Inverse relationship between HBV DNA levels and liver histopathological changes in immune-tolerant CHB patients.

Limited data exist regarding the association between hepatitis B virus (HBV) DNA levels and liver histopathological changes in patients with chronic hepatitis B (CHB) during the immune tolerant (IT) phase. In this study, we retrospectively analysed liver biopsy results from 150 adult IT-CHB patients. The liver tissue necroinflammation and fibrosis were evaluated by the Scheuer scoring system. Multivariate logistic regression, smooth curve fitting, and segmented regression models were used to examine the association between HBV DNA levels and liver histopathological changes. A total of 26%, 30.67% and 42% of IT patients had significant necroinflammation (≥G2), significant fibrosis (≥S2) and significant histopathological changes (≥G2 and/or ≥S2), respectively. HBV DNA levels were independently and non-linear inversely associated with significant necroinflammation and histopathological changes in IT-CHB patients. Patients with HBV DNA levels <107 IU/mL had a higher risk of significant histopathological changes compared to those with levels >107 IU/mL. The findings were further confirmed by smooth curve fitting analyses, subgroup and sensitivity analyses. In segmented regression model analyses, the optimal DNA value for the lowest odds ratio of significant histopathological changes was 7.26 log10 IU/mL. A non-linear inverse association between HBV DNA levels and significant histopathological changes in IT-CHB patients. DNA 7.26 log10 IU/mL may serve as a potential cut-off point to define a 'true immune tolerant phase' with minimal liver histopathological changes.

[Design and implementation of a modular pulse wave preprocessing and analysis system based on a new detection algorithm].

As one of the standard electrophysiological signals in the human body, the photoplethysmography contains detailed information about the blood microcirculation and has been commonly used in various medical scenarios, where the accurate detection of the pulse waveform and quantification of its morphological characteristics are essential steps. In this paper, a modular pulse wave preprocessing and analysis system is developed based on the principles of design patterns. The system designs each part of the preprocessing and analysis process as independent functional modules to be compatible and reusable. In addition, the detection process of the pulse waveform is improved, and a new waveform detection algorithm composed of screening-checking-deciding is proposed. It is verified that the algorithm has a practical design for each module, high accuracy of waveform recognition and high anti-interference capability. The modular pulse wave preprocessing and analysis software system developed in this paper can meet the individual preprocessing requirements for various pulse wave application studies under different platforms. The proposed novel algorithm with high accuracy also provides a new idea for the pulse wave analysis process.

Global Quantification of Glutathione S-Transferases in Human Serum Using LC-MS/MS Coupled with Affinity Enrichment.

The members of the glutathione S-transferase (GST) superfamily often exhibit functional overlap and can compensate for each other. Their concentrations in serum are considered as disease biomarkers. A global and quantitative evaluation of serum GSTs is therefore urgent, but there is a lack of efficient approaches due to technological limitations. GSH magnetic beads were examined for their affinity to enrich GSTs in serum, and the enriched GSTs were quantitatively targeted using a Q Exactive HF-X mass spectrometer in parallel reaction monitoring (PRM) mode. To optimize the quantification of GST peptides, sample types, trypsin digestion, and serum loading were carefully assessed; a biosynthetic method was employed to generate isotope-labeled GST peptides, and instrumental parameters were systematically optimized. A total of 134 clinical sera were collected for GST quantification from healthy donors and patients with four liver diseases. Using the new approach, GSTs in healthy sera were profiled: 14 GST peptides were quantified, and the abundance of five GST families was ranked GSTM > GSTP > GSTA > MGST1 > GSTT1, ranging from 0.1 to 4 pmol/L. Furthermore, combining the abundance of multiple GST peptides could effectively distinguish different types of liver diseases. Quantification of serum GSTs through targeted proteomics, therefore, has apparent clinical potential for disease diagnosis.

MELD score < 18 rule out 28-day ACLF development among inpatients with hepatitis B-related previous compensated liver disease.

The acute-on-chronic liver failure (ACLF) development is highly dynamic. Currently, no satisfactory algorithm identifies patients with HBV at risk of this complication. The aim of the study was to characterize ACLF development in hospitalized HBV-related patients without previous decompensation and to test the performance of traditional prognostic models in ruling out ACLF development within 28 days on admission we conducted a cohort study. Two multi-center cohorts with hospitalized HBV-related previous compensated patients were analyzed. Performances of MELD, MELD-Na, CLIF-C AD, and CLIF-C ACLF-D in ruling out ACLF development within 28 days were compared and further validated by ROC analyses. In the derivation cohort (n = 892), there were 102 patients developed ACLF within 28 days, with profound systemic inflammatory levels and higher 28-day mortality rate (31.4% vs. 1.0%) than those without ACLF development. The MELD score (cut-off = 18) achieved acceptable missing rate (missed/total ACLF development) at 2.9%. In the validation cohort (n = 1656), the MELD score (<18) was able to rule out ACLF development within 28 days with missing rate at 3.0%. ACLF development within 28 days were both lower than 1% (0.6%, derivation cohort; 0.5%, validation cohort) in patients with MELD < 18. While in patients with MELD ≥ 18, 26.6% (99/372, derivation cohort) and 17.8% (130/732, validation cohort) developed into ACLF within 28 days, respectively. While MELD-Na score cut-off at 20 and CLIF-AD score cut-off at 42 did not have consistent performance in our two cohorts. MELD < 18 was able to safely rule out patients with ACLF development within 28 days in HBV-related patients without previous decompensation, which had a high 28-day mortality.

Tri-typing of hepatitis B-related acute-on-chronic liver failure defined by the World Gastroenterology Organization.

BACKGROUND AND AIM: Tri-typing of acute-on-chronic liver failure (ACLF), as proposed by the World Gastroenterology Organization (WGO), has not been validated in patients infected with hepatitis B virus (HBV). We aim to compare the three types of ACLF patients in clinic characteristics. METHODS: Hospitalized ACLF patients with chronic hepatitis B from five hepatology centers were retrospectively selected and grouped according to the WGO classification. For each group, we investigated laboratory tests, precipitating events, organ failure, and clinical outcome. RESULTS: Compared with type-B (n = 262, compensated cirrhosis) and type-C (n = 129, decompensated cirrhosis) ACLF, type-A patients (n = 195, non-cirrhosis) were associated with a younger age, the highest platelet counts, the highest aminotransferase levels, and the most active HBV replications. HBV reactivation were more predominant in type-A, while bacterial infections in type-B and type-C ACLF cases. Liver failure (97.4%) and coagulation failure (86.7%) were most common in type-A compared with type-B or type-C ACLF patients. Kidney failure was predominantly identified in type-C subjects (41.9%) and was highest (23/38, 60.5%) in grade 1 ACLF patients. Furthermore, type-C ACLF showed the highest 28-day (65.2%) and 90-day (75.3%) mortalities, compared with type-A (48.7% and 54.4%, respectively) and type-B (48.4% and 62.8%, respectively) ACLF cases. Compared with type-A (11.7%) ACLF patients, the increased mortality from 28 to 90 days was higher in type-B (31.6%) and type-C (37.5%). CONCLUSION: Tri-typing of HBV-related ACLF in accordance with the WGO definition was able to distinguish clinical characteristics, including precipitating events, organ failure, and short-term prognosis in ACLF patients.

COVID-19: Abnormal liver function tests.

BACKGROUND & AIMS: Recent data on the coronavirus disease 2019 (COVID-19) outbreak caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has begun to shine light on the impact of the disease on the liver. But no studies to date have systematically described liver test abnormalities in patients with COVID-19. We evaluated the clinical characteristics of COVID-19 in patients with abnormal liver test results. METHODS: Clinical records and laboratory results were obtained from 417 patients with laboratory-confirmed COVID-19 who were admitted to the only referral hospital in Shenzhen, China from January 11 to February 21, 2020 and followed up to March 7, 2020. Information on clinical features of patients with abnormal liver tests were collected for analysis. RESULTS: Of 417 patients with COVID-19, 318 (76.3%) had abnormal liver test results and 90 (21.5%) had liver injury during hospitalization. The presence of abnormal liver tests became more pronounced during hospitalization within 2 weeks, with 49 (23.4%), 31 (14.8%), 24 (11.5%) and 51 (24.4%) patients having alanine aminotransferase, aspartate aminotransferase, total bilirubin and gamma-glutamyl transferase levels elevated to more than 3× the upper limit of normal, respectively. Patients with abnormal liver tests of hepatocellular type or mixed type at admission had higher odds of progressing to severe disease (odds ratios [ORs] 2.73; 95% CI 1.19-6.3, and 4.44, 95% CI 1.93-10.23, respectively). The use of lopinavir/ritonavir was also found to lead to increased odds of liver injury (OR from 4.44 to 5.03, both p <0.01). CONCLUSION: Patients with abnormal liver tests were at higher risk of progressing to severe disease. The detrimental effects on liver injury mainly related to certain medications used during hospitalization, which should be monitored and evaluated frequently. LAY SUMMARY: Data on liver tests in patients with COVID-19 are scarce. We observed a high prevalence of liver test abnormalities and liver injury in 417 patients with COVID-19 admitted to our referral center, and the prevalence increased substantially during hospitalization. The presence of abnormal liver tests and liver injury were associated with the progression to severe pneumonia. The detrimental effects on liver injury were related to certain medications used during hospitalization, which warrants frequent monitoring and evaluation for these patients.

Chloride intracellular channel 4 participate in the protective effect of Ginkgolide B in MPP+ injured MN9D cells: insight from proteomic analysis.

BACKGROUND: Ginkgolide B (GB), the extract of G. biloba leaves, has been shown to be protective against many neurological disorders, including Parkinson's disease (PD). Efforts have been made to synthesized ginkgolides analogs and derivatives with more targeted and smaller molecular weight. In the present study, four GB derivatives (GBHC-1-GBHC-4) were synthesized, and their protective roles in N-methyl-4-phenylpyridinium (MPP +) injured MN9D dopaminergic neuronal cell line were evaluated. Also, cell response mechanisms upon these GB derivatives treatment were analyzed by iTRAQ proteomics. METHODS: MN9D cells were treated with MPP + to induce in vitro cell models of PD. Four GB derivatives (GBHC-1-GBHC-4) were synthesized, and their protective roles on cell viability and apoptosis in in vitro PD model cells were evaluated by CCK8 assay, fluorescence-activated cell sorting and DAPI staining, respectively. The proteomic profiles of MPP+ injured MN9D cells pretreated with or without GB and GB derivatives were detected using the isobaric tags for relative and absolute quantification (iTRAQ) labeling technique. RESULTS: Pretreatment with GBHC-1-GBHC-4 noticeably increased cell viability and attenuated cell apoptosis in MPP+ -injured MN9D cells. Using proteomic analysis, we identified differentially expressed proteins upon GB and GB derivatives treatment. Chloride intracellular channel 4 (CLIC4) and "protein processing in endoplasmic reticulum" pathways participated in the protective roles of GB and GBHC-4. GB and GBHC-4 pretreatment could significantly reverse MPP+ -induced CLIC4 expression and translocation from cytoplasm to nucleus of MN9D cells. CONCLUSIONS: Quantitative comparative proteomic analysis identified differentially expressed proteins associated with GB and GB derivatives. We further verified the expression of CLIC4 by western blotting and immunocytochemistry assay. This bio-information on the identified pathways and differentially expressed proteins such as CLIC4 provide more targeted directions for the synthesis of more effective and targeted GB derivatives for the treatment of neurological disorders.

COVID-19 in a designated infectious diseases hospital outside Hubei Province, China.

BACKGROUND: The clinical characteristics of novel coronavirus disease (COVID-2019) patients outside the epicenter of Hubei Province are less understood. METHODS: We analyzed the epidemiological and clinical features of all COVID-2019 cases in the only referral hospital in Shenzhen City, China, from January 11, 2020, to February 6, 2020, and followed until March 6, 2020. RESULTS: Among the 298 confirmed cases, 233 (81.5%) had been to Hubei, while 42 (14%) did not have a clear travel history. Only 218 (73.15%) cases had a fever as the initial symptom. Compared with the nonsevere cases, severe cases were associated with older age, those with underlying diseases, and higher levels of C-reactive protein, interleukin-6, and erythrocyte sedimentation rate. Slower clearance of the virus was associated with a higher risk of progression to critical condition. As of March 6, 2020, 268 (89.9%) patients were discharged and the overall case fatality ratio was 1.0%. CONCLUSIONS: In a designated hospital outside Hubei Province, COVID-2019 patients could be effectively managed by properly using the existing hospital system. Mortality may be lowered when cases are relatively mild, and there are sufficient medical resources to care and treat the disease.

Solitary solid pulmonary nodules: a CT-based deep learning nomogram helps differentiate tuberculosis granulomas from lung adenocarcinomas.

OBJECTIVES: To evaluate the differential diagnostic performance of a computed tomography (CT)-based deep learning nomogram (DLN) in identifying tuberculous granuloma (TBG) and lung adenocarcinoma (LAC) presenting as solitary solid pulmonary nodules (SSPNs). METHODS: Routine CT images of 550 patients with SSPNs were retrospectively obtained from two centers. A convolutional neural network was used to extract deep learning features from all lesions. The training set consisted of data for 218 patients. The least absolute shrinkage and selection operator logistic regression was used to create a deep learning signature (DLS). Clinical factors and CT-based subjective findings were combined in a clinical model. An individualized DLN incorporating DLS, clinical factors, and CT-based subjective findings was constructed to validate the diagnostic ability. The performance of the DLN was assessed by discrimination and calibration using internal (n = 140) and external validation cohorts (n = 192). RESULTS: DLS, gender, age, and lobulated shape were found to be independent predictors and were used to build the DLN. The combination showed better diagnostic accuracy than any single model evaluated using the net reclassification improvement method (p < 0.05). The areas under the curve in the training, internal validation, and external validation cohorts were 0.889 (95% confidence interval [CI], 0.839-0.927), 0.879 (95% CI, 0.813-0.928), and 0.809 (95% CI, 0.746-0.862), respectively. Decision curve analysis and stratification analysis showed that the DLN has potential generalization ability. CONCLUSIONS: The CT-based DLN can preoperatively distinguish between LAC and TBG in patients presenting with SSPNs. KEY POINTS: • The deep learning nomogram was developed to preoperatively differentiate TBG from LAC in patients with SSPNs. • The performance of the deep learning feature was superior to that of the radiomics feature. • The deep learning nomogram achieved superior performance compared to the deep learning signature, the radiomics signature, or the clinical model alone.

Segmentation of small ground glass opacity pulmonary nodules based on Markov random field energy and Bayesian probability difference.

BACKGROUND: Image segmentation is an important part of computer-aided diagnosis (CAD), the segmentation of small ground glass opacity (GGO) pulmonary nodules is beneficial for the early detection of lung cancer. For the segmentation of small GGO pulmonary nodules, an integrated active contour model based on Markov random field energy and Bayesian probability difference (IACM_MRFEBPD) is proposed in this paper. METHODS: First, the Markov random field (MRF) is constructed on the computed tomography (CT) images, then the MRF energy is calculated. The MRF energy is used to construct the region term. It can not only enhance the contrast between pulmonary nodule and the background region, but also solve the problem of intensity inhomogeneity using local spatial correlation information between neighboring pixels in the image. Second, the Gaussian mixture model is used to establish the probability model of the image, and the model parameters are estimated by the expectation maximization (EM) algorithm. So the Bayesian posterior probability difference of each pixel can be calculated. The probability difference is used to construct the boundary detection term, which is 0 at the boundary. Therefore, the blurred boundary problem can be solved. Finally, under the framework of the level set, the integrated active contour model is constructed. RESULTS: To verify the effectiveness of the proposed method, the public data of the lung image database consortium and image database resource initiative (LIDC-IDRI) and the clinical data of the Affiliated Jiangmen Hospital of Sun Yat-sen University are used to perform experiments, and the intersection over union (IOU) score is used to evaluate the segmentation methods. Compared with other methods, the proposed method achieves the best results with the highest average IOU of 0.7444, 0.7503, and 0.7450 for LIDC-IDRI test set, clinical test set, and all test sets, respectively. CONCLUSIONS: The experiment results show that the proposed method can segment various small GGO pulmonary nodules more accurately and robustly, which is helpful for the accurate evaluation of medical imaging.

Correction to: LncRNA NEAT1 mediates progression of oral squamous cell carcinoma via VEGF-A and Notch signaling pathway.

An amendment to this paper has been published and can be accessed via the original article.

LncRNA NEAT1 mediates progression of oral squamous cell carcinoma via VEGF-A and Notch signaling pathway.

BACKGROUND: lncRNAs and VEGF have been shown to have close connections with oral squamous cell carcinoma (OSCC). We explored the interaction between lncRNA NEAT1 and VEGF-A in OSCC. METHODS: RT-qPCR was implemented to measure levels of lncRNA NEAT1 and VEGF-A in OSCC cell lines and normal cell lines. Cell functions then were checked after regulating the expressions of lncRNA NEAT1 and VEGF-A separately. Cell viabilities were examined with CCK-8 and apoptosis rate was checked with flow cytometry. Meanwhile, EMT-related genes E-cadherin, N-cadherin, Vimentin, and Snail and Notch signaling genes Notch1, Notch2, and Jagged were evaluated by RT-qPCR. IMR-1 was applied for impeding Notch signaling pathway. Later, cell viabilities, apoptosis, and EMT were assessed. RESULTS: Expressions of lncRNA NEAT1 and VEGF-A were both increased significantly in OSCC cell lines especially in TSCC1 cell line. Suppression of lncNRA NEAT1 was associated with lower cell viabilities and EMT and higher apoptosis rate in the TSCC1 cell line. Meanwhile, knockdown of VEGF-A significantly repressed cell viabilities and EMT in the TSCC1 cell line. Magnifying functions of inhibited lncRNA NEAT1 Notch signaling pathway was obviously activated with overexpressions of lncRNA NEAT1 and VEGF-A. Adding IMR-1 significantly downregulated cell viabilities and EMT and sharply increased apoptosis in the context of lncRNA NEAT1 and VEGF-A overexpression. CONCLUSION: LncRNA NEAT1 may upregulate proliferation and EMT and repress apoptosis through activating VEGF-A and Notch signaling pathway in vitro, suggesting an underlying regulatory factor in OSCC. Nevertheless, further research is necessary to gain a greater understanding of lncRNA NEAT1 and connections with VEGF-A in vivo and in clinical study.

The CSP-Based New Features Plus Non-Convex Log Sparse Feature Selection for Motor Imagery EEG Classification.

The common spatial pattern (CSP) is a very effective feature extraction method in motor imagery based brain computer interface (BCI), but its performance depends on the selection of the optimal frequency band. Although a lot of research works have been proposed to improve CSP, most of these works have the problems of large computation costs and long feature extraction time. To this end, three new feature extraction methods based on CSP and a new feature selection method based on non-convex log regularization are proposed in this paper. Firstly, EEG signals are spatially filtered by CSP, and then three new feature extraction methods are proposed. We called them CSP-wavelet, CSP-WPD and CSP-FB, respectively. For CSP-Wavelet and CSP-WPD, the discrete wavelet transform (DWT) or wavelet packet decomposition (WPD) is used to decompose the spatially filtered signals, and then the energy and standard deviation of the wavelet coefficients are extracted as features. For CSP-FB, the spatially filtered signals are filtered into multiple bands by a filter bank (FB), and then the logarithm of variances of each band are extracted as features. Secondly, a sparse optimization method regularized with a non-convex log function is proposed for the feature selection, which we called LOG, and an optimization algorithm for LOG is given. Finally, ensemble learning is used for secondary feature selection and classification model construction. Combing feature extraction and feature selection methods, a total of three new EEG decoding methods are obtained, namely CSP-Wavelet+LOG, CSP-WPD+LOG, and CSP-FB+LOG. Four public motor imagery datasets are used to verify the performance of the proposed methods. Compared to existing methods, the proposed methods achieved the highest average classification accuracy of 88.86, 83.40, 81.53, and 80.83 in datasets 1-4, respectively. The feature extraction time of CSP-FB is the shortest. The experimental results show that the proposed methods can effectively improve the classification accuracy and reduce the feature extraction time. With comprehensive consideration of classification accuracy and feature extraction time, CSP-FB+LOG has the best performance and can be used for the real-time BCI system.

An accelerated alternating direction method of multiplier for MRI with TV regularisation.

Compressed Sensing (CS) is important in the field of image processing and signal processing, and CS-Magnetic Resonance Imaging (MRI) is used to reconstruct image from undersampled k-space data. Total Variation (TV) regularisation is a common technique to improve the sparsity of image, and the Alternating Direction Multiplier Method (ADMM) plays a key role in the variational image processing problem. This paper aims to improve the quality of MRI and shorten the reconstruction time. We consider MRI to solve a linear inverse problem, we convert it into a constrained optimization problem based on TV regularisation, then an accelerated ADMM is established. Through a series of theoretical derivations, we verify that the algorithm satisfies the convergence rate of O1/k2 under the condition that one objective function is quadratically convex and the other is strongly convex. We select five undersampled templates for testing in MRI experiment and compare it with other algorithms, experimental results show that our proposed method not only improves the running speed but also gives better reconstruction results.

Secoemestrin C Ameliorates Psoriasis-like Skin Inflammation in Mice by Suppressing the TNF-α/NF-κB Signaling Pathway.

OBJECTIVE: Secoemestrin C (SC), an epitetrathiodioxopiperazine isolated from Aspergillus nidulans, has been previously reported to have immunomodulatory and hepatoprotective effects against acute autoimmune hepatitis. However, the effect of SC on regulating the inflammation and its underlying mechanisms in the pathogenesis of psoriasis remain unclear. This study aimed to evaluate the effects of SC on inflammatory dermatosis both in vitro and in vivo. METHODS: In vitro, HaCaT cells were induced with tumor necrosis factor-alpha (TNF-α, 10 ng/mL) to establish an inflammatory injury model, and the expression of nuclear transcription factor-κB (NF-κB) pathway components was measured using qRT-PCR and Western blotting. An in vivo mouse model of imiquimod (IMQ)-induced psoriasis-like skin inflammation was used to evaluate the effectiveness of SC in alleviating psoriasis. RESULTS: SC significantly blocked the activation of NF-κB signaling in TNF-α-stimulated HaCaT cells. In addition, systemic and local administration of SC improved psoriatic dermatitis in the IMQ-induced mouse model. SC reduced skin scale and significantly inhibited the secretion of inflammatory factors in skin lesions. CONCLUSION: The protective effect of SC against psoriatic-associated inflammation reveals its potential therapeutic value for treating psoriasis.

A transformer-guided cross-modality adaptive feature fusion framework for esophageal gross tumor volume segmentation.

BACKGROUND AND OBJECTIVE: Accurate segmentation of esophageal gross tumor volume (GTV) indirectly enhances the efficacy of radiotherapy for patients with esophagus cancer. In this domain, learning-based methods have been employed to fuse cross-modality positron emission tomography (PET) and computed tomography (CT) images, aiming to improve segmentation accuracy. This fusion is essential as it combines functional metabolic information from PET with anatomical information from CT, providing complementary information. While the existing three-dimensional (3D) segmentation method has achieved state-of-the-art (SOTA) performance, it typically relies on pure-convolution architectures, limiting its ability to capture long-range spatial dependencies due to convolution's confinement to a local receptive field. To address this limitation and further enhance esophageal GTV segmentation performance, this work proposes a transformer-guided cross-modality adaptive feature fusion network, referred to as TransAttPSNN, which is based on cross-modality PET/CT scans. METHODS: Specifically, we establish an attention progressive semantically-nested network (AttPSNN) by incorporating the convolutional attention mechanism into the progressive semantically-nested network (PSNN). Subsequently, we devise a plug-and-play transformer-guided cross-modality adaptive feature fusion model, which is inserted between the multi-scale feature counterparts of a two-stream AttPSNN backbone (one for the PET modality flow and another for the CT modality flow), resulting in the proposed TransAttPSNN architecture. RESULTS: Through extensive four-fold cross-validation experiments on the clinical PET/CT cohort. The proposed approach acquires a Dice similarity coefficient (DSC) of 0.76 ± 0.13, a Hausdorff distance (HD) of 9.38 ± 8.76 mm, and a Mean surface distance (MSD) of 1.13 ± 0.94 mm, outperforming the SOTA competing methods. The qualitative results show a satisfying consistency with the lesion areas. CONCLUSIONS: The devised transformer-guided cross-modality adaptive feature fusion module integrates the strengths of PET and CT, effectively enhancing the segmentation performance of esophageal GTV. The proposed TransAttPSNN has further advanced the research of esophageal GTV segmentation.

Combining quantitative and qualitative analysis for scoring pleural line in lung ultrasound.

Objective.Accurate assessment of pleural line is crucial for the application of lung ultrasound (LUS) in monitoring lung diseases, thereby aim of this study is to develop a quantitative and qualitative analysis method for pleural line.Approach.The novel cascaded deep learning model based on convolution and multilayer perceptron was proposed to locate and segment the pleural line in LUS images, whose results were applied for quantitative analysis of textural and morphological features, respectively. By using gray-level co-occurrence matrix and self-designed statistical methods, eight textural and three morphological features were generated to characterize the pleural lines. Furthermore, the machine learning-based classifiers were employed to qualitatively evaluate the lesion degree of pleural line in LUS images.Main results.We prospectively evaluated 3770 LUS images acquired from 31 pneumonia patients. Experimental results demonstrated that the proposed pleural line extraction and evaluation methods all have good performance, with dice and accuracy of 0.87 and 94.47%, respectively, and the comparison with previous methods found statistical significance (P< 0.001 for all). Meanwhile, the generalization verification proved the feasibility of the proposed method in multiple data scenarios.Significance.The proposed method has great application potential for assessment of pleural line in LUS images and aiding lung disease diagnosis and treatment.

Liver histological changes in untreated chronic hepatitis B patients in indeterminate phase.

BACKGROUND: Studies with large size samples on the liver histological changes of indeterminate phase chronic hepatitis B (CHB) patients were not previously conducted. AIM: To assess the liver histological changes in the indeterminate phase CHB patients using liver biopsy. METHODS: The clinical and laboratory data of 1532 untreated CHB patients were collected, and all patients had least once liver biopsy from January 2015 to December 2021. The significant differences among different phases of CHB infection were compared with t-test, and the risk factors of significant liver histological changes were analyzed by the multivariate logistic regression analysis. RESULTS: Among 1532 untreated CHB patients, 814 (53.13%) patients were in the indeterminate phase. Significant liver histological changes (defined as biopsy score ≥ G2 and/or ≥ S2) were found in 488/814 (59.95%) CHB patients in the indeterminate phase. Significant liver histological changes were significant differences among different age, platelets (PLTs), and alanine aminotransferase (ALT) subgroup in indeterminate patient. Multivariate logistic regression analysis indicated that age ≥ 40 years old [adjust odd risk (aOR), 1.44; 95% confidence interval (CI): 1.06-1.97; P = 0.02], PLTs ≤ 150 × 109/L (aOR, 2.99; 95%CI: 1.85-4.83; P < 0.0001), and ALT ≥ upper limits of normal (aOR, 1.48; 95%CI: 1.08, 2.05, P = 0.0163) were independent risk factors for significant liver histological changes in CHB patients in the indeterminate phase. CONCLUSION: Our results suggested that significant liver histological changes were not rare among the untreated CHB patients in indeterminate phase, and additional strategies are urgently required for the management of these patients.

Computer-aided diagnosis of cystic lung diseases using CT scans and deep learning.

BACKGROUND: Auxiliary diagnosis of different types of cystic lung diseases (CLDs) is important in the clinic and is instrumental in facilitating early and specific treatments. Current clinical methods heavily depend on accumulated experience, restricting their applicability in regions with less developed medical resources. Thus, how to realize the computer-aided diagnosis of CLDs is of great clinical value. PURPOSE: This work proposed a deep learning-based method for automatically segmenting the lung parenchyma in computed tomography (CT) slice images and accurately diagnosing the CLDs using CT scans. METHODS: A two-stage deep learning method was proposed for the automatic classification of normal cases and five different CLDs using CT scans. Lung parenchyma segmentation is the foundation of CT image analysis and auxiliary diagnosis. To meet the requirements of different sizes of the lung parenchyma, an adaptive region-growing and improved U-Net model was employed for mask acquisition and automatic segmentation. The former was achieved by a self-designed adaptive seed point selection method based on similarity measurement, and the latter introduced multiscale input and multichannel output into the original U-Net model and effectively achieved the lightweight design by adjusting the structure and parameters. After that, the middle 30 consecutive CT slice images of each sample were segmented to obtain lung parenchyma, which was employed for training and testing the proposed multichannel parallel input recursive MLP-Mixer network (MPIRMNet) model, achieving the computer-aided diagnosis of CLDs. RESULTS: A total of 4718 and 16 290 CT slice images collected from 543 patients were employed to validate the proposed segmentation and classification methods, respectively. Experimental results showed that the improved U-Net model can accurately segment the lung parenchyma in CT slice images, with the Dice, precision, volumetric overlap error, and relative volume difference of 0.96 ± 0.01, 0.93 ± 0.04, 0.05 ± 0.02, and 0.05 ± 0.03, respectively. Meanwhile, the proposed MPIRMNet model achieved appreciable classification effect for normal cases and different CLDs, with the accuracy, sensitivity, specificity, and F1 score of 0.8823 ± 0.0324, 0.8897 ± 0.0325, 0.9746 ± 0.0078, and 0.8831 ± 0.0334, respectively. Compared with classical machine learning and convolutional neural networks-based methods for this task, the proposed classification method had a preferable performance, with a significant improvement of accuracy of 10.74%. CONCLUSIONS: The work introduced a two-stage deep learning method, which can achieve the segmentation of lung parenchyma and the classification of CLDs. Compared to previous diagnostic tasks targeting single CLD, this work can achieve various CLDs' diagnosis in the early stage, thereby achieving targeted treatment and increasing the potential and value of clinical applications.

Global temporal trends and projections of acute hepatitis E incidence among women of childbearing age: Age-period-cohort analysis 2021.

BACKGROUND & AIMS: Acute hepatitis E (AHE) poses a significant threat to global public health, particularly among women of childbearing age (WCBA), who are at heightened risk for severe pregnancy-related complications. This study aimed to delineate the temporal trends and project future incidence of AHE in WCBA, providing insights crucial for targeted prevention and control strategies. METHODS: Data on AHE incidence from the Global Health data 2021. The age-period-cohort (APC) model was applied to analyze trends across different age groups, periods, and birth cohorts, and the Bayesian APC model was utilized for forecasting future epidemiological trajectories. RESULTS: Globally, AHE incidence numbers among WCBA rose from 2,831,075 in 1992 to 3,420,786 in 2021, while the age-standardized incidence rate (ASIR) declined from 194.66 to 179.54 per 100,000 with a global net drift of -0.28%. However, high SDI regions showed a contrasting trend with a positive net drift of 0.02%. The age effect was consistent across SDI regions and globally, showing a decrease with advancing age, while unfavorable period and cohort effects were exhibited in high-SDI region. At the national level, locations exhibited varying trends of change. The BAPC model predicted a total of 3,759,384 AHE global cases in WCBA by 2030, with an expected mild increase in the ASIR. The outlook for the management and containment of AHE is grim in certain countries, including India. CONCLUSIONS: The study revealed a complex epidemiological landscape of AHE in WCBA, with increasing global incidence numbers juxtaposed against a declining ASIR. The AHE burden by 2030 remain severe among WCBA. Young WCBA and high SDI region merit particular attention. The findings underscore the need for region-specific strategies to curb the projected rise in AHE incidence and align with the 2030 WHO goals.