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2022 ◽  
Vol 262 ◽  
pp. 107439
Cristina Vásquez ◽  
Rolando Célleri ◽  
Mario Córdova ◽  
Galo Carrillo-Rojas

2022 ◽  
Vol 12 (4) ◽  
pp. 778-787
Jiang-Hong An ◽  
Fu-Rong Qi ◽  
Xiao-Ya Cheng ◽  
Xun-Qi Liu ◽  
Pu Luo ◽  

Background and purpose: Coronavirus disease 2019 (COVID-19) was spreading all over the world. Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) primarily invades and infects the lungs of humans leading to COVID-19. Mild to severe clinical symptoms such as fever, cough, and shortness of breath were existed in those patients. One of the most common changes in these patients was abnormal blood routine. However, uncertainty remains regarding the dynamic characteristics of platelet in COVID-19 patients due to limited data. Therefore, we aimed to analyze the association between dynamic characteristics of blood platelet and disease severity, and to identify new monitoring indicators to treat the COVID-19 patients. Methods: In this cohort study, 398 COVID-19 patients treated in the Shenzhen Third People’s hospital from December 16, 2019 to March 26, 2020 were collected and participated. All data of participants including the clinical characteristics, imaging and laboratory information were collected. All patients included in our study were classified as four groups (mild, common, severe, and critical types) regarding clinical symptoms and relevant severe failures based on the Diagnosis Criteria. Platelet count was examined at the baseline and every 3–5 days during hospitalization. Results: The platelet count varied with clinical classifications. The platelet count in mild type was normal without significant fluctuation. While the blood platelet count of most common and severe patients had obvious fluctuations, showing as a dynamic change that first rose and then fell to the level at admission, which was consistent with the trend of lung inflammation. Bone marrow smears further showed that bone marrow hyperplasia was normal in mild, common and severe type patients, and megakaryocytes and their platelet-producing functions were not abnormal. Conclusions: Our results suggested that the dynamic changes of platelet count might be a predictor of lung inflammation alteration for COVID-19 patients. The changes in platelet count might be a responsive pattern secondary to lung inflammation. The function of bone marrow may be slightly affected by SARS-CoV-2 infection.

2022 ◽  
Vol 37 ◽  
pp. 37-54
Alex Grenyer ◽  
Oliver Schwabe ◽  
John A. Erkoyuncu ◽  
Yifan Zhao

Pharmacy ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 16
Sarah Grace Gunter ◽  
Mary Joyce B. Wingler ◽  
David A. Cretella ◽  
Jamie L. Wagner ◽  
Katie E. Barber ◽  

Limited data are available regarding optimal antimicrobial therapy for Staphylococcus aureus bacteremia (SAB) in pediatric patients. The purpose of this study was to assess clinical characteristics and outcomes associated with intravenous (IV) versus oral step-down treatment of pediatric SAB. This study evaluated patients aged 3 months to 18 years that received at least 72 h of inpatient treatment for SAB. The primary endpoint was 30-day readmission. Secondary endpoints included hospital length of stay and inpatient mortality. One hundred and one patients were included in this study. The median age was 7.9 years. Patients who underwent oral step-down were less likely to be immunocompromised and more likely to have community-acquired SAB from osteomyelitis or skin and soft tissue infection (SSTI). More patients in the IV therapy group had a 30-day readmission (10 (25.6%) vs. 3 (5.3%), p = 0.006). Mortality was low (5 (5%)) and not statistically different between groups. Length of stay was greater in patients receiving IV therapy only (11 vs. 7 days, p = 0.001). In this study, over half of the patients received oral step-down therapy and 30-day readmission was low for this group. Oral therapy appears to be safe and effective for patients with SAB from osteomyelitis or SSTIs.

2022 ◽  
Vol 2022 ◽  
pp. 1-14
Zhenzhou Yuan ◽  
Kun He ◽  
Yang Yang

With the development of freeway system informatization, it is easier to obtain the traffic flow data of freeway, which are widely used to study the relationship between traffic flow state and traffic safety. However, as the development degree of the freeway system is different in different regions, the sample size of traffic data collected in some regions is insufficient, and the precision of data is relatively low. In order to study the influence of limited data on the real-time freeway traffic crash risk modeling, three data sets including high precision data, small sample data, and low precision data were considered. Firstly, Bayesian Logistic regression was used to identify and predict the risk of three data sets. Secondly, based on the Bayesian updating method, the migration test towards high and low precision data sets was established. Finally, the applicability of machine learning and statistical methods to low precision data set was compared. The results show that the prediction performance of Bayesian Logistic regression improves with the increasing of sample size. Bayesian Logistic regression can identify various significant risk factors when data sets are of different precision. Comparatively, the prediction performance of the support vector machine is better than that of Bayesian Logistic. In addition, Bayesian updating method can improve the prediction performance of the transplanted model.

2022 ◽  
Vol 2022 ◽  
pp. 1-9
Samira Zamani ◽  
Anis Mohammadi ◽  
Bahareh Hajikhani ◽  
Parnaz Abiri ◽  
Maryam Fazeli ◽  

The spread of mupirocin-resistant Staphylococcus aureus strains in hospitals and communities is a universal challenge. Limited data is available on the genetic features of high-level mupirocin resistant- (HLMUPR-) S. aureus isolates in Tehran. In the present research, we investigated 48 high-level mupirocin resistance S. aureus by antimicrobial activity, virulence analysis, biofilm formation, multilocus sequence typing (MLST), and staphylocoagulase (SC) typing. All the HLMUPR strains were positive for mupA gene. The frequency of multidrug resistance was 97.9%. Twenty-one (43.8%) were toxinogenic with 14 producing pvl (29.2%), 5 tst (10.4%), and two eta (4.2%). Among the HLMUPR isolates, biofilm production was detected in 45 (89.6%) isolates with complete dominance clfB, clfA genes, and a noticeably high frequency fnbA (95.8%), followed by fnbB (93.8%), eno and icaD (each 83.3%), sdrC (81.3%), ebps (79.2%), icaA (75%), sdrD (66.7%), fib (60.4%), sdrE (50%), cna (41.7%), and bap (4.2%). Coagulase typing distinguished isolates into four genotypic patterns including III (50%), II (27.1%), and type IVa (22.9%). A total of three clonal complexes (CCs) and 4 sequence types (STs) including CC/ST22 as the most prevalent (52.1%), CC8/ST239 (20.8%), CC/ST8 (16.7%), and CC/ST5 (10.4%) were identified in current work. According to our analysis, nonbiofilm producer isolates belonged to CC8/ST239 (6.3%) and CC/ST8 (4.2%). Fusidic acid-resistant isolates belonged to CC/ST45 ( n = 3 ) and CC8/ST239 ( n = 1 ). Observations highlighted the circulation of the CC/ST22 HLMUPR S. aureus strains with strong biofilm-production ability in our hospitals, indicating the possibility of transmission of this type between community and hospital.

Jarrel Seah ◽  
Tom Boeken ◽  
Marc Sapoval ◽  
Gerard S. Goh

AbstractMachine learning techniques, also known as artificial intelligence (AI), is about to dramatically change workflow and diagnostic capabilities in diagnostic radiology. The interest in AI in Interventional Radiology is rapidly gathering pace. With this early interest in AI in procedural medicine, IR could lead the way to AI research and clinical applications for all interventional medical fields. This review will address an overview of machine learning, radiomics and AI in the field of interventional radiology, enumerating the possible applications of such techniques, while also describing techniques to overcome the challenge of limited data when applying these techniques in interventional radiology. Lastly, this review will address common errors in research in this field and suggest pathways for those interested in learning and becoming involved about AI.

2022 ◽  
Vol 11 ◽  
Liting Zhong ◽  
Xiaoyu Liu ◽  
Zelei Li ◽  
Xuebing Zhang ◽  
Yuli Wang ◽  

Gallbladder carcinoma (GBC) with proficient mismatch repair (pMMR)/microsatellite stable (MSS) is associated with limited response to programmed death-1 (PD-1) inhibitor monotherapy. Limited data of PD-1 blockade combined with anti-angiogenic therapy in GBC are reported. One recurrent GBC patient with pMMR/MSS was treated with camrelizumab plus apatinib. After 4 cycles of combination therapy, the patient achieved a durable complete response with manageable toxicity. The next-generation sequencing and immunohistochemistry analysis showed that tumor mutation burden (TMB) was 7.26 mutants/Mb and PD-L1 expression was 10% (tumor proportion score) and 20% (immune proportion score). This case suggests that camrelizumab in combination with apatinib may be an effective treatment option for GBC patients with pMMR/MSS status, who have moderate expression of TMB and PD-L1. Additionally, TMB and PD-L1 expression may serve as potential biomarkers for predicting PD-1 inhibitor response of GBC. Furthermore, this needs to be verified in future studies.

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