Performance of rapid on-site evaluation of touch imprints of bronchoscopic biopsies or lung tissue biopsies for the diagnosis of invasive pulmonary filamentous fungi infections in non-neutropenic patients.

The diagnosis of invasive pulmonary fungal disease depends on histopathology and mycological culture; there are few studies on touch imprints of bronchoscopic biopsies or lung tissue biopsies for the diagnosis of pulmonary filamentous fungi infections. The purpose of the present study was to explore the detection accuracy of rapid on-site evaluation of touch imprints of bronchoscopic biopsies or lung tissue biopsies for the filamentous fungi, and it aims to provide a basis for initiating antifungal therapy before obtaining microbiological evidence. We retrospectively analyzed the diagnosis and treatment of 44 non-neutropenic patients with invasive pulmonary filamentous fungi confirmed by glactomannan assay, histopathology, and culture from February 2017 to December 2023. The diagnostic positive rate and sensitivity of rapid on-site evaluation for these filamentous fungi identification, including diagnostic turnaround time, were calculated. Compared with the final diagnosis, the sensitivity of rapid on-site evaluation was 81.8%, and the sensitivity of histopathology, culture of bronchoalveolar lavage fluid, and glactomannan assay of bronchoalveolar lavage fluid was 86.4%, 52.3%, and 68.2%, respectively. The average turnaround time of detecting filamentous fungi by rapid on-site evaluation was 0.17 ± 0.03 hours, which was significantly faster than histopathology, glactomannan assay, and mycological culture. A total of 29 (76.3%) patients received earlier antifungal therapy based on ROSE diagnosis and demonstrated clinical improvement. Rapid on-site evaluation showed good sensitivity and accuracy that can be comparable to histopathology in identification of pulmonary filamentous fungi. Importantly, it contributed to the triage of biopsies for further microbial culture or molecular detection based on the preliminary diagnosis, and the decision on early antifungal therapy before microbiological evidence is available.

Machine learning models for diagnosis of essential tremor and dystonic tremor using grey matter morphological networks.

BACKGROUND: Essential tremor (ET) and dystonic tremor (DT) are the two most common tremor disorders, and misdiagnoses are very common due to similar tremor symptoms. In this study, we explore the structural network mechanisms of ET and DT using brain grey matter (GM) morphological networks and combine those with machine learning models. METHODS: 3D-T1 structural images of 75 ET patients, 71 DT patients, and 79 healthy controls (HCs) were acquired. We used voxel-based morphometry to obtain GM images and constructed GM morphological networks based on the Kullback-Leibler divergence-based similarity (KLS) method. We used the GM volumes, morphological relations, and global topological properties of GM-KLS morphological networks as input features. We employed three classifiers to perform the classification tasks. Moreover, we conducted correlation analysis between discriminative features and clinical characteristics. RESULTS: 16 morphological relations features and 1 global topological metric were identified as the discriminative features, and mainly involved the cerebello-thalamo-cortical circuits and the basal ganglia area. The Random Forest (RF) classifier achieved the best classification performance in the three-classification task, achieving a mean accuracy (mACC) of 78.7%, and was subsequently used for binary classification tasks. Specifically, the RF classifier demonstrated strong classification performance in distinguishing ET vs. HCs, ET vs. DT, and DT vs. HCs, with mACCs of 83.0 %, 95.2 %, and 89.3 %, respectively. Correlation analysis demonstrated that four discriminative features were significantly associated with the clinical characteristics. CONCLUSION: This study offers new insights into the structural network mechanisms of ET and DT. It demonstrates the effectiveness of combining GM-KLS morphological networks with machine learning models in distinguishing between ET, DT, and HCs.

Multimodal deep learning-based drought monitoring research for winter wheat during critical growth stages.

Wheat is a major grain crop in China, accounting for one-fifth of the national grain production. Drought stress severely affects the normal growth and development of wheat, leading to total crop failure, reduced yields, and quality. To address the lag and limitations inherent in traditional drought monitoring methods, this paper proposes a multimodal deep learning-based drought stress monitoring S-DNet model for winter wheat during its critical growth periods. Drought stress images of winter wheat during the Rise-Jointing, Heading-Flowering and Flowering-Maturity stages were acquired to establish a dataset corresponding to soil moisture monitoring data. The DenseNet-121 model was selected as the base network to extract drought features. Combining the drought phenotypic characteristics of wheat in the field with meteorological factors and IoT technology, the study integrated the meteorological drought index SPEI, based on WSN sensors, and deep image learning data to build a multimodal deep learning-based S-DNet model for monitoring drought stress in winter wheat. The results show that, compared to the single-modal DenseNet-121 model, the multimodal S-DNet model has higher robustness and generalization capability, with an average drought recognition accuracy reaching 96.4%. This effectively achieves non-destructive, accurate, and rapid monitoring of drought stress in winter wheat.

Case report: Endobronchial closure of postoperative bronchopleural fistula with embolization coil: a sandwich-like approach.

Background: Embolization Coil has been reported to effectively treat postoperative bronchopleural fistula (BPF). Little detailed information was available on computer tomography (CT) imaging features of postoperative BPF and treating procedures with pushable Embolization Coil. Objective: We aimed to specify the imaging characteristics of postoperative BPFs and present our experience treating them with the pushable Embolization Coil. Methods: Six consecutive patients (four males and two females aged 29-56 years) diagnosed with postoperative BPF receiving bronchoscopic treatment with the pushable Nester® Embolization Coil (Cook Medical, Bloomington, Indiana) were included in this single-center, retrospective study. Multiplanar reconstruction of multidetector CT scans was reviewed for the presence, location, size, and radiological complications of each BPF, including air collection, pneumothorax, bronchiectasis, and chest tube. Using standardized data abstraction forms, demographic traits and clinical outcomes were extracted from the medical files of these patients. Results: The underlying diseases for lung resection surgery were pulmonary tuberculosis (n = 3), lung adenocarcinoma (n = 2), and pulmonary aspergillosis (n = 1). All patients had air or air-fluid collection with chest tubes on radiological findings. Multiplanar reconstruction identified the presence of postoperative BPF in all patients. Five fistulas were central, located proximal to the main or lobar bronchus, while one was peripheral, distant from the lobar bronchus. Fistula sizes ranged from 0.8 to 5.8 mm. Subsequent bronchoscopy and occlusion testing confirmed fistula openings in the bronchial stump: right main bronchus (n = 1), right upper lobe (n = 2), and left upper lobe (n = 3). The angioplasty catheter-based procedure allows precise fistula occlusion "like a sandwich" with the pushable Embolization Coil. Five patients with BPF sizes ranging from 0.8 to 1.5 mm were successfully treated with a pushable Embolization Coil, except for one with a BPF size of 5.8 mm. No adverse events or complications were observed throughout follow-up, ranging from 29 to 1,307 days. Conclusion: The pushable Nester® Embolization Coil seems a minimally invasive, cost-effective, and relatively easy-to-perform bronchoscopic treatment for postoperative BPF with a size less than 2 mm. Further studies are required to ensure the use of pushable Embolization Coil in treating postoperative BPF.

Clinical values of different specimen preparation methods for the diagnosis of lung cancer by EBUS-TBNA.

BACKGROUND AND OBJECTIVE: EBUS-TBNA has emerged as an important minimally invasive procedure for the diagnosis and staging of lung cancer. Our objective was to evaluate the effect of different specimen preparation from aspirates on the diagnosis of lung cancer. METHODS: 181 consecutive patients with known or suspected lung cancer accompanied by hilar / mediastinal lymphadenopathy underwent EBUS-TBNA from January 2019 to December 2022. Specimens obtained by EBUS-TBNA were processed by three methods: Traditional smear cytology of aspirates (TSC), liquid-based cytology of aspirates (LBC) and histopathology of core biopsies. RESULTS: EBUS-TBNA was performed in 181 patients on 213 lymph nodes, the total positive rate of the combination of three specimen preparation methods was 80.7%. The diagnostic positive rate of histopathology was 72.3%, TSC was 68.1%, and LBC was 65.3%, no significant differences was observed (p = 0.29); however, statistically significant difference was noted between the combination of three preparation methods and any single specimen preparation methods (p = 0.002). The diagnostic sensitivity of histopathology combined with TSC and histopathology combined with LBC were 96.5 and 94.8%, the specificity was 95.0% and 97.5%, the PPV was 98.8% and 99.4%, the NPV was 86.4% and 81.2%, the diagnostic accuracy was 96.2% and 95.3%, respectively; The sensitivity and accuracy of above methods were higher than that of single specimen preparation, but lower than that of combination of three preparation methods. CONCLUSION: When EBUS-TBNA is used for the diagnosis and staging of lung cancer, histopathology combined with TSC can achieve enough diagnostic efficiency and better cost-effectiveness.

A semiparametric Gaussian mixture model for chest CT-based 3D blood vessel reconstruction.

Computed tomography (CT) has been a powerful diagnostic tool since its emergence in the 1970s. Using CT data, 3D structures of human internal organs and tissues, such as blood vessels, can be reconstructed using professional software. This 3D reconstruction is crucial for surgical operations and can serve as a vivid medical teaching example. However, traditional 3D reconstruction heavily relies on manual operations, which are time-consuming, subjective, and require substantial experience. To address this problem, we develop a novel semiparametric Gaussian mixture model tailored for the 3D reconstruction of blood vessels. This model extends the classical Gaussian mixture model by enabling nonparametric variations in the component-wise parameters of interest according to voxel positions. We develop a kernel-based expectation-maximization algorithm for estimating the model parameters, accompanied by a supporting asymptotic theory. Furthermore, we propose a novel regression method for optimal bandwidth selection. Compared to the conventional cross-validation-based (CV) method, the regression method outperforms the CV method in terms of computational and statistical efficiency. In application, this methodology facilitates the fully automated reconstruction of 3D blood vessel structures with remarkable accuracy.

Combined brain topological metrics with machine learning to distinguish essential tremor and tremor-dominant Parkinson's disease.

BACKGROUND: Essential tremor (ET) and Parkinson's disease (PD) are the two most prevalent movement disorders, sharing several overlapping tremor clinical features. Although growing evidence pointed out that changes in similar brain network nodes are associated with these two diseases, the brain network topological properties are still not very clear. OBJECTIVE: The combination of graph theory analysis with machine learning (ML) algorithms provides a promising way to reveal the topological pathogenesis in ET and tremor-dominant PD (tPD). METHODS: Topological metrics were extracted from Resting-state functional images of 86 ET patients, 86 tPD patients, and 86 age- and sex-matched healthy controls (HCs). Three steps were conducted to feature dimensionality reduction and four frequently used classifiers were adopted to discriminate ET, tPD, and HCs. RESULTS: A support vector machine classifier achieved the best classification performance of four classifiers for discriminating ET, tPD, and HCs with 89.0% mean accuracy (mACC) and was used for binary classification. Particularly, the binary classification performances among ET vs. tPD, ET vs. HCs, and tPD vs. HCs were with 94.2% mACC, 86.0% mACC, and 86.3% mACC, respectively. The most power discriminative features were mainly located in the default, frontal-parietal, cingulo-opercular, sensorimotor, and cerebellum networks. Correlation analysis results showed that 2 topological features negatively and 1 positively correlated with clinical characteristics. CONCLUSIONS: These results demonstrated that combining topological metrics with ML algorithms could not only achieve high classification accuracy for discrimination ET, tPD, and HCs but also help to reveal the potential brain topological network pathogenesis in ET and tPD.

Pulmonary cryptococcosis coexisting with lung adenocarcinoma: A case report and review of the literature.

Pulmonary cryptococcosis (PC) is an invasive pulmonary fungal disease caused by Cryptococcus neoformans or Cryptococcus gattii. It often presents as a single nodule or mass on radiology, which is easily misdiagnosed as lung cancer or metastases. However, cases of PC coexisting with lung cancer are rare and when this scenario is encountered in clinical practice, it is easy to be misdiagnosed as metastatic lung cancer. The present study reported the case of a 65-year-old immunocompetent patient with PC coexisting with lung adenocarcinoma. Percutaneous lung biopsy was performed on the nodule in the anterior segment of the left upper lobe and the nodule in the posterior basal segment of the left lower lobe, which were diagnosed as primary adenocarcinoma and cryptococcus, respectively. Lung cancer was treated by surgery and PC was treated successfully by antifungal treatment. During the 5-year follow-up, contrast-enhanced CT showed no recurrence of either disease. This case reminds us of the possibility of dualism in the diagnosis of multiple pulmonary nodules based on CT examination, such as the coexistence of lung carcinoma and PC. In addition, early diagnosis and treatment contribute to good prognosis.

Endobronchial pigmented mass in a patient with primary malignant melanoma of the lung: A case report.

Malignant melanoma (MM) commonly presents as a primary skin tumor and respiratory MM cases are almost all metastatic. Primary lung MM (PMML) is quite rare, especially when manifested as an endobronchial pigmented mass, its diagnosis is relatively difficult and MM has a poor prognosis. Only a few cases have been described previously and the pathologic features, clinical behavior and therapeutic options are not well established. The present study reports the case of a 72-year-old female patient with PMML who denied any history of tumors. The patient complained of chest pain and coughing for 2 weeks. Chest computed tomography (CT) revealed a mass in the right upper lobe and an enlarged mediastinal lymph node. Positron emission tomogram-CT suggested a hypermetabolic tumor. To confirm the diagnosis, the patient underwent a transbronchial forceps biopsy and endobronchial ultrasound-guided transbronchial needle aspiration, which confirmed the diagnosis of PMML. Genetic testing identified a BRAF V600E mutation, so the patient received treatment with dabrafenib plus trametinib. PMML is extremely rare and is easily misdiagnosed as lung cancer due to its nonspecific clinical manifestations and imaging features. The diagnosis of PMML remains challenging due to its morphologic and immunophenotypic variability. Targeted therapy is a good option for advanced PMML patients with BRAF V600E mutations.

Identification of winter wheat pests and diseases based on improved convolutional neural network.

Wheat pests and diseases are one of the main factors affecting wheat yield. According to the characteristics of four common pests and diseases, an identification method based on improved convolution neural network is proposed. VGGNet16 is selected as the basic network model, but the problem of small dataset size is common in specific fields such as smart agriculture, which limits the research and application of artificial intelligence methods based on deep learning technology in the field. Data expansion and transfer learning technology are introduced to improve the training mode, and then attention mechanism is introduced for further improvement. The experimental results show that the transfer learning scheme of fine-tuning source model is better than that of freezing source model, and the VGGNet16 based on fine-tuning all layers has the best recognition effect, with an accuracy of 96.02%. The CBAM-VGGNet16 and NLCBAM-VGGNet16 models are designed and implemented. The experimental results show that the recognition accuracy of the test set of CBAM-VGGNet16 and NLCBAM-VGGNet16 is higher than that of VGGNet16. The recognition accuracy of CBAM-VGGNet16 and NLCBAM-VGGNet16 is 96.60 and 97.57%, respectively, achieving high precision recognition of common pests and diseases of winter wheat.

An ensemble deep learning model for risk stratification of invasive lung adenocarcinoma using thin-slice CT.

Lung cancer screening using computed tomography (CT) has increased the detection rate of small pulmonary nodules and early-stage lung adenocarcinoma. It would be clinically meaningful to accurate assessment of the nodule histology by CT scans with advanced deep learning algorithms. However, recent studies mainly focus on predicting benign and malignant nodules, lacking of model for the risk stratification of invasive adenocarcinoma. We propose an ensemble multi-view 3D convolutional neural network (EMV-3D-CNN) model to study the risk stratification of lung adenocarcinoma. We include 1075 lung nodules (≤30 mm and ≥4 mm) with preoperative thin-section CT scans and definite pathology confirmed by surgery. Our model achieves a state-of-art performance of 91.3% and 92.9% AUC for diagnosis of benign/malignant and pre-invasive/invasive nodules, respectively. Importantly, our model outperforms senior doctors in risk stratification of invasive adenocarcinoma with 77.6% accuracy [i.e., Grades 1, 2, 3]). It provides detailed predictive histological information for the surgical management of pulmonary nodules. Finally, for user-friendly access, the proposed model is implemented as a web-based system ( https://seeyourlung.com.cn ).

Exudative pleural effusion caused by lung fluke infection: A practical diagnostic approach.

OBJECTIVES: Pleural effusion caused by lung fluke is a rare etiology of exudative pleural effusion (EPE), which is often misdiagnosed or delayed. We aim to summarize the diagnosis and treatment course of EPE caused by lung fluke infection and put forward a practical diagnosis approach. METHODS: We retrospectively analyzed the diagnosis and treatment of 14 cases of EPE caused by lung fluke infection diagnosed by enzyme-linked immunosorbent assay of serum antibodies or egg detection. RESULTS: All patients (100%) with an absolute count of eosinophils in peripheral blood exceeded 0.5 × 109/l, and 10 patients (71.4%) had a history of special ingestion. Eosinophilic PE occurred in 11 patients (78.6%), pleural biopsy of medical thoracoscopic demonstrated eosinophils infiltration in nine patients (64.3%), and parasite eggs in one patient. All patients showed positive intradermal tests for Paragonimus-specific antigens and enzyme-linked immunosorbent assay of serum antibodies to Paragonimus. CONCLUSION: For patients with unexplained PE, lung fluke infection should be highly suspected when pleural fluid or pleural biopsy shows eosinophilic PE or eosinophils infiltration, especially for patients with certain diet history.

Using histogram analysis of the intrinsic brain activity mapping to identify essential tremor.

Background: Essential tremor (ET) is one of the most common movement disorders. Histogram analysis based on brain intrinsic activity imaging is a promising way to identify ET patients from healthy controls (HCs) and further explore the spontaneous brain activity change mechanisms and build the potential diagnostic biomarker in ET patients. Methods: The histogram features based on the Resting-state functional magnetic resonance imaging (Rs-fMRI) data were extracted from 133 ET patients and 135 well-matched HCs as the input features. Then, a two-sample t-test, the mutual information, and the least absolute shrinkage and selection operator methods were applied to reduce the feature dimensionality. Support vector machine (SVM), logistic regression (LR), random forest (RF), and k-nearest neighbor (KNN) were used to differentiate ET and HCs, and classification performance of the established models was evaluated by the mean area under the curve (AUC). Moreover, correlation analysis was carried out between the selected histogram features and clinical tremor characteristics. Results: Each classifier achieved a good classification performance in training and testing sets. The mean accuracy and area under the curve (AUC) of SVM, LR, RF, and KNN in the testing set were 92.62%, 0.948; 92.01%, 0.942; 93.88%, 0.941; and 92.27%, 0.939, respectively. The most power-discriminative features were mainly located in the cerebello-thalamo-motor and non-motor cortical pathways. Correlation analysis showed that there were two histogram features negatively and one positively correlated with tremor severity. Conclusion: Our findings demonstrated that the histogram analysis of the amplitude of low-frequency fluctuation (ALFF) images with multiple machine learning algorithms could identify ET patients from HCs and help to understand the spontaneous brain activity pathogenesis mechanisms in ET patients.

Rapid on-site evaluation of touch imprints of medical thoracoscopy biopsy tissue for the management of pleural disease.

Background and objective: Medical thoracoscopy (MT) plays an important role in the diagnosis and treatment of pleural diseases, and rapid on-site evaluation (ROSE) has long been used for transbronchial needle aspiration or fine-needle aspiration to evaluate the adequacy of biopsy materials for the diagnosis of peripheral lung lesions. However, research on ROSE combined with MT for the management of pleural disease has been rarely reported. We aimed to evaluate the diagnostic performance of ROSE for pleura biopsies and visual diagnosis by thoracoscopists for gross thoracoscopic appearance. The secondary objective was to assess the intermodality agreement between ROSE and the final histopathologic diagnosis. Methods: A total of 579 patients with exudative pleural effusion (EPE) who underwent MT combined with ROSE from February 2017 to December 2020 at Taihe Hospital were included in the study. Thoracoscopists' visual diagnosis of gross thoracoscopic appearance, ROSE results, histopathologic findings, and the final diagnosis was recorded. Results: Thoracoscopic pleural biopsies were performed in 565 patients (97.6%); 183 patients were confirmed to have malignant pleural effusion (MPE), and 382 patients were confirmed to have benign pleural effusion (BPE). The area under the curve of ROSE for the diagnosis of MPE was 0.96 (95% CI: 0.94-0.98, p < 0.001), with a sensitivity of 98.7%, a specificity of 97.2%, a diagnostic accuracy of 97.1%, a positive predictive value of 97.2%, and a negative predictive value of 97.2%. Diagnostic consistency between ROSE and histopathology was good (κ ± SE = 0.93 ± 0.02, p < 0.001). The area under the curve of the thoracoscopists' visual diagnosis of gross thoracoscopic appearance was 0.79 (95% CI: 0.75-0.83, p < 0.01), with a sensitivity of 76.7%, a specificity of 80.9%, a positive predictive value of 62.4%, and a negative predictive value of 89.3%. Conclusion: ROSE of touch imprints of MT biopsy tissue during MT showed high accuracy for distinguishing between benign and malignant lesions. In addition, ROSE was in good agreement with the histopathological diagnosis, which may help thoracoscopists perform pleurodesis (talc poudrage) directly during the procedure, especially in patients with malignant results.

Identification of essential tremor based on resting-state functional connectivity.

Currently, machine-learning algorithms have been considered the most promising approach to reach a clinical diagnosis at the individual level. This study aimed to investigate whether the whole-brain resting-state functional connectivity (RSFC) metrics combined with machine-learning algorithms could be used to identify essential tremor (ET) patients from healthy controls (HCs) and further revealed ET-related brain network pathogenesis to establish the potential diagnostic biomarkers. The RSFC metrics obtained from 127 ET patients and 120 HCs were used as input features, then the Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) methods were applied to reduce feature dimensionality. Four machine-learning algorithms were adopted to identify ET from HCs. The accuracy, sensitivity, specificity and the area under the curve (AUC) were used to evaluate the classification performances. The support vector machine, gradient boosting decision tree, random forest and Gaussian naïve Bayes algorithms could achieve good classification performances with accuracy at 82.8%, 79.4%, 78.9% and 72.4%, respectively. The most discriminative features were primarily located in the cerebello-thalamo-motor and non-motor circuits. Correlation analysis showed that two RSFC features were positively correlated with tremor frequency and four RSFC features were negatively correlated with tremor severity. The present study demonstrated that combining the RSFC matrices with multiple machine-learning algorithms could not only achieve high classification accuracy for discriminating ET patients from HCs but also help us to reveal the potential brain network pathogenesis in ET.

Direct-Maternal Genetic Parameters for Litter Size and Body Weight of Piglets of a New Black Breed for the Taiwan Black Hog Market.

The objective of this study was to estimate the genetic parameters of litter size and piglet weight from farrowing to weaning in KHAPS Black sows. The genetic parameters investigated were the direct (h2d), maternal (h2m), realized (h2r), and total (h2T) heritability, as well as correlations (rd, rm, and rdm) within and between traits. The analyses were performed using single- and three-trait animal models with and without maternal genetic effects. In the three-trait model with maternal genetic effect, all estimates of h2d and h2m were significantly different from zero except the h2d of mean birth weight. Positive values of rd and rm between traits were observed as expected in the range of 0.322-1.000. Negative values of rdm were found within and between traits and were less associated with mean piglet weight traits than litter size traits. Estimates of h2T were consistently larger than those of h2r in both the single- and three-trait model analyses. In addition, the three-trait model can take into account the association between the traits, so the estimates are more accurate with smaller SEs. In conclusion, maternal genetic effects were not negligible in this study, and thus, a multiple-trait animal model with maternal genetic effects and full pedigree is recommended to assist future pig breeding decisions in this new breed.

Combined brain network topological metrics with machine learning algorithms to identify essential tremor.

Background and objective: Essential tremor (ET) is a common movement syndrome, and the pathogenesis mechanisms, especially the brain network topological changes in ET are still unclear. The combination of graph theory (GT) analysis with machine learning (ML) algorithms provides a promising way to identify ET from healthy controls (HCs) at the individual level, and further help to reveal the topological pathogenesis in ET. Methods: Resting-state functional magnetic resonance imaging (fMRI) data were obtained from 101 ET and 105 HCs. The topological properties were analyzed by using GT analysis, and the topological metrics under every single threshold and the area under the curve (AUC) of all thresholds were used as features. Then a Mann-Whitney U-test and least absolute shrinkage and selection operator (LASSO) were conducted to feature dimensionality reduction. Four ML algorithms were adopted to identify ET from HCs. The mean accuracy, mean balanced accuracy, mean sensitivity, mean specificity, and mean AUC were used to evaluate the classification performance. In addition, correlation analysis was carried out between selected topological features and clinical tremor characteristics. Results: All classifiers achieved good classification performance. The mean accuracy of Support vector machine (SVM), logistic regression (LR), random forest (RF), and naïve bayes (NB) was 84.65, 85.03, 84.85, and 76.31%, respectively. LR classifier achieved the best classification performance with 85.03% mean accuracy, 83.97% sensitivity, and an AUC of 0.924. Correlation analysis results showed that 2 topological features negatively and 1 positively correlated with tremor severity. Conclusion: These results demonstrated that combining topological metrics with ML algorithms could not only achieve high classification accuracy for discrimination ET from HCs but also help us to reveal the potential topological pathogenesis of ET.

Combined Intrinsic Local Functional Connectivity With Multivariate Pattern Analysis to Identify Depressed Essential Tremor.

Background: Although depression is one of the most common neuropsychiatric symptoms in essential tremor (ET), the diagnosis biomarker and intrinsic brain activity remain unclear. We aimed to combine multivariate pattern analysis (MVPA) with local brain functional connectivity to identify depressed ET. Methods: Based on individual voxel-level local brain functional connectivity (regional homogeneity, ReHo) mapping from 41 depressed ET, 43 non-depressed ET, and 45 healthy controls (HCs), the binary support vector machine (BSVM) and multiclass Gaussian Process Classification (MGPC) algorithms were used to identify depressed ET patients from non-depressed ET and HCs, the accuracy and permutations test were used to assess the classification performance. Results: The MGPC algorithm was able to classify the three groups (depressed ET, non-depressed ET, and HCs) with a total accuracy of 84.5%. The BSVM algorithm achieved a better classification performance with total accuracy of 90.7, 88.64, and 90.48% for depressed ET vs. HCs, non-depressed ET vs. HCs, and depressed ET vs. non-depressed ET, and the sensitivity for them at 80.49, 76.64, and 80.49%, respectively. The significant discriminative features of depressed ET vs. HCs were primarily located in the cerebellar-motor-prefrontal gyrus-anterior cingulate cortex pathway, and for depressed ET vs. non-depressed ET located in the cerebellar-prefrontal gyrus-anterior cingulate cortex circuits. The partial correlation showed that the ReHo values in the bilateral middle prefrontal gyrus (positive) and the bilateral cerebellum XI (negative) were significantly correlated with clinical depression severity. Conclusion: Our findings suggested that combined individual ReHo maps with MVPA not only could be used to identify depressed ET but also help to reveal the intrinsic brain activity changes and further act as the potential diagnosis biomarker in depressed ET patients.

Application of 3-Dimensionally Printed Coplanar Template Improves Diagnostic Yield of CT-Guided Percutaneous Core Needle Biopsy for Pulmonary Nodules.

Background and Objective: Computed tomography-guided percutaneous lung biopsy is a commonly used method for clarifying the nature of nodules, masses or lung consolidation. However, the diagnostic yield of nodules needs to be improved when compared with masses during percutaneous lung biopsy. In recent years, 3D-printed coplanar templates have been gradually utilized in radioactive seed implantation for lung cancer treatment. However, there is little research on the application of 3D-printed coplanar templates in pulmonary nodules biopsy. Therefore, we conducted a single center and retrospective study to explore the application value of 3D-printed coplanar puncture template-assisted computed tomography-guided percutaneous core needle biopsy of small pulmonary nodules. Methods: 210 patients hospitalized in Taihe Hospital with pulmonary nodules underwent percutaneous core needle biopsy for histopathology diagnosis and were included in the study. 106 patients underwent conventional percutaneous lung biopsy (control group) and 104 patients underwent 3D-PCT-assisted percutaneous lung biopsy (3D-PCT group). The diagnostic yield and incidence of complications were recorded and compared between the two groups. Results: The overall diagnostic yield significantly improved in 3D-PCT group (95.2%) compared with Control group (87.7%) (P < .05); the diagnostic yield for lung nodules smaller than 2 cm in the 3D-PCT group and the control group was 94.4% and 80.5%, respectively, (P < .05). Incidence of pneumothorax (17.3% vs 18.9%) and pulmonary hemorrhage (7.7% vs 9.4%) were not significantly difference between the two groups (P > .05). Conclusions: Studies indicated that application of 3-Dimensionally printed coplanar template improves diagnostic yield of CT-guided percutaneous core needle biopsy for pulmonary nodules, especially for pulmonary nodule smaller than 2 cm.