DeepSS2GO: protein function prediction from secondary structure.

Predicting protein function is crucial for understanding biological life processes, preventing diseases and developing new drug targets. In recent years, methods based on sequence, structure and biological networks for protein function annotation have been extensively researched. Although obtaining a protein in three-dimensional structure through experimental or computational methods enhances the accuracy of function prediction, the sheer volume of proteins sequenced by high-throughput technologies presents a significant challenge. To address this issue, we introduce a deep neural network model DeepSS2GO (Secondary Structure to Gene Ontology). It is a predictor incorporating secondary structure features along with primary sequence and homology information. The algorithm expertly combines the speed of sequence-based information with the accuracy of structure-based features while streamlining the redundant data in primary sequences and bypassing the time-consuming challenges of tertiary structure analysis. The results show that the prediction performance surpasses state-of-the-art algorithms. It has the ability to predict key functions by effectively utilizing secondary structure information, rather than broadly predicting general Gene Ontology terms. Additionally, DeepSS2GO predicts five times faster than advanced algorithms, making it highly applicable to massive sequencing data. The source code and trained models are available at https://github.com/orca233/DeepSS2GO.

CodonBERT: a BERT-based architecture tailored for codon optimization using the cross-attention mechanism.

MOTIVATION: Due to the varying delivery methods of mRNA vaccines, codon optimization plays a critical role in vaccine design to improve the stability and expression of proteins in specific tissues. Considering the many-to-one relationship between synonymous codons and amino acids, the number of mRNA sequences encoding the same amino acid sequence could be enormous. Finding stable and highly expressed mRNA sequences from the vast sequence space using in silico methods can generally be viewed as a path-search problem or a machine translation problem. However, current deep learning-based methods inspired by machine translation may have some limitations, such as recurrent neural networks, which have a weak ability to capture the long-term dependencies of codon preferences. RESULTS: We develop a BERT-based architecture that uses the cross-attention mechanism for codon optimization. In CodonBERT, the codon sequence is randomly masked with each codon serving as a key and a value. In the meantime, the amino acid sequence is used as the query. CodonBERT was trained on high-expression transcripts from Human Protein Atlas mixed with different proportions of high codon adaptation index codon sequences. The result showed that CodonBERT can effectively capture the long-term dependencies between codons and amino acids, suggesting that it can be used as a customized training framework for specific optimization targets. AVAILABILITY AND IMPLEMENTATION: CodonBERT is freely available on https://github.com/FPPGroup/CodonBERT.

Soil properties constrain predicted poleward migration of plants under climate change.

Many plant species are predicted to migrate poleward in response to climate change. Species distribution models (SDMs) have been widely used to quantify future suitable habitats, but they often neglect soil properties, despite the importance of soil for plant fitness. As soil properties often change along latitudinal gradients, higher-latitude soils might be more or less suitable than average conditions within the current ranges of species, thereby accelerating or slowing potential poleward migration. In this study, we built three SDMs - one with only climate predictors, one with only soil predictors, and one with both - for each of 1870 plant species in Eastern North America, in order to investigate the relative importance of soil properties in determining plant distributions and poleward shifts under climate change. While climate variables were the most important predictors, soil properties also had a substantial influence on continental-scale plant distributions. Under future climate scenarios, models including soil predicted much smaller northward shifts in distributions than climate-only models (c. 40% reduction). Our findings strongly suggest that high-latitude soils are likely to impede ongoing plant migration, and they highlight the necessity of incorporating soil properties into models and predictions for plant distributions and migration under environmental change.

Association between genetic variants of transmembrane transporters and susceptibility to anthracycline-induced cardiotoxicity: Current understanding and existing evidence.

Anthracyclines remain the cornerstone of numerous chemotherapeutic protocols, with beneficial effects against haematological malignancies and solid tumours. Unfortunately, the clinical usefulness of anthracyclines is compromised by the development of cardiotoxic side effects, leading to dose limitations or treatment discontinuation. There is no absolute linear correlation between the incidence of cardiotoxicity and the threshold dose, suggesting that genetic factors may modify the association between anthracyclines and cardiotoxicity risk. And the majority of single nucleotide polymorphisms (SNPs) associated with anthracycline pharmacogenomics were identified in the ATP-binding cassette (ABC) and solute carrier (SLC) transporters, generating increasing interest in the pharmacogenetic implications of their genetic variations for anthracycline-induced cardiotoxicity (AIC). This review focuses on the influence of SLC and ABC polymorphisms on AIC and highlights the prospects and clinical significance of pharmacogenetics for individualised preventive approaches.

Nanopore sequencing of forensic short tandem repeats using QNome of Qitan Technology.

Devices of nanopore sequencing can be highly portable and of low cost. Thus, nanopore sequencing is promising in in-field forensic applications. Previous investigations have demonstrated that nanopore sequencing is feasible for genotyping forensic short tandem repeats (STRs) by using sequencers of Oxford Nanopore Technologies. Recently, Qitan Technology launched a new portable nanopore sequencer and became the second supplier in the world. Here, for the first time, we assess the QNome (QNome-3841) for its accuracy in nanopore sequencing of STRs and compare with MinION (MinION Mk1B). We profile 54 STRs of 21 unrelated individuals and 2800M standard DNA. The overall accuracy for diploid STRs and haploid STRs were 53.5% (378 of 706) and 82.7% (134 of 162), respectively, by using QNome. The accuracies were remarkably lower than those of MinION (diploid STRs, 84.5%; haploid, 90.7%), with a similar amount of sequencing data and identical bioinformatics analysis. Although it was not reliable for diploid STRs typing by using QNome, the haploid STRs were consistently correctly typed. The majority of errors (58.8%) in QNome-based STR typing were one-repeat deviations of repeat units in the error from true allele, related with homopolymers in repeats of STRs.

Pilot validation of on-field STR typing and human identity testing by MinION nanopore sequencing.

Nanopore sequencing technology has broad application prospects in forensic medicine due to its small size, portability, fast speed, real-time result analysis capabilities, single-molecule sequencing abilities, and simple operation. Here, we demonstrate for the first time that nanopore sequencing platforms can be used to identify individuals in the field. Through scientific and reasonable design, a nanopore MinION MK1B device and other auxiliary devices are integrated into a portable detection box conducive to individual identification at the accident site. Individual identification of 12 samples could be completed within approximately 24 h by jointly detecting 23 short tandem repeat (STR) loci. Through double-blinded experiments, the genotypes of 49 samples were successfully determined, and the accuracy of the STR genotyping was verified by the gold standard. Specifically, the typing success rate for 1150 genotypes was 95.3%, and the accuracy rate was 86.87%. Although this study focused primarily on demonstrating the feasibility of full-process testing, it can be optimistically predicted that further improvements in bioinformatics workflows and nanopore sequencing technology will help enhance the feasibility of Oxford Nanopore Technologies equipment for real-time individual identification at accident sites.

Associations of CSF BACE1 with amyloid pathology, neurodegeneration, and cognition in Alzheimer's disease.

Β-site amyloid precursor protein (APP) cleaving enzyme (BACE1) is a crucial protease in the production of amyloid-ß (Aß) in Alzheimer's disease (AD) patients. However, the side effects observed in clinical trials of BACE1 inhibitors, including reduction in brain volume and cognitive worsening, suggest that the exact role of BACE1 in AD pathology is not fully understood. To further investigate this, we examined cerebrospinal fluid (CSF) levels of BACE1 and its cleaved product sAPPß that reflects BACE1 activity in the China Aging and Neurodegenerative Disorder Initiative cohort. We found significant correlations between CSF BACE1 or sAPPß levels and CSF Aß40, Aß42, and Aß42/Aß40 ratio, but not with amyloid deposition detected by 18F-Florbetapir PET. Additionally, CSF BACE1 and sAPPß levels were positively associated with cortical thickness in multiple brain regions, and higher levels of sAPPß were linked to increased cortical glucose metabolism in frontal and supramarginal areas. Interestingly, individuals with higher baseline levels of CSF BACE1 exhibited slower rates of brain volume reduction and cognitive worsening over time. This suggests that increased levels and activity of BACE1 may not be the determining factor for amyloid deposition, but instead, may be associated with increased neuronal activity and potentially providing protection against neurodegeneration in AD.

Predictive Value of the Diffusion Magnetic Resonance Imaging Technique for the Postoperative Outcome of Cervical Spondylotic Myelopathy.

BACKGROUND: Diffusion magnetic resonsance imaging (dMRI) can potentially predict the postoperative outcome of cervical spondylotic myelopathy (CSM). PURPOSE: To explore preoperative dMRI parameters to predict the postoperative outcome of CSM through multifactor correlation analysis. STUDY TYPE: Prospective. POPULATION: Post-surgery CSM patients; 102 total, 73 male (52.42 ± 10.60 years old) and 29 female (52.0 ± 11.45 years old). FIELD STRENGTH/SEQUENCE: 3.0 T/Turbo spin echo T1/T2-weighted, T2*-weighted multiecho gradient echo and dMRI. ASSESSMENT: Spinal cord function was evaluated using modified Japanese Orthopedic Association (mJOA) scoring at different time points: preoperative and 3, 6, and 12 months postoperative. Single-factor correlation and t test analyses were conducted based on fractional anisotropy (FA), mean diffusivity, intracellular volume fraction, isotropic volume fraction, orientation division index, increased signal intensity, compression ratio, age, sex, symptom duration and operation method, and multicollinearity was calculated. The linear quantile mixed model (LQMM) and the linear mixed-effects regression model (LMER) were used for multifactor correlation analysis using the combinations of the above variables. STATISTICAL TESTS: Distance correlation, Pearson's correlation, multiscale graph correlation and t tests were used for the single-factor correlation analyses. The variance inflation factor (VIF) was used to calculate multicollinearity. LQMM and LMER were used for multifactor correlation analyses. P < 0.05 was considered statistically significant. RESULTS: The single-factor correlation between all variables and the postoperative mJOA score was weak (all r < 0.3). The linear relationship was stronger than the nonlinear relationship, and there was no significant multicollinearity (VIF = 1.10-1.94). FA values in the LQMM and LMER models had a significant positive correlation with the mJOA score (r = 5.27-6.04), which was stronger than the other variables. DATA CONCLUSION: The FA value based on dMRI significantly positively correlated with CSM patient postoperative outcomes, helping to predict the surgical outcome and formulate a treatment plan before surgery. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.

Prevalence, contemporary trends and associated factors of potentially inappropriate prescription of edoxaban in real-world clinical practice: A subanalysis of the SUNSHINE registry.

AIM: As the direct oral anticoagulant most recently approved in China, data pertaining to clinical edoxaban use are still scarce. This study investigated the prevalence of and contemporary trends in edoxaban prescription among Chinese patients as well as factors associated with its inappropriate use in a multicentre registry of patients treated in real-world clinical practice. METHODS: This real-world, prospective, multicentre and non-interventional study included 1005 inpatients treated with edoxaban. According to National Medical Products Administration and European Heart Rhythm Association guidelines, edoxaban therapy was determined to be appropriate or inappropriate in each case. RESULTS: The median patient age was 70.0 years (interquartile range 61.0-78.0 years) and 46.3% were women. Overall, 456 (45.4%) patients received inappropriate edoxaban therapy, and common issues included an inappropriately low dosage (183, 18.2%) or wrong drug selection (109, 10.8%), high dosage (73, 7.3%), unreasonable off-label use (49, 4.9%), contraindicated medication combinations (27, 2.7%) and incorrect administration timing (16, 1.6%). Several factors, such as age ≥75 years (odds ratio [OR] = 1.921, 95% confidence interval [CI] 1.355-2.723, P < 0.001), weight >60 kg (OR = 2.657, 95%CI 1.970-3.583, P < 0.001), severe renal insufficiency (OR = 1.988, 95% CI 1.043-3.790, P = 0.037), current anaemia (OR = 1.556, 95% CI 1.151-2.102, P = 0.004) and history of bleeding (OR = 2.931, 95% CI 1.605-5.351, P < 0.001) were associated with an increased risk of inappropriate edoxaban therapy, whereas factors associated with cardiovascular specialties, such as admission to a cardiovascular department (OR = 0.637, 95% CI 0.464-0.873, P = 0.005), dronedarone use (OR = 0.065, 95% CI 0.026-0.165, P < 0.001) and amiodarone use (OR = 0.365, 95% CI 0.209-0.637, P < 0.001) decreased this risk. CONCLUSION: In this real-world study, 45.4% of patients received an inappropriate treatment with edoxaban. Multiple clinical characteristics can help identify patients who should receive edoxaban. Further development and implantation of educational activities and management strategies are needed to ensure the correct use of edoxaban.

A Smartphone Application-Based Remote Rehabilitation System for Post-Total Knee Arthroplasty Rehabilitation: A Randomized Controlled Trial.

BACKGROUND: Remote rehabilitation after total knee arthroplasty has gradually gained popularity in recent years. This study aimed to determine whether smartphone application-based remote rehabilitation could outperform home-based rehabilitation and outpatient guidance in terms of 12-week outcomes following primary unilateral total knee arthroplasty. METHODS: Patients who underwent primary unilateral total knee arthroplasty were recruited and randomly divided into a telerehabilitation group and a control group. A total of 100 patients were examined, with 50 each assigned to the telerehabilitation and control groups. In the telerehabilitation group, a telerehabilitation application was installed on the smartphones of the participants to allow postdischarge guidance. The primary outcomes were knee range of motion (ROM) at 12 weeks postoperatively. Secondary outcomes included the Western Ontario and McMaster Universities Osteoarthritis Index, Knee Society Score, The Medical Outcomes Study 36-Item Short-Form Health Survey (SF-36), Five Times Sit-to-Stand Test (5xSST), Single-Leg Stance Test (SLST), satisfaction, rehabilitation costs, complication rate, and 90-day readmission rate. All outcomes were collected at 2, 6, and 12 weeks after surgery. RESULTS: At 12 weeks postoperatively, the telerehabilitation patients significantly outperformed the controls in terms of knee ROM (124 ± 8.7 versus 119 ± 5.5 P = .01), SF-36 (physiological function) (61.5 ± 20.3 versus 45.5 ± 18.1 P = .000), SF-36 (role-physical) (49.3 ± 41.5 versus 27.7 ± 28.9 P = .012), SLST (13.0 ± 9.1 versus 9.1 ± 5.9 P = .026), and 5xSST (17.7 ± 4.3 versus 19.4 ± 3.5 P = .043). No significant differences were found between groups in the Western Ontario and McMaster Universities Osteoarthritis Index score, Knee Society Score, rehabilitation costs, 90-day readmission rate, or incidence of adverse events. CONCLUSION: Our study showed that smartphone app-based remote rehabilitation worked better than home-based rehabilitation with outpatient guidance in terms of short-term results in ROM, SLST, and 5xSST.

Preliminary exploration of deep learning-assisted recognition of superior labrum anterior and posterior lesions in shoulder MR arthrography.

PURPOSE: MR arthrography (MRA) is the most accurate method for preoperatively diagnosing superior labrum anterior-posterior (SLAP) lesions, but diagnostic results can vary considerably due to factors such as experience. In this study, deep learning was used to facilitate the preliminary identification of SLAP lesions and compared with radiologists of different seniority. METHODS: MRA data from 636 patients were retrospectively collected, and all patients were classified as having/not having SLAP lesions according to shoulder arthroscopy. The SLAP-Net model was built and tested on 514 patients (dataset 1) and independently tested on data from two other MRI devices (122 patients, dataset 2). Manual diagnosis was performed by three radiologists with different seniority levels and compared with SLAP-Net outputs. Model performance was evaluated by the receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), etc. McNemar's test was used to compare performance among models and between radiologists' models. The intraclass correlation coefficient (ICC) was used to assess the radiologists' reliability. p < 0.05 was considered statistically significant. RESULTS: SLAP-Net had AUC = 0.98 and accuracy = 0.96 for classification in dataset 1 and AUC = 0.92 and accuracy = 0.85 in dataset 2. In dataset 1, SLAP-Net had diagnostic performance similar to that of senior radiologists (p = 0.055) but higher than that of early- and mid-career radiologists (p = 0.025 and 0.011). In dataset 2, SLAP-Net had similar diagnostic performance to radiologists of all three seniority levels (p = 0.468, 0.289, and 0.495, respectively). CONCLUSIONS: Deep learning can be used to identify SLAP lesions upon initial MR arthrography examination. SLAP-Net performs comparably to senior radiologists.

Macrophage nuclear factor erythroid 2-related factor 2 deficiency promotes innate immune activation by tissue inhibitor of metalloproteinase 3-mediated RhoA/ROCK pathway in the ischemic liver.

BACKGROUND AND AIMS: Nuclear factor erythroid 2-related factor 2 (Nrf2) is a master regulator of reactive oxygen species (ROS) and inflammation and has been implicated in both human and murine inflammatory disease models. We aimed to characterize the roles of macrophage-specific Nrf2 in liver ischemia/reperfusion injury (IRI). APPROACH AND RESULTS: First, macrophage Nrf2 expression and liver injury in patients undergoing OLT or ischemia-related hepatectomy were analyzed. Subsequently, we created a myeloid-specific Nrf2-knockout (Nrf2M-KO ) strain to study the function and mechanism of macrophage Nrf2 in a murine liver IRI model. In human specimens, macrophage Nrf2 expression was significantly increased in liver tissues after transplantation or hepatectomy. Interestingly, lower Nrf2 expressions correlated with more severe liver injury postoperatively. In a mouse model, we found Nrf2M-KO mice showed worse hepatocellular damage than Nrf2-proficient controls based on serum biochemistry, pathology, ROS, and inflammation. In vitro, Nrf2 deficiency promoted innate immune activation and migration in macrophages on toll-like receptor (TLR) 4 stimulation. Microarray profiling showed Nrf2 deletion caused markedly lower transcriptional levels of tissue inhibitor of metalloproteinase 3 (Timp3). ChIP-seq, PCR, and luciferase reporter assay further demonstrated Nrf2 bound to the promoter region of Timp3. Moreover, a disintegrin and metalloproteinase (ADAM) 10/ROCK1 was specifically increased in Nrf2-deficient macrophages. Increasing Timp3 expression effectively inhibited ADAM10/ROCK1 expression and rescued the Nrf2M-KO -mediated inflammatory response on TLR4 stimulation in vitro. Importantly, Timp3 overexpression, recombinant Timp3 protein, or ROCK1 knockdown rescued Nrf2M-KO -related liver IRI by inhibiting macrophage activation. CONCLUSIONS: In conclusion, macrophage Nrf2 mediates innate proinflammatory responses, attenuates liver IRI by binding to Timp3, and inhibits the RhoA/ROCK pathway, which provides a therapeutic target for clinical organ IRI.

Deep Learning Approach for MRI in the Classification of Anterior Talofibular Ligament Injuries.

BACKGROUND: Diagnosing anterior talofibular ligament (ATFL) injuries differs among radiologists. Further assessment of ATFL tears is valuable for clinical decision-making. PURPOSE: To establish a deep learning method for classifying ATFL injuries based on magnetic resonance imaging (MRI). STUDY TYPE: Retrospective. POPULATION: One thousand seventy-three patients from a single center with ankle MRI within 1 month of reference standard arthroscopy (in-group dataset), were divided into training, validation, and test sets in a ratio of 8:1:1. Additionally, 167 patients from another center were used as an independent out-group dataset. FIELD STRENGTH/SEQUENCE: Fat-saturation proton density-weighted fast spin-echo sequence at 1.5/3.0 T. ASSESSMENT: Patients were divided into normal, strain and degeneration, partial tear and complete tear groups (groups 0-3). The complete tear group was divided into five sub-groups by location and the potential avulsion fracture (groups 3.1-3.5). All images were input into AlexNet, VGG11, Small-Sample-Attention Net (SSA-Net), and SSA-Net + Weight Loss for classification. The results were compared with four radiologists with 5-30 years of experience. STATISTICAL TESTS: Model performance was evaluated by the receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC), and so on. McNemar's test was used to compare performance among the different models, and between the radiologists and models. The intraclass correlation coefficient (ICC) was used to assess the reliability of the radiologists. P < 0.05 was considered statistically significant. RESULTS: The average AUC of AlexNet, VGG11, SAA-Net, and SSA-Net + Weight Loss was 0.95, 0.99, 0.99, 0.99 in groups 0-3 and 0.96, 0.99, 0.99, 0.99 in groups 3.1-3.5. The effect of SSA-Net + Weight Loss was similar to SSA-Net but better than AlexNet and VGG11. In the out-group test set, the AUC of SSA-Net + Weight Loss ranged from 0.89 to 0.99. The ICC of radiologists was 0.97-1.00. The effect of SSA-Net + Weight Loss was better than each radiologist in the in-group and out-group test sets. DATA CONCLUSION: Deep learning has potential to be used for classifying ATFL injuries. SSA-Net + Weight Loss has a better diagnostic effect than radiologists with different experience levels. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 2.

MRI-based automated multitask deep learning system to evaluate supraspinatus tendon injuries.

OBJECTIVE: To establish an automated, multitask, MRI-based deep learning system for the detailed evaluation of supraspinatus tendon (SST) injuries. METHODS: According to arthroscopy findings, 3087 patients were divided into normal, degenerative, and tear groups (groups 0-2). Group 2 was further divided into bursal-side, articular-side, intratendinous, and full-thickness tear groups (groups 2.1-2.4), and external validation was performed with 573 patients. Visual geometry group network 16 (VGG16) was used for preliminary image screening. Then, the rotator cuff multitask learning (RC-MTL) model performed multitask classification (classifiers 1-4). A multistage decision model produced the final output. Model performance was evaluated by receiver operating characteristic (ROC) curve analysis and calculation of related parameters. McNemar's test was used to compare the differences in the diagnostic effects between radiologists and the model. The intraclass correlation coefficient (ICC) was used to assess the radiologists' reliability. p < 0.05 indicated statistical significance. RESULTS: In the in-group dataset, the area under the ROC curve (AUC) of VGG16 was 0.92, and the average AUCs of RC-MTL classifiers 1-4 were 0.99, 0.98, 0.97, and 0.97, respectively. The average AUC of the automated multitask deep learning system for groups 0-2.4 was 0.98 and 0.97 in the in-group and out-group datasets, respectively. The ICCs of the radiologists were 0.97-0.99. The automated multitask deep learning system outperformed the radiologists in classifying groups 0-2.4 in both the in-group and out-group datasets (p < 0.001). CONCLUSION: The MRI-based automated multitask deep learning system performed well in diagnosing SST injuries and is comparable to experienced radiologists. CLINICAL RELEVANCE STATEMENT: Our study established an automated multitask deep learning system to evaluate supraspinatus tendon (SST) injuries and further determine the location of SST tears. The model can potentially improve radiologists' diagnostic efficiency, reduce diagnostic variability, and accurately assess SST injuries. KEY POINTS: • A detailed classification of supraspinatus tendon tears can help clinical decision-making. • Deep learning enables the detailed classification of supraspinatus tendon injuries. • The proposed automated multitask deep learning system is comparable to radiologists.

Application research of AI-assisted compressed sensing technology in MRI scanning of the knee joint: 3D-MRI perspective.

OBJECTIVE: To investigate the potential applicability of AI-assisted compressed sensing (ACS) in knee MRI to enhance and optimize the scanning process. METHODS: Volunteers and patients with sports-related injuries underwent prospective MRI scans with a range of acceleration techniques. The volunteers were subjected to varied ACS acceleration levels to ascertain the most effective level. Patients underwent scans at the determined optimal 3D-ACS acceleration level, and 3D compressed sensing (CS) and 2D parallel acquisition technology (PAT) scans were performed. The resultant 3D-ACS images underwent 3.5 mm/2.0 mm multiplanar reconstruction (MPR). Experienced radiologists evaluated and compared the quality of images obtained by 3D-ACS-MRI and 3D-CS-MRI, 3.5 mm/2.0 mm MPR and 2D-PAT-MRI, diagnosed diseases, and compared the results with the arthroscopic findings. The diagnostic agreement was evaluated using Cohen's kappa correlation coefficient, and both absolute and relative evaluation methods were utilized for objective assessment. RESULTS: The study involved 15 volunteers and 53 patients. An acceleration factor of 10.69 × was identified as optimal. The quality evaluation showed that 3D-ACS provided poorer bone structure visualization, and improved cartilage visualization and less satisfactory axial images with 3.5 mm/2.0 mm MPR than 2D-PAT. In terms of objective evaluation, the relative evaluation yielded satisfactory results across different groups, while the absolute evaluation revealed significant variances in most features. Nevertheless, high levels of diagnostic agreement (κ: 0.81-0.94) and accuracy (0.83-0.98) were observed across all diagnoses. CONCLUSION: ACS technology presents significant potential as a replacement for traditional CS in 3D-MRI knee scans, allowing thinner MPRs and markedly faster scans without sacrificing diagnostic accuracy. CLINICAL RELEVANCE STATEMENT: 3D-ACS-MRI of the knee can be completed in the 160 s with good diagnostic consistency and image quality. 3D-MRI-MPR can replace 2D-MRI and reconstruct images with thinner slices, which helps to optimize the current MRI examination process and shorten scanning time. KEY POINTS: • AI-assisted compressed sensing technology can reduce knee MRI scan time by over 50%. • 3D AI-assisted compressed sensing MRI and related multiplanar reconstruction can replace traditional accelerated MRI and yield thinner 2D multiplanar reconstructions. • Successful application of 3D AI-assisted compressed sensing MRI can help optimize the current knee MRI process.

Sex-specific outbreeding advantages and sexual dimorphism in the seedlings of dioecious trees.

PREMISE: Dioecious trees are important components of many forest ecosystems. Outbreeding advantage and sexual dimorphism are two major mechanisms that explain the persistence of dioecious plants; however, they have rarely been studied in dioecious trees. METHODS: We investigated the influence of sex and genetic distance between parental trees (GDPT) on the growth and functional traits of multiple seedlings of a dioecious tree, Diospyros morrisiana. RESULTS: We found significant positive relationships between GDPT and seedling sizes and tissue density. However, the positive outbreeding effects on seedling growth mainly manifested in female seedlings, but were not prominent in males. Among seedlings, the male ones generally had higher biomass and leaf area than female seedlings, but such differences diminished as GDPT increased. CONCLUSIONS: Our research highlights that outbreeding advantage in plants can be sex-specific and that sexual dimorphism begins from the seedling stage of dioecious trees.

High temperature can improve the performance of invasive plants by facilitating root growth.

PREMISE: The ever-increasing temperatures of the Anthropocene may facilitate plant invasions. To date, studies of temperature effects on alien plants have mainly focused on aboveground plant traits but ignored belowground traits, which may confound predictions of plant invasion risks. METHODS: The temperature effects on the root growth dynamics of two alien shrubs, invasive Mimosa sepiaria and naturalized Corchorus capsulari, were studied using a 3D, transparent growth system under five temperature treatments (day/night: 18°C/13°C to 34°C/29°C) that cover the present and future warming temperature scenarios in China. We measured root depth and width growth in response to temperature treatments over 84 days. We also investigated intra- and interspecific competition of paired plants of the two species grown together at the five temperatures. RESULTS: Shoot growth of M. sepiaria and C. capsularis was optimal at the mid-range temperature. Root growth, however, was faster at the highest temperature (34°C/29°C) for M. sepiaria, but decreased for C. capsularis as temperatures increased. Root depth growth was more sensitive than root width for both species during neighbor competition. Compared to C. capsularis, M. sepiaria had relatively greater advantage during intra- and interspecific competition with increasing temperature, possibly because of its better root growth at high temperatures. CONCLUSIONS: These results suggest that temperature increases can improve the performance of some alien plants by facilitating width and depth growth of their roots. This enhancement requires serious attention when managing and predicting invasion risk.

Risk factors and molecular epidemiology of fecal carriage of carbapenem resistant Enterobacteriaceae in patients with liver disease.

BACKGROUND: Carbapenem resistant Enterobacteriaceae (CRE) colonization is a risk factor for CRE infection. CRE infection results in an increase in mortality in patients with cirrhosis. However, minimal data regarding the prevalence and the risk factors of CRE colonization in patients with liver disease yet without liver transplantation are available. The present study aimed to investigate the prevalence, risk factors and molecular epidemiology characteristics of CRE fecal carriage among patients with liver disease. METHODS: Stool specimens from 574 adult inpatients with liver disease were collected from December 2020 to April 2021. CRE were screened using selective chromogenic agar medium and identified by the Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS). Antimicrobial susceptibility was determined using the broth microdilution method. Carbapenemase genes were characterized by polymerase chain reaction (PCR) and DNA sequencing. Multilocus sequence typing (MLST) was performed for Carbapenem Resistant Klebsiella pneumoniae (CR-KPN) isolates and Carbapenem Resistant Escherichia Coli (CR-ECO) isolates. RESULTS: The total number of stool specimens (732) were collected from 574 patients with liver disease. 43 non-duplicated CRE strains were isolated from 39 patients with a carriage rate of 6.79% (39/574). The carriage rate was 15.60% (17/109) in patients with acute-on-chronic liver failure (ACLF). Multivariate analysis indicated that ACLF (P = 0.018), the history of pulmonary infection within past 3 months (P = 0.001) and the use of third generation cephalosporin/ß-lactamases inhibitor within past 3 months (P = 0.000) were independent risk factors of CRE colonization in patients with liver disease. Klebsiella Pnuemoniae (KPN) (51.28%) and Escherichia coli (ECO) (30.77%) were main strains in these patients. All CRE strains showed high resistance to most antimicrobials except for polymyxin B and tigecycline. Most (83.72%, 36/43) of the CRE carried carbapenemase genes. blaKPC-2 was the major carbapenemase gene. The molecular epidemiology of KPN were dominated by ST11, while the STs of ECO were scattered. CONCLUSIONS: The present study revealed that CRE fecal carriage rates were higher in patients with ACLF than in patients without liver failure. ACLF, the history of pulmonary infection within past 3 months and the use of third generation cephalosporin/ß-lactamases inhibitor within past 3 months were independent risk factors of CRE colonization in patients with liver disease. Regular CRE screening for hospitalized patients with liver disease should be conducted to limit the spread of CRE strain.

Deep learning model for measuring the sagittal Cobb angle on cervical spine computed tomography.

PURPOSES: To develop a deep learning (DL) model to measure the sagittal Cobb angle of the cervical spine on computed tomography (CT). MATERIALS AND METHODS: Two VB-Net-based DL models for cervical vertebra segmentation and key-point detection were developed. Four-points and line-fitting methods were used to calculate the sagittal Cobb angle automatically. The average value of the sagittal Cobb angle was manually measured by two doctors as the reference standard. The percentage of correct key points (PCK), matched samples t test, intraclass correlation coefficient (ICC), Pearson correlation coefficient, mean absolute error (MAE), and Bland‒Altman plots were used to evaluate the performance of the DL model and the robustness and generalization of the model on the external test set. RESULTS: A total of 991 patients were included in the internal data set, and 112 patients were included in the external data set. The PCK of the DL model ranged from 78 to 100% in the test set. The four-points method, line-fitting method, and reference standard measured sagittal Cobb angles were - 1.10 ± 18.29°, 0.30 ± 13.36°, and 0.50 ± 12.83° in the internal test set and 4.55 ± 20.01°, 3.66 ± 18.55°, and 1.83 ± 12.02° in the external test set, respectively. The sagittal Cobb angle calculated by the four-points method and the line-fitting method maintained high consistency with the reference standard (internal test set: ICC = 0.75 and 0.97; r = 0.64 and 0.94; MAE = 5.42° and 3.23°, respectively; external test set: ICC = 0.74 and 0.80, r = 0.66 and 0.974, MAE = 5.25° and 4.68°, respectively). CONCLUSIONS: The DL model can accurately measure the sagittal Cobb angle of the cervical spine on CT. The line-fitting method shows a higher consistency with the doctors and a minor average absolute error.

Primary Total Hip Arthroplasty With Ceramic-On-Ceramic Articulations: Analysis of a Single-Center Series of 1,083 Hips at a Minimum of 10-Year Follow-Up.

BACKGROUND: There are limited long-term results of using ceramic-on-ceramic (CoC) bearings in total hip arthroplasty (THA) in a large number of patient cohorts. The purpose of this study was to evaluate the minimum 10-year clinical and radiological outcomes and survivorship in a single surgeon series of CoC-THA. METHODS: Among the 1,039 patients (1,391 hips) who underwent primary THA at our institution between 2008 and 2011, 49 patients (69 hips) experienced paralysis or death, and 194 patients (239 hips, 19%) were lost to follow-up. The remaining 796 patients (1,083 hips) were assessed at a mean of 11 years (range, 10 to 13 years) using the modified Harris hip score (mHHS), the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and a questionnaire on articular noises. Survival analysis was used to estimate the survivorship. Radiological evaluation was performed on 869 hips at the final follow-up. RESULTS: Survivorship at 11 years was 98.3% for revision or aseptic loosening, and 98.2% for reoperation. At the final follow-up, the mean mHHS and WOMAC scores were 93 (range, 12 to 100) and 14.4 (range, 3 to 66), respectively. There were 131 (12%) hips that experienced squeaking, but no patient required revision. No fracture of the ceramic was observed. Radiological evaluation at the final follow-up revealed that 3 (0.3%) hips exhibited loosening, 2 (0.2%) had femoral osteolysis, 81(9.3%) acquired radiolucencies, and 35 (4%) showed heterotopic ossification. CONCLUSION: This CoC bearing for THA had a high survivorship and excellent functional outcomes for at least 10 years.