Abdominal ultrasound in amazonian manatee (Trichechus inunguis) (Natterer, 1883)

Morphophysiological species researches are fundamental, and diagnostic imaging is an excellent technique, already used in wild animals, with great application, not invasive and provide real-time information of each body. Amazonian manatees are on the list of endangered animals classified in the vulnerable category and knowledge of the normal pattern of ultrasound anatomy of organs and tissues is important for the maintenance and well-being of captive specimens contributing to reintroduction actions. The objective of the study was to standardize the examination technique and describe the ultrasound findings of the liver, gallbladder, stomach, urinary bladder and the subcutaneous tissue of the abdominal region in Trichechus inunguis, in order to contribute with the anatomical and sonographic knowledge and assist in the diagnosis and prognosis diseases. The study used 18 animals to describe the normal sonographic anatomy in the abdominal cavity of the Amazonian manatee. During abdominal scan, it was possible to visualize the features of the liver, gallbladder, stomach, urinary bladder obtained satisfactory results in this study. Therefore, other structures were not primarily identified by the reduced time, lots of fat and gases in intestines of animals.

The Assessment, Management and Prevention of Calf Muscle Strain Injuries: A Qualitative Study of the Practices and Perspectives of 20 Expert Sports Clinicians

Background Despite calf muscle strain injuries (CMSI) being problematic in many sports, there is a dearth of research to guide clinicians dealing with these injuries. The aim of this study was to evaluate the current practices and perspectives of a select group of international experts regarding the assessment, management and prevention of CMSI using in-depth semi-structured interviews. Results Twenty expert clinicians working in elite sport and/or clinician-researchers specialising in the field completed interviews. A number of key points emerged from the interviews. Characteristics of CMSI were considered unique compared to other muscle strains. Rigor in the clinical approach clarifies the diagnosis, whereas ongoing monitoring of calf capacity and responses to loading exposure provides the most accurate estimate of prognosis. Athlete intrinsic characteristics, injury factors and sport demands shaped rehabilitation across six management phases, which were guided by key principles to optimise performance at return to play (RTP) while avoiding subsequent injury or recurrence. To prevent CMSI, periodic monitoring is common, but practices vary and data are collected to inform load-management and exercise selection rather than predict future CMSI. A universal injury prevention program for CMSI may not exist. Instead, individualised strategies should reflect athlete intrinsic characteristics and sport demands. Conclusions Information provided by experts enabled a recommended approach to clinically evaluate CMSI to be outlined, highlighting the injury characteristics considered most important for diagnosis and prognosis. Principles for optimal management after CMSI were also identified, which involved a systematic approach to rehabilitation and the RTP decision. Although CMSI were reportedly difficult to prevent, on- and off-field strategies were implemented by experts to mitigate risk, particularly in susceptible athletes.

Generation and Application of Inducible Chimeric RNA ASTN2-PAPPAas Knockin Mouse Model

Chimeric RNAs (chiRNAs) play many previously unrecognized roles in different diseases including cancer. They can not only be used as biomarkers for diagnosis and prognosis of various diseases but also serve as potential therapeutic targets. In order to better understand the roles of chiRNAs in pathogenesis, we inserted human sequences into mouse genome and established a knockin mouse model of the tamoxifen-inducible expression of ASTN2-PAPPA antisense chimeric RNA (A-PaschiRNA). Mice carrying the A-PaschiRNA knockin gene do not display any apparent abnormalities in growth, fertility, histological, hematopoietic, and biochemical indices. Using this model, we dissected the role of A-PaschiRNA in chemical carcinogen 4-nitroquinoline 1-oxide (4NQO)-induced carcinogenesis of esophageal squamous cell carcinoma (ESCC). To our knowledge, we are the first to generate a chiRNA knockin mouse model using the Cre-loxP system. The model could be used to explore the roles of chiRNA in pathogenesis and potential targeted therapies.

An automated COVID-19 triage pipeline using artificial intelligence based on chest radiographs and clinical data

While COVID-19 diagnosis and prognosis artificial intelligence models exist, very few can be implemented for practical use given their high risk of bias. We aimed to develop a diagnosis model that addresses notable shortcomings of prior studies, integrating it into a fully automated triage pipeline that examines chest radiographs for the presence, severity, and progression of COVID-19 pneumonia. Scans were collected using the DICOM Image Analysis and Archive, a system that communicates with a hospital’s image repository. The authors collected over 6,500 non-public chest X-rays comprising diverse COVID-19 severities, along with radiology reports and RT-PCR data. The authors provisioned one internally held-out and two external test sets to assess model generalizability and compare performance to traditional radiologist interpretation. The pipeline was evaluated on a prospective cohort of 80 radiographs, reporting a 95% diagnostic accuracy. The study mitigates bias in AI model development and demonstrates the value of an end-to-end COVID-19 triage platform.

Circulating Blood-Based Biomarkers in Pulmonary Hypertension

Pulmonary hypertension (PH) is a serious hemodynamic condition, characterized by increased pulmonary vascular resistance (PVR), leading to right heart failure (HF) and death when not properly treated. The prognosis of PH depends on etiology, hemodynamic and biochemical parameters, as well as on response to specific treatment. Biomarkers appear to be useful noninvasive tools, providing information about the disease severity, treatment response, and prognosis. However, given the complexity of PH, it is impossible for a single biomarker to be adequate for the broad assessment of patients with different types of PH. The search for novel emerging biomarkers is still ongoing, resulting in a few potential biomarkers mirroring numerous pathophysiological courses. In this review, markers related to HF, myocardial remodeling, inflammation, hypoxia and tissue damage, and endothelial and pulmonary smooth muscle cell dysfunction are discussed in terms of diagnosis and prognosis. Extracellular vesicles and other markers with complex backgrounds are also reviewed. In conclusion, although many promising biomarkers have been identified and studied in recent years, there are still insufficient data on the application of multimarker strategies for monitoring and risk stratification in PH patients.

Clinical Relevance of Soluble Forms of Immune Checkpoint Molecules sPD-1, sPD-L1, and sCTLA-4 in the Diagnosis and Prognosis of Ovarian Cancer

It is crucial to find new diagnostic and prognostic biomarkers. A total of 80 patients were enrolled in the study. The study group consisted of 37 patients with epithelial ovarian cancer, and the control group consisted of 43 patients with benign ovarian cystic lesions. Three proteins involved in the immune response were studied: PD-1, PD-L1, and CTLA-4. The study material was serum and peritoneal fluid. The ROC curve was plotted, and the area under the curve was calculated to characterize the sensitivity and specificity of the studied parameters. Univariate and multivariate analyses were performed simultaneously using the Cox regression model. The cut-off level of CTLA-4 was 0.595 pg/mL, with the sensitivity and specificity of 70.3% and 90.7% (p = 0.000004). Unfavorable prognostic factors determined in serum were: PD-L1 (for PFS: HR 1.18, 95% CI 1.11–1.21, p = 0.016; for OS: HR 1.17, 95% CI 1.14–1.19, p = 0.048) and PD-1 (for PFS: HR 1.01, 95% CI 0.91–1.06, p = 0.035). Unfavorable prognostic factors determined in peritoneal fluid were: PD-L1 (for PFS: HR 1.08, 95% CI 1.01–1.11, p = 0.049; for OS: HR 1.14, 95% CI 1.10–1.17, p = 0.045) and PD-1 (for PFS: HR 1.21, 95% CI 1.19–1.26, p = 0.044). We conclude that CTLA-4 should be considered as a potential biomarker in the diagnosis of ovarian cancer. PD-L1 and PD-1 concentrations are unfavorable prognostic factors for ovarian cancer.

Differences in gene expression between high- and low-grade serous ovarian cancers: implications for diagnosis and prognosis

Background: The clinical presentations of high-grade serous ovarian cancer (HGSOC) and low-grade serous ovarian cancer (LGSOC) differ. In this study, we aimed to identify the essential molecules for the diagnosis and prognosis of these OC subtypes. Methods: Differentially expressed genes between HGSOC and LGSOC were identified using three GEO series. The functional enrichment analysis was performed to investigate different biological processes and pathways. The protein-protein interaction network was constructed, and the discovered hub genes were frequently validated using prognostic correlation and immunohistochemistry (IHC) in GEPIA and HPA databases. Finally, we screened out BIRC5 and used IHC to detect its expression in 20 cases of borderline serous tumors, 20 cases of LGSOC, and 38 cases of HGSOC, and further analyzed its correlation with clinical characteristics.Results: In comparison with LGSOC, 79 upregulated and 85 downregulated genes were identified in HGSOC. The biological roles of these genes were mainly centered on the cell cycle process and chromosomal segregation. Among the 10 hub genes chosen, BIRC5 is positively related to the overall survival of patients with OC (p = 0.014) and can distinguish OC from normal ovarian tissue. In addition to database analysis, we verify BIRC5 through the specimen resources in our case database. According to the IHC results of our specimens, we found that BIRC5 can not only distinguish HGSOC and LGSOC but also positively correlate with the age, preoperative CA125 level, FIGO stage，and TP53/Ki-67 expression in tumor specimens.Conclusions: BIRC5 is a reliable marker that can distinguish HGSOC from LGSOC, guide prognosis, and be utilized in clinical IHC.

Two-Stage Classification Model for the Prediction of Heart Disease Using IoMT and Artificial Intelligence

Internet of Things (IoT) technology has recently been applied in healthcare systems as an Internet of Medical Things (IoMT) to collect sensor information for the diagnosis and prognosis of heart disease. The main objective of the proposed research is to classify data and predict heart disease using medical data and medical images. The proposed model is a medical data classification and prediction model that operates in two stages. If the result from the first stage is efficient in predicting heart disease, there is no need for stage two. In the first stage, data gathered from medical sensors affixed to the patient’s body were classified; then, in stage two, echocardiogram image classification was performed for heart disease prediction. A hybrid linear discriminant analysis with the modified ant lion optimization (HLDA-MALO) technique was used for sensor data classification, while a hybrid Faster R-CNN with SE-ResNet-101 modelwass used for echocardiogram image classification. Both classification methods were carried out, and the classification findings were consolidated and validated to predict heart disease. The HLDA-MALO method obtained 96.85% accuracy in detecting normal sensor data, and 98.31% accuracy in detecting abnormal sensor data. The proposed hybrid Faster R-CNN with SE-ResNeXt-101 transfer learning model performed better in classifying echocardiogram images, with 98.06% precision, 98.95% recall, 96.32% specificity, a 99.02% F-score, and maximum accuracy of 99.15%.

Predicting 3D Structure, Cross Talks, and Prognostic Significance of KLF9 in Cervical Cancer

Our study aimed to identify the new blood-based biomarkers for the diagnosis and prognosis of cervical cancer. Moreover, the three-dimensional (3D) structure of Kruppel-like factor 9 (KLF9) was also determined in order to better understand its function, and a signaling pathway was constructed to identity its upstream and downstream targets. In the current study, the co-expressions of tumor protein D52 (TPD52), KLF9, microRNA 223 (miR-223), and protein kinase C epsilon (PKCϵ) were evaluated in cervical cancer patients and a possible relation with disease outcome was revealed. The expressions of TPD52, KLF9, miR-223, and PKCϵ were studied in the blood of 100 cervical cancer patients and 100 healthy controls using real-time PCR. The 3D structure of KLF9 was determined through homology modeling via the SWISS-MODEL and assessed using the Ramachandran plot. The predicted 3D structure of KLF9 had a similarity index of 62% with its template (KLF4) with no bad bonds in it. In order to construct a genetic pathway, depicting the crosstalk between understudied genes, STRING analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG), and DAVID software were used. The constructed genetic pathway showed that all the understudied genes are linked to each other and involved in the PI3K/Akt signaling pathway. There was a 23-fold increase in TPD52 expression, a 2-fold increase in miR-223 expression, a 0.14-fold decrease in KLF9 expression, and a 0.05-fold decrease of PKCϵ expression in cervical cancer. In the present study, we observed an association of the expressions of TPD52, KLF9, miR-223, and PKCϵ with tumor stage, metastasis, and treatment status of cervical cancer patients. Elevated expressions of TPD52 and miR-223 and reduced expressions of KLF9 and PKCϵ in peripheral blood of cervical cancer patients may serve as predictors of disease diagnosis and prognosis. Nevertheless, further in vitro and tissue-level studies are required to strengthen their role as potential diagnostic and prognostic biomarkers.