Homogeneous Space Construction and Projection for Single-Cell Expression Prediction Based on Deep Learning.

Predicting cellular responses to perturbations is an unsolved problem in biology. Traditional approaches assume that different cell types respond similarly to perturbations. However, this assumption does not take into account the context of genome interactions in different cell types, which leads to compromised prediction quality. More recently, deep learning models used to discover gene-gene relationships can yield more accurate predictions of cellular responses. The huge difference in biological information between different cell types makes it difficult for deep learning models to encode data into a continuous low-dimensional feature space, which means that the features captured by the latent space may not be continuous. Therefore, the mapping relationship between the two conditional spaces learned by the model can only be applied where the real reference data resides, leading to the wrong mapping of the predicted target cells because they are not in the same domain as the reference data. In this paper, we propose an information-navigated variational autoencoder (INVAE), a deep neural network for cell perturbation response prediction. INVAE filters out information that is not conducive to predictive performance. For the remaining information, INVAE constructs a homogeneous space of control conditions, and finds the mapping relationship between the control condition space and the perturbation condition space. By embedding the target unit into the control space and then mapping it to the perturbation space, we can predict the perturbed state of the target unit. Comparing our proposed method with other three state-of-the-art methods on three real datasets, experimental results show that INVAE outperforms existing methods in cell state prediction after perturbation. Furthermore, we demonstrate that filtering out useless information not only improves prediction accuracy but also reveals similarities in how genes in different cell types are regulated following perturbation.

Fatty liver is associated with significant liver inflammation and increases the burden of advanced fibrosis in chronic HBV infection.

BACKGROUND: Concurrent non-alcoholic fatty liver disease (NAFLD) is common in patients with chronic HBV infection. But the impact of fatty liver on the histologic progression of HBV infection remains controversial. METHODS: Consecutive HBV-infected patients who underwent liver biopsy between 2016 and 2021 were included. Alcohol consumption and other types of viral hepatitis were excluded. All biopsies were scored for grading and staging by Scheuer's score, and the steatosis was scored as an estimate of the percentage of liver parenchyma replaced by fat. Logistic regression analyses were applied to assess the associated factors for significant liver inflammation (G ≥ 2), significant fibrosis (S ≥ 2) and advanced fibrosis (S ≥ 3). RESULTS: Among the 871 HBV-infected patients, hepatic steatosis was prevalent in 255 patients (29.28%). Significant liver inflammation was present in 461 patients (52.93%). Significant fibrosis was observed in 527 patients (60.51%), while advanced liver fibrosis was observed in 171 patients (19.63%). Patients with concomitant NAFLD were more likely to have significant liver inflammation and advanced fibrosis. Fatty liver was an independent risk factor for significant liver inflammation (OR: 2.117, 95% CI: 1.500-2.988), but it could not predict the development of fibrosis. Especially, in HBV-infected patients with persistent normal ALT (immune tolerant and inactive carrier phase), the presence of significant liver inflammation was higher in NAFLD than those without NAFLD. The prevalence of advanced liver fibrosis was higher in NAFLD than non-NAFLD only in the immune tolerant phase, while NAFLD did not increase fibrosis burden in other stages of HBV infection. We developed a predictive model for significant liver inflammation with the area under receiver operating characteristic curve (AUROC) of 0.825, and a model for significant fibrosis with the AUROC of 0.760. CONCLUSIONS: NAFLD is independently associated with significant liver inflammation, and increases the burden of advanced liver fibrosis in HBV-infected patients. The influence of NAFLD on the degree of liver inflammation and fibrosis is different in distinct clinical phases of chronic HBV infection.

Deep Learning-Based Recognition of Periodontitis and Dental Caries in Dental X-ray Images.

Dental X-ray images are important and useful for dentists to diagnose dental diseases. Utilizing deep learning in dental X-ray images can help dentists quickly and accurately identify common dental diseases such as periodontitis and dental caries. This paper applies image processing and deep learning technologies to dental X-ray images to propose a simultaneous recognition method for periodontitis and dental caries. The single-tooth X-ray image is detected by the YOLOv7 object detection technique and cropped from the periapical X-ray image. Then, it is processed through contrast-limited adaptive histogram equalization to enhance the local contrast, and bilateral filtering to eliminate noise while preserving the edge. The deep learning architecture for classification comprises a pre-trained EfficientNet-B0 and fully connected layers that output two labels by the sigmoid activation function for the classification task. The average precision of tooth detection using YOLOv7 is 97.1%. For the recognition of periodontitis, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve is 98.67%, and the AUC of the precision-recall (PR) curve is 98.38%. For the recognition of dental caries, the AUC of the ROC curve is 98.31%, and the AUC of the PR curve is 97.55%. Different from the conventional deep learning-based methods for a single disease such as periodontitis or dental caries, the proposed approach can provide the recognition of both periodontitis and dental caries simultaneously. This recognition method presents good performance in the identification of periodontitis and dental caries, thus facilitating dental diagnosis.

Metabolic dysfunction is associated with steatosis but no other histologic features in nonalcoholic fatty liver disease.

BACKGROUND: Recently, nonalcoholic fatty liver disease (NAFLD) has been renamed metabolic-associated fatty liver disease (MAFLD). Based on the definition for MAFLD, a group of non-obese and metabolically healthy individuals with fatty liver are excluded from the newly proposed nomenclature. AIM: To analyze the histologic features in the MAFLD and non-MAFLD subgroups of NAFLD. METHODS: Eighty-three patients with biopsy-proven NAFLD were separated into MAFLD and non-MAFLD groups. The diagnosis of MAFLD was established as hepatic steatosis along with obesity/diabetes or evidence of metabolic dysfunction. The histologic features were compared according to different metabolic disorders and liver enzyme levels. RESULTS: MAFLD individuals had a higher NAFLD activity score (P = 0.002) and higher severity of hepatic steatosis (42.6% Grade 1, 42.6% Grade 2, and 14.8% Grade 3 in MAFLD; 81.8% Grade 1, 13.6% Grade 2, and 4.5% Grade 3 in non-MAFLD; P = 0.007) than the non-MAFLD group. Lobular and portal inflammation, hepatic ballooning, fibrosis grade, and the presence of nonalcoholic steatohepatitis (NASH) and significant fibrosis were comparable between the two groups. The higher the liver enzyme levels, the more severe the grades of hepatic steatosis (75.0% Grade 1 and 25.0% Grade 2 in normal liver function; 56.6% Grade 1, 39.6% Grade 2, and 3.8% Grade 3 in increased liver enzyme levels; 27.8% Grade 1, 27.8% Grade 2, and 44.4% Grade 3 in liver injury; P < 0.001). Patients with liver injury (alanine aminotransferase > 3 × upper limit of normal) presented a higher severity of hepatocellular ballooning (P = 0.021). Moreover, the grade of steatosis correlated significantly with hepatocellular ballooning degree (r = 0.338, P = 0.002) and the presence of NASH (r = 0.466, P < 0.001). CONCLUSION: Metabolic dysfunction is associated with hepatic steatosis but no other histologic features in NAFLD. Further research is needed to assess the dynamic histologic characteristics in NAFLD based on the presence or absence of metabolic disorders.

[Characteristics and Sources of Nitrated Phenols in Atmospheric Fine Particles of Northern Suburban Nanjing].

Nitrated phenols are a group of nitrogen-containing organics ubiquitously present in ambient air, which are also important components of atmospheric light-absorbing organic matter (brown carbon) that have significant impacts on climate change, air quality, and human health. In this study, we collected a total of 265 daily filter samples of fine particles (PM2.5) in northern suburban Nanjing from March 2019 to January 2020. We used ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) to detect and quantify eight nitrated phenolic species. The results showed that the average annual concentration of total nitrated phenols in the sampling site was 18.77 ng·m-3, and the average concentrations in spring, summer, autumn, and winter were 16.82, 8.59, 17.28, and 44.79 ng·m-3, respectively. Such concentrations were obviously higher than those determined in other countries but were similar to those in domestic cities, such as Jinan. 4-Nitrophenol was the most abundant nitrated phenol, followed by 4-nitrocatechol and 2-methoxy-5-nitrophenol. Correlation analysis showed that 3-nitrosalicylic acid was from a specific source different from that of other species. Finally, we used a positive matrix factorization model to quantify the source contributions of nitrated phenols. The major sources were vehicle emissions (32%), mixed coal and biomass burning emissions (44%), and industrial emissions (24%). The mixed coal and biomass burning emissions were dominant in autumn and winter. The mass fraction of 3-nitrosalicylic acid in the factor of industrial emissions was>90%, consistent with the results of the correlation analysis. Overall, this study provides valuable insights into the understanding of concentrations, characteristics, and sources of atmospheric nitrated phenols in ambient air.

Multicenter analysis and a rapid screening model to predict early novel coronavirus pneumonia using a random forest algorithm.

ABSTRACT: Early determination of coronavirus disease 2019 (COVID-19) pneumonia from numerous suspected cases is critical for the early isolation and treatment of patients.The purpose of the study was to develop and validate a rapid screening model to predict early COVID-19 pneumonia from suspected cases using a random forest algorithm in China.A total of 914 initially suspected COVID-19 pneumonia in multiple centers were prospectively included. The computer-assisted embedding method was used to screen the variables. The random forest algorithm was adopted to build a rapid screening model based on the training set. The screening model was evaluated by the confusion matrix and receiver operating characteristic (ROC) analysis in the validation.The rapid screening model was set up based on 4 epidemiological features, 3 clinical manifestations, decreased white blood cell count and lymphocytes, and imaging changes on chest X-ray or computed tomography. The area under the ROC curve was 0.956, and the model had a sensitivity of 83.82% and a specificity of 89.57%. The confusion matrix revealed that the prospective screening model had an accuracy of 87.0% for predicting early COVID-19 pneumonia.Here, we developed and validated a rapid screening model that could predict early COVID-19 pneumonia with high sensitivity and specificity. The use of this model to screen for COVID-19 pneumonia have epidemiological and clinical significance.

Primary bone anaplastic lymphoma kinase positive anaplastic large-cell lymphoma: A case report and review of the literature.

BACKGROUND: Primary bone lymphoma (PBL) is an uncommon extranodal disease that represents approximately 1%-3% of lymphomas. Anaplastic lymphoma kinase (ALK) positive anaplastic large-cell lymphoma (ALCL) is an extremely rare type of PBL. The aim of this report is describe the symptoms, diagnosis, and treatment of primary bone ALK-positive ALCL. CASE SUMMARY: A 66-year-old man presented to our hospital with neck and shoulder pain and intermittent fever that lasted for 1 mo. After extensive evaluation, positron emission tomography-computed tomography (CT) examination showed multiple osteolytic bone lesions without other sites lesions. CT-guided biopsy of the T10 vertebral body was performed, and the pathology results showed that neoplastic cells were positive for ALK-1, CD30, and CD3. A diagnosis of primary bone ALK positive ALCL was ultimately made. The patient was in partial response after four cycle soft cyclophosphamide, doxorubicin, vincristine, and prednisone chemotherapy, and we planned to repeat the biopsy and radiological examination after completion of the fifth cycle of therapy. CONCLUSION: Primary bone ALK positive ALCL is a rare disease and physicians should keep in mind that ALCL can present with isolated osseous involvement without nodal involvement, and lymphoma should be considered in the differential diagnosis of primary bone lesions.

A rapid screening model for early predicting novel coronavirus pneumonia in Zhejiang Province of China: a multicenter study.

Novel coronavirus pneumonia (NCP) has been widely spread in China and several other countries. Early finding of this pneumonia from huge numbers of suspects gives clinicians a big challenge. The aim of the study was to develop a rapid screening model for early predicting NCP in a Zhejiang population, as well as its utility in other areas. A total of 880 participants who were initially suspected of NCP from January 17 to February 19 were included. Potential predictors were selected via stepwise logistic regression analysis. The model was established based on epidemiological features, clinical manifestations, white blood cell count, and pulmonary imaging changes, with the area under receiver operating characteristic (AUROC) curve of 0.920. At a cut-off value of 1.0, the model could determine NCP with a sensitivity of 85% and a specificity of 82.3%. We further developed a simplified model by combining the geographical regions and rounding the coefficients, with the AUROC of 0.909, as well as a model without epidemiological factors with the AUROC of 0.859. The study demonstrated that the screening model was a helpful and cost-effective tool for early predicting NCP and had great clinical significance given the high activity of NCP.

CYP2C9, a Metabolic CYP450s Enzyme, Plays Critical Roles in Activating Ellagic Acid in Human Intestinal Epithelial Cells.

BACKGROUND The metabolic processing of ellagic acid (EA) by cytochrome P450s (CYP450s) expressed in the intestines is unclear. This study aimed to investigate the effects of CYP450s that are highly expressed in HIEC cells on metabolic activity of EA. MATERIAL AND METHODS HIEC cell models expressing 2B6, 2C9, 2D6, and 3A4 were generated by stably transfecting with CYP450 genes using a lentivirus system. PCR and Western blot assay were used to detect expression of CYP450s. Cell Counting Kit-8 (CCK-8) assay was used to examine the cytotoxic effect of EA on CYP450s-expressing HIEC cells. Flow cytometry was employed to evaluate apoptosis of CYP450s-expressing HIEC cells after addition of EA. Metabolic clearance rate of EA in vitro by the constructed HIEC cell models was measured using UPLC-MS method. RESULTS CYP450s expression HIEC cell models, including CYP2B6, CYP2C9, CYP2D6, and CYP3A4, were successfully established. EA treatment at different concentrations (10 µg/mL and 50 µg/mL) remarkably decreased cell viability of HIEC cells expressing CYP2C9 compared to the untreated control (p<0.01), in a concentration-dependent and time-dependent manner. Expression of CYP2C9 significantly increased the apoptosis rate of HIEC cells treated with EA compared to that in HIEC cells without any CYP450s expression (p<0.01). The clearance rate of EA in CYP2B6-expressing (p<0.05) and CYP2C9-expressing (p<0.001) HIEC cell models was remarkably reduced after 120 min. CONCLUSIONS Ellagic acid was effectively activated by CYP2C9 in HIEC cells and caused cytotoxicity and apoptosis of HIEC cells. Therefore, CYP2C9 is main metabolic enzyme of EA when compared to other CYP450 HIEC cell models.

Machine Learning for Long Cycle Maintenance Prediction of Wind Turbine.

Within Internet of Things (IoT) sensors, the challenge is how to dig out the potentially valuable information from the collected data to support decision making. This paper proposes a method based on machine learning to predict long cycle maintenance time of wind turbines for efficient management in the power company. Long cycle maintenance time prediction makes the power company operate wind turbines as cost-effectively as possible to maximize the profit. Sensor data including operation data, maintenance time data, and event codes are collected from 31 wind turbines in two wind farms. Data aggregation is performed to filter out some errors and get significant information from the data. Then, the hybrid network is built to train the predictive model based on the convolutional neural network (CNN) and support vector machine (SVM). The experimental results show that the prediction of the proposed method reaches high accuracy, which helps drive up the efficiency of wind turbine maintenance.

Serum Proteomic Changes as Candidate Biomarkers of Intermediate Liver Fibrosis in Chronic Hepatitis B Infection.

Chronic hepatitis B (CHB) is a major global health burden. Liver fibrosis, an insidious process, is the main histopathological change in CHB that might lead to the end-stage liver disease if left untreated. The intermediate liver fibrosis (S2) is the optimal time to start antiviral therapy. The aim of the present study was to examine the proteomic changes in patients with CHB at different fibrotic stages, with a view to identify future serum biomarkers for S2. Ninety CHB patients were grouped into mild (S0-1), intermediate (S2), and severe liver fibrosis (S3-4) (61 men and 29 women; age 25-63 years). Isobaric tagging for relative and absolute quantitation was applied to screen proteins differentially expressed among the patient groups. Another 46 patients with CHB (age 25-59 years; 31 men and 15 women), and 16 healthy controls (age 26-61 years; 11 men and 5 women) were enrolled in a validation group. Enzyme-linked immunosorbent assay was used to verify the diagnostic value of the candidate biomarkers. We found 139 proteins that were differentially expressed between various fibrotic stage-paired comparisons. Five protein candidates were selected as potential biomarkers of S2 for further verification. Notably, ficolin-2 (FCN2) and carboxypeptidase B2 (CPB2) showed differential expression between patients and healthy controls. In conclusion, serum proteomic changes reported here offer new molecular leads for future research on biomarker candidates to identify liver fibrotic stages in CHB. In particular, FCN2 and CPB2 warrant further research on their possible mechanistic involvement in CHB pathogenesis.

Transforming Retinal Photographs to Entropy Images in Deep Learning to Improve Automated Detection for Diabetic Retinopathy.

Entropy images, representing the complexity of original fundus photographs, may strengthen the contrast between diabetic retinopathy (DR) lesions and unaffected areas. The aim of this study is to compare the detection performance for severe DR between original fundus photographs and entropy images by deep learning. A sample of 21,123 interpretable fundus photographs obtained from a publicly available data set was expanded to 33,000 images by rotating and flipping. All photographs were transformed into entropy images using block size 9 and downsized to a standard resolution of 100 × 100 pixels. The stages of DR are classified into 5 grades based on the International Clinical Diabetic Retinopathy Disease Severity Scale: Grade 0 (no DR), Grade 1 (mild nonproliferative DR), Grade 2 (moderate nonproliferative DR), Grade 3 (severe nonproliferative DR), and Grade 4 (proliferative DR). Of these 33,000 photographs, 30,000 images were randomly selected as the training set, and the remaining 3,000 images were used as the testing set. Both the original fundus photographs and the entropy images were used as the inputs of convolutional neural network (CNN), and the results of detecting referable DR (Grades 2-4) as the outputs from the two data sets were compared. The detection accuracy, sensitivity, and specificity of using the original fundus photographs data set were 81.80%, 68.36%, 89.87%, respectively, for the entropy images data set, and the figures significantly increased to 86.10%, 73.24%, and 93.81%, respectively (all p values <0.001). The entropy image quantifies the amount of information in the fundus photograph and efficiently accelerates the generating of feature maps in the CNN. The research results draw the conclusion that transformed entropy imaging of fundus photographs can increase the machinery detection accuracy, sensitivity, and specificity of referable DR for the deep learning-based system.

Machine learning to relate PM2.5 and PM10 concentrations to outpatient visits for upper respiratory tract infections in Taiwan: A nationwide analysis.

AIM: To examine the accuracy of machine learning to relate particulate matter (PM) 2.5 and PM10 concentrations to upper respiratory tract infections (URIs). METHODS: Daily nationwide and regional outdoor PM2.5 and PM10 concentrations collected over 30 consecutive days obtained from the Taiwan Environment Protection Administration were the inputs for machine learning, using multilayer perceptron (MLP), to relate to the subsequent one-week outpatient visits for URIs. The URI data were obtained from the Centers for Disease Control datasets in Taiwan between 2009 and 2016. The testing used the middle month dataset of each season (January, April, July and October), and the training used the other months' datasets. The weekly URI cases were classified by tertile as high, moderate, and low volumes. RESULTS: Both PM concentrations and URI cases peak in winter and spring. In the nationwide data analysis, MLP machine learning can accurately relate the URI volumes of the elderly (89.05% and 88.32%, respectively) and the overall population (81.75% and 83.21%, respectively) with the PM2.5 and PM10 concentrations. In the regional data analyses, greater accuracy is found for PM2.5 than for PM10 for the elderly, particularly in the Central region (78.10% and 74.45%, respectively), whereas greater accuracy is found for PM10 than for PM2.5 for the overall population, particularly in the Northern region (73.19% and 63.04%, respectively). CONCLUSION: Short-term PM2.5 and PM10 concentrations were accurately related to the subsequent occurrence of URIs by using machine learning. Our findings suggested that the effects of PM2.5 and PM10 on URI may differ by age, and the mechanism needs further evaluation.

Identification of potential metabolic biomarkers of cerebrospinal fluids that differentiate tuberculous meningitis from other types of meningitis by a metabolomics study.

Tuberculous meningitis (TBM) is caused by tuberculosis infection of of the meninges, which are the membrane systems that encircle the brain, with a high morbidity and mortality rate. It is challenging to diagnose TBM among other types of meningitis, such as viral meningitis, bacterial meningitis and cryptococcal meningitis. We aimed to identify metabolites that are differentially expressed between TBM and the other types of meningitis by a global metabolomics analysis. The cerebrospinal fluids (CSF) from 50 patients with TBM, 17 with viral meningitis, 17 with bacterial meningitis, and 16 with cryptococcal meningitis were analyzed using ultra high performance liquid chromatography coupled with quadrupole time of flight mass spectrometry (UHPLC-QTOF-MS). A total of 1161 and 512 features were determined in positive and negative electrospray ionization mode, respectively. A clear separation between TBM and viral, bacterial or cryptococcal meningitis was achieved by orthogonal projections to latent structures-discriminate analysis (OPLS-DA) analysis. Potential metabolic markers and related pathways were identified, which were mainly involved in the metabolism of amino acid, lipids and nucleosides. In summary, differential metabolic profiles of the CSF exist between TBM and other types of meningitis, and potential metabolic biomarkers were identified to differentiate TBM from other types of meningitis.

Development and validation of a model for staging hepatic fibrosis for chronic hepatitis B patients with E antigen-positive.

BACKGROUND: Interest is growing in the use of non-invasive techniques for complementing liver biopsy for liver fibrosis assessment. We aimed to prospectively evaluate liver histology in chronic hepatitis B (CHB) patients with e-antigen positivity, and develop and validate a novel scoring system-e-antigen-positive CHB liver fibrosis (EPLF) score-for noninvasively predicting the fibrosis stages. METHODS: We identified the baseline variables associated with fibrosis stage (MATAVIR score, F0-F4) in 212 CHB patients with e-antigen positivity. These significant variables were used to develop the EPLF scoring system. The EPLF score equation was developed based on the prediction of fibrosis stages via multivariate ordered logistic regression analysis. The diagnostic powers of the EPLF score and several non-invasive markers were assessed through an area under the receiver operating characteristic curve (AUROC) analyses. This EPLF score model was validated in another set of 208 similar patients. RESULTS: The natural logarithms of serum albumin, HBeAg, and HBsAg levels were selected as significant independent variables for the EPLF score equation. The EPLF score had good diagnostic power (AUROC, 0.72-0.90, p<0.001) and good diagnostic accuracy (72-85%), with a high positive predictive value (80.8-92.8%) for each fibrosis stage in the test group. Similar results were observed in the validation group (AUROC, 0.73-0.89, p<0.001). The EPLF score exhibited a strong correlation with fibrosis stage (r=0.67, p<0.001), and was the preferable non-invasive marker for staging liver fibrosis. CONCLUSION: In e-antigen-positive patients with CHB, the EPLF score could serve as a potential non-invasive marker of liver fibrosis stage.

An Inexpensive Digital Image Analysis Technique for Liver Fibrosis Quantification in Chronic Hepatitis B Patients.

BACKGROUND AND AIM: Quantitative digital imaging analysis to evaluate liver fibrosis is accurate, but its clinical use is limited by its high cost and lack of standardization. We aimed to validate an inexpensive digital imaging analysis technique for fibrosis quantification in chronic hepatitis B patients. MATERIAL AND METHODS: In total, 142 chronic hepatitis B patients who underwent liver biopsy and analysis of serum fibrosis markers were included. Images of Sirius red stain sections were captured and processed using Adobe Photoshop CS3 software. The percentage of fibrosis (fibrosis index) was determined by the ratio of the fibrosis area to the total sample area, expressed in pixels, and calculated automatically. RESULTS: A strong correlation between the fibrosis index and the Ishak, Metavir, and Laennec histological staging systems were observed (r = 0.83, 0.86, and 0.84, respectively; < 0.001). The cutoff value associated with cirrhosis was 7.7% with an area under the receiver operating characteristic curve (AUROC) of 0.95 (95% confidence interval [CI], 0.92-0.99, p < 0.001). Furthermore, the fibrosis index yielded a cutoff value of 8.9% (AUROC, 0.74; 95% CI, 0.66-0.86), 12% (AUROC, 0.84; 95% CI, 0.75-0.93), and 14% (AUROC, 0.97; 95% CI, 0.92-1.0) for the diagnosis of cirrhosis 4a, 4b, and 4c, respectively. No serum markers or fibrosis models were correlated with the fibrosis index in Metavir F2-F4. CONCLUSIONS: The present digital imaging analysis technique is reproducible and available worldwide, allowing its use in clinical practice, and can be considered as a complementary tool to traditional histological methods.

Old age and hydrocephalus are associated with poor prognosis in patients with tuberculous meningitis: A retrospective study in a Chinese adult population.

Tuberculous meningitis (TBM) is the most common form of central nervous system tuberculosis with a very poor prognosis. We aimed at assessing risk factors related to the prognosis of patients with TBM.Forty-five inpatients with TBM in our institution from January 2013 to December 2015 were enrolled retrospectively. The good or poor prognosis in the patients was defined, based on Glasgow Outcome Scale System at discharge. Patients with a GOS score less than 5 were defined as "poor prognosis." Univariate and multivariate logistic regression analyses were performed to assess the predictors for TBM outcome.Among 45 TBM patients, 35 (77.8%) and 10 (22.2%) were in good, poor prognoses, respectively. Old age, disturbance of consciousness, moderate to severe electroencephalogram abnormality, hydrocephalus, remarkable increase of protein (≥ 236 mg/dL) and white blood cell counts (≥ 243 /µL) in cerebral spinal fluid were associated with poor prognosis. Multivariate analysis indicated that old age (odds ratio (OR) = 18.395, P = .036) and hydrocephalus (OR = 32.995, P = .049) were independent factors for a poor outcome of TBM.In conclusion, old age and hydrocephalus are the predictors for poor prognosis of TBM. Patients with these risk factors should be treated promptly with a special care paid to improve their outcomes.

Peliosis hepatis: 2 case reports of a rare liver disorder and its differential diagnosis.

RATIONALE: Peliosis hepatis (PH) is a rare tumor-like liver lesion composed of multiple blood-filled cavities within the liver parenchyma. It is hard to differentiate PH from other liver lesions by imaging, such as carcinoma, metastases, or abscess. PATIENT CONCERNS: Here, we reported 2 cases that presented with liver lesions under ultrasound and computed tomography (CT) scanning, without any history of liver diseases or drug usage traced back. DIAGNOSES: Liver biopsy and laparoscopy were processed, and the lesions were eventually diagnosed as PH by histopathology, which microscopically presented with multiple sinusoidal dilatations with blood-filled cystic spaces. INTERVENTIONS: After the liver biopsy or laparoscopy, the patients were discharged and followed up in the clinic. OUTCOMES: Both patients were followed up for at least 1 year with good recovery. LESSONS: PH should always be recognized in the differentiation of liver lesions, particularly indistinctive lesion(s) without any history of liver-related diseases.

Promoter structures and differential responses to viral and non-viral inducers of chicken melanoma differentiation-associated gene 5.

Melanoma differentiation-associated gene 5 (MDA5) is a member of the retinoic acid-inducible gene I (RIG-I)-like receptors (RLRs) family and plays a pivotal role in the anti-viral innate immune response. As RIG-I is absent in chickens, MDA5 is hypothesized to be important in detecting viral nucleic acids in the cytoplasm. However, the molecular mechanism of the regulation of chicken MDA5 (chMDA5) expression has yet to be fully elucidated. With this in mind, a ∼2.5kb chMDA5 gene promoter region was examined and PCR amplified to assess its role in immune response. A chMDA5 promoter reporter plasmid (piggybac-MDA5-DsRed) was constructed and transfected into DF-1 cells to establish a Piggybac-MDA5-DsRed cell line. The MDA5 promoter activity was extremely low under basal condition, but was dramatically increased when cells were stimulated with polyinosinic: polycytidylic acid (poly I:C), interferon beta (IFN-ß) or Infectious Bursal Disease Virus (IBDV). The DsRed mRNA level represented the promoter activity and was remarkably increased, which matched the expression of endogenous MDA5. However, Infectious Bronchitis Virus (IBV) and Newcastle disease virus (NDV) failed to increase the MDA5 promoter activity and the expression of endogenous MDA5. The results indicated that the promoter and the Piggybac-MDA5-DsRed cell line could be utilized to determine whether a ligand regulates MDA5 expression. For the first time, this study provides a tool for testing chMDA5 expression and regulation.

Effectiveness of Antipsychotic Drugs for 24-Month Maintenance Treatment in First-Episode Schizophrenia: Evidence From a Community-Based "Real-World" Study.

OBJECTIVE: Maintenance treatment of schizophrenia with antipsychotic medications has become a standard for the prevention of psychotic relapse. However, little is known about the effectiveness of antipsychotic drugs for maintenance treatment in "real-world" populations with schizophrenia. We carried out a prospective study to assess the effectiveness of the most frequently prescribed antipsychotic drugs in the maintenance treatment of schizophrenia from 2 community settings. METHODS: This study was conducted from October 2011 to December 2014. All participants were diagnosed with schizophrenia according to DSM-IV, were treated with an antipsychotic monotherapy, and were registered in a case management program with monthly monitoring for 24 months. The primary outcome measure, Positive and Negative Syndrome Scale (PANSS), and the Clinical Global Impressions-Severity of Illness (CGI-S) and -Improvement (CGI-I) scales were used to evaluate symptom severity and treatment response. The Personal and Social Performance scale (PSP) was used to evaluate the patients' social functioning. The Medication Adherence Rating Scale (MARS) was used to assess medication adherence behavior. On the basis of antipsychotic used at baseline, patients were clustered into 7 groups: aripiprazole (n = 21), clozapine (n = 84), chlorpromazine (n = 61), olanzapine (n = 34), perphenazine (n = 21), quetiapine (n = 27), and risperidone (n = 99). RESULTS: Of the 347 patients enrolled in the study, 312 completed the 24-month follow-up. There were no significant differences among the treatment groups in the PANSS total and subscale scores or the CGI-S and CGI-I scores over 24 months (all P values > .05). There were also no significant differences in interactions between PSP scores and antipsychotic drugs (P = .17). The remission rates increased as the follow-time lapsed in all groups, but no significant difference was observed in remission rates at each time point among the 7 groups (P values > .05). At the endpoint, MARS total scores were over 6, but did not significantly differ among the studied drugs (P = .24). CONCLUSIONS: These findings suggest that antipsychotic drugs can achieve equivalent effectiveness in maintenance treatment of first-episode schizophrenia through a well-organized case management program and family participation.