Central precocious puberty with hypothalamic hamartoma: the first case reports of 2 siblings with different phenotypes of Seckel syndrome 5.

Hypothalamic hamartomas (HHs) are nonneoplastic mass lesions located in the hypothalamus that can cause central precocious puberty (CPP) and/or gelastic seizures. Seckel syndrome 5 (OMIM210600, SCKL5) is a rare autosomal recessive genetic spectrum disorder characterized by intrauterine growth retardation, proportionate osteodysplastic primordial dwarfism, a wide range of intellectual disability, "bird-headed" facial features, and microcephaly with various structural brain abnormalities. Two siblings presented with short stature and small head circumference and were diagnosed with SCKL 5. The younger sister had HH with CPP and experienced a slipped capital femoral epiphysis during treatment. The 2 siblings had the same genetic variant but showed different phenotypes, which has not been reported previously; this study also as presents the first cases of SCKL5 diagnosed by genetic confirmation in Korea.

PCW-A1001, AI-assisted de novo design approach to design a selective inhibitor for FLT-3(D835Y) in acute myeloid leukemia.

Treating acute myeloid leukemia (AML) by targeting FMS-like tyrosine kinase 3 (FLT-3) is considered an effective treatment strategy. By using AI-assisted hit optimization, we discovered a novel and highly selective compound with desired drug-like properties with which to target the FLT-3 (D835Y) mutant. In the current study, we applied an AI-assisted de novo design approach to identify a novel inhibitor of FLT-3 (D835Y). A recurrent neural network containing long short-term memory cells (LSTM) was implemented to generate potential candidates related to our in-house hit compound (PCW-1001). Approximately 10,416 hits were generated from 20 epochs, and the generated hits were further filtered using various toxicity and synthetic feasibility filters. Based on the docking and free energy ranking, the top compound was selected for synthesis and screening. Of these three compounds, PCW-A1001 proved to be highly selective for the FLT-3 (D835Y) mutant, with an IC50 of 764 nM, whereas the IC50 of FLT-3 WT was 2.54 µM.

Machine-Learning-Based Approach to Differential Diagnosis in Tuberculous and Viral Meningitis.

BACKGROUND: Tuberculous meningitis (TBM) is the most severe form of tuberculosis, but differentiating between the diagnosis of TBM and viral meningitis (VM) is difficult. Thus, we have developed machine-learning modules for differentiating TBM from VM. MATERIAL AND METHODS: For the training data, confirmed or probable TBM and confirmed VM cases were retrospectively collected from five teaching hospitals in Korea between January 2000 - July 2018. Various machine-learning algorithms were used for training. The machine-learning algorithms were tested by the leave-one-out cross-validation. Four residents and two infectious disease specialists were tested using the summarized medical information. RESULTS: The training study comprised data from 60 patients with confirmed or probable TBM and 143 patients with confirmed VM. Older age, longer symptom duration before the visit, lower serum sodium, lower cerebrospinal fluid (CSF) glucose, higher CSF protein, and CSF adenosine deaminase were found in the TBM patients. Among the various machine-learning algorithms, the area under the curve (AUC) of the receiver operating characteristics of artificial neural network (ANN) with ImperativeImputer for matrix completion (0.85; 95% confidence interval 0.79 - 0.89) was found to be the highest. The AUC of the ANN model was statistically higher than those of all the residents (range 0.67 - 0.72, P <0.001) and an infectious disease specialist (AUC 0.76; P = 0.03). CONCLUSION: The machine-learning techniques may play a role in differentiating between TBM and VM. Specifically, the ANN model seems to have better diagnostic performance than the non-expert clinician.