Traumatic interposition of a rotator cuff tendon in the glenohumeral joint without recognizable glenohumeral dislocation is an unusual complication after shoulder trauma. Here we report the clinical and imaging presentations of a 17-year-old man with trapped rotator cuff tendons in the glenohumeral joint after a bicycle accident. The possible trauma mechanism is also discussed.
Adolescent ; Bicycling/*injuries ; Humans ; Magnetic Resonance Imaging/*methods ; Male ; Range of Motion, Articular/physiology ; Rotator Cuff/*injuries ; Shoulder Dislocation/*diagnosis/etiology/rehabilitation

Background/Aims: Chronic hepatitis C (CHC) patients who failed antiviral therapy are at increased risk for hepatocellular carcinoma (HCC). This study assessed the potential role of metformin and statins, medications for diabetes mellitus (DM) and hyperlipidemia (HLP), in reducing HCC risk among these patients. Methods: We included CHC patients from the T-COACH study who failed antiviral therapy. We tracked the onset of HCC 1.5 years post-therapy by linking to Taiwan’s cancer registry data from 2003 to 2019. We accounted for death and liver transplantation as competing risks and employed Gray’s cumulative incidence and Cox subdistribution hazards models to analyze HCC development. Results: Out of 2,779 patients, 480 (17.3%) developed HCC post-therapy. DM patients not using metformin had a 51% increased risk of HCC compared to non-DM patients, while HLP patients on statins had a 50% reduced risk compared to those without HLP. The 5-year HCC incidence was significantly higher for metformin non-users (16.5%) versus non-DM patients (11.3%; adjusted sub-distribution hazard ratio [aSHR]=1.51; P=0.007) and metformin users (3.1%; aSHR=1.59; P=0.022). Statin use in HLP patients correlated with a lower HCC risk (3.8%) compared to non-HLP patients (12.5%; aSHR=0.50; P<0.001). Notably, the increased HCC risk associated with non-use of metformin was primarily seen in non-cirrhotic patients, whereas statins decreased HCC risk in both cirrhotic and non-cirrhotic patients. Conclusions Metformin and statins may have a chemopreventive effect against HCC in CHC patients who failed antiviral therapy. These results support the need for personalized preventive strategies in managing HCC risk.

This report presents the case of a 59-year-old man with severe COVID-19 that gradually progressed to cytokine release syndrome and then acute respiratory distress syndrome; he was successfully treated via integration of therapeutic plasma exchange and traditional Chinese medicine. The patient initially presented with a sore throat, severe muscle aches, productive cough and fever. On the worsening of symptoms, remdesivir was administered. However, as the symptoms continued to worsen and a cytokine release syndrome was suspected, oxygen was provided through a high-flow nasal cannula (50 L/min) and therapeutic plasma exchange was performed to prevent worsening of the acute respiratory distress syndrome. On the same day, a course of traditional Chinese medicine was introduced in consultation with the infectious house staff. The patient's symptoms gradually improved; the levels of C-reactive protein and D-dimers reduced, and the patient was weaned to a simple oxygen mask and eventually to room air. This is the first reported case of the integration of these treatments. Together, they prevented the patient from requiring intubation, played a role in cytokine management, and also improved the clinical symptoms, including productive purulent sputum, cough, frequent stool passage and intermittent fever, with no adverse effects. As a result, the patient was discharged within two weeks of the integration of these treatments. Therefore, the integration of therapeutic plasma exchange and traditional Chinese medicine is an effective therapy for patients with severe COVID-19.
Male ; Humans ; Middle Aged ; COVID-19/therapy* ; Cytokine Release Syndrome ; Plasma Exchange ; Medicine, Chinese Traditional ; Cough/drug therapy* ; Respiratory Distress Syndrome/therapy* ; Oxygen/therapeutic use*

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.