms of the area under the receiver operating characteristic curve and the area under the precision-recall curve.

Inferring disease-associated circRNAs by multi-source aggregation based on heterogeneous graph neural network.

Emerging evidence has proved that circular RNAs (circRNAs) are implicated in pathogenic processes. They are regarded as promising biomarkers for diagnosis due to covalently closed loop structures. As opposed to traditional experiments, computational approaches can identify circRNA-disease associations at a lower cost. Aggregating multi-source pathogenesis data helps to alleviate data sparsity and infer potential associations at the system level. The majority of computational approaches construct a homologous network using multi-source data, but they lose the heterogeneity of the data. Effective methods that use the features of multi-source data are considered as a matter of urgency. In this paper, we propose a model (CDHGNN) based on edge-weighted graph attention and heterogeneous graph neural networks for potential circRNA-disease association prediction. The circRNA network, micro RNA network, disease network and heterogeneous network are constructed based on multi-source data. To reflect association probabilities between nodes, an edge-weighted graph attention network model is designed for node features. To assign attention weights to different types of edges and learn contextual meta-path, CDHGNN infers potential circRNA-disease association based on heterogeneous neural networks. CDHGNN outperforms state-of-the-art algorithms in terms of accuracy. Edge-weighted graph attention networks and heterogeneous graph networks have both improved performance significantly. Furthermore, case studies suggest that CDHGNN is capable of identifying specific molecular associations and investigating biomolecular regulatory relationships in pathogenesis. The code of CDHGNN is freely available at https://github.com/BioinformaticsCSU/CDHGNN.

Benchmarking of computational methods for predicting circRNA-disease associations.

Accumulating evidences demonstrate that circular RNA (circRNA) plays an important role in human diseases. Identification of circRNA-disease associations can help for the diagnosis of human diseases, while the traditional method based on biological experiments is time-consuming. In order to address the limitation, a series of computational methods have been proposed in recent years. However, few works have summarized these methods or compared the performance of them. In this paper, we divided the existing methods into three categories: information propagation, traditional machine learning and deep learning. Then, the baseline methods in each category are introduced in detail. Further, 5 different datasets are collected, and 14 representative methods of each category are selected and compared in the 5-fold, 10-fold cross-validation and the de novo experiment. In order to further evaluate the effectiveness of these methods, six common cancers are selected to compare the number of correctly identified circRNA-disease associations in the top-10, top-20, top-50, top-100 and top-200. In addition, according to the results, the observation about the robustness and the character of these methods are concluded. Finally, the future directions and challenges are discussed.

JLONMFSC: Clustering scRNA-seq data based on joint learning of non-negative matrix factorization and subspace clustering.

The development of single cell RNA sequencing (scRNA-seq) has provided new perspectives to study biological problems at the single cell level. One of the key issues in scRNA-seq data analysis is to divide cells into several clusters for discovering the heterogeneity and diversity of cells. However, the existing scRNA-seq data are high-dimensional, sparse, and noisy, which challenges the existing single-cell clustering methods. In this study, we propose a joint learning framework (JLONMFSC) for clustering scRNA-seq data. In our method, the dimension of the original data is reduced to minimize the effect of noise. In addition, the graph regularized matrix factorization is used to learn the local features. Further, the Low-Rank Representation (LRR) subspace clustering is utilized to learn the global features. Finally, the joint learning of local features and global features is performed to obtain the results of clustering. We compare the proposed algorithm with eight state-of-the-art algorithms for clustering performance on six datasets, and the experimental results demonstrate that the JLONMFSC achieves better performance in all datasets. The code is avalable at https://github.com/lanbiolab/JLONMFSC.

Artificial neuromorphic cognitive skins based on distributed biaxially stretchable elastomeric synaptic transistors.

Cephalopod (e.g., squid, octopus, etc.) skin is a soft cognitive organ capable of elastic deformation, visualizing, stealth, and camouflaging through complex biological processes of sensing, recognition, neurologic processing, and actuation in a noncentralized, distributed manner. However, none of the existing artificial skin devices have shown distributed neuromorphic processing and cognition capabilities similar to those of a cephalopod skin. Thus, the creation of an elastic, biaxially stretchy device with embedded, distributed neurologic and cognitive functions mimicking a cephalopod skin can play a pivotal role in emerging robotics, wearables, skin prosthetics, bioelectronics, etc. This paper introduces artificial neuromorphic cognitive skins based on arrayed, biaxially stretchable synaptic transistors constructed entirely out of elastomeric materials. Systematic investigation of the synaptic characteristics such as the excitatory postsynaptic current, paired-pulse facilitation index of the biaxially stretchable synaptic transistor under various levels of biaxial mechanical strain sets the operational foundation for stretchy distributed synapse arrays and neuromorphic cognitive skin devices. The biaxially stretchy arrays here achieved neuromorphic cognitive functions, including image memorization, long-term memorization, fault tolerance, programming, and erasing functions under 30% biaxial mechanical strain. The stretchy neuromorphic imaging sensory skin devices showed stable neuromorphic pattern reinforcement performance under both biaxial and nonuniform local deformation.

NGCN: Drug-target interaction prediction by integrating information and feature learning from heterogeneous network.

Drug-target interaction (DTI) prediction is essential for new drug design and development. Constructing heterogeneous network based on diverse information about drugs, proteins and diseases provides new opportunities for DTI prediction. However, the inherent complexity, high dimensionality and noise of such a network prevent us from taking full advantage of these network characteristics. This article proposes a novel method, NGCN, to predict drug-target interactions from an integrated heterogeneous network, from which to extract relevant biological properties and association information while maintaining the topology information. It focuses on learning the topology representation of drugs and targets to improve the performance of DTI prediction. Unlike traditional methods, it focuses on learning the low-dimensional topology representation of drugs and targets via graph-based convolutional neural network. NGCN achieves substantial performance improvements over other state-of-the-art methods, such as a nearly 1.0% increase in AUPR value. Moreover, we verify the robustness of NGCN through benchmark tests, and the experimental results demonstrate it is an extensible framework capable of combining heterogeneous information for DTI prediction.

Location of Phosphorylation Sites within Long Polypeptide Chains by Binder-Assisted Nanopore Detection.

The detection and mapping of protein phosphorylation sites are essential for understanding the mechanisms of various cellular processes and for identifying targets for drug development. The study of biopolymers at the single-molecule level has been revolutionized by nanopore technology. In this study, we detect protein phosphorylation within long polypeptides (>700 amino acids), after the attachment of binders that interact with phosphate monoesters; electro-osmosis is used to drive the tagged chains through engineered protein nanopores. By monitoring the ionic current carried by a nanopore, phosphorylation sites are located within individual polypeptide chains, providing a valuable step toward nanopore proteomics.

Interlayer and Phase Engineering Modifications of K-MoS2@C Nanoflowers for High-Performance Degradable Zn-Ion Batteries.

2D transition metal dichalcogenides (TMDs) have garnered significant interest as cathode materials for aqueous zinc-ion batteries (AZIBs) due to their open transport channels and abundant Zn2+ intercalation sites. However, unmodified TMDs exhibit low electrochemical activity and poor kinetics owing to the high binding energy and large hydration radius of divalent Zn2+. To overcome these limitations, an interlayer engineering strategy is proposed where K+ is preintercalated into K-MoS2 nanosheets, which then undergo in situ growth on carbon nanospheres (denoted as K-MoS2@C nanoflowers). This strategy stimulates in-plane redox-active sites, expands the interlayer spacing (from 6.16 to 9.42 Å), and induces the formation of abundant MoS2 1T-phase. The K-MoS2@C cathode demonstrates excellent redox activity and fast kinetics, attributed to the potassium ions acting as a structural "stabilizer" and an electrostatic interaction "shield," accelerating charge transfer, promoting Zn2+ diffusion, and ensuring structural stability. Meanwhile, the carbon nanospheres serve as a 3D conductive network for Zn2+ and enhance the cathode's hydrophilicity. More significantly, the outstanding electrochemical performance of K-MoS2@C, along with its superior biocompatibility and degradability of its related components, can enable an implantable energy supply, providing novel opportunities for the application of transient electronics.

High-Performance Monolithic 3D Integrated Complementary Inverters Based on Monolayer n-MoS2 and p-WSe2.

Herein, the structure of integrated M3D inverters are successfully demonstrated where a chemical vapor deposition (CVD) synthesized monolayer WSe2 p-type nanosheet FET is vertically integrated on top of CVD synthesized monolayer MoS2 n-type film FET arrays (2.5 × 2.5 cm) by semiconductor industry techniques, such as transfer, e-beam evaporation (EBV), and plasma etching processes. A low temperature (below 250 °C) is employed to protect the WSe2 and MoS2 channel materials from thermal decomposition during the whole fabrication process. The MoS2 NMOS and WSe2 PMOS device fabricated show an on/off current ratio exceeding 106 and the integrated M3D inverters indicate an average voltage gain of ≈9 at VDD = 2 V. In addition, the integrated M3D inverter demonstrates an ultra-low power consumption of 0.112 nW at a VDD of 1 V. Statistical analysis of the fabricated inverters devices shows their high reliability, rendering them suitable for large-area applications. The successful demonstration of M3D inverters based on large-scale 2D monolayer TMDs indicate their high potential for advancing the application of 2D TMDs in future integrated circuits.

Epidemiology of carbapenem-resistant Klebsiella pneumoniae in China and the evolving trends of predominant clone ST11: a multicentre, genome-based study.

OBJECTIVES: Carbapenem-resistant Klebsiella pneumoniae (CRKP) is a major nosocomial infectious pathogen with rapidly increasing prevalence. The genomic epidemiological characteristics of CRKP nationwide, especially the evolving trends within the predominant clones, should be evaluated clearly. METHODS: We collected 3415 K. pneumoniae strains from 28 hospitals across China. Antimicrobial susceptibility testing and WGS were performed. Subsequent genomic analyses, including sequence typing, K-locus (KL) identification, antimicrobial resistance gene screening, and virulence score assessment were performed. The phylogenetic relationship of clonal group 11 was determined based on core-genome analysis, and the presence of the pLVPK-like virulence plasmid in ST11 isolates was confirmed using plasmid core-gene analysis. Additionally, the trends of the ST11 lineage with different KL types on a global scale were investigated using Beast2. RESULTS: Of the K. pneumoniae strains, 708 were identified as CRKP isolates (20.7%), of which 97.7% were MDR. ST11 was the predominant clone, and KPC-2 was the prevalent carbapenemase in China, although the prevalence of specific clones and carbapenemases varied by geographic region. Among ST11 isolates, KL47 and KL64 were the predominant KL types, and KL64 gradually replaced KL47, with a higher percentage of KL64 isolates harbouring the pLVPK-like plasmid. Global genome data showed a significant increase in the effective population size of KL64 over the last 5 years. CONCLUSIONS: The prevalence of CRKP was very high in certain regions in China. The increasing convergence of virulence and resistance, particularly in ST11-KL64 isolates, should be given more attention and further investigation.

KGANCDA: predicting circRNA-disease associations based on knowledge graph attention network.

Increasing evidences have proved that circRNA plays a significant role in the development of many diseases. In addition, many researches have shown that circRNA can be considered as the potential biomarker for clinical diagnosis and treatment of disease. Some computational methods have been proposed to predict circRNA-disease associations. However, the performance of these methods is limited as the sparsity of low-order interaction information. In this paper, we propose a new computational method (KGANCDA) to predict circRNA-disease associations based on knowledge graph attention network. The circRNA-disease knowledge graphs are constructed by collecting multiple relationship data among circRNA, disease, miRNA and lncRNA. Then, the knowledge graph attention network is designed to obtain embeddings of each entity by distinguishing the importance of information from neighbors. Besides the low-order neighbor information, it can also capture high-order neighbor information from multisource associations, which alleviates the problem of data sparsity. Finally, the multilayer perceptron is applied to predict the affinity score of circRNA-disease associations based on the embeddings of circRNA and disease. The experiment results show that KGANCDA outperforms than other state-of-the-art methods in 5-fold cross validation. Furthermore, the case study demonstrates that KGANCDA is an effective tool to predict potential circRNA-disease associations.

Distinct characteristics of the gut virome in patients with osteoarthritis and gouty arthritis.

BACKGROUND/PURPOSE(S): The gut microbiota and its metabolites play crucial roles in pathogenesis of arthritis, highlighting gut microbiota as a promising avenue for modulating autoimmunity. However, the characterization of the gut virome in arthritis patients, including osteoarthritis (OA) and gouty arthritis (GA), requires further investigation. METHODS: We employed virus-like particle (VLP)-based metagenomic sequencing to analyze gut viral community in 20 OA patients, 26 GA patients, and 31 healthy controls, encompassing a total of 77 fecal samples. RESULTS: Our analysis generated 6819 vOTUs, with a considerable proportion of viral genomes differing from existing catalogs. The gut virome in OA and GA patients differed significantly from healthy controls, showing variations in diversity and viral family abundances. We identified 157 OA-associated and 94 GA-associated vOTUs, achieving high accuracy in patient-control discrimination with random forest models. OA-associated viruses were predicted to infect pro-inflammatory bacteria or bacteria associated with immunoglobulin A production, while GA-associated viruses were linked to Bacteroidaceae or Lachnospiraceae phages. Furthermore, several viral functional orthologs displayed significant differences in frequency between OA-enriched and GA-enriched vOTUs, suggesting potential functional roles of these viruses. Additionally, we trained classification models based on gut viral signatures to effectively discriminate OA or GA patients from healthy controls, yielding AUC values up to 0.97, indicating the clinical utility of the gut virome in diagnosing OA or GA. CONCLUSION: Our study highlights distinctive alterations in viral diversity and taxonomy within gut virome of OA and GA patients, offering insights into arthritis etiology and potential treatment and prevention strategies.

Right ventricular-pulmonary arterial coupling ratio derived from 3-dimensional echocardiography predicts outcomes in systemic lupus erythematosus-associated pulmonary arterial hypertension patients.

BACKGROUND: Systemic lupus erythematosus (SLE) is a complex autoimmune connective tissue disease (CTD) that is an important cause of devastating pulmonary arterial hypertension (PAH), and persistent progression of PAH can lead to right heart failure, predicting a poor prognosis for SLE patients. Right ventricular-pulmonary arterial (RV-PA) coupling with echocardiography has been demonstrated to be a noninvasive alternative method for evaluating PAH patients' predictive outcomes. Whether the ratio of right ventricular stroke volume (RVSV) to right ventricular end-systolic volume (RVESV) measured by three-dimensional echocardiography (3DE) is a new index of RV-PA coupling has not been discussed as a new predictor for the clinical outcome of systemic lupus erythematosus-associated pulmonary arterial hypertension (SLE-PAH). METHODS: From June 2019 to February 2023, 46 consecutive patients with SLE-PAH were enrolled prospectively, and their clinical data and echocardiographs were studied and analyzed. The control group consisted of 30 healthy subjects matched for age, sex, and body surface area (BSA). The main endpoints of this study were a composite of all-cause mortality and adverse clinical events. Baseline clinical characteristics and echocardiographic assessments were analyzed. RESULTS: During a median of 24 months (IQR 18-31), 16 of 46 SLE-PAH patients (34.7%) experienced endpoint-related events. At baseline, patients who experienced mortality or adverse events had a worse WHO functional class (WHO FC) and lower anti-double-stranded DNA (dsDNA) antibody levels. The right ventricular (RV) systolic dysfunction in SLE-PAH subjects was significantly worse than that in the healthy control group, especially in SLE-PAH patients in the endpoint event group. Compared to controls, patients with SLE-PAH had a lower RVSV/RVESV ratio. In the group comparison, patients who had experienced an endpoint event had a sequentially worse ratio (1.86 (1.65-2.3) versus 1.30 (1.09-1.46) versus 0.64 (0.59-0.67), p < .001). There were statistically significant associations between the RVSV/RVESV ratio to routine RV systolic function and clinical parameters. The RVSV/RVESV ratio was negatively correlated with the WHO FC (r = -0.621, p < .001) and positively correlated with the anti-dsDNA level. The ROC curve showed that the optimal cutoff for RVSV/RVESV < 0.712 determined a higher risk of poor prognosis. Kaplan‒Meier survival curves showed that an RVSV/RVESV ratio >0.712 was associated with more favorable long-term outcomes. CONCLUSIONS: The 3DE-derived SV/ESV ratio as a noninvasive alternative surrogate of RV-PA coupling was an eximious indicator for identifying endpoint events in SLE-PAH patients and can provide a diagnostic basis for clinical intervention.

A rare case of anti-DPPX encephalitis combined with neuroleptospirosis.

BACKGROUND: Neuroleptospirosis and anti-dipeptidyl-peptidase-like protein 6 (DPPX) encephalitis are both very rare and have only been reported in the form of respective case reports. There are no reports of anti-DPPX encephalitis combined with neuroleptospirosis in the literature. We reported the first case of neuroleptospirosis combined with elevated DPPX antibodies in serum and cerebrospinal fluid (CSF). CASE PRESENTATION: A previously healthy 53-year-old Chinese male farmer with a history of drinking raw stream water and flood sewage exposure was brought to the hospital due to an acute onset of neuropsychiatric symptoms. No fever or meningeal irritation signs were detected on physical examination. Routine laboratory investigations, including infection indicators, leukocyte and protein in CSF, electroencephalogram and gadolinium-enhanced magnetic resonance imaging of the brain, all revealed normal. While metagenomic next-generation sequencing (mNGS) identified the DNA genome of Leptospira interrogans in the CSF. Anti-DPPX antibody was detected both in blood and in CSF. A diagnosis of neuroleptospirosis combined with autoimmune encephalitis associated with DPPX-Ab was eventually made. He resolved completely after adequate amount of penicillin combined with immunotherapy. CONCLUSION: We highlight that in patients with acute or subacute behavioral changes, even in the absence of fever, if the most recent freshwater exposure is clear, physicians should pay attention to leptospirosis. Due to the low sensitivity of routine microscopy, culture, polymerase chain reaction and antibody testing, mNGS may have more advantages in diagnosing neuroleptospirosis. As autoimmune encephalitis can be triggered by various infections, neuroleptospirosis may be one of the causes of autoimmune encephalitis. Since neuronal antibody measurements themselves are not that common in neuroleptospirosis, future studies are needed to determine whether the detection of anti-DPPX antibodies is a rare event in leptospirosis. Early identification of autoimmune encephalitis and timely administration of immunotherapy may lead to a better outcome.

ß-catenin mediates monocrotaline-induced pulmonary hypertension via glycolysis in rats.

BACKGROUND: Metabolic abnormalities and immune inflammation are deeply involved in pulmonary vascular remodelling and the development of pulmonary hypertension (PH). However, the regulatory mechanisms of glycolysis in macrophages are still elusive. Cumulative evidence indicates that ß-catenin plays a crucial role in metabolic reprogramming. This study aimed to investigate the effect of ß-catenin on macrophage glycolysis in PH. METHODS: LPS-induced BMDMs were generated via in vitro experiments. A monocrotaline (MCT)-induced PH rat model was established, and the ß-catenin inhibitor XAV939 was administered in vivo. The role of ß-catenin in glycolysis was analysed. The degree of pulmonary vascular remodelling was measured. RESULTS: ß-catenin was significantly increased in both in vitro and in vivo models. In LPS-induced BMDMs, ß-catenin increased the levels of hexokinase 2 (HK2), phosphofructokinase (PFK), M2-pyruvate kinase (PKM2), lactate dehydrogenase (LDH), and lactate (LA) and the expression of inflammatory cytokines and promoted PASMC proliferation and migration in vitro. XAV939 decreased the level of glycolysis and downregulated the expression of inflammatory cytokines in vivo. MCT promoted pulmonary arterial structural remodelling and right ventricular hypertrophy, and XAV939 alleviated these changes. CONCLUSIONS: Our findings suggest that ß-catenin is involved in the development of PH by promoting glycolysis and the inflammatory response in macrophages. Inhibition of ß-catenin could improve the progression of PH.

Desmoplastic fibroma in a child: a 9-year follow-up case report.

BACKGROUND: Desmoplastic fibroma is an extremely rare primary bone tumor. Its characteristic features include bone destruction accompanied by the formation of soft tissue masses. This condition predominantly affects individuals under the age of 30. Since its histology is similar to desmoid-type fibromatosis, an accurate diagnosis before operation is difficult. Desmoplastic fibroma is resistant to chemotherapy, and the efficacy of radiotherapy is uncertain. Surgical excision is preferred for treatment, but it entails high recurrence. Further, skeletal reconstruction post-surgery is challenging, especially in pediatric cases. CASE PRESENTATION: Nine years ago, a 14-year-old male patient presented with a 4-year history of progressive pain in his left wrist. Initially diagnosed as fibrous dysplasia by needle biopsy, the patient underwent tumor resection followed by free vascularized fibular proximal epiphyseal transfer for wrist reconstruction. However, a histological examination confirmed a diagnosis of desmoplastic fibroma. The patient achieved bone union and experienced a recurrence in the ipsilateral ulna 5 years later, accompanied by a wrist deformity. He underwent a second tumor resection and wrist arthrodesis in a single stage. The most recent annual follow-up was in September 2023; the patient had no recurrence and was satisfied with the surgery. CONCLUSIONS: Desmoplastic fibroma is difficult to diagnose and treat, and reconstruction surgery after tumor resection is challenging. Close follow-up by experienced surgeons may be beneficial for prognosis.

Trichoderma reesei Rad51 tolerates mismatches in hybrid meiosis with diverse genome sequences.

Most eukaryotes possess two RecA-like recombinases (ubiquitous Rad51 and meiosis-specific Dmc1) to promote interhomolog recombination during meiosis. However, some eukaryotes have lost Dmc1. Given that mammalian and yeast Saccharomyces cerevisiae (Sc) Dmc1 have been shown to stabilize recombination intermediates containing mismatches better than Rad51, we used the Pezizomycotina filamentous fungus Trichoderma reesei to address if and how Rad51-only eukaryotes conduct interhomolog recombination in zygotes with high sequence heterogeneity. We applied multidisciplinary approaches (next- and third-generation sequencing technology, genetics, cytology, bioinformatics, biochemistry, and single-molecule biophysics) to show that T. reesei Rad51 (TrRad51) is indispensable for interhomolog recombination during meiosis and, like ScDmc1, TrRad51 possesses better mismatch tolerance than ScRad51 during homologous recombination. Our results also indicate that the ancestral TrRad51 evolved to acquire ScDmc1-like properties by creating multiple structural variations, including via amino acid residues in the L1 and L2 DNA-binding loops.

Artificial Intelligence-Assisted Prescription Determination for Orthokeratology Lens Fitting: From Algorithm to Clinical Practice.

OBJECTIVES: To explore the potential of artificial intelligence (AI) to assist prescription determination for orthokeratology (OK) lenses. METHODS: Artificial intelligence algorithm development followed by a real-world trial. A total of 11,502 OK lenses fitting records collected from seven clinical environments covering major brands. Records were randomly divided in a three-way data split. Cross-validation was used to identify the most accurate algorithm, followed by an evaluation using an independent test data set. An online AI-assisted system was implemented and assessed in a real-world trial involving four junior and three senior clinicians. RESULTS: The primary outcome measure was the algorithm's accuracy (ACC). The ACC of the best performance of algorithms to predict the targeted reduction amplitude, lens diameter, and alignment curve of the prescription was 0.80, 0.82, and 0.83, respectively. With the assistance of the AI system, the number of trials required to determine the final prescription significantly decreased for six of the seven participating clinicians (all P <0.01). This reduction was more significant among junior clinicians compared with consultants (0.76±0.60 vs. 0.32±0.60, P <0.001). Junior clinicians achieved clinical outcomes comparable to their seniors, as 93.96% (140/149) and 94.44% (119/126), respectively, of the eyes fitted achieved unaided visual acuity no worse than 0.8 ( P =0.864). CONCLUSIONS: AI can improve prescription efficiency and reduce discrepancies in clinical outcomes among clinicians with differing levels of experience. Embedment of AI in practice should ultimately help lessen the medical burden and improve service quality for myopia boom emerging worldwide.