Applying machine-learning models to differentiate benign and malignant thyroid nodules classified as C-TIRADS 4 based on 2D-ultrasound combined with five contrast-enhanced ultrasound key frames.

Objectives: To apply machine learning to extract radiomics features from thyroid two-dimensional ultrasound (2D-US) combined with contrast-enhanced ultrasound (CEUS) images to classify and predict benign and malignant thyroid nodules, classified according to the Chinese version of the thyroid imaging reporting and data system (C-TIRADS) as category 4. Materials and methods: This retrospective study included 313 pathologically diagnosed thyroid nodules (203 malignant and 110 benign). Two 2D-US images and five CEUS key frames ("2nd second after the arrival time" frame, "time to peak" frame, "2nd second after peak" frame, "first-flash" frame, and "second-flash" frame) were selected to manually label the region of interest using the "Labelme" tool. A total of 7 images of each nodule and their annotates were imported into the Darwin Research Platform for radiomics analysis. The datasets were randomly split into training and test cohorts in a 9:1 ratio. Six classifiers, namely, support vector machine, logistic regression, decision tree, random forest (RF), gradient boosting decision tree and extreme gradient boosting, were used to construct and test the models. Performance was evaluated using a receiver operating characteristic curve analysis. The area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy (ACC), and F1-score were calculated. One junior radiologist and one senior radiologist reviewed the 2D-US image and CEUS videos of each nodule and made a diagnosis. We then compared their AUC and ACC with those of our best model. Results: The AUC of the diagnosis of US, CEUS and US combined CEUS by junior radiologist and senior radiologist were 0.755, 0.750, 0.784, 0.800, 0.873, 0.890, respectively. The RF classifier performed better than the other five, with an AUC of 1 for the training cohort and 0.94 (95% confidence interval 0.88-1) for the test cohort. The sensitivity, specificity, accuracy, PPV, NPV, and F1-score of the RF model in the test cohort were 0.82, 0.93, 0.90, 0.85, 0.92, and 0.84, respectively. The RF model with 2D-US combined with CEUS key frames achieved equivalent performance as the senior radiologist (AUC: 0.94 vs. 0.92, P = 0.798; ACC: 0.90 vs. 0.92) and outperformed the junior radiologist (AUC: 0.94 vs. 0.80, P = 0.039, ACC: 0.90 vs. 0.81) in the test cohort. Conclusions: Our model, based on 2D-US and CEUS key frames radiomics features, had good diagnostic efficacy for thyroid nodules, which are classified as C-TIRADS 4. It shows promising potential in assisting less experienced junior radiologists.

Significant improvements in the olfactory sensitivity of bipolar I disorder patients during euthymia versus manic episodes: a longitudinal study.

Introduction: Research has indicated that individuals diagnosed with bipolar disorder (BD) might experience alterations in their olfaction or levels of serum tumor necrosis factor-α (TNF-α), but no studies have investigated olfactory function and serum TNF-α in BD patients simultaneously. Moreover, there is a lack of existing research that compares the longitudinal olfactory function between individuals with manic and euthymic BD I. Methods: Patients with manic BD I (BDM, n=44) and healthy controls (HCs, n=32) were evaluated symptoms (measured via the Young Manic Rating Scale, YRMS), social function (measured via the Global Assessment Function, GAF), serum TNF-α, and olfactory function (via the Sniffin' Sticks test) including olfactory sensitivity (OS) and olfactory identification (OI). The BDM patients were followed up to the remission period and re-evaluated again. We compared OS, OI and serum TNF-α in manic and euthymic patients with BD I and HCs. We examined the correlation between olfactory function and symptoms, social function, and serum TNF-α in patients with BD I. Results: The BDM patients exhibited significantly lower OS and OI compared to the HCs (Z = -2.235, P = 0.025; t = -6.005, P < 0.001), while a positive correlation was observed between OS and GAF score (r = 0.313, P = 0.039). The OS in the BD I remission group (n=25) exhibited significantly superior performance compared to the BDM group (t = -4.056, P < 0.001), and the same as that in the HCs (P = 0.503). The change in OS showed a positive correlation with the decrease in YMRS score (r = 0.445, P = 0.026), and a negative correlation with the course of disease (r = -0.594, P = 0.002). The TNF-α in BD I patients was significantly lower compared to HCs (P < 0.001), and not significantly correlated with olfactory function (all P > 0.05). Conclusion: The findings suggest that OS and OI are impaired in BDM patients, and the impaired OS in those patients can be recovered in the remission stage. OI may serve as a potential characteristic marker of BD. OS might be useful as an index for BDM treatment efficacy and prognosis.

Applying contrast-enhanced ultrasound model to distinguish atypical focal adenomyosis from uterine leiomyomas.

Background: Different from conventional ultrasound, contrast-enhanced ultrasound (CEUS) is better in observing microperfusion. For atypical focal adenomyosis and uterine leiomyomas that are difficult to be distinguished by conventional ultrasound, this study aims to further improve the differential diagnosis performance by using CEUS model. Methods: After screening the cases with difficulties in identifying focal myometrium lesions through conventional ultrasound, the number of cases covered in the focal adenomyosis group and leiomyomas group were 60 and 30 in derivation cohort, 14 and 7 in validation cohort. The qualitative and quantitative characteristics of CEUS were analyzed according to the surgical pathology. The qualitative characteristics include: the enhancement level based on the myometrium, the contrast enhancement pattern, the enhanced time of the lesion based on the myometrium, post-contrast lesion border, the distribution of the contrast agent, and the wash-out time based on the myometrium. The quantitative characteristics include: arrive time (AT), time to peak (TTP), peak intensity (PI), ΔAT, ΔTTP, ΔPI, |ΔAT|, |ΔTTP|, |ΔPI| and lesion temporal variability. Multiple logistic regression analysis was used to screen the independent risk factors, and a risk prediction model for the differential diagnosis of the two diseases was established. The area under the receiver operating characteristic (ROC) curve (AUC) was used to assess the diagnostic performance of the model. The validation cohort was used to further evaluate the diagnostic performance of the model. Results: Through the multivariate analysis, it concluded that short-term vessels first enhanced enhancement mode, unclear boundary, lesion temporal variability under CEUS >9.5 s, uneven contrast agent distribution could be independent risk factors for the diagnosis of adenomyosis [AUC =0.908, 95% confidence interval (CI): 0.833-0.982]. We also determined the sensitivity (98.33%), specificity (70.00%), positive predictive value (PPV) (86.76%), negative predictive value (NPV) (95.45%), and accuracy (87.78%) of this model. Based on pathological diagnosis, the sensitivity and specificity of the model in the validation cohort were both 85.71%, with NPV of 75% and PPV of 92.3%. The area under the ROC curve was 0.898 (95% CI: 0.742-1.000). Conclusions: The establishment of CEUS model has certain clinical application value in differentiating atypical focal adenomyosis from leiomyomas.

Estimation and Potential Analysis of Land Population Carrying Capacity in Shanghai Metropolis.

It is of great practical significance to understand the current situation of urban land carrying capacity, explore its potential space, and continuously improve the economic adaptability and resilience and population carrying capacity of megacities. Based on the guiding principle of territorial spatial division and the concept of moderate-scale resilient cities, combined with GIS technology, this study aims to divide land spaces into three types and construct different index systems to evaluate the land carrying capacity of Shanghai in different spaces. Furthermore, we propose different schemes of estimating subspace land population carrying capacity, and the carrying potential of land population is analysed as well. The acquired results demonstrate three key points. Firstly, the total land population capacity of Shanghai is estimated at 25,476.61-32,047.27 people, with urban land space being the most dominant for the city's population carrying capacity. Furthermore, the inner suburbs carry the largest population, and the urban centre carries a larger population density than other areas. Secondly, there are significant spatial differences in land population carrying potential. Compared with the demographic data from 2017, Shanghai still has a population carrying potential of 1293.30-7863.97 people and a suitable population carrying potential of 4578.64 people. The population of the urban centre is near the upper limit of the estimated population carrying capacity, and the suburbs, especially the outer suburbs, have large population carrying potential. Thirdly, the estimation method adopted in this study can effectively reveal the spatial differences in population carrying capacity and the potential of different land spaces and different regions in Shanghai, with the estimation results being highly credible. The results will provide references for the improvement of the multi-scenario population planning strategy in Shanghai, as well as enrich the research span and methods currently employed in land carrying capacity.

The complete mitochondrial genome of the least horseshoe bat (Rhinolophus pusillus).

In this study, we generated the complete mitochondrial genome of Rhinolophus pusillus using next-generation sequencing. The mitochondrial genome was 16,833 bp in length and contained 13 protein-coding genes, 22 tRNA, 2 rRNA, and a non-coding control region. Phylogenetic analyses supported the taxonomic status of Rhinolophus pusillus among genus Rhinolophus, and the grouping with the sister taxon R. monoceros, which was highly restricted to Taiwan Island.

A Review of Passive RFID Tag Antenna-Based Sensors and Systems for Structural Health Monitoring Applications.

In recent few years, the antenna and sensor communities have witnessed a considerable integration of radio frequency identification (RFID) tag antennas and sensors because of the impetus provided by internet of things (IoT) and cyber-physical systems (CPS). Such types of sensor can find potential applications in structural health monitoring (SHM) because of their passive, wireless, simple, compact size, and multimodal nature, particular in large scale infrastructures during their lifecycle. The big data from these ubiquitous sensors are expected to generate a big impact for intelligent monitoring. A remarkable number of scientific papers demonstrate the possibility that objects can be remotely tracked and intelligently monitored for their physical/chemical/mechanical properties and environment conditions. Most of the work focuses on antenna design, and significant information has been generated to demonstrate feasibilities. Further information is needed to gain deep understanding of the passive RFID antenna sensor systems in order to make them reliable and practical. Nevertheless, this information is scattered over much literature. This paper is to comprehensively summarize and clearly highlight the challenges and state-of-the-art methods of passive RFID antenna sensors and systems in terms of sensing and communication from system point of view. Future trends are also discussed. The future research and development in UK are suggested as well.

Multiprotein-bridging factor 1 regulates vegetative growth, osmotic stress, and virulence in Magnaporthe oryzae.

Multiprotein bridging factor 1 (MBF1) is a transcriptional co-activator that mediates transcriptional activation by bridging sequence-specific activator like proteins and the TATA-box binding protein (TBP). MBF1 has been well-studied in Arabidopsis thaliana, Saccharomyces cerevisiae, Drosophila melanogaster, and Homo sapiens, but it is not well understood in filamentous fungi. In this study, we report the identification and characterization of a MBF1 ortholog (MoMBF1) in the rice blast fungus Magnaporthe oryzae), which causes the devastating rice blast disease and is an ideal model for studying the growth, development and pathogenic mechanisms of filamentous fungi. MoMBF1 encodes a 161 amino acid protein with a typical MBF1 domain and HTH domain. Bioinformatics were used to analyze the structural domains in MoMBF1 and its phylogenetic relationship to other homologs from different organisms. We have generated MoMBF1 deletion mutants (ΔMoMBF1) and functional complementation transformants, and found that the deletion mutants showed significant defects in vegetative growth and tolerance to exogenous stresses, such as 1 M sorbitol, 0.5 M NaCl, and 5 mM H2O2. Moreover, ΔMoMBF1 showed reduced pathogenicity with smaller infection lesions than wild type and the complementation strain, and decreased response to the accumulation of ROS (reactive oxygen species) in planta at the initial infection stage. Taken together, our data indicate that MoMBF1 is required for vegetative growth, pathogenicity and stress response in M. oryzae.