Mycoplasma hominis, a well known cause of neonatal infection, has been reported as a pathogen in urogenital infections in adults; however, central nervous system (CNS) infections are rare. We report here the first case of M. hominis meningitis in China, post neurosurgical treatment for an intracerebral haemorrhage in a 71-year-old male. We describe a 71-year-old man who developed M. hominis meningitis after neurosurgical treatment and was successfully treated with combined azithromycin and minocycline therapy of 2 weeks duration, despite delayed treatment because the Gram stain of cerebrospinal fluid (CSF) yielded no visible organisms. The diagnosis required 16S rDNA sequencing analysis of the cultured isolate from CSF. Literature review of M. hominis CNS infections yielded 19 cases (13 instances of brain abscess, 3 of meningitis, 1 spinal cord abscess and 1 subdural empyema each). Delay in diagnosis and initial treatment failure was evident in all cases. With appropriate microbiological testing, antibiotic therapy (ranging from 5 days to 12 weeks) and often, multiple surgical interventions, almost all the patients improved immediately. Both our patient findings and the literature review, highlighted the pathogenic potential of M. hominis together with the challenges prompted by rare infectious diseases in particular for developing countries laboratories with limited diagnostic resources.

Erdheim–Chester disease (ECD) is a rare multi-systemic form of histiocytosis. Treatment with BRAF inhibitors has markedly improved outcomes of ECD; however, this targeted therapy is expensive (estimated annual cost is $50,000). Since estimated annual cost of interferon-α (IFN-α) is only approximately $1600 in China, we retrospectively evaluated the long-term therapeutic efficacy of IFN-α and the value of 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) as an assessment method among 32 ECD patients who received high dose IFN-α therapy at Peking Union Medical College Hospital. The median age at diagnosis was 48 years (range, 6–66 years). The median duration of treatment was 18.5 months (range, 1–51 months). The overall clinical response rates were 80.0%, including 33.3% complete response, 36.7% partial response and 10.0% stable disease. Thirty-one patients underwent a total of 81 scans by FDG-PET. Seventeen patients had serial FDG-PET results, nine patients had experienced a partial metabolic response at the last follow-up. The median reduction of ratios between the most active target lesion standardized uptake value (SUV) and liver SUV from baseline to last FDG-PET scan was 61.4% (range, 8.8–86.6%). Eight of thirteen patients who experienced continuous clinical improvement during follow-up had at least one target lesion SUV increased by FDG-PET which decreased in subsequent scans without changing treatment strategy. The estimated 3-year progression-free survival (PFS) and overall survival (OS) were 64.1 and 84.5%, respectively. Central nervous system (CNS) involvement was the only predictor for poor PFS and OS. High-dose IFN-α treatment is a cost-effective option, especially for patients without CNS involvement. Single target lesion SUV elevation according to FDG-PET do not accurately demonstrate disease progression, but serial FDG-PET imaging effectively discriminate treatment response.

Objective: To investigate whether bone age (BA) of children living in Tibet Highland could be accurately assessed using a fully automated artificial intelligence (AI) system. Methods：: Left hand radiographs of 385 children (300 Tibetan and 85 immigrant Han) aged 4–18 years who presented to the largest medical center of Tibet between September 2013 and November 2019 were consecutively collected. From these radiographs, BA was determined using the Greulich and Pyle (GP) method by experts in a consensus manner; furthermore, BA was estimated by a previously reported artificial intelligence (AI) BA system based on Han children from southern China. The performance of the AI system was compared with that of experts by using statistical analysis. Results: Compared with the experts’ results, the accuracy of the AI system for Tibetan and Han children within 1 year was 84.67 and 89.41%, respectively, and its mean absolute difference (MAD) was 0.65 and 0.56 years, respectively. The discrepancy in hand-wrist bone maturation was the main cause of low accuracy of the system in the 4- to 6-year-old group. Conclusion: The AI BA system developed for Han Chinese children living in flat regions could enable to assess BA accurately in Tibet where medical resources are limited. Advances in knowledge: AI-based BA system may serve as an effective and efficient solution to assess BA in Tibet.

Objective: Dermatomyositis (DM) and polymyositis (PM) make up the largest group of potentially treatable myopathies and require early diagnosis. This study investigates whether the edema of thigh muscles in DM/PM can be quantitatively assessed by a novel accelerated T2 mapping technique—GRAPPATINI. Methods: Three conventional MR sequences and GRAPPATINI accelerated T2 mapping of bilateral thighs from 20 patients (7 DM and 13 PM) and 10 healthy volunteers were prospectively carried out on a 3 T MR scanner. Afterwards, T2 values of 477 thigh muscles from the patients and the healthy controls were manually measured. In addition, the correlations between T2 values and serum muscle enzymes in patients were also analyzed. Results: The new GRAPPATINI technique made quantitative T2 mapping of bilateral thighs feasible with a scanning time of only 2 min 18 s. Moreover, GRAPPATINI-generated T2 values of muscles from patients were markedly higher than those from healthy subjects (p < 0.001). GRAPPATINI accelerated T2 mapping appeared a more sensitive technique in that some DM/PM muscles appearing normal per conventional MRI had increased T2 relaxation time. Furthermore, GRAPPATINI-generated T2 values of DM/PM thigh muscles positively correlated with serum enzyme levels (p < 0.001), which reflected the severity of myopathy. Conclusion: GRAPPATINI can significantly shorten acquisition time of T2 mapping and may potentially be applied clinically in DM and PM. Advances in knowledge: GRAPPATINI acceleration makes T2 mapping feasible in clinical practice in providing quantitative information regarding thigh muscle inflammation in DM and PM.

Background: Manual bone age assessment (BAA) is associated with longer interpretation time and higher cost and variability, thus posing challenges in areas with restricted medical facilities, such as the high-altitude Tibetan Plateau. The application of artificial intelligence (AI) for automating BAA could facilitate resolving this issue. This study aimed to develop an AI-based BAA model for Han and Tibetan children.Methods: A model named “EVG-BANet” was trained using three datasets, including the Radiology Society of North America (RSNA) dataset (training set n = 12611, validation set n = 1425, and test set n = 200), the Radiological Hand Pose Estimation (RHPE) dataset (training set n = 5491, validation set n = 713, and test set n = 79), and a self-established local dataset [training set n = 825 and test set n = 351 (Han n = 216 and Tibetan n = 135)]. An open-access state-of-the-art model BoNet was used for comparison. The accuracy and generalizability of the two models were evaluated using the abovementioned three test sets and an external test set (n = 256, all were Tibetan). Mean absolute difference (MAD) and accuracy within 1 year were used as indicators. Bias was evaluated by comparing the MAD between the demographic groups.Results: EVG-BANet outperformed BoNet in the MAD on the RHPE test set (0.52 vs. 0.63 years, p < 0.001), the local test set (0.47 vs. 0.62 years, p < 0.001), and the external test set (0.53 vs. 0.66 years, p < 0.001) and exhibited a comparable MAD on the RSNA test set (0.34 vs. 0.35 years, p = 0.934). EVG-BANet achieved accuracy within 1 year of 97.7% on the local test set (BoNet 90%, p < 0.001) and 89.5% on the external test set (BoNet 85.5%, p = 0.066). EVG-BANet showed no bias in the local test set but exhibited a bias related to chronological age in the external test set.Conclusion: EVG-BANet can accurately predict the bone age (BA) for both Han children and Tibetan children living in the Tibetan Plateau with limited healthcare facilities.

Objectives: To validate the correlation between histopathological findings and quantitative magnetic resonance imaging (qMRI) fat fraction (FF) and water T2 mapping in patients with idiopathic inflammatory myopathy (IIM). Methods: The study included 13 patients with histopathologically confirmed IIM who underwent dedicated thigh qMRI scanning within 1 month before open muscle biopsy. For the biopsied muscles, FF derived from the iterative decomposition of water and fat with echo asymmetry and least-squares estimation quantitation (IDEAL-IQ) and T2 time from T2 mapping with chemical shift selective fat saturation were measured using a machine learning software. Individual histochemical and immunohistochemical slides were evaluated using a 5-point Likert score. Inter-reader agreement and the correlation between qMRI markers and histopathological scores were analyzed. Results: Readers showed good to perfect agreement in qMRI measurements and most histopathological scores. FF of the biopsied muscles was positively correlated with the amount of fat in histopathological slides (p = 0.031). Prolonged T2 time was associated with the degree of variation in myofiber size, inflammatory cell infiltration, and amount of connective tissues (p ≤ 0.008 for all). Conclusions: Using the machine learning-based muscle segmentation method, a positive correlation was confirmed between qMRI biomarkers and histopathological findings of patients with IIM. This finding provides a basis for using qMRI as a non-invasive tool in the diagnostic workflow of IIM. Relevance statement: By using ML-based muscle segmentation, a correlation between qMRI biomarkers and histopathology was found in patients with IIM: qMRI is a potential non-invasive tool in this clinical setting. Key points: • Quantitative magnetic resonance imaging measurements using machine learning-based muscle segmentation have good consistency and reproductivity. • Fat fraction of idiopathic inflammatory myopathy (IIM) correlated with the amount of fat at histopathology. • Prolonged T2 time was associated with muscle inflammation in IIM. Graphical