A landmark-supervised registration framework for multi-phase CT images with cross-distillation.

Multi-phase computed tomography (CT) has become a leading modality for identifying hepatic tumors. Nevertheless, the presence of misalignment in the images of different phases poses a challenge in accurately identifying and analyzing the patient's anatomy. Conventional registration methods typically concentrate on either intensity-based features or landmark-based features in isolation, so imposing limitations on the accuracy of the registration process. In this paper, we establish a nonrigid cycle-registration network that leverages semi-supervised learning techniques, wherein a point distance term based on Euclidean distance between registered landmark points is introduced into the loss function. Additionally, a cross-distillation strategy is proposed in network training to further improve registration performance which incorporates response-based knowledge concerning the distances between feature points. We conducted experiments using multi-centered liver CT datasets to evaluate the performance of the proposed method. The results demonstrate that our method outperforms baseline methods in terms of Target Registration Error (TRE). Additionally, Dice scores of the warped tumor masks were calculated. Our method consistently achieved the highest scores among all the comparing methods. Specifically, it achieved scores of 82.9% and 82.5% in the hepatocellular carcinoma (HCC) and the intrahepatic cholangiocarcinoma (ICC) dataset, respectively. The superior registration performance indicates its potential to serve as an important tool in hepatic tumor identification and analysis.

Phylogenetic relationships, selective pressure and molecular markers development of six species in subfamily Polygonoideae based on complete chloroplast genomes.

The subfamily Polygonoideae encompasses a diverse array of medicinal and horticultural plants that hold significant economic value. However, due to the lack of a robust taxonomy based on phylogenetic relationships, the classification within this family is perplexing, and there is also a scarcity of reports on the chloroplast genomes of many plants falling under this classification. In this study, we conducted a comprehensive analysis by sequencing and characterizing the complete chloroplast genomes of six Polygonoideae plants, namely Pteroxygonum denticulatum, Pleuropterus multiflorus, Pleuropterus ciliinervis, Fallopia aubertii, Fallopia dentatoalata, and Fallopia convolvulus. Our findings revealed that these six plants possess chloroplast genomes with a typical quadripartite structure, averaging 162,931 bp in length. Comparative chloroplast analysis, codon usage analysis, and repetitive sequence analysis demonstrated a high level of conservation within the chloroplast genomes of these plants. Furthermore, phylogenetic analysis unveiled a distinct clade occupied by P. denticulatum, while P. ciliinrvis displayed a closer relationship to the three plants belonging to the Fallopia genus. Selective pressure analysis based on maximum likelihood trees showed that a total of 14 protein-coding genes exhibited positive selection, with psbB and ycf1 having the highest number of positive amino acid sites. Additionally, we identified four molecular markers, namely petN-psbM, psal-ycf4, ycf3-trnS-GGA, and trnL-UAG-ccsA, which exhibit high variability and can be utilized for the identification of these six plants.

Dynamic radiomics based on contrast-enhanced MRI for predicting microvascular invasion in hepatocellular carcinoma.

OBJECTIVE: To exploit the improved prediction performance based on dynamic contrast-enhanced (DCE) MRI by using dynamic radiomics for microvascular invasion (MVI) in hepatocellular carcinoma (HCC). METHODS: We retrospectively included 175 and 75 HCC patients who underwent preoperative DCE-MRI from September 2019 to August 2022 in institution 1 (development cohort) and institution 2 (validation cohort), respectively. Static radiomics features were extracted from the mask, arterial, portal venous, and equilibrium phase images and used to construct dynamic features. The static, dynamic, and dynamic-static radiomics (SR, DR, and DSR) signatures were separately constructed based on the feature selection method of LASSO and classification algorithm of logistic regression. The receiver operating characteristic (ROC) curves and the area under the curve (AUC) were plotted to evaluate and compare the predictive performance of each signature. RESULTS: In the three radiomics signatures, the DSR signature performed the best. The AUCs of the SR, DR, and DSR signatures in the training set were 0.750, 0.751 and 0.805, respectively, while in the external validation set, the corresponding AUCs were 0.706, 0756 and 0.777. The DSR signature showed significant improvement over the SR signature in predicting MVI status (training cohort: P = 0.019; validation cohort: P = 0.044). After external validation, the AUC value of the SR signature decreased from 0.750 to 0.706, while the AUC value of the DR signature did not show a decline (AUCs: 0.756 vs. 0.751). CONCLUSIONS: The dynamic radiomics had an improved effect on the MVI prediction in HCC, compared with the static DCE MRI-based radiomics models.

Structural Elucidation of a Polysaccharide from Flammulina velutipes and Its Lipid-Lowering and Immunomodulation Activities.

FVPT1, a novel heteropolysaccharide, was purified from the fruiting body of Flammulina velutipes using magnetic-field-assisted three-phase partitioning and gel permeation chromatography. The structure was characterized using monosaccharide composition and methylation analysis, infrared spectroscopy and nuclear magnetic resonance (NMR). The FVPT1 (~1.64 × 104 Da) was composed of L-fucose, D-galactose, D-glucose and D-mannose at a molar ratio of 1.0:3.5:1.0:1.4. The polysaccharide repeating unit of FVPT1 was established with methylation analyses and NMR spectroscopy. Moreover, a zebrafish larva hyperlipidemia model test demonstrated that FVPT1 can show appreciable lipid-lowering effects. In addition, the FVPT1 exhibited remarkable immunoregulatory activity by increasing nitric oxide, interleukin (IL)-1ß and IL-1 secretion in macrophages. Therefore, these results suggest that FVPT1 has the potential to be developed into a new immune or hypolipidemic health product.

Acute Helicobacter pylori infection prevalence and association with metabolic abnormality in general Chinese population: A retrospective study.

Helicobacter pylori (H pylori) infection was common worldwide and previous researches on the correlation between H pylori infection and metabolic abnormality provided inconsistent conclusions. We assessed acute H pylori infection prevalence and the relationship with metabolic abnormality in general Chinese population. Participants attending for the physical examination underwent a carbon-13 urea breath test. For individual, the following data were collected: age, gender, body mass index (BMI), systolic blood pressure, diastolic blood pressure, total protein, albumin, globulin (GLB), total bilirubin, direct bilirubin (DBIL), indirect bilirubin, alanine transaminase, glutamyl transpeptidase, alkaline phosphatase, cholesterol, triglyceride, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, urea nitrogen, creatinine, uric acid, fasting plasma glucose (FPG), and homocysteine. A total of 29,154 participants were enrolled. The prevalence of acute H pylori infection was 29.79% (8684/29,154). Spearson correlation analysis showed that gender, BMI, ALB, GLB, total bilirubin, DBIL, indirect bilirubin, and FPG were closely related to H pylori infection. Multinomial logistic regressions analysis with stepwise subset selection further identified gender, BMI, ALB, GLB, DBIL, and FPG as independent risk factors for acute H pylori infection. Our results indicated that acute H pylori infection might has a significant impact on metabolic abnormalities, which should be further confirmed.

Domain-Adversarial Transformer Network for Multiphase Liver Tumor Segmentation.

Accurate liver tumor segmentation is a prerequisite for data-driven tumor analysis. Multiphase computed tomography (CT) with extensive liver tumor characteristics is typically used as the most crucial diagnostic basis. However, the large variations in contrast, texture, and tumor structure between CT phases limit the generalization capabilities of the associated segmentation algorithms. Inadequate feature integration across phases might also lead to a performance decrease. To address these issues, we present a domain-adversarial transformer (DA-Tran) network for segmenting liver tumors from multiphase CT images. A DA module is designed to generate domain-adapted feature maps from the non-contrast-enhanced (NC) phase, arterial (ART) phase, portal venous (PV) phase, and delay phase (DP) images. These domain-adapted feature maps are then combined with 3D transformer blocks to capture patch-structured similarity and global context attention. The experimental findings show that DA-Tran produces cutting-edge tumor segmentation outcomes, making it an ideal candidate for this co-segmentation challenge.

Phase Difference Network for Efficient Differentiation of Hepatic Tumors with Multi-Phase CT.

Liver cancer has been one of the top causes of cancer-related death. For developing an accurate treatment strategy and raising the survival rate, the differentiation of liver cancers is essential. Multiphase CT recently acts as the primary examination method for clinical diagnosis. Deep learning techniques based on multiphase CT have been proposed to distinguish hepatic cancers. However, due to the recurrent mechanism, RNN-based approaches require expensive calculations whereas CNN-based models fail to explicitly establish temporal correlations among phases. In this paper, we proposed a phase difference network, termed as Phase Difference Network (PDN), to identify two liver cancer, hepatocellular carcinoma and intrahepatic cholangiocarcinoma, from four-phase CT. Specifically, the phase difference was used as interphase temporal information in a differential attention module, which enhanced the feature representation. Additionally, utilizing a multihead self-attention module, a transformer-based classification module was employed to explore the long-term context and capture the temporal relation between phases. Clinical datasets are used in experiments to compare the performance of the proposed strategy versus conventional approaches. The results indicate that the proposed method outperforms the traditional deep learning based methods.

Quantitative Relaxometry Assessment of Brain Microstructural Abnormality of Preschool Children With Autism Spectrum Disorder With Synthetic Magnetic Resonance Imaging.

OBJECTIVE: This study aimed to perform an assessment of brain microstructure in children with autism aged 2 to 5 years using relaxation times acquired by synthetic magnetic resonance imaging. MATERIALS AND METHODS: Thirty-four children with autism spectrum disorder (ASD) (ASD group) and 17 children with global developmental delay (GDD) (GDD group) were enrolled, and synthetic magnetic resonance imaging was performed to obtain T1 and T2 relaxation times. The differences in brain relaxation times between the 2 groups of children were compared, and the correlation between significantly changed T1/T2 and clinical neuropsychological scores in the ASD group was analyzed. RESULTS: Compared with the GDD group, shortened T1 relaxation times in the ASD group were distributed in the genu of corpus callosum (GCC) ( P = 0.003), splenium of corpus callosum ( P = 0.002), and right thalamus (TH) ( P = 0.014), whereas shortened T2 relaxation times in the ASD group were distributed in GCC ( P = 0.011), left parietal white matter ( P = 0.035), and bilateral TH (right, P = 0.014; left, P = 0.016). In the ASD group, the T2 of the left parietal white matter is positively correlated with gross motor (developmental quotient [DQ] 2) and personal-social behavior (DQ5), respectively ( r = 0.377, P = 0.028; r = 0.392, P = 0.022); the T2 of the GCC was positively correlated with DQ5 ( r = 0.404, P = 0.018); and the T2 of the left TH is positively correlated with DQ2 and DQ5, respectively ( r = 0.433, P = 0.009; r = 0.377, P = 0.028). All significantly changed relaxation values were not significantly correlated with Childhood Autism Rating Scale scores. CONCLUSIONS: The shortened relaxometry times in the brain of children with ASD may be associated with the increased myelin content and decreased water content in the brain of children with ASD in comparison with GDD, contributing the understanding of the pathophysiology of ASD. Therefore, the T1 and T2 relaxometry may be used as promising imaging markers for ASD diagnosis.

Computed tomography texture-based models for predicting KIT exon 11 mutation of gastrointestinal stromal tumors.

Background: KIT exon 11 mutation in gastrointestinal stromal tumors (GISTs) is associated with treatment strategies. However, few studies have shown the role of imaging-based texture analysis in KIT exon 11 mutation in GISTs. In this study, we aimed to show the association between computed tomography (CT)-based texture features and KIT exon 11 mutation. Methods: Ninety-five GISTs confirmed by surgery and identified with mutational genotype of KIT were included in this study. By amplifying the samples using over-sampling technique, a total of 183 region of interest (ROI) segments were extracted from 63 patients as training cohort. The 63 new ROI segments were extracted from the 63 patients as internal validation cohort. Thirty-two patients who underwent KIT exon 11 mutation test during 2021-2023 was selected as external validation cohort. The textural parameters were evaluated both in training cohort and validation cohort. Least absolute shrinkage and selection operator (LASSO) algorithms and logistic regression analysis were used to select the discriminant features. Results: Three of textural features were obtained using LASSO analysis. Logistic regression analysis showed that patients' age, tumor location and radiomics features were significantly associated with KIT exon 11 mutation (p < 0.05). A nomogram was developed based on the associated factors. The area under the curve (AUC) of clinical features, radiomics features and their combination in training cohort was 0.687 (95 % CI: 0.604-0.771), 0.829 (95 % CI: 0.768-0.890) and 0.874 (95 % CI: 0.822-0.926), respectively. The AUC of radiomics features in internal validation cohort and external cohort was 0.880 (95 % CI: 0.796-0.964) and 0.827 (95%CI: 0.667-0.987), respectively. Conclusion: The CT texture-based model can be used to predict KIT exon 11 mutation in GISTs.

Time course efficiency of MICE and HIIE on inhibitory control and HRV in adolescents with obesity and different cardiorespiratory fitness.

Background: Adolescent obesity is associated with impaired inhibitory control. Acute exercise can improve executive function. However, due to the influence of exercise intensity, cognitive test timing, and cardiorespiratory fitness (CF) level, the most effective exercise program remains controversial. Methods: The current study investigated the time-course effects of moderate-intensity continuous exercise (MICE) and high-intensity interval exercise (HIIE) on inhibitory control (Stroop) and task-related heart rate variability (HRV) in adolescents with different CF. A mixed experimental design of 2 CF levels (high CF, HCF; low CF, LCF) × 3 exercise methods (MICE, HIIE, CON) × 3 test timing (pre, post-0, post-20) was adopted. Heart rate variability (HRV) and Stroop task tests were conducted before exercise (pre), immediately after exercise (post-0), and 20 min after exercise (post-20). Results: Individuals with HCF exhibited a positive decrease in Stroop response time immediately and 20 min after MICE and HIIE, compared to pretest response times (RT). Conversely, individuals with LCF showed a slight increase in Stroop task (RT) only immediately after HIIE. All individuals had a slight increase in ACC after MICE and HIIE compared to before exercise. In addition, compared with the control group, the time-domain index (the square root of the mean squared differences of successive NN intervals, RMSSD) of HRV was significantly decreased, the frequency-domain index (the absolute power of the Low-Frequency band/the absolute power of the High-Frequency band ratio, LF/HF) was significantly increased after MICE and HIIE, and the effect of HIIE on RMSSD and LF/HF was significantly greater than that of MICE. Conclusion: The current study found that the acute effects of MICE and HIIE on inhibitory control in obese adolescents were influenced by the interaction of cognitive test timing and cardiorespiratory fitness. Individuals with high cardiorespiratory fitness performed better on the Stroop task than individuals with low cardiorespiratory fitness. The inhibitory control of HIIE in high-cardiorespiratory obese adolescents produced positive effects similar to those in MICE but more lasting, suggesting that HIIE is more beneficial for high-cardiorespiratory obese adolescents. MICE promoted inhibitory control in obese adolescents with low cardiorespiratory fitness, but HIIE impaired inhibitory control in obese adolescents with low cardiorespiratory fitness immediately after exercise, suggesting that low cardiopulmonary fitness obese adolescents may be suitable for MICE rather than HIIE exercise intervention. The shift from balanced HRV to sympathetic dominance after acute exercise reflects increased arousal levels and may be one of the underlying mechanisms by which acute exercise brings benefits to executive function.

From guest to host: parasite Cistanche deserticola shapes and dominates bacterial and fungal community structure and network complexity.

BACKGROUND: Rhizosphere and plant microbiota are assumed to play an essential role in deciding the well-being of hosts, but effects of parasites on their host microbiota have been rarely studied. Also, the characteristics of the rhizosphere and root microbiota of parasites and hosts under parasitism is relatively unknown. In this study, we used Cistanche deserticola and Haloxylon ammodendron from cultivated populations as our model parasites and host plants, respectively. We collected samples from BULK soil (BULK), rhizosphere soil of H. ammodendron not parasitized (NCD) and parasitized (RHA) to study how the parasite influenced the rhizosphere microbiota of the host. We also collected samples from the rhizosphere soil and roots of C. deserticola (RCD and ECD) and Haloxylon ammodendron (RHA and EHA) to explore the difference between the microbiota of the parasite and its host under parasitism. RESULTS: The parasite reduced the compositional and co-occurrence network complexities of bacterial and fungal microbiota of RHA. Additionally, the parasite increased the proportion of stochastic processes mainly belonging to dispersal limitation in the bacterial microbiota of RHA. Based on the PCoA ordinations and permutational multivariate analysis of variance, the dissimilarity between microbiota of C. deserticola and H. ammodendron were rarely evident (bacteria, R2 = 0.29971; fungi, R2 = 0.15631). Interestingly, four hub nodes of H. ammodendron in endosphere fungal microbiota were identified, while one hub node of C. deserticola in endosphere fungal microbiota was identified. It indicated that H. ammodendron played a predominant role in the co-occurrence network of endosphere fungal microbiota. Source model of plant microbiome suggested the potential source percentage from the parasite to the host (bacteria: 52.1%; fungi: 16.7%) was lower than host-to-parasite (bacteria: 76.5%; fungi: 34.3%), illustrating that microbial communication was bidirectional, mainly from the host to the parasite. CONCLUSIONS: Collectively, our results suggested that the parasite C. deserticola shaped the diversity, composition, co-occurrence network, and community assembly mechanisms of the rhizosphere microbiota of H. ammodendron. Additionally, the microbiota of C. deserticola and H. ammodendron were highly similar and shared. Our findings on parasite and host microbiota provided a novel line of evidence supporting the influence of parasites on the microbiota of their hosts.

Development and external validation of a multiparametric MRI-based radiomics model for preoperative prediction of microsatellite instability status in rectal cancer: a retrospective multicenter study.

OBJECTIVES: To establish and validate a radiomics model based on multiparametric magnetic resonance imaging (MRI), and to predict microsatellite instability (MSI) status in rectal cancer patients. METHODS: A total of 199 patients with pathologically confirmed rectal cancer were included. The MSI status was confirmed by immunohistochemistry (IHC) staining. Clinical factors and laboratory data associated with MSI status were analyzed. The imaging data of 100 patients from one of the hospitals were used as the training set. The remaining 99 patients from the other two hospitals were used as the external validation set. The regions of interest (ROIs) were delineated from T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and contrast-enhanced T1WI (CE-T1WI) sequence to extract the radiomics features. The Tree-based approach was used for feature selection. The models were constructed based on the four single sequences and a combination of the four sequences using the random forest (RF) algorithm. The external validation set was used to verify the generalization ability of each model. The receiver operating characteristic (ROC) curves and the area under the curve (AUC) were plotted to evaluate and compare the predictive performance of each model. RESULTS: In the four single-series models, the CE-T1WI model performed the best. The AUCs of the T1WI, T2WI, DWI, and CE-T1WI prediction models in the training set were 0.74, 0.71, 0.71, and 0.78, respectively, while in the external validation set, the corresponding AUCs were 0.67, 0.66, 0.70, and 0.77. The prediction and generalization performance of the combined model of multi-sequences was comparable to that of the CE-T1WI model and it was better than that of the remaining three single-series models, with AUC values of 0.78 and 0.78 in the training and validation sets, respectively. CONCLUSION: The established radiomics models based on CE-T1WI or multiparametric MRI have similar predictive performance. They have the potential to predict MSI status in rectal cancer patients. KEY POINTS: • A radiomics model for the prediction of MSI status in patients with rectal cancer was established and validated using external validation. • The models based on CE-T1WI or multiparametric MRI have better predictive performance than those based on single unenhanced sequence images. • The radiomics model has the potential to suggest MSI status in rectal cancer patients; however, it is not yet a substitute for histological confirmation.

The Complete Chloroplast Genome Sequence of Laportea bulbifera (Sieb. et Zucc.) Wedd. and Comparative Analysis with Its Congeneric Species.

Laportea bulbifera (L. bulbifera) is an important medicinal plant of Chinese ethnic minorities, with high economic and medicinal value. However, the medicinal materials of the genus Laportea are prone to be misidentified due to the similar morphological characteristics of the original plants. Thus, it is crucial to discover their molecular marker points and to precisely identify these species for their exploitation and conservation. Here, this study reports detailed information on the complete chloroplast (cp) of L. bulbifera. The result indicates that the cp genome of L. bulbifera of 150,005 bp contains 126 genes, among them, 37 tRNA genes and 81 protein-coding genes. The analysis of repetition demonstrated that palindromic repeats are more frequent. In the meantime, 39 SSRs were also identified, the majority of which were mononucleotides Adenine-Thymine (A-T). Furthermore, we compared L. bulbifera with eight published Laportea plastomes, to explore highly polymorphic molecular markers. The analysis identified four hypervariable regions, including rps16, ycf1, trnC-GCA and trnG-GCC. According to the phylogenetic analysis, L. bulbifera was most closely related to Laportea canadensis (L. canadensis), and the molecular clock analysis speculated that the species originated from 1.8216 Mya. Overall, this study provides a more comprehensive analysis of the evolution of L. bulbifera from the perspective of phylogenetic and intrageneric molecular variation in the genus Laportea, which is useful for providing a scientific basis for further identification, taxonomic, and evolutionary studies of the genus.

Explore the Relationship Between Short-Term Ambient Air Pollution Exposure and Daily Outpatient Visits for Metabolic Related Fatty Liver.

Background: Many studies have shown that the pollution of fine particles in the air is related to the incidence of chronic diseases. However, research on air pollution and metabolism-associated fatty liver disease (MAFLD) is limited. Objective: The purpose of this study was to explore the relationship between short-term ambient air pollution and daily outpatient visits for metabolic-related fatty liver. Methods: We used a quasi-Poisson regression generalized additive model to stratify analyses by season, age, and gender. Results: From January 1, 2017, to August 31, 2019, 10,562 confirmed MAFLD outpatient visits were recorded. A 10 µg/m3 increase of fine particular matter (PM10and PM2.5) and NO2 concentrations corresponding with percent change were 0.82 (95% confidence interval [CI], 0.49-1.15), 0.57 (95% CI, 0.18-0.98), and 0.86 (95% CI, 0.59-1.13) elevation in MAFLD outpatient visits. In terms of season, the impact estimates of NO2 and PM2.5% change were 3.55 (95% CI, 1.23-5.87) and 1.12 (95% CI, 0.78-1.46) in the hot season and transition season, respectively. Compared with the warm season, the impact estimates of PM10were more significant in the cool season: 2.88 (95% CI, 0.66-5.10). NO2 has the greatest effect in the transition season, whereas PM10 has the greatest highest effect in the cool and hot seasons. Compared with other pollutants, PM2.5 has the greatest impact in the age stratification, which percent change are 2.69 (95% CI, 0.77-5.61) and 2.88 (95% CI, 0.37-6.40) respectively. The impact values of PM2.5 in male and female percent change were 3.60 (95% CI, 0.63-6.57) and 1.65 (95% CI, 1.05-2.25), respectively. Conclusion: This study shows that the air pollutants are related to the number of outpatient visits for MAFLD. The effects of different air pollutants on MAFLD outpatient visits were different by season, ages, and gender.

Preoperative prediction of invasive behavior of pancreatic solid pseudopapillary neoplasm by MRI-based multiparametric radiomics models.

OBJECTIVE: A log-combined model was developed to predict the invasive behavior of pancreatic solid pseudopapillary neoplasm (pSPN) based on clinical and radiomic features extracted from multiparametric magnetic resonance imaging (MRI). MATERIALS AND METHODS: A total of 111 patients with pathologically confirmed pSPN who underwent preoperative plain and contrast-enhanced MRI were included, and divided into an invasive group (n = 34) and non-invasive group (n = 77). Clinical features and laboratory data related to pSPN invasive behavior were analyzed. Regions of interest were delineated based on T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and contrast-enhanced T1WI (CE-T1WI) to extract radiomic features. Correlation analysis was performed for these features, followed by L1_based feature selection (C = 0.15). A logistic regression algorithm was used to construct models based on each of the four sequences and a log-combined model was used to integrate the sequences. A receiver operating characteristic (ROC) curve was plotted to evaluate the model performance, and the Brier score was used to assess the overall accuracy of the model predictions. RESULTS: The area under the ROC curve was 0.68, 0.73, 0.71, and 0.49 for Log-T1WI, Log-T2WI, Log-DWI, and Log-CE models, respectively, and 0.81 for the log-combined model. The accuracy, precision, sensitivity, and specificity of the log-combined model were 0.77, 0.88, 0.75, and 0.78, respectively. The best performance was obtained with the log-combined model with a Brier score of 0.18. Tumor location was identified as a significant clinical feature in comparison between the two groups (p < 0.05), and invasive pSPN was more frequent in the tail of the pancreas. CONCLUSION: The log-combined model based on multiparametric MRI and clinical features can be used as a non-invasive diagnostic tool for preoperative prediction of pSPN invasive behavior and to facilitate the development of individualized treatment strategies and monitoring management plans.

Complete chloroplast genome of Gentianopsis barbata and comparative analysis with related species from Gentianaceae.

Gentianopsis barbata is an essential medicinal plant in China with high ornamental and medicinal values. Unfortunately, the study of the chloroplast genome of this plant still has a gap. This study sequenced and characterized the complete chloroplast genome of G. barbata. The complete chloroplast genome of G. barbata is a typical circular structure of 151 123 bp. It consists of a large single-copy region (82 690 bp) and a small single-copy region (17 887 bp) separated by a pair of inverted repeats (25 273 bp), which covers 78 protein-coding genes, 30 tRNAs, and 4 rRNAs. The long repeat sequence analysis showed that the P-type (palindromic) sequences were the major long repeat sequences. Thirty-seven simple sequence repeats were identified, most of which were single nucleotides. The Bayesian inference tree, maximum likelihood tree, and neighbor-joining tree suggested that G. barbata is grouped with Gentianopsis grandis and Gentianopsis paludosa. The divergence time analysis showed that G. barbata diverged at 1.243 Mya. Comparative analysis of chloroplast genomes can reveal interspecific diversity, and regions with high variation can be used to develop molecular markers applicable to various research areas. Our results provide a new insight into plastome evolution and a valuable resource for further studies on G. barbata.

The complete mitochondrial genome of the edible mushroom Grifola frondosa.

The culinary-medicinal mushroom Grifola frondosa is widely cultivated in East Asia. In this study, the complete mitochondrial genome of G. frondosa was determined using Illumina sequencing. The circular molecule was 197,486 bp in length with a content of 25.01% GC, which was one of the largest mitochondrial genomes in the order Polyporales. A total of 39 known genes encoding 13 common mitochondrial genes, 24 tRNA genes, 1 ribosomal protein s3 gene (rps3), and 1 DNA polymerase gene (dpo) were predicted in this genome. The phylogenetic analysis showed that G. frondosa clustered together with Sparassis crispa, Laetiporus sulphureus, Wolfiporia cocos, and Taiwanofungus camphoratus. The complete mitochondrial genome reported here may provide new insight into genetic information and evolution for further studies.

Factors associated with medical students' self-regulated learning and its relationship with clinical performance: a cross-sectional study.

BACKGROUND: The importance of self-regulated learning (SRL) has been broadly recognised by medical education institutions and medical professionals. Self-regulated learning, which is a context-specific process, is affected by personal, contextual and social factors. Although many studies on exploring the factors that influenced SRL and the relationship of between SRL and clinical achievement levels have been carried out in western countries, little is known about the factors associated with self-regulated learning and its relationship with clinical performance among medical students in China. METHODS: A cross-sectional online survey was distributed to 3rd year clinical medicine students who were in the clinical clerkship stage in a medical college in Wuhan. We used Self-regulated Learning Scale for Undergraduates (SLSU) to measure the self-regulated learning of students and Objective Structured Clinical Examination (OSCE) in the national proficiency test to assess the clinical performance of students. The participation rate was 73.95% (193 students). An independent t-test and analysis of variance were used to analyse the factors associated with self-regulated learning. The relationship between self-regulated learning and clinical performance was analysed with multilinear regression analysis. RESULTS: Univariate analysis showed that having a clear career planning and a professional idol, providing full-time teaching clinical teachers in the clerkship department and seeking the help of the surrounding classmates and the guidance of teachers or senior students were significant predictors of self-regulated learning. Multilinear regression analysis has revealed a positive relationship among extrinsic goals (partial r = 0.171), clinical clerkship evaluation (partial r = 0.197) and clinical performance (F = 4.070, p = 0.004). CONCLUSIONS: Motivation-related personal and social factors related to clinical context could promote the SRL level of medical students in China. Extrinsic goals and clinical clerkship evaluation could facilitate students' clinical achievements on clinical skills. External support, such as clinical clerkship management, might improve clinical performance on clinical skills in clinical clerkship context.