Depression and suicide risk prediction models using blood-derived multi-omics data

More than 300 million people worldwide experience depression; annually, ~800,000 people die by suicide. Unfortunately, conventional interview-based diagnosis is insufficient to accurately predict a psychiatric status. We developed machine learning models to predict depression and suicide risk using blood methylome and transcriptome data from 56 suicide attempters (SAs), 39 patients with major depressive disorder (MDD), and 87 healthy controls. Our random forest classifiers showed accuracies of 92.6% in distinguishing SAs from MDD patients, 87.3% in distinguishing MDD patients from controls, and 86.7% in distinguishing SAs from controls. We also developed regression models for predicting psychiatric scales with R2 values of 0.961 and 0.943 for Hamilton Rating Scale for Depression???17 and Scale for Suicide Ideation, respectively. Multi-omics data were used to construct psychiatric status prediction models for improved mental health treatment

3D Cascaded U-Net with a Squeeze-and-Exicitation Block for Semantic Segmentation on Kidney and Renal Cell Carcinoma in Abdonimal Volumetric CT

Segmentation is a fundamental process in medical image analysis. Recently, convolutional neural networks (CNNs) has allowed for automatic segmentation; however, segmentaiton of complex organs and diseases including the kidney or renal cell carcinoma (RCC) remains a different task due to limited data and labor-intensive labeling work. The purpose of this study is to segment kideny and RCC in CT using cascaded 3D U-Net with a squeeze-and-excitation (SE) block using a cascaded method. 210 kidneys and their RCC in abdominal CT images were used as training and validation sets. The Dice similarity coefficients (DSCs) of kidney and RCC in test set were 0.963 and 0.734 respectively. The cascaded semantic segmentation can potentially reduce segmentation efforts and increase the efficiency in clinical workflow

Increased Transforming Growth Factor-beta1 in Alcohol Dependence

Ethanol and its metabolite acetaldehyde increase transforming growth factor beta1 (TGF-β1) expression in animal studies. TGF-β1 is related with the hepatic stellate cell (the key element of hepatic fibrogenesis) and the radial glia (the key element of neuronal migration). Blood samples were collected from 41 patients with alcohol dependence, TGF-β1 levels measured by ELISA were compared with 41 normal subjects. Plasma TGF-β1 levels in the patients with alcohol dependence (1,653.11±532.45 pg/mL) were significantly higher than those of healthy subjects (669.87±366.53 pg/mL) (P=0.000). Patients with or without liver pathology showed no difference in TGF-β1 (P=0.36). Increased TGF-β1 may mediate deleterious effect of alcohol such as hepatic fibrosis and suppressed neuronal developments in alcohol dependence patients

Solid-phase synthesis of combinatorial 2,4-disubstituted-1,3,5-triazine via amine nucleophilic reaction

10.1002/bkcs.10091Bulletin of the Korean Chemical Society361435-43

Non-Convulsive Status Epilepticus following Liver Transplantation

Neurological complications following liver transplantation are more common than after other organ transplants. These complications include seizure in about 8% of cases, which is associated with morbidity and mortality. Seizure should be treated immediately, and the process of differential diagnosis has to be performed appropriately in order to avoid permanent neurologic deficit. We herein report a case of status epilepticus after liver transplantation. The status epilepticus was treated promptly and the cause of seizure was assessed. The patient was discharged without any complication

Recurrent Desaturation Events due to Opioid-Induced Chest Wall Rigidity after Low Dose Fentanyl Administration

Opioid-induced chest wall rigidity is an uncommon complication of opioids. Because of this, it is often difficult to make a differential diagnosis in a mechanically ventilated patient who experiences increased airway pressure and difficulty with ventilation. A 76-year-old female patient was admitted to the intensive care unit (ICU) after surgery for periprosthetic fracture of the femur neck. On completion of the surgery, airway pressure was increased, and oxygen saturation fell below 95% after a bolus dose of fentanyl. After ICU admission, the same event recurred. Manual ventilation was immediately started, and a muscle relaxant relieved the symptoms. There was no sign or symptom suggesting airway obstruction or asthma on physical examination. Early recognition and treatment should be made in a mechanically ventilated patient experiencing increased airway pressure in order to prevent further deterioration

Fully automated condyle segmentation using 3D convolutional neural networks

Abstract The aim of this study was to develop an auto-segmentation algorithm for mandibular condyle using the 3D U-Net and perform a stress test to determine the optimal dataset size for achieving clinically acceptable accuracy. 234 cone-beam computed tomography images of mandibular condyles were acquired from 117 subjects from two institutions, which were manually segmented to generate the ground truth. Semantic segmentation was performed using basic 3D U-Net and a cascaded 3D U-Net. A stress test was performed using different sets of condylar images as the training, validation, and test datasets. Relative accuracy was evaluated using dice similarity coefficients (DSCs) and Hausdorff distance (HD). In the five stages, the DSC ranged 0.886–0.922 and 0.912–0.932 for basic 3D U-Net and cascaded 3D U-Net, respectively; the HD ranged 2.557–3.099 and 2.452–2.600 for basic 3D U-Net and cascaded 3D U-Net, respectively. Stage V (largest data from two institutions) exhibited the highest DSC of 0.922 ± 0.021 and 0.932 ± 0.023 for basic 3D U-Net and cascaded 3D U-Net, respectively. Stage IV (200 samples from two institutions) had a lower performance than stage III (162 samples from one institution). Our results show that fully automated segmentation of mandibular condyles is possible using 3D U-Net algorithms, and the segmentation accuracy increases as training data increases

Automated detection of intracranial aneurysms using skeleton-based 3D patches, semantic segmentation, and auxiliary classification for overcoming data imbalance in brain TOF-MRA

Abstract Accurate and reliable detection of intracranial aneurysms is vital for subsequent treatment to prevent bleeding. However, the detection of intracranial aneurysms can be time-consuming and even challenging, and there is great variability among experts, especially in the case of small aneurysms. This study aimed to detect intracranial aneurysms accurately using a convolutional neural network (CNN) with 3D time-of-flight magnetic resonance angiography (TOF-MRA). A total of 154 3D TOF-MRA datasets with intracranial aneurysms were acquired, and the gold standards were manually drawn by neuroradiologists. We also obtained 113 subjects from a public dataset for external validation. These angiograms were pre-processed by using skull-stripping, signal intensity normalization, and N4 bias correction. The 3D patches along the vessel skeleton from MRA were extracted. Values of the ratio between the aneurysmal and the normal patches ranged from 1:1 to 1:5. The semantic segmentation on intracranial aneurysms was trained using a 3D U-Net with an auxiliary classifier to overcome the imbalance in patches. The proposed method achieved an accuracy of 0.910 in internal validation and external validation accuracy of 0.883 with a 2:1 ratio of normal to aneurysmal patches. This multi-task learning method showed that the aneurysm segmentation performance was sufficient to be helpful in an actual clinical setting