Endosymbionts of citrus leafminer Phyllocnistis citrella Stainton among different citrus orchards in China.

Endosymbionts regulate the behavior of pest species, which could provide insights into their control. The citrus leafminer (Phyllocnistis citrella Stainton) is a widely distributed pest associated with diseases of citrus, especially of young trees. Here, we determined the endosymbiont composition of P. citrella in citrus orchards across China. The resulting dataset comprised average 50,430 high-quality reads for bacterial 16S rRNA V3-V4 regions of endosymbionts from 36 P. citrella larvae sampled from 12 citrus orchards across China. The sequencing depth and sampling size of this dataset were sufficient to reveal most of the endosymbionts of P. citrella. In total, 2,875 bacterial amplicon sequence variants were obtained; taxonomic analysis revealed a total of 372 bacterial genera, most of which were Proteobacteria phylum with Undibacterium being the most abundant genus. This dataset provides the first evidence of P. citrella endosymbionts that could support the development of pest management approaches in citrus orchards.

Leveraging Brain Modularity Prior for Interpretable Representation Learning of fMRI.

Resting-state functional magnetic resonance imaging (rs-fMRI) can reflect spontaneous neural activities in the brain and is widely used for brain disorder analysis. Previous studies focus on extracting fMRI representations using machine/deep learning methods, but these features typically lack biological interpretability. The human brain exhibits a remarkable modular structure in spontaneous brain functional networks, with each module comprised of functionally interconnected brain regions-of-interest (ROIs). However, existing learning-based methods cannot adequately utilize such brain modularity prior. In this paper, we propose a brain modularity-constrained dynamic representation learning framework for interpretable fMRI analysis, consisting of dynamic graph construction, dynamic graph learning via a novel modularity-constrained graph neural network (MGNN), and prediction and biomarker detection. The designed MGNN is constrained by three core neurocognitive modules (i.e., salience network, central executive network, and default mode network), encouraging ROIs within the same module to share similar representations. To further enhance discriminative ability of learned features, we encourage the MGNN to preserve network topology of input graphs via a graph topology reconstruction constraint. Experimental results on 534 subjects with rs-fMRI scans from two datasets validate the effectiveness of the proposed method. The identified discriminative brain ROIs and functional connectivities can be regarded as potential fMRI biomarkers to aid in clinical diagnosis.

A Systematic Review and Meta-Analysis of MRI Radiomics for Predicting Microvascular Invasion in Patients with Hepatocellular Carcinoma.

BACKGROUND: The prediction power of MRI radiomics for microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) remains uncertain. OBJECTIVE: To investigate the prediction performance of MRI radiomics for MVI in HCC. METHODS: Original studies focusing on preoperative prediction performance of MRI radiomics for MVI in HCC, were systematically searched from databases of PubMed, Embase, Web of Science and Cochrane Library. Radiomics quality score (RQS) and risk of bias of involved studies were evaluated. Meta-analysis was carried out to demonstrate the value of MRI radiomics for MVI prediction in HCC. Influencing factors of the prediction performance of MRI radiomics were identified by subgroup analyses. RESULTS: 13 studies classified as type 2a or above according to the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis statement were eligible for this systematic review and meta-analysis. The studies achieved an average RQS of 14 (ranging from 11 to 17), accounting for 38.9% of the total points. MRI radiomics achieved a pooled sensitivity of 0.82 (95%CI: 0.78 - 0.86), specificity of 0.79 (95%CI: 0.76 - 0.83) and area under the summary receiver operator characteristic curve (AUC) of 0.88 (95%CI: 0.84 - 0.91) to predict MVI in HCC. Radiomics models combined with clinical features achieved superior performances compared to models without the combination (AUC: 0.90 vs 0.85, P < 0.05). CONCLUSION: MRI radiomics has the potential for preoperative prediction of MVI in HCC. Further studies with high methodological quality should be designed to improve the reliability and reproducibility of the radiomics models for clinical application. The systematic review and meta-analysis was registered prospectively in the International Prospective Register of Systematic Reviews (No. CRD42022333822).

A randomized, double-blind, placebo-controlled phase I clinical trial of rotavirus inactivated vaccine (Vero cell) in a healthy adult population aged 18-49 years to assess safety and preliminary observation of immunogenicity.

We conducted a phase I, randomized, double-blind, placebo-controlled trial including healthy adults in Sui County, Henan Province, China. Ninety-six adults were randomly assigned to one of three groups (high-dose, medium-dose, and low-dose) at a 3:1 ratio to receive one vaccine dose or placebo. Adverse events up to 28 days after each dose and serious adverse events up to 6 months after all doses were reported. Geometric mean titers and seroconversion rates were measured for anti-rotavirus neutralizing antibodies using microneutralization tests. The rates of total adverse events in the placebo group, low-dose group, medium-dose group, and high-dose group were 29.17 % (12.62 %-51.09 %), 12.50 % (2.66 %-32.36 %), 50.00 % (29.12 %-70.88 %), and 41.67 % (22.11 %-63.36 %), respectively, with no significant difference in the experimental groups compared with the placebo group. The results of the neutralizing antibody assay showed that in the adult group, the neutralizing antibody geometric mean titer at 28 days after full immunization in the low-dose group was 583.01 (95 % confidence interval [CI]: 447.12-760.20), that in the medium-dose group was 899.34 (95 % CI: 601.73-1344.14), and that in the high-dose group was 1055.24 (95 % CI: 876.28-1270.75). The GMT of serum-specific IgG at 28 days after full immunization in the low-dose group was 3444.26 (95 % CI: 2292.35-5175.02), that in the medium-dose group was 6888.55 (95 % CI: 4426.67-10719.6), and that in the high-dose group was 7511.99 (95 % CI: 3988.27-14149.0). The GMT of serum-specific IgA at 28 days after full immunization in the low-dose group was 2332.14 (95 % CI: 1538.82-3534.45), that in the medium-dose group was 4800.98 (95 % CI: 2986.64-7717.50), and that in the high-dose group was 3204.30 (95 % CI: 2175.66-4719.27). In terms of safety, adverse events were mainly Grades 1 and 2, indicating that the safety of the vaccine is within the acceptable range in the healthy adult population. Considering the GMT and positive transfer rate of neutralizing antibodies for the main immunogenicity endpoints in the experimental groups, it was initially observed that the high-dose group had higher levels of neutralizing antibodies than the medium- and low-dose groups in adults aged 18-49 years. This novel inactivated rotavirus vaccine was generally well-tolerated in adults, and the vaccine was immunogenic in adults (ClinicalTrials.gov number, NCT04626856).

Chinese expert consensus on imaging diagnosis of drug-resistant pulmonary tuberculosis.

Tuberculosis (TB) remains one of the major infectious diseases in the world with a high incidence rate. Drug-resistant tuberculosis (DR-TB) is a key and difficult challenge in the prevention and treatment of TB. Early, rapid, and accurate diagnosis of DR-TB is essential for selecting appropriate and personalized treatment and is an important means of reducing disease transmission and mortality. In recent years, imaging diagnosis of DR-TB has developed rapidly, but there is a lack of consistent understanding. To this end, the Infectious Disease Imaging Group, Infectious Disease Branch, Chinese Research Hospital Association; Infectious Diseases Group of Chinese Medical Association of Radiology; Digital Health Committee of China Association for the Promotion of Science and Technology Industrialization, and other organizations, formed a group of TB experts across China. The conglomerate then considered the Chinese and international diagnosis and treatment status of DR-TB, China's clinical practice, and evidence-based medicine on the methodological requirements of guidelines and standards. After repeated discussion, the expert consensus of imaging diagnosis of DR-PB was proposed. This consensus includes clinical diagnosis and classification of DR-TB, selection of etiology and imaging examination [mainly X-ray and computed tomography (CT)], imaging manifestations, diagnosis, and differential diagnosis. This expert consensus is expected to improve the understanding of the imaging changes of DR-TB, as a starting point for timely detection of suspected DR-TB patients, and can effectively improve the efficiency of clinical diagnosis and achieve the purpose of early diagnosis and treatment of DR-TB.

An improved model based on quantitative features of right liver lobe, maximum varices, and portal vein system measured on magnetic resonance imaging to predict oesophagogastric variceal haemorrhage secondary to hepatitis B-related cirrhosis.

Background: In patients with hepatitis B-related cirrhosis, it is important to predict those at high-risk of oesophagogastric variceal haemorrhage (OVH) to decide upon prophylactic treatment. Our published model developed with right liver lobe volume and diameters of portal vein system did not incorporate maximum variceal size as a factor. This study thus aimed to develop an improved model based on right liver lobe volume, diameters of maximum oesophagogastric varices (OV) and portal vein system obtained at magnetic resonance imaging (MRI) to predict OVH. Methods: Two hundred and thirty consecutive individuals with hepatitis B-related cirrhosis undergoing abdominal enhanced MRI were randomly grouped into training (n=160) and validation sets (n=70). OVH was confirmed in 51 and 23 participants in the training and validation sets during 2-year follow-up period, respectively. Spleen, total liver, right lobe, caudate lobe, left lateral lobe, and left medial lobe volumes, together with diameters of maximum OV and portal venous system were measured on MRI. In the training set, univariate analyses and binary logistic regression analyses were conducted to determine independent predictors. The performance of the model for predicting OVH constructed based on independent predictors from the training set was evaluated with receiver operating characteristic (ROC) analysis and validated in the validation set. Results: The model for predicting OVH was established based on right liver lobe volume and diameters of the maximum OV, left gastric vein, and portal vein [odds ratio (OR) =0.991, 2.462, 1.434, and 1.582, respectively; all P values <0.05]. The logistic regression model equation [-0.009 × right liver lobe volume + 0.901 × maximum OV diameter (MOVD) + 0.361 × left gastric vein diameter (LGVD) + 0.459 × portal vein diameter (PVD) - 7.842] with a cutoff value of -0.656 for predicting OVH obtained excellent performance with an area under ROC curve (AUC) of 0.924 [95% confidence interval (CI): 0.878-0.971]. The Delong test showed negative statistical difference in the model performance between the training and validation sets, with a P value >0.99. Conclusions: The model could help well screen those patients at high risk of OVH for timely intervention and avoiding the fatal complications.

CT radiomics for prediction of microvascular invasion in hepatocellular carcinoma: A systematic review and meta-analysis

Abstract The power of computed tomography (CT) radiomics for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) demonstrated in current research is variable. This systematic review and meta-analysis aim to evaluate the value of CT radiomics for MVI prediction in HCC, and to investigate the methodologic quality in the workflow of radiomics research. Databases of PubMed, Embase, Web of Science, and Cochrane Library were systematically searched. The methodologic quality of included studies was assessed. Validation data from studies with Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) statement type 2a or above were extracted for meta-analysis. Eleven studies were included, among which nine were eligible for meta-analysis. Radiomics quality scores of the enrolled eleven studies varied from 6 to 17, accounting for 16.7%-47.2% of the total points, with an average score of 14. Pooled sensitivity, specificity, and Area Under the summary receiver operator Characteristic Curve (AUC) were 0.82 (95% CI 0.77-0.86), 0.79 (95% CI 0.75-0.83), and 0.87 (95% CI 0.84-0.91) for the predictive performance of CT radiomics, respectively. Meta-regression and subgroup analyses showed radiomics model based on 3D tumor segmentation, and deep learning model achieved superior performances compared to 2D segmentation and non-deep learning model, respectively (AUC: 0.93 vs. 0.83, and 0.97 vs. 0.83, respectively). This study proves that CT radiomics could predict MVI in HCC. The heterogeneity of the included studies precludes a definition of the role of CT radiomics in predicting MVI, but methodology warrants uniformization in the radiology community regarding radiomics in HCC.