Search

Introduction: Psittacosis is a zoonosis caused by Chlamydia psittaci, the clinical manifestations of Psittacosis range from mild illness to fulminant severe pneumonia with multiple organ failure. This study aimed to evaluate the clinical characteristics of Chlamydia psittaci infection diagnosed based on metagenomic next-generation sequencing(mNGS), as well as the risk factors affecting the progress of Chlamydia psittaci infection, in order to improve the effect of therapeutics. Methods: We retrospectively analyzed the clinical data of patients infected with chlamydia psittaci in the First Affiliated Hospital of Nanchang University from January 2021 to December 2021. The patient's past medical history, clinical manifestations, laboratory examinations, chest CT results, treatment status, and prognosis data were collected. we also investigated both the pathogenic profile characteristics and the lower respiratory tract microbiota of patients with Chlamydia psittaci pneumonia using mNGS. Results: All cases of Chlamydia psittaci in our research have been confirmed by mNGS. Among 46 cases of Chlamydia psittaci pneumonia, Poultry exposure was reported in 35 cases. In severe cases of Chlamydia psittaci pneumonia, Neutrophils, Procalcitonin (PCT), Lactate Dehydrogenase (LDH), Hydroxybutyrate Dehydrogenase (HBDH), Creatine Kinase Isoenzymes-B (CK-MB) and D-Dimer levels were remarkably higher than that of non-severe cases, except for lymphocytes (all P < 0.05). Chest CT scans showed Bilateral (77.8%), multiple lobar lungs (85.2%), pleural effusions (44.4%) involvement in those suffering from severe Chlamydia psittaci pneumonia, whereas its incidence was 0%, 21.1% and 10.5% in non-severe patients, respectively (P < 0.05). Multivariate analysis revealed that higher lymphocyte concentrations (OR 0.836, 95% CI 0.714–0.962, P = 0.041) were the only protective factor for survival. mNGS results indicated that 41.3% of patients (19/46) had suspected coinfections with a coinfection rate of 84.2% (16/19) in the severe group, much higher than that in the non severe group (p < 0.05). No significantly different profiles of lower respiratory tract microbiota diversity were found between non severe group and severe group. Conclusion: A history of poultry exposure in patients can serve as an important basis for diagnosing Chlamydia psittaci pneumonia, and patients with severe Chlamydia psittaci pneumonia are more likely to develop elevated inflammatory biomarkers as well as elevated cardiac markers. Higher lymphocyte concentrations are protective factors associated with severe C. psittaci pneumonia. The higher proportion of patients with coinfections in our study supports the use of mNGS for comprehensive early detection of respiratory infections in patients with C. psittaci pneumonia. [ABSTRACT FROM AUTHOR]

Long noncoding RNA HOTTIP plays important roles in the generation and progression of human cancers. Exosomes participate in cellular communication by transmitting moleculars between cells and are regarded as suitable candidates for non-invasive diagnosis. However, the existence of HOTTIP in the circulating exosomes and the potential roles of exosomal HOTTIP in gastric cancer (GC) was poorly understood. This study aims at investigating the clinical roles of exosomal HOTTIP in GC. Serum exosomal HOTTIP from 246 subjects (126 GC patients and 120 healthy people) were detected by reverse transcription real-time quantitative polymerase chain reaction (RT-qPCR). Our results showed that expression levels of exosomal HOTTIP were typically upregulated in GC than in normal control (P < 0.001). And its expression levels were significantly correlated with invasion depth (P = 0.0298) and TNM stage (P < 0.001). The AUC for exosomal HOTTIP was 0.827, which demonstrated a higher diagnostic capability than CEA, CA 19–9 and CA72–4 (AUC = 0.653, 0.685 and 0.639, respectively) (P < 0.001). The Kaplan–Meier analysis showed a correlation between increased exosomal HOTTIP levels and poor overall survival (OS) (logrank P < 0.001). And univariate and multivariate COX analysis revealed exosomal HOTTIP overexpression was an independent prognostic factor in GC patients (P = 0.027). These findings demonstrated that exosomal HOTTIP may be a potential biomarker for GC in diagnosis and prognosis. [ABSTRACT FROM AUTHOR]

Background: Thoracic outlet syndrome (TOS) with the lower trunk compression of brachial plexus (BP) is difficult to diagnosis. This study aimed to summarize the features of thoracic outlet syndrome (TOS) with the lower trunk compression of brachial plexus observed on high-frequency ultrasonography (HFUS). Methods: The ultrasound data of 27 patients who had TOS with the lower trunk compression of brachial plexus were collected and eventually confirmed by surgery. The imaging data were compared, and the pathogenesis of TOS was analyzed on the basis of surgical data. Results: TOS occurred predominantly in females (70.4%). Most cases had unilateral involvement (92.6%), mainly on the right side (66.7%). The HFUS features of TOS can be summarized as follows: (1) Lower trunk compression. HFUS revealed focal thinning that reflected compression at the level of the lower trunk; furthermore, the distal part of the nerve was thickened for edema (Affected side: 0.49 ± 0.12 cm vs. Healthy side: 0.38 ± 0.06, P = 0.009), and the cross-sectional area of brachial plexus cords was markedly greater on the injured side than on the healthy side (0.95 ± 0.08 cm² vs. 0.65 ± 0.11 cm², P = 0.004). (2) Hyperechoic fibromuscular bands behind the compressed nerve (mostly the scalenus minimus muscle). (3) Abnormal bony structures: cervical ribs or elongated transverse processes of the 7th cervical vertebra (C7). Surgical results showed that the etiological factors contributing to TOS were (1) muscle hypertrophy and/or fibrosis (100%) and (2) cervical ribs/elongated C7 transverse processes (20.7%). Conclusion: TOS with the lower trunk compression of brachial plexus can be diagnosed accurately and reliably by high-frequency ultrasound. [ABSTRACT FROM AUTHOR]

Background: Scrub typhus is caused by O. tsutsugamushi and spreads through mite larvae biting the skin. Classic symptoms of the disease are eschar and lymphadenopathy. Previous reports have revealed clinical manifestations of scrub typhus, including gastrointestinal symptoms, meningoencephalitis, ocular flutter, pneumonitis, acute respiratory distress syndrome, and acute kidney injury. However, cases of scrub typhus presenting as a urinary tract infection (UTI) with high D-dimer levels could be easily misdiagnosed when clinical attention is insufficient, resulting in difficulty in making a timely diagnosis of the infection. Metagenomics next-generation sequencing (mNGS) is a revolutionary and highly sensitive method that may help in diagnosing atypical cases, even when trace amounts of pathogens are present.Case Presentation: A 52-year-old female presented with a 10-day history of fever, chills, headache and myalgia. She was initially diagnosed with influenza at a local clinic. Various antibacterials were used on the 2nd-12th day onwards; however, her symptoms persisted and were followed by increased urination duration, frequency, urgency and dysuria for 2 days. Orientia tsutsugamushi was confirmed as the pathogen responsible for the infection through mNGS analysis of her blood samples from Day 13 onwards. The patient's temperature changed remarkably 24 h after the initiation of doxycycline. Over the next 48 h (i.e., Day 15 onwards), the patient showed clinical improvement. She recovered and was discharged from the hospital.Conclusions: Scrub typhus can present atypical clinical symptoms, such as UTIs, in a febrile patient. mNGS may be a useful method for identifying O. tsutsugamushi infection in patients with atypical clinical manifestations. [ABSTRACT FROM AUTHOR]

Background: COVID-19 has posed an enormous threat to public health around the world. Some severe and critical cases have bad prognoses and high case fatality rates, unraveling risk factors for severe COVID-19 are of significance for predicting and preventing illness progression, and reducing case fatality rates. Our study focused on analyzing characteristics of COVID-19 cases and exploring risk factors for developing severe COVID-19. Methods: The data for this study was disease surveillance data on symptomatic cases of COVID-19 reported from 30 provinces in China between January 19 and March 9, 2020, which included demographics, dates of symptom onset, clinical manifestations at the time of diagnosis, laboratory findings, radiographic findings, underlying disease history, and exposure history. We grouped mild and moderate cases together as non-severe cases and categorized severe and critical cases together as severe cases. We compared characteristics of severe cases and non-severe cases of COVID-19 and explored risk factors for severity. Results: The total number of cases were 12 647 with age from less than 1 year old to 99 years old. The severe cases were 1662 (13.1%), the median age of severe cases was 57 years [Inter-quartile range(IQR): 46–68] and the median age of non-severe cases was 43 years (IQR: 32–54). The risk factors for severe COVID-19 were being male [adjusted odds ratio (aOR) = 1.3, 95% CI: 1.2–1.5]; fever (aOR = 2.3, 95% CI: 2.0–2.7), cough (aOR = 1.4, 95% CI: 1.2–1.6), fatigue (aOR = 1.3, 95% CI: 1.2–1.5), and chronic kidney disease (aOR = 2.5, 95% CI: 1.4–4.6), hypertension (aOR = 1.5, 95% CI: 1.2–1.8) and diabetes (aOR = 1.96, 95% CI: 1.6–2.4). With the increase of age, risk for the severity was gradually higher [20–39 years (aOR = 3.9, 95% CI: 1.8–8.4), 40–59 years (aOR = 7.6, 95% CI: 3.6–16.3), ≥ 60 years (aOR = 20.4, 95% CI: 9.5–43.7)], and longer time from symtem onset to diagnosis [3–5 days (aOR = 1.4, 95% CI: 1.2–1.7), 6–8 days (aOR = 1.8, 95% CI: 1.5–2.1), ≥ 9 days(aOR = 1.9, 95% CI: 1.6–2.3)]. Conclusions: Our study showed the risk factors for developing severe COVID-19 with large sample size, which included being male, older age, fever, cough, fatigue, delayed diagnosis, hypertension, diabetes, chronic kidney diasease. Based on these factors, the severity of COVID-19 cases can be predicted. So cases with these risk factors should be paid more attention to prevent severity. [ABSTRACT FROM AUTHOR]

Background: COVID-19 has posed an enormous threat to public health around the world. Some severe and critical cases have bad prognoses and high case fatality rates, unraveling risk factors for severe COVID-19 are of significance for predicting and preventing illness progression, and reducing case fatality rates. Our study focused on analyzing characteristics of COVID-19 cases and exploring risk factors for developing severe COVID-19. Methods: The data for this study was disease surveillance data on symptomatic cases of COVID-19 reported from 30 provinces in China between January 19 and March 9, 2020, which included demographics, dates of symptom onset, clinical manifestations at the time of diagnosis, laboratory findings, radiographic findings, underlying disease history, and exposure history. We grouped mild and moderate cases together as non-severe cases and categorized severe and critical cases together as severe cases. We compared characteristics of severe cases and non-severe cases of COVID-19 and explored risk factors for severity. Results: The total number of cases were 12 647 with age from less than 1 year old to 99 years old. The severe cases were 1662 (13.1%), the median age of severe cases was 57 years [Inter-quartile range(IQR): 46–68] and the median age of non-severe cases was 43 years (IQR: 32–54). The risk factors for severe COVID-19 were being male [adjusted odds ratio (aOR) = 1.3, 95% CI: 1.2–1.5]; fever (aOR = 2.3, 95% CI: 2.0–2.7), cough (aOR = 1.4, 95% CI: 1.2–1.6), fatigue (aOR = 1.3, 95% CI: 1.2–1.5), and chronic kidney disease (aOR = 2.5, 95% CI: 1.4–4.6), hypertension (aOR = 1.5, 95% CI: 1.2–1.8) and diabetes (aOR = 1.96, 95% CI: 1.6–2.4). With the increase of age, risk for the severity was gradually higher [20–39 years (aOR = 3.9, 95% CI: 1.8–8.4), 40–59 years (aOR = 7.6, 95% CI: 3.6–16.3), ≥ 60 years (aOR = 20.4, 95% CI: 9.5–43.7)], and longer time from symtem onset to diagnosis [3–5 days (aOR = 1.4, 95% CI: 1.2–1.7), 6–8 days (aOR = 1.8, 95% CI: 1.5–2.1), ≥ 9 days(aOR = 1.9, 95% CI: 1.6–2.3)]. Conclusions: Our study showed the risk factors for developing severe COVID-19 with large sample size, which included being male, older age, fever, cough, fatigue, delayed diagnosis, hypertension, diabetes, chronic kidney diasease, early case identification and prompt medical care. Based on these factors, the severity of COVID-19 cases can be predicted. So cases with these risk factors should be paid more attention to prevent severity. [ABSTRACT FROM AUTHOR]