UNASSIGNED: Epidemiological and clinical analyses of brucellosis are vital for public health leaders to reinforce disease surveillance and case management strategies.
UNASSIGNED: In this study, we aimed to analyse the epidemiology and clinical features of 1,590 cases of human brucellosis.
UNASSIGNED: Approximately 72.08% (1,146) of the patients were male and 27.92% (444) were female. At least 88.18% (1,402/1,590) of the patients had a history of contact with sheep/goats and cattle, which was identified as the main risk factor for infection. The most common age group affected was 30-69 years, comprising 83.90% of all cases, with a median age of 47.3 years. Meanwhile, 75.03% (1,193/1,590) of the patients were farmers, followed by workers (10.50%, 167/1,590). The spectrum of clinical manifestations varied, and the major symptoms were fatigue (42.96%), joint pain (37.30%), and fever (23.33%). Arthritis was diagnosed in 989 patients, spondylitis was diagnosed in 469 patients, and external genital complications were found in at least 53.96% (858/1,590) of patients. In addition, approximately 41.25% (625/1,515) and 24.53% (390/1,590) of cases exhibited elevated CRP and D-dimer levels, respectively. Conversely, a significant decrease was observed in fibrinogen, total protein, and albumin levels, affecting 48.36% (769/1,590), 77.30% (1,226/1,586), and 91.80% (1,456/1,586) of the patients, respectively. These data demonstrate that brucellosis is a severe wasting disease that leads to an imbalance in nutritional metabolism and a decline in immunity. In total, 86.73% (1,379/1,590) of patients showed improvement with antibiotic therapy, while 13.27% (211/1,590) of patients experienced relapses or treatment failure.
UNASSIGNED: Brucellosis often presents with non-specific symptoms and laboratory findings, accompanied by multiple organ invasions, as well as being a vital challenge for diagnosis and treatment; thus, it is essential for a high degree of suspicion to be placed on brucellosis for a timely diagnosis and treatment. This study provides basic data and resources for developing tailored countermeasures to curb its further spread.

OBJECTIVE: This study aimed to summarize and analyze the clinical data of intestinal tuberculosis (ITB) in order to provide guidance for accurate diagnosis and treatment of ITB.
METHODS: This study consecutively included patients with ITB who were admitted to our hospital from 2008 to 2021 and retrospectively analyzed their clinical features.
RESULTS: Forty-six patients were included. The most common clinical symptom was weight loss (67.4%). Seventy percent of 20 patients were positive for tuberculin skin test; 57.1% of 14 patients were positive for mycobacterium tuberculosis specific cellular immune response test, while 84.6% of 26 patients were positive for tuberculosis infection T cell spot test. By chest computed tomography (CT) examination, 25% and 5.6% of 36 patients were diagnosed with active pulmonary tuberculosis and with inactive pulmonary tuberculosis, respectively. By abdominal CT examination, the most common sign was abdominal lymph node enlargement (43.2%). Forty-two patients underwent colonoscopy, and the most common endoscopic manifestation was ileocecal ulcer (59.5%), followed by colonic ulcer (35.7%) and ileocecal valve deformity (26.2%). ITB most frequently involved the terminal ileum/ileocecal region (76.1%). Granulomatous inflammation with multinucleated giant cells and caseous necrosis was found via endoscopic biopsies, the ultrasound-guided percutaneous biopsy of enlarged mesentery lymph nodes, and surgical interventions. The acid-fast bacilli were discovered in 53.1% of 32 samples. Twenty-one cases highly suspected of ITB were confirmed after responding to empiric anti-tuberculosis therapy.
CONCLUSIONS: It was necessary to comprehensively analyze clinical features to make an accurate diagnosis of ITB and aid in distinguishing ITB from diseases such as Crohn\'s disease and malignant tumors.

UNASSIGNED: In December 2021, a large-scale epidemic broke out in Xi\'an, China, due to SARS-CoV-2 infection. This study reports the effect of vaccination on COVID-19 and evaluates the impact of different vaccine doses on routine laboratory markers.
UNASSIGNED: The laboratory data upon admission, of 231 cases with COVID-19 hospitalized from December 8, 2021 to January 20, 2022 in Xi\'an, including blood routine, lymphocyte subtypes, coagulative function tests, virus specific antibodies and blood biochemical tests were collected and analyzed.
UNASSIGNED: Of the 231 patients, 21 were not vaccinated, 158 were vaccinated with two doses and 52 with three doses. Unvaccinated patients had a higher proportion of moderate and severe symptoms than vaccinated patients, while two-dose vaccinated patients had a higher proportion than three-dose vaccinated patients. SARS-CoV-2 specific IgG levels were significantly elevated in vaccinated patients compared with unvaccinated patients. Particularly, unvaccinated patients had lower counts and percentages of lymphocytes, eosinophils and CD8+ T-lymphocytes, and elevated coagulation-related markers. In addition, vaccination had no effect on liver and kidney function.
UNASSIGNED: Vaccination against SARS-CoV-2, inducing high IgG level and increased CD8+ T cells and eosinophils, and regulating coagulation function, can significantly attenuate symptoms of COVID-19, suggesting that the vaccine remains protective against SARS-CoV-2.

The aim of this study is to propose a model based on machine learning (ML) and Internet of Things (IoT) to diagnose patients with COVID-19 in smart hospitals. In this sense, it was emphasized that by the representation for the role of ML models and IoT relevant technologies in smart hospital environment. The accuracy rate of diagnosis (classification) based on laboratory findings can be improved via light ML models. Three ML models, namely, naive Bayes (NB), Random Forest (RF), and support vector machine (SVM), were trained and tested on the basis of laboratory datasets. Three main methodological scenarios of COVID-19 diagnoses, such as diagnoses based on original and normalized datasets and those based on feature selection, were presented. Compared with benchmark studies, our proposed SVM model obtained the most substantial diagnosis performance (up to 95%). The proposed model based on ML and IoT can be served as a clinical decision support system. Furthermore, the outcomes could reduce the workload for doctors, tackle the issue of patient overcrowding, and reduce mortality rate during the COVID-19 pandemic.

Objectives: COVID-19 is highly infectious and has been widely spread worldwide, with more than 159 million confirmed cases and more than 3 million deaths as of May 11, 2021. It has become a serious public health event threatening people\'s lives and safety. Due to the rapid transmission and long incubation period, shortage of medical resources would easily occur in the short term of discovering disease cases. Therefore, we aimed to construct an artificial intelligent framework to rapidly distinguish patients with COVID-19 from common pneumonia and non-pneumonia populations based on computed tomography (CT) images. Furthermore, we explored artificial intelligence (AI) algorithms to integrate CT features and laboratory findings on admission to predict the clinical classification of COVID-19. This will ease the burden of doctors in this emergency period and aid them to perform timely and appropriate treatment on patients. Methods: We collected all CT images and clinical data of novel coronavirus pneumonia cases in Inner Mongolia, including domestic cases and those imported from abroad; then, three models based on transfer learning to distinguish COVID-19 from other pneumonia and non-pneumonia population were developed. In addition, CT features and laboratory findings on admission were combined to predict clinical types of COVID-19 using AI algorithms. Lastly, Spearman\'s correlation test was applied to study correlations of CT characteristics and laboratory findings. Results: Among three models to distinguish COVID-19 based on CT, vgg19 showed excellent diagnostic performance, with area under the curve (AUC) of the receiver operating characteristic (ROC) curve at 95%. Together with laboratory findings, we were able to predict clinical types of COVID-19 with AUC of the ROC curve at 90%. Furthermore, biochemical markers, such as C-reactive protein (CRP), LYM, and lactic dehydrogenase (LDH) were identified and correlated with CT features. Conclusion: We developed an AI model to identify patients who were positive for COVID-19 according to the results of the first CT examination after admission and predict the progression combined with laboratory findings. In addition, we obtained important clinical characteristics that correlated with the CT image features. Together, our AI system could rapidly diagnose COVID-19 and predict clinical types to assist clinicians perform appropriate clinical management.

Brucellosis is a zoonosis with a wide spectrum of clinical manifestations. However, it is still unclear whether the clinical manifestations in children are significantly different from those in adults.
Patients with brucellosis and treated at the General Hospital of Ningxia Medical University between 2009 and 2019 were divided into two groups; children (88) and adults (354). Thereafter, the records of the two groups were analyzed retrospectively.
The findings showed that: 1. School-age children, young and middle-aged individuals were more likely to suffer from brucellosis and most were male; 2. Fever and arthralgia were the most common manifestations in the two groups. In addition, fatigue and low back pain were rare in children although fever and lymphadenopathy were more common in this group. However, hepatomegaly and splenomegaly were common in both groups; 3. The most common complication was osteoarthritis and peripheral arthritis occurred more frequently in children. On the other hand, spondylitis was the most common in adults (this particularly involved the lumbar and sacral vertebrae); 4. An increase in the erythrocyte sedimentation rate, levels of the C-reactive protein and liver enzymes was common in both two groups; 5. There was no significant difference in the positive rate of the standard agglutination test between children (96.59%) and adults (95.20%). However, the positive rate of blood culture was higher in children (65.85%) than in adults (51.00%).
Brucellosis causes damage to multiple systems and differences in clinical characteristics were found between children and adults.

The COVID-19 outbreak has brought great challenges to healthcare resources around the world. Patients with COVID-19 exhibit a broad spectrum of clinical characteristics. In this study, the Factor Analysis of Mixed Data (FAMD)-based cluster analysis was applied to demographic information, laboratory indicators at the time of admission, and symptoms presented before admission. Three COVID-19 clusters with distinct clinical features were identified by FAMD-based cluster analysis. The FAMD-based cluster analysis results indicated that the symptoms of COVID-19 were roughly consistent with the laboratory findings of COVID-19 patients. Furthermore, symptoms for mild patients were atypical. Different hospital stay durations and survival differences among the three clusters were also found, and the more severe the clinical characteristics were, the worse the prognosis. Our aims were to describe COVID-19 clusters with different clinical characteristics, and a classifier model according to the results of FAMD-based cluster analysis was constructed to help provide better individualized treatments for numerous COVID-19 patients in the future.

Background and Objectives: To investigate whether coronavirus disease 2019 (COVID-19) survivors who had different disease severities have different levels of pulmonary sequelae at 3 months post-discharge. Methods: COVID-19 patients discharged from four hospitals 3 months previously, recovered asymptomatic patients from an isolation hotel, and uninfected healthy controls (HCs) from the community were prospectively recruited. Participants were recruited at Wuhan Union Hospital and underwent examinations, including quality-of-life evaluation (St. George Respiratory Questionnaire [SGRQ]), laboratory examination, chest computed tomography (CT) imaging, and pulmonary function tests. Results: A total of 216 participants were recruited, including 95 patients who had recovered from severe/critical COVID-19 (SPs), 51 who had recovered from mild/moderate disease (MPs), 28 who had recovered from asymptomatic disease (APs), and 42 HCs. In total, 154 out of 174 (88.5%) recovered COVID-19 patients tested positive for serum SARS-COV-2 IgG, but only 19 (10.9%) were still positive for IgM. The SGRQ scores were highest in the SPs, while APs had slightly higher SGRQ scores than those of HCs; 85.1% of SPs and 68.0% of MPs still had residual CT abnormalities, mainly ground-glass opacity (GGO) followed by strip-like fibrosis at 3 months after discharge, but the pneumonic lesions were largely absorbed in the recovered SPs or MPs relative to findings in the acute phase. Pulmonary function showed that the frequency of lung diffusion capacity for carbon monoxide abnormalities were comparable in SPs and MPs (47.1 vs. 41.7%), while abnormal total lung capacity (TLC) and residual volume (RV) were more frequent in SPs than in MPs (TLC, 18.8 vs. 8.3%; RV, 11.8 vs. 0%). Conclusions: Pulmonary abnormalities remained after recovery from COVID-19 and were more frequent and conspicuous in SPs at 3 months after discharge.

UNASSIGNED: The insufficient understanding and misdiagnosis of clinically diagnosed pulmonary tuberculosis (PTB) without an aetiological evidence is a major problem in the diagnosis of tuberculosis (TB). This study aims to confirm the value of Long non-coding RNA (lncRNA) n344917 in the diagnosis of PTB and construct a rapid, accurate, and universal prediction model.
UNASSIGNED: A total of 536 patients were prospectively and consecutively recruited, including clinically diagnosed PTB, PTB with an aetiological evidence and non-TB disease controls, who were admitted to West China hospital from Dec 2014 to Dec 2017. The expression levels of lncRNA n344917 of all patients were analyzed using reverse transcriptase quantitative real-time PCR. Then, the laboratory findings, electronic health record (EHR) information and expression levels of n344917 were used to construct a prediction model through the Least Absolute Shrinkage and Selection Operator algorithm and multivariate logistic regression.
UNASSIGNED: The factors of n344917, age, CT calcification, cough, TBIGRA, low-grade fever and weight loss were included in the prediction model. It had good discrimination (area under the curve = 0.88, cutoff = 0.657, sensitivity = 88.98%, specificity = 86.43%, positive predictive value = 85.61%, and negative predictive value = 89.63%), consistency and clinical availability. It also showed a good replicability in the validation cohort. Finally, it was encapsulated as an open-source and free web-based application for clinical use and is available online at https://ziruinptb.shinyapps.io/shiny/.
UNASSIGNED: Combining the novel potential molecular biomarker n344917, laboratory and EHR variables, this web-based prediction model could serve as a user-friendly, accurate platform to improve the clinical diagnosis of PTB.

Background: Predicting the risk of progression to severe coronavirus disease 2019 (COVID-19) could facilitate personalized diagnosis and treatment options, thus optimizing the use of medical resources. Methods: In this prospective study, 206 patients with COVID-19 were enrolled from regional medical institutions between December 20, 2019, and April 10, 2020. We collated a range of data to derive and validate a predictive model for COVID-19 progression, including demographics, clinical characteristics, laboratory findings, and cytokine levels. Variation analysis, along with the least absolute shrinkage and selection operator (LASSO) and Boruta algorithms, was used for modeling. The performance of the derived models was evaluated by specificity, sensitivity, area under the receiver operating characteristic (ROC) curve (AUC), Akaike information criterion (AIC), calibration plots, decision curve analysis (DCA), and Hosmer-Lemeshow test. Results: We used the LASSO algorithm and logistic regression to develop a model that can accurately predict the risk of progression to severe COVID-19. The model incorporated alanine aminotransferase (ALT), interleukin (IL)-6, expectoration, fatigue, lymphocyte ratio (LYMR), aspartate transaminase (AST), and creatinine (CREA). The model yielded a satisfactory predictive performance with an AUC of 0.9104 and 0.8792 in the derivation and validation cohorts, respectively. The final model was then used to create a nomogram that was packaged into an open-source and predictive calculator for clinical use. The model is freely available online at https://severeconid-19predction.shinyapps.io/SHINY/. Conclusion: In this study, we developed an open-source and free predictive calculator for COVID-19 progression based on ALT, IL-6, expectoration, fatigue, LYMR, AST, and CREA. The validated model can effectively predict progression to severe COVID-19, thus providing an efficient option for early and personalized management and the allocation of appropriate medical resources.