Development and validation of an ultrasound-based deep learning radiomics nomogram for predicting the malignant risk of ovarian tumours.

BACKGROUND: The timely identification and management of ovarian cancer are critical determinants of patient prognosis. In this study, we developed and validated a deep learning radiomics nomogram (DLR_Nomogram) based on ultrasound (US) imaging to accurately predict the malignant risk of ovarian tumours and compared the diagnostic performance of the DLR_Nomogram to that of the ovarian-adnexal reporting and data system (O-RADS). METHODS: This study encompasses two research tasks. Patients were randomly divided into training and testing sets in an 8:2 ratio for both tasks. In task 1, we assessed the malignancy risk of 849 patients with ovarian tumours. In task 2, we evaluated the malignancy risk of 391 patients with O-RADS 4 and O-RADS 5 ovarian neoplasms. Three models were developed and validated to predict the risk of malignancy in ovarian tumours. The predicted outcomes of the models for each sample were merged to form a new feature set that was utilised as an input for the logistic regression (LR) model for constructing a combined model, visualised as the DLR_Nomogram. Then, the diagnostic performance of these models was evaluated by the receiver operating characteristic curve (ROC). RESULTS: The DLR_Nomogram demonstrated superior predictive performance in predicting the malignant risk of ovarian tumours, as evidenced by area under the ROC curve (AUC) values of 0.985 and 0.928 for the training and testing sets of task 1, respectively. The AUC value of its testing set was lower than that of the O-RADS; however, the difference was not statistically significant. The DLR_Nomogram exhibited the highest AUC values of 0.955 and 0.869 in the training and testing sets of task 2, respectively. The DLR_Nomogram showed satisfactory fitting performance for both tasks in Hosmer-Lemeshow testing. Decision curve analysis demonstrated that the DLR_Nomogram yielded greater net clinical benefits for predicting malignant ovarian tumours within a specific range of threshold values. CONCLUSIONS: The US-based DLR_Nomogram has shown the capability to accurately predict the malignant risk of ovarian tumours, exhibiting a predictive efficacy comparable to that of O-RADS.

Ultrasound-based deep learning radiomics model for differentiating benign, borderline, and malignant ovarian tumours: a multi-class classification exploratory study.

BACKGROUND: Accurate preoperative identification of ovarian tumour subtypes is imperative for patients as it enables physicians to custom-tailor precise and individualized management strategies. So, we have developed an ultrasound (US)-based multiclass prediction algorithm for differentiating between benign, borderline, and malignant ovarian tumours. METHODS: We randomised data from 849 patients with ovarian tumours into training and testing sets in a ratio of 8:2. The regions of interest on the US images were segmented and handcrafted radiomics features were extracted and screened. We applied the one-versus-rest method in multiclass classification. We inputted the best features into machine learning (ML) models and constructed a radiomic signature (Rad_Sig). US images of the maximum trimmed ovarian tumour sections were inputted into a pre-trained convolutional neural network (CNN) model. After internal enhancement and complex algorithms, each sample's predicted probability, known as the deep transfer learning signature (DTL_Sig), was generated. Clinical baseline data were analysed. Statistically significant clinical parameters and US semantic features in the training set were used to construct clinical signatures (Clinic_Sig). The prediction results of Rad_Sig, DTL_Sig, and Clinic_Sig for each sample were fused as new feature sets, to build the combined model, namely, the deep learning radiomic signature (DLR_Sig). We used the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) to estimate the performance of the multiclass classification model. RESULTS: The training set included 440 benign, 44 borderline, and 196 malignant ovarian tumours. The testing set included 109 benign, 11 borderline, and 49 malignant ovarian tumours. DLR_Sig three-class prediction model had the best overall and class-specific classification performance, with micro- and macro-average AUC of 0.90 and 0.84, respectively, on the testing set. Categories of identification AUC were 0.84, 0.85, and 0.83 for benign, borderline, and malignant ovarian tumours, respectively. In the confusion matrix, the classifier models of Clinic_Sig and Rad_Sig could not recognise borderline ovarian tumours. However, the proportions of borderline and malignant ovarian tumours identified by DLR_Sig were the highest at 54.55% and 63.27%, respectively. CONCLUSIONS: The three-class prediction model of US-based DLR_Sig can discriminate between benign, borderline, and malignant ovarian tumours. Therefore, it may guide clinicians in determining the differential management of patients with ovarian tumours.

The gene spectrum of thalassemia in Yangjiang of western Guangdong Province.

Background: Thalassemia presents a higher incidence in southern China. The objective of this study is to analyze the genotype distribution of thalassemia in Yangjiang, a western city of Guangdong Province in China. Methods: The genotypes of suspected cases with thalassemia were tested by PCR and reverse dot blot (RDB). Unidentified rare thalassemia genotypes of the samples were further ascertained by PCR and direct DNA sequencing. Results: Among 22467 suspected cases with thalassemia, 7658 cases were found with thalassemia genotypes using our PCR-RDB kit. Among these 7658 cases, 5313 cases were found with α-thalassemia (α-thal) alone, --SEA/αα was the most common genotype, accounting for 61.75% of α-thal genotypes, and the following mutations were found: α3.7/αα, -α4.2/αα, αCSα/αα, αWSα/αα, and αQSα/αα. A total of 2032 cases were found with ß-thalassemia (ß-thal) alone. ßCD41-42/ßN, ßIVS-II-654/ßN, and ß-28/ßN accounted for 80.9% of all ß-thal genotypes, and the following genotypes were found: ßCD17/ßN, ßCD71-72/ßN, and ßE/ßN. Compound heterozygotes of ß-thal and ß-thalassemia homozygotes were identified in 11 and five cases, respectively, in this study. α-thal combined with ß-thal was identified in 313 cases, showing 57 genotype combinations of the coincidence of both Hb disorders; one extreme patient had a genotype of --SEA/αWSα and ßCD41-42/ß-28. In addition, four rare α-mutations (--THAI, HKαα, Hb Q-Thailand, and CD31 AGG>AAG) and six rare ß-mutations (CD39 CAG>TAG, IVS-Ⅱ-2 (-T), -90(C>T), Chinese Gγ+(Aγδß)0, CD104 (-G), and CD19 A>G) were also found in this study population. Conclusion: This study provided detailed genotypes of thalassemia in Yangjiang of western Guangdong Province in China and reflected the complexity of genotypes in this high-prevalence region, and this would be valuable for diagnosis and counseling for thalassemia in this area.

A simple and effective aerosol pathogen disinfection test for a flowing air disinfector.

Aerosol transmission is an important disease transmission route and has been especially pertinent to hospital and biosafety laboratories during the SARS-CoV-2 pandemic. The thermal resistance of airborne SARS-CoV-2 is lower than that of Bacillus subtilis spores, which are often used to test the effectiveness of SARS-CoV-2 and other pathogen disinfection methods. Herein, we propose a new method to test the disinfection ability of a flowing air disinfector (a digital electromagnetic induction air heater) using B. subtilis spores. The study provides an alternative air disinfection test method. The new test system combined an aerosol generator and a respiratory filter designed in-house and could effectively recover spores on the filter membrane at the air outlet after passing through the flowing air disinfector. The total number of bacterial spores used in the test was within the range of 5 × 105-5 × 106 colony-forming units (CFUs) specified in the technical standard for disinfection. The calculation was based on the calculation method in Air Disinfection Effect Appraisal Test in Technical Standard for Disinfection (2002 Edition). At an air speed of 3.5 m/s, we used a digital electromagnetic induction air heater to disinfect flowing air containing 4.100 × 106 CFUs of B. subtilis spores and determined that the minimum disinfection temperature was 350 °C for a killing rate of 99.99%. At 400 °C, additional experiments using higher spore concentrations (4.700 × 106 ± 1.871 × 105 CFU) and a higher airspeed (4 m/s) showed that the killing rate remained>99.99%. B. subtilis spores, as a biological indicator for testing the efficiency of dry-heat sterilization, were killed by the high temperatures used in this system. The proposed method used to test the flowing air disinfector is simple, stable, and effective. This study provides a reference for the development of test systems that can assess the disinfection ability of flowing air disinfectors.

Molecular epidemiological investigation of G6PD deficiency in Yangjiang region, western Guangdong province.

Objectives: The prevalence of G6PD deficiency has not been reported in Yangjiang, a western city in Guangdong province. This study aims to investigate the molecular characteristics of G6PD deficiency in this region. Methods: Blood samples were collected from adults at a local hospital to screen for G6PD deficiency. The deficient samples were subjected to further analysis using PCR and reverse dot blot to determine the specific G6PD variants. Results: Among the 3314 male subjects, 250 cases of G6PD deficiency were found using the G6PD enzyme quantitative assay, resulting in a prevalence of 7.54% (250/3314) in the Yangjiang region. The prevalence of G6PD deficiency in females was 3.42% (176/5145). Out of the 268 cases of G6PD deficiency tested for G6PD mutations, reverse dot blot identified 20 different G6PD variants. The most common G6PD variant was c.1388G>A (81/268), followed by c.1376G>T (48/268), c.95A>G (32/268), c.1024C>T (9/268), c.392G>T (7/268), and c.871G>A/c.1311C>T (6/268). It was observed that c.871G>A was always linked to the polymorphism of c.1311C>T in this population. Conclusion: This investigation into G6PD deficiency in this area is expected to significantly improve our understanding of the prevalence and molecular characterization of this condition.

Vimentin Inhibits Dengue Virus Type 2 Invasion of the Blood-Brain Barrier.

Dengue virus (DENV) causes dengue fever, which is prevalent in the tropical and subtropical regions, and in recent years, has resulted in several major epidemics. Vimentin, a cytoskeletal component involved in DENV infection, is significantly reorganized during infection. However, the mechanism underlying the association between DENV infection and vimentin is still poorly understood. We generated vimentin-knockout (Vim-KO) human brain microvascular endothelial cells (HBMECs) and a Vim-KO SV129 suckling mouse model, combining the dynamic vimentin changes observed in vitro and differences in disease course in vivo, to clarify the role of vimentin in DENV-2 infection. We found that the phosphorylation and solubility of vimentin changed dynamically during DENV-2 infection of HBMECs, suggesting the regulation of vimentin by DENV-2 infection. The similar trends observed in the phosphorylation and solubility of vimentin showed that these characteristics are related. Compared with that in control cells, the DENV-2 viral load was significantly increased in Vim-KO HBMECs, and after DENV-2 infection, Vim-KO SV129 mice displayed more severe disease signs than wild-type SV129 mice, as well as higher viral loads in their serum and brain tissue, demonstrating that vimentin can inhibit DENV-2 infection. Moreover, Vim-KO SV129 mice had more disordered cerebral cortical nerve cells, confirming that Vim-KO mice were more susceptible to DENV-2 infection, which causes severe brain damage. The findings of our study help clarify the mechanism by which vimentin inhibits DENV-2 infection and provides guidance for antiviral treatment strategies for DENV infections.

High-specificity targets in SARS-CoV-2 N protein for serological detection and distinction from SARS-CoV.

Numerous serological detection kits are being rapidly developed and approved for screening and diagnosing suspected coronavirus disease 2019 (COVID-19) cases. However, cross-reactivity between pre-existing antibodies against other coronaviruses and the captured antigens in these kits can affect detection accuracy, emphasizing the necessity for identifying highly specific antigen fragments for antibody detection. Thus, we performed a conservation and specificity analysis of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleocapsid (N) protein. We also integrated various B-cell epitope prediction methods to obtain possible dominant epitope regions for the N protein, analyzed the differences in serological antibody levels for different epitopes using ELISA, and identified N protein epitopes for IgG and IgM with high-specificity. The SARS-CoV-2 N protein showed low mutation rates and shared the highest amino acid similarity with SARS-CoV; however, it differed substantially from other coronaviruses. Tests targeting the SARS-CoV-2 N protein produce strong positive results in patients recovering from SARS-CoV. The N18-39 and N183-197 epitopes for IgG and IgM detection, respectively, can effectively overcome cross-reactivity, and even exhibit good specificity between SARS-CoV-2 and SARS-CoV. The antibody levels detected with these were consistent with those detected using the complete N protein. These findings provide a basis for serological diagnosis and determining the kinetics of SARS-CoV-2 antibody detection in patients.

Epidemiological and clinical characteristics of respiratory viruses in 4403 pediatric patients from multiple hospitals in Guangdong, China.

BACKGROUND: Acute respiratory infections (ARI) cause considerable morbidity and mortality worldwide, especially in children. Unfortunately, there are limited multi-center data on common viral respiratory infections in south China. METHODS: A total of 4403 nasal swabs were collected from children in 10 cities in Guangdong, China in 2019. Seven respiratory viruses, influenza A virus (IFA), influenza B virus (IFB), respiratory syncytial virus (RSV), adenoviruses (ADV) and parainfluenza virus types 1-3 (PIV1, PIV2 and PIV3), were detected by direct immunofluorescence antibody assay. The personal information and clinical characteristics were recorded and analyzed. RESULTS: The results showed that at least one virus was detected in 1099 (24.96 %) samples. The detection rates of RSV, IFA, ADV, PIV3, PIV1 and PIV2 were 7.13 % (314/4403), 5.31 % (234/4403), 4.02 % (177/4403), 3.04 % (134/4403), 1.70 % (75/4403) and 1.16 % (51/4403), respectively. The detection rate of RSV was highest in 0-6-month-old children at 18.18 % (106/583), while the detection rate of IFA was highest in 12-18-year-old children at 20.48 % (17/83). The total detection rates in winter and spring were 35.67 % (219/614) and 34.56 % (403/1166), higher than those in summer, 17.41 % (284/1631), and autumn, 19.46 % (193/992). CONCLUSIONS: RSV and IFA were the main respiratory viruses in children. With increasing age the detection rate of RSV decreased in children, but the trends for the detection rates of IFA and IFB were the opposite. This study provided the viral etiology and epidemiology of pediatric patients with ARI in Guangdong, China.

Intra-host variation and evolutionary dynamics of SARS-CoV-2 populations in COVID-19 patients.

BACKGROUND: Since early February 2021, the causative agent of COVID-19, SARS-CoV-2, has infected over 104 million people with more than 2 million deaths according to official reports. The key to understanding the biology and virus-host interactions of SARS-CoV-2 requires the knowledge of mutation and evolution of this virus at both inter- and intra-host levels. However, despite quite a few polymorphic sites identified among SARS-CoV-2 populations, intra-host variant spectra and their evolutionary dynamics remain mostly unknown. METHODS: Using high-throughput sequencing of metatranscriptomic and hybrid captured libraries, we characterized consensus genomes and intra-host single nucleotide variations (iSNVs) of serial samples collected from eight patients with COVID-19. The distribution of iSNVs along the SARS-CoV-2 genome was analyzed and co-occurring iSNVs among COVID-19 patients were identified. We also compared the evolutionary dynamics of SARS-CoV-2 population in the respiratory tract (RT) and gastrointestinal tract (GIT). RESULTS: The 32 consensus genomes revealed the co-existence of different genotypes within the same patient. We further identified 40 intra-host single nucleotide variants (iSNVs). Most (30/40) iSNVs presented in a single patient, while ten iSNVs were found in at least two patients or identical to consensus variants. Comparing allele frequencies of the iSNVs revealed a clear genetic differentiation between intra-host populations from the respiratory tract (RT) and gastrointestinal tract (GIT), mostly driven by bottleneck events during intra-host migrations. Compared to RT populations, the GIT populations showed a better maintenance and rapid development of viral genetic diversity following the suspected intra-host bottlenecks. CONCLUSIONS: Our findings here illustrate the intra-host bottlenecks and evolutionary dynamics of SARS-CoV-2 in different anatomic sites and may provide new insights to understand the virus-host interactions of coronaviruses and other RNA viruses.

SARS-CoV-2: characteristics and current advances in research.

Coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 infection has spread rapidly across the world and become an international public health emergency. Both SARS-CoV-2 and SARS-CoV belong to subfamily Coronavirinae in the family Coronaviridae of the order Nidovirales and they are classified as the SARS-like species while belong to different cluster. Besides, viral structure, epidemiology characteristics and pathological characteristics are also different. We present a comprehensive survey of the latest coronavirus-SARS-CoV-2-from investigating its origin and evolution alongside SARS-CoV. Meanwhile, pathogenesis, cardiovascular disease in COVID-19 patients, myocardial injury and venous thromboembolism induced by SARS-CoV-2 as well as the treatment methods are summarized in this review.

A signal amplification system on a lateral flow immunoassay detecting for hepatitis e-antigen in human blood samples.

Hepatitis B e-antigen (HBeAg) is the secretory form of the nucleocapsid of the hepatitis B virus (HBV), which is a marker of viral replication. In this study, a novel signal amplification system (SAS) based on the lateral flow immunoassay (LFIA) was used for rapid detection of HBeAg in blood samples from patients or blood donors. In this assay, the detection antibody was conjugated with gold nanoparticles (GNPs), and the capture antibody was labeled with biotin. The presence of targeting antigen HBeAg in blood sample would act as a bridge with biotinylated captured antibody and GNP-conjugated detection antibody to form the dendritic nanoparticle complex. The dendritic complexes in the sample solution were migrated and immobilized on the testing line of strip coated with antibiotin antibodies. Signal intensity was massively amplified by the SAS, which was positively correlated with the concentration of targeting antigen in the blood sample and was assessed by eyes or strip scanner. The SAS worked only when targeting antigens were present in the sample. By using this SAS-LFIA, we were able to detect a very low concentration of HBeAg (9 ng/mL), which was 27-fold sensitive than that by conventional LFIA (cLFIA). A number of 420 blood samples were detested by this novel SAS-LFIA, the results were in accordance with those of enzyme-linked immunosorbent assay (ELISA) completely, while the cLFIA missed an HBeAg-positive sample. In conclusion, the novel SAS has high specificity and sensitivity, which can be used to replace the conventional rapid test and ELISA in clinical diagnosis.