Improvement of Flowering Stage in Japonica Rice Variety Jiahe212 by Using CRISPR/Cas9 System.

The flowering period of rice significantly impacts variety adaptability and yield formation. Properly shortening the reproductive period of rice varieties can expand their ecological range without significant yield reduction. Targeted genome editing, like CRISPR/Cas9, is an ideal tool to fine-tune rice growth stages and boost yield synergistically. In this study, we developed a CRISPR/Cas9-mediated multiplex genome-editing vector containing five genes related to three traits, Hd2, Ghd7, and DTH8 (flowering-stage genes), along with the recessive rice blast resistance gene Pi21 and the aromatic gene BADH2. This vector was introduced into the high-quality japonica rice variety in Zhejiang province, Jiahe212 (JH212), resulting in 34 T0 plants with various effective mutations. Among the 17 mutant T1 lines, several displayed diverse flowering dates, but most exhibited undesirable agronomic traits. Notably, three homozygous mutant lines (JH-C15, JH-C18, and JH-C31) showed slightly earlier flowering dates without significant differences in yield-related traits compared to JH212. Through special Hyg and Cas marker selection of T2 plants, we identified seven, six, and two fragrant glutinous plants devoid of transgenic components. These single plants will serve as sib lines of JH212 and potential resources for breeding applications, including maintenance lines for indica-japonica interspecific three-line hybrid rice. In summary, our research lays the foundation for the creation of short-growth-period CMS (cytoplasmic male sterility, CMS) lines, and also provides materials and a theoretical basis for indica-japonica interspecific hybrid rice breeding with wider adaptability.

Associations between serum albumin level trajectories and clinical outcomes in sepsis patients in ICU: insights from longitudinal group trajectory modeling.

Background: Sepsis triggers a strong inflammatory response, often leading to organ failure and high mortality. The role of serum albumin levels in sepsis is critical but not fully understood, particularly regarding the significance of albumin level changes over time. This study utilized Group-based Trajectory Modeling (GBTM) to investigate the patterns of serum albumin changes and their impact on sepsis outcomes. Methods: We conducted a retrospective analysis on ICU patients from West China Hospital (2015-2022), employing GBTM to study serum albumin fluctuations within the first week of ICU admission. The study factored in demographics, clinical parameters, and comorbidities, handling missing data through multiple imputation. Outcomes assessed included 28-day mortality, overall hospital mortality, and secondary complications such as AKI and the need for mechanical ventilation. Results: Data from 1,950 patients revealed four serum albumin trajectories, showing distinct patterns of consistently low, increasing, moderate, and consistently high levels. These groups differed significantly in mortality, with the consistently low level group experiencing the highest mortality. No significant difference in 28-day mortality was observed among the other groups. Subgroup analysis did not alter these findings. Conclusion: The study identified four albumin trajectory groups in sepsis patients, highlighting that those with persistently low levels had the worst outcomes, while those with increasing levels had the best. Stable high levels above 30 g/L did not change outcomes significantly. These findings can inform clinical decisions, helping to identify high-risk patients early and tailor treatment approaches.

Effect of supercritical carbon dioxide on protein structure modification and antimicrobial peptides production of Mongolian cheese and its in vitro digestion.

The aim of this research was to investigate the effects of supercritical carbon dioxide (SC-CO2) treatment on protein structure in Mongolian cheese. The peptides during the digestive process of the SC-CO2 treated cheese were also studied. SC-CO2 technology was utilized to treat Mongolian cheese at three temperatures (45, 55 and 65 °C) and three pressures (7.5, 12.5 and 17.5 MPa). The results of fluorescence, ultraviolet-visible, Fourier transform infrared spectroscopy and free sulfhydryl groups showed that SC-CO2, particularly at 65 °C and 17.5 MPa, modified the protein structure in Mongolian cheese effectively. The data of LC-MS/MS-based peptidomics showed that the content of antimicrobial peptides found in the SC-CO2 treated Mongolian cheese was 1.55 times that of the untreated Mongolian cheese; the content of unique antimicrobial peptides in the digested SC-CO2 treated Mongolian cheese was 1.46 times that of the digested untreated Mongolian cheese, which proved that SC-CO2 could help produce antimicrobial peptides in cheese not only during the process of SC-CO2 treatment but during subsequent simulated gastrointestinal digestion as well. In conclusion, SC-CO2 could be considered a promising method to develop cheese products with potential health benefits.

Role of chitinase expression in the virulence of Lecanicillium lecanii against citrus black aphid (Toxoptera aurantii).

Chitinase plays a vital role in the virulence of entomopathogenic fungi (EPF) when it infects host insects. We used gene recombination technology to express chitinase of three strains of Lecanicillium lecanii: Vl6063, V3450, and Vp28. The ORF of ChitVl6063, ChitV3450 and ChitVp28 were inserted into the fungal expression vector pBARGPE-1, which contained strong promoter and terminator, respectively, to construct a chitinase overpressing plasmid, then transformed the wild-type strain with blastospore transformation method. The virulence of the three recombinant strains against Toxoptera aurantii was improved by overproduction of ChitVl6063, ChitV3450, and ChitVp28, as demonstrated by significantly lower 3.43 %, 1.72 %, and 1.23 % fatal doses, respectively, according to an insect bioassay. Similarly, lethal times of recombinants (ChitVl6063, ChitV3450 and ChitVp28) were also decreased up to 29.51 %, 30.46 % and 33.90 %, respectively, compared to the wild-type strains. Improving the expression of chitinase is considered as an effective method for the enhancement of the EPF value. The efficacy could be enhanced using recombinant technology, which provides a prospecting view for future insecticidal applications.

DWARF AND LESS TILLERS ON CHROMOSOME 3 Promotes Tillering in Rice by Sustaining FLORAL ORGAN NUMBER 1 Expression.

Three key factors determine yield in rice (Oryza sativa): panicle number, grain number, and grain weight. Panicle number is strongly associated with tiller number. Although many genes regulating tillering have been identified, whether Dof proteins are involved in controlling plant architecture remains unknown. The dwarf and less tillers on chromosome 3 (dlt3) rice mutant produces fewer tillers than the wild type. We cloned DLT3, which encodes a Dof protein that interacts with MONOCULM 3 (MOC3) in vivo and in vitro and recruits MOC1, forming a DLT3-MOC3-MOC1 complex. DLT3 binds to the promoter of FLORAL ORGAN NUMBER 1 (FON1) to activate its transcription and positively regulate tiller number. The overexpression of MOC1, MOC3, or FON1 in the dlt3 mutant increased tiller number. Collectively, these results suggest a model in which DLT3 regulates tiller number by maintaining the expression of MOC1, MOC3, and FON1. We discovered that DLT3 underwent directional selection in the Xian/indica and Geng/japonica populations during rice domestication. To provide genetic resources for breeding varieties with optimal panicle numbers, we performed large-scale diversity sequencing of the 1080-bp DLT3 coding region of 531 accessions from different countries and regions. Haplotype analysis showed that the superior haplotype, DLT3H1, produced the most tillers, while haplotype DLT3H6 produced the fewest tillers. Our study provides important germplasm resources for breeding super high-yielding rice varieties with combinations of superior haplotypes in different target genes, which will help overcome the challenge of food and nutritional security in the future.

Comprehensive genetic analysis of facioscapulohumeral muscular dystrophy by Nanopore long-read whole-genome sequencing.

BACKGROUND: Facioscapulohumeral muscular dystrophy (FSHD) is a high-prevalence autosomal dominant neuromuscular disease characterized by significant clinical and genetic heterogeneity. Genetic diagnosis of FSHD remains a challenge because it cannot be detected by standard sequencing methods and requires a complex diagnosis workflow. METHODS: We developed a comprehensive genetic FSHD detection method based on Oxford Nanopore Technologies (ONT) whole-genome sequencing. Using a case-control design, we applied this procedure to 29 samples and compared the results with those from optical genome mapping (OGM), bisulfite sequencing (BSS), and whole-exome sequencing (WES). RESULTS: Using our ONT-based method, we identified 59 haplotypes (35 4qA and 24 4qB) among the 29 samples (including a mosaic sample), as well as the number of D4Z4 repeat units (RUs). The pathogenetic D4Z4 RU contraction identified by our ONT-based method showed 100% concordance with OGM results. The methylation levels of the most distal D4Z4 RU and the double homeobox 4 gene (DUX4) detected by ONT sequencing are highly consistent with the BSS results and showed excellent diagnostic efficiency. Additionally, our ONT-based method provided an independent methylation profile analysis of two permissive 4qA alleles, reflecting a more accurate scenario than traditional BSS. The ONT-based method detected 17 variations in three FSHD2-related genes from nine samples, showing 100% concordance with WES. CONCLUSIONS: Our ONT-based FSHD detection method is a comprehensive method for identifying pathogenetic D4Z4 RU contractions, methylation level alterations, allele-specific methylation of two 4qA haplotypes, and variations in FSHD2-related genes, which will all greatly improve genetic testing for FSHD.

Design and Analysis of 5-DOF Compact Electromagnetic Levitation Actuator for Lens Control of Laser Cutting Machine.

In laser beam processing, the angle or offset between the auxiliary gas and the laser beam axis have been proved to be two new process optimization parameters for improving cutting speed and quality. However, a traditional electromechanical actuator cannot achieve high-speed and high-precision motion control with a compact structure. This paper proposes a magnetic levitation actuator which could realize the 5-DOF motion control of a lens using six groups of differential electromagnets. At first, the nonlinear characteristic of a magnetic driving force was analyzed by establishing an analytical model and finite element calculation. Then, the dynamic model of the magnetic levitation actuator was established using the Taylor series. And the mathematical relationship between the detected distance and five-degree-of-freedom was determined. Next, the centralized control system based on PID control was designed. Finally, a driving test was carried out to verify the five-degrees-of-freedom motion of the proposed electromagnetic levitation actuator. The results show it can achieve a stable levitation and precision positioning with a desired command motion. It also proves that the proposed magnetic levitation actuator has the potential application in an off-axis laser cutting machine tool.

[Determination of multiple residual solvents in ibandronate sodium using headspace-gas chromatography].

Ibandronate sodium, a third-generation diphosphate drug used worldwide to treat osteoporosis, has the advantages of convenient use, low toxicity, and significant therapeutic effects. However, the residual organic solvents in the synthesis process of sodium ibandronate not only have a negative impact on the efficacy of the drug, but also lead to a decrease in drug stability. Moreover, if the residual amounts of these solvents exceed safety standards, they may pose serious threats to human health. This study successfully established a convenient and efficient method based on headspace-gas chromatography (HS-GC) for the simultaneous determination of five residual solvents (methanol, acetone, benzene, toluene, 1-pentanol) in the raw materials of ibandronate sodium. The results indicated that satisfactory analytical performance can be achieved by using DB-624 capillary column (30 m×0.32 mm×1.8 µm) and a flame ionization detector in conjunction with headspace autosampling and a temperature program. The specific operating conditions included an initial temperature of 40 ℃, with a hold of 2 min, followed by a temperature ramp first to 200 ℃ at a rate of 5 ℃/min and then to 240 ℃ at a rate of 20 ℃/min, with a hold of 5 min. Nitrogen with a flow rate of 1 mL/min and split ratio of 14∶1 was used as the carrier gas. The headspace vial temperature was maintained at 80 ℃, and the sample equilibration time was 20 min. Under the established analytical conditions, good linear relationships were obtained between the mass concentrations of methanol (72-216 µg/mL), acetone (120-360 µg/mL), benzene (0.048-0.144 µg/mL), toluene (21.36-64.08 µg/mL), and 1-pentanol (120-360 µg/mL) and their corresponding peak areas, with correlation coefficients (r) greater than 0.990. The limits of detection for these solvents were 2.88, 0.011, 0.90, 0.24, and 0.024 ng/mL, respectively, with limits of quantification of 11.5, 0.043, 3.6, 0.96, and 0.096 ng/mL, respectively. Furthermore, the recoveries of these solvents ranged from 86.3% to 101.9%, with relative standard deviations (RSDs, n=3) of less than 2.49%. The proposed method is simple, accurate, reliable, and suitable for the rapid and simultaneous determination of five residual solvents in the raw materials of ibandronate sodium. This study has important practical significance in improving drug safety and ensuring public health.

Critical Role of Configurational Disorder in Stabilizing Chemically Unfavorable Coordination in Complex Compounds.

The crystal structure of a material is essentially determined by the nature of its chemical bonding. Consequently, the atomic coordination intimately correlates with the degree of ionicity or covalency of the material. Based on this principle, materials with similar chemical compositions can be successfully categorized into different coordination groups. However, counterexamples have recently emerged in complex ternary compounds. For instance, covalent IB-IIIA-VIA2 compounds, such as AgInS2, prefer a tetrahedrally coordinated structure (TCS), while ionic IA-VA-VIA2 compounds, such as NaBiS2, would favor an octahedrally coordinated structure (OCS). One naturally expects that IB-VA-VIA2 compounds with intermediate ionicity or covalency, such as AgBiS2, should then have a mix-coordinated structure (MCS) consisting of covalent AgS4 tetrahedra and ionic BiS6 octahedra. Surprisingly, only the experimental presence of the OCS was observed for AgBiS2. To resolve this puzzle, we perform first-principles studies of the phase stabilities of ternary compounds at finite temperatures. We find that AgBiS2 indeed prefers MCS at the ground state, in agreement with the typical expectation, but under experimental synthesis conditions, disordered OCS becomes energetically more favorable because of its low mixing energy and high configurational entropy. Our work elucidates the critical role of configurational disorder in stabilizing chemically unfavorable coordination, providing a rigorous rationale for the anomalous coordination preference in IB-VA-VIA2 compounds.

Dual uncertainty-guided multi-model pseudo-label learning for semi-supervised medical image segmentation.

Semi-supervised medical image segmentation is currently a highly researched area. Pseudo-label learning is a traditional semi-supervised learning method aimed at acquiring additional knowledge by generating pseudo-labels for unlabeled data. However, this method relies on the quality of pseudo-labels and can lead to an unstable training process due to differences between samples. Additionally, directly generating pseudo-labels from the model itself accelerates noise accumulation, resulting in low-confidence pseudo-labels. To address these issues, we proposed a dual uncertainty-guided multi-model pseudo-label learning framework (DUMM) for semi-supervised medical image segmentation. The framework consisted of two main parts: The first part is a sample selection module based on sample-level uncertainty (SUS), intended to achieve a more stable and smooth training process. The second part is a multi-model pseudo-label generation module based on pixel-level uncertainty (PUM), intended to obtain high-quality pseudo-labels. We conducted a series of experiments on two public medical datasets, ACDC2017 and ISIC2018. Compared to the baseline, we improved the Dice scores by 6.5% and 4.0% over the two datasets, respectively. Furthermore, our results showed a clear advantage over the comparative methods. This validates the feasibility and applicability of our approach.

A self-supervised fusion network for carotid plaque ultrasound image classification.

Carotid plaque classification from ultrasound images is crucial for predicting ischemic stroke risk. While deep learning has shown effectiveness, it heavily relies on substantial labeled datasets. Achieving high performance with limited labeled images is essential for clinical use. Self-supervised learning (SSL) offers a potential solution; however, the existing works mainly focus on constructing the SSL tasks, neglecting the use of multiple tasks for pretraining. To overcome these limitations, this study proposed a self-supervised fusion network (Fusion-SSL) for carotid plaque ultrasound image classification with limited labeled data. Fusion-SSL consists of two SSL tasks: classifying image block order (Ordering) and predicting image rotation angle (Rotating). A dual-branch residual neural network was developed to fuse feature presentations learned by the two tasks, which can extract richer visual boundary shape and contour information than a single task. In this experiment, 1270 carotid plaque ultrasound images were collected from 844 patients at Zhongnan Hospital (Wuhan, China). The results showed that Fusion-SSL outperforms single SSL methods across different percentages of labeled training data, ranging from 10 to 100%. Moreover, with only 40% labeled training data, Fusion-SSL achieved comparable results to a single SSL method (predicting image rotation angle) with 100% labeled data. These results indicate that Fusion-SSL could be beneficial for the classification of carotid plaques and the early warning of a stroke in clinical practice.

Active whey protein/hydroxypropyl methylcellulose edible films incorporated with cinnamaldehyde: Characterization, release kinetics and application to Mongolian cheese preservation.

Edible films with modulated release of antimicrobial agents are important for food preservation. Herein, antimicrobial edible films were prepared using whey protein (WP) and hydroxypropyl methylcellulose (HM) as polymer matrix materials and cinnamaldehyde (CIN) as antimicrobial agent. The mass ratios of WP and HM were 100/0, 75/25, 50/50, 25/75 and 0/100. The release kinetics of CIN through the film was studied, applying the Fickian model, power law and Weibull model. The films were also characterized by physical and structural characteristics, and antibacterial activity. In comparison to other films, the CIN-loaded film with a WP/HM ratio of 50/50 had better moisture resistance, water vapor barrier properties and mechanical properties. High correlation factors were obtained by fitting the CIN release data with the power law (R2 > 0.96) and Weibull model (R2 > 0.97). The diffusion mechanism of CIN was pseudo-Fickian. The diffusion coefficients (D1 and D2) had a positive linear relationship with the HM ratio, suggesting that a high HM ratio was beneficial to the CIN release. Finally, the WH50-C film was successfully used to preserve Mongolian cheese. This research provides a new perspective on the design of active packaging film with sustained-release characteristics.

Decoding the Cell Atlas and Inflammatory Features of Human Intracranial Aneurysm Wall by Single-Cell RNA Sequencing.

BACKGROUND: Intracranial aneurysm (IA) is common and occasionally results in life-threatening hemorrhagic strokes. However, the cell architecture and inflammation in the IA dome remain less understood. METHODS AND RESULTS: Single-cell RNA sequencing was performed on ruptured and unruptured human IA domes for delineating the cell atlas, gene expression perturbations, and inflammation features. Two external bulk mRNA sequencing-based data sets and serological results of 126 patients were collected for validation. As a result, a total of 21 332 qualified cells were captured. Vascular cells, including endothelial cells, smooth muscle cells, fibroblasts, and pericytes, were assigned in extremely sparse numbers (4.84%), and were confirmed by immunofluorescence staining. Pericytes, characterized by ABCC9 and HIGD1B, were identified in the IA dome for the first time. Abundant immune cells were identified, with the proportion of monocytes/macrophages and neutrophils being remarkably higher in ruptured IA. The lymphocyte compartment was also thoroughly categorized. By leveraging external data sets and machine learning algorithms, macrophages were robustly associated with IA rupture, irrespective of their polarization status. The single nucleotide polymorphism rs2280543, which is identified in East Asian populations, was associated with macrophage metabolic reprogramming through regulating TALDO1 expression. CONCLUSIONS: This study provides insights into the cellular architecture and inflammatory features in the IA dome and may enlighten novel therapeutics for unruptured IA.

An Alzheimer's Disease classification network based on MRI utilizing diffusion maps for multi-scale feature fusion in graph convolution.

Graph convolutional networks (GCN) have been widely utilized in Alzheimer's disease (AD) classification research due to its ability to automatically learn robust and powerful feature representations. Inter-patient relationships are effectively captured by constructing patients magnetic resonance imaging (MRI) data as graph data, where nodes represent individuals and edges denote the relationships between them. However, the performance of GCNs might be constrained by the construction of the graph adjacency matrix, thereby leading to learned features potentially overlooking intrinsic correlations among patients, which ultimately causes inaccurate disease classifications. To address this issue, we propose an Alzheimer's disease Classification network based on MRI utilizing diffusion maps for multi-scale feature fusion in graph convolution. This method aims to tackle the problem of features neglecting intrinsic relationships among patients while integrating features from diffusion mapping with different neighbor counts to better represent patients and achieve an accurate AD classification. Initially, the diffusion maps method conducts diffusion information in the feature space, thus breaking free from the constraints of diffusion based on the adjacency matrix. Subsequently, the diffusion features with different neighbor counts are merged, and a self-attention mechanism is employed to adaptively adjust the weights of diffusion features at different scales, thereby comprehensively and accurately capturing patient characteristics. Finally, metric learning techniques enhance the similarity of node features within the same category in the graph structure and bring node features of different categories more distant from each other. This study aims to enhance the classification accuracy of AD, by providing an effective tool for early diagnosis and intervention. It offers valuable information for clinical decisions and personalized treatment. Experimentation on the publicly accessible Alzheimer's disease neuroimaging initiative (ADNI) dataset validated our method's competitive performance across various AD-related classification tasks. Compared to existing methodologies, our approach captures patient characteristics more effectively and demonstrates superior generalization capabilities.

JoCoRank: Joint correlation learning with ranking similarity regularization for imbalanced fetal brain age regression.

Estimating fetal brain age based on sulci by magnetic resonance imaging (MRI) is clinically crucial in determining the normal development of fetal brains. Deep learning provides a possible way for fetal brain age estimation using MRI. Previous studies have mainly emphasized optimizing individual-wise correlation criteria, such as mean square error. However, they ignored the very important global and peer-wise criterion, which are essential for learning the structured relationships among regression samples. Moreover, the imbalanced label distribution introduces an adverse bias, which impairs the reliability and interpretation of correlation estimation and the model's fairness and generalizability. In this work, we propose a novel joint correlation learning with ranking similarity regularization (JoCoRank) algorithm for deep imbalanced regression of fetal brain age. Joint correlation learning concurrently captures individual, global, and peer-level valuable relationship information, and the customized optimization scheme for each criterion exhibits strong robustness against outliers and imbalanced regression. Ranking similarity regularization is designed to calibrate the biased feature representations by aligning the sorted list of neighbors in the label space with those in the feature space. A total of 1327 MRI images from 157 healthy fetuses between 22 and 34 weeks were collected at Wuhan Children's Hospital and utilized to evaluate the performance of JoCoRank in fetal brain age estimation. JoCoRank achieved promising results with an average mean absolute error of 0.693±0.064 weeks and R2 coefficient of 0.930±0.019. Our fetal brain age estimation algorithm would be useful for identifying abnormalities in fetal brain development and undertaking early intervention in clinical practice.

Dual-process modeling of sequential decision making in the balloon analogue risk task.

People are often faced with repeated risky decisions that involve uncertainty. In sequential risk-taking tasks, like the Balloon Analogue Risk Task (BART), the underlying decision process is not yet fully understood. Dual-process theory proposes that human cognition involves two main families of processes, often referred to as System 1 (fast and automatic) and System 2 (slow and conscious). We cross models of the BART with different architectures of the two systems to yield a pool of computational dual-process models that are evaluated on multiple performance measures (e.g., parameter identifiability, model recovery, and predictive accuracy). Results show that the best-performing model configuration assumes the two systems are competitively connected, an evaluation process based on the Scaled Target Learning model of the BART, and an assessment rate that incorporates sensitivity to the trial number, pumping opportunity, and bias to engage in System 1. Findings also shed light on how modeling choices and response times in a dual-process framework can benefit our understanding of sequential risk-taking behavior.

Value of preoperative evaluation of FEV1 in patients with destroyed lung undergoing pneumonectomy - a 20-year real-world study.

BACKGROUND: Clinical guidelines recommend a preoperative forced expiratory volume in one second (FEV1) of > 2 L as an indication for left or right pneumonectomy. This study compares the safety and long-term prognosis of pneumonectomy for destroyed lung (DL) patients with FEV1 ≤ 2 L or > 2 L. METHODS: A total of 123 DL patients who underwent pneumonectomy between November 2002 and February 2023 at the Department of Thoracic Surgery, Beijing Chest Hospital were included. Patients were sorted into two groups: the FEV1 > 2 L group (n = 30) or the FEV1 ≤ 2 L group (n = 96). Clinical characteristics and rates of mortality, complications within 30 days after surgery, long-term mortality, occurrence of residual lung infection/tuberculosis (TB), bronchopleural fistula/empyema, readmission by last follow-up visit, and modified Medical Research Council (mMRC) dyspnea scores were compared between groups. RESULTS: A total of 96.7% (119/123) of patients were successfully discharged, with 75.6% (93/123) in the FEV1 ≤ 2 L group. As compared to the FEV1 > 2 L group, the FEV1 ≤ 2 L group exhibited significantly lower proportions of males, patients with smoking histories, patients with lung cavities as revealed by chest imaging findings, and patients with lower forced vital capacity as a percentage of predicted values (FVC%pred) (P values of 0.001, 0.027, and 0.023, 0.003, respectively). No significant intergroup differences were observed in rates of mortality within 30 days after surgery, incidence of postoperative complications, long-term mortality, occurrence of residual lung infection/TB, bronchopleural fistula/empyema, mMRC ≥ 1 at the last follow-up visit, and postoperative readmission (P > 0.05). CONCLUSIONS: As most DL patients planning to undergo left/right pneumonectomy have a preoperative FEV1 ≤ 2 L, the procedure is generally safe with favourable short- and long-term prognoses for these patients. Consequently, the results of this study suggest that DL patient preoperative FEV1 > 2 L should not be utilised as an exclusion criterion for pneumonectomy.

Optical Genome Mapping for Chromosomal Aberrations Detection-False-Negative Results and Contributing Factors.

Optical genome mapping (OGM) has been known as an all-in-one technology for chromosomal aberration detection. However, there are also aberrations beyond the detection range of OGM. This study aimed to report the aberrations missed by OGM and analyze the contributing factors. OGM was performed by taking both GRCh37 and GRCh38 as reference genomes. The OGM results were analyzed in blinded fashion and compared to standard assays. Quality control (QC) metrics, sample types, reference genome, effective coverage and classes and locations of aberrations were then analyzed. In total, 154 clinically reported variations from 123 samples were investigated. OGM failed to detect 10 (6.5%, 10/154) aberrations with GRCh37 assembly, including five copy number variations (CNVs), two submicroscopic balanced translocations, two pericentric inversion and one isochromosome (mosaicism). All the samples passed pre-analytical and analytical QC. With GRCh38 assembly, the false-negative rate of OGM fell to 4.5% (7/154). The breakpoints of the CNVs, balanced translocations and inversions undetected by OGM were located in segmental duplication (SD) regions or regions with no DLE-1 label. In conclusion, besides variations with centromeric breakpoints, structural variations (SVs) with breakpoints located in large repetitive sequences may also be missed by OGM. GRCh38 is recommended as the reference genome when OGM is performed. Our results highlight the necessity of fully understanding the detection range and limitation of OGM in clinical practice.