Background/Aims: Biliary tract cancer (BTC) is a rare malignancy with poor prognosis. We investigated genomic determinants of clinical benefit from gemcitabine, cisplatin, and nab-paclitaxel (GAP) versus gemcitabine and cisplatin (GC) in advanced BTC. Methods: Clinical and genomic data using TruSight Oncology 500 were analyzed from patients treated with GAP (N=198) or GC (N=89) as first-line therapy. Results: With a median follow-up of 33.0 months, GAP modestly improved progression-free survival (PFS) (hazard ratio [HR] 0.764; 95% confidence interval [CI] 0.591–0.989) without significant overall survival (OS) difference compared to GC. Genomic profiling revealed frequent alterations in TP53 (35.2%), KRAS (16.4%), SMAD4 (10.5%), and TNFRSF14 (10.5%), involving RTK/RAS (44.3%), TP53 (41.8%), and PI3K (20.2%) pathways. Single-gene mutations did not predict treatment benefit. However, pathway-level analysis identified PI3K pathway activation as significantly associated with inferior PFS (HR 2.148; 95% CI 1.478–3.124) and OS (HR 2.096; 95% CI 1.413–3.109) in patients receiving GAP, an effect not observed with GC. Importantly, GAP conferred clinical benefit only in patients without PI3K pathway activation, while no survival advantage was seen in those with such alterations (Pinteraction=0.023 for PFS, Pinteraction=0.003 for OS). Similar results were obtained in the independent validation cohort treated with GAP (N=103) or GC (N=64) for BTC. Conclusions Genomic profiling using next-generation sequencing identified PI3K pathway activation as key molecular determinant that differentiates patient outcomes between GAP and GC treatments in advanced BTC.

Objectives: In this paper, we aimed to investigate the evolving debate over border closure in Korea during the coronavirus disease 2019 (COVID-19) pandemic, to address the main themes associated with border closure, and to discuss the factors that need to be considered when making such decisions. Methods: We collated and reviewed previously conducted review studies on border closures during infectious disease outbreaks to derive relevant themes and factors. Results: According to our systematic review on border closures and travel restrictions, the effects of such containment efforts are limited. We suggest considering the following factors when determining whether to impose border closure measures: (1) disease characteristics, (2) timeliness of implementation, (3) transmission delay and the basic reproduction number, (4) globalization and pandemics, and (5) social and economic costs. Conclusions Our assessment indicates that the effects of border closures are at best temporary and limited. Alternative measures must be contemplated and implemented to suppress the spread of COVID-19 in particular and infectious diseases more broadly.

PURPOSE: Head and neck squamous cell carcinoma (HNSCC) is a deadly disease in which precision medicine needs to be incorporated. We aimed to implement next-generation sequencing (NGS) in determining actionable targets to guide appropriate molecular targeted therapy in HNSCC patients. MATERIALS AND METHODS: Ninety-three tumors and matched blood samples underwent targeted sequencing of 244 genes using the Illumina HiSeq 2500 platform with an average depth of coverage of greater than 1,000×. Clinicopathological data from patients were obtained from 17 centers in Korea, and were analyzed in correlation with NGS data. RESULTS: Ninety-two of the 93 tumors were amenable to data analysis. TP53 was the most common mutation, occurring in 47 (51%) patients, followed by CDKN2A (n=23, 25%), CCND1 (n=22, 24%), and PIK3CA (n=19, 21%). The total mutational burden was similar between human papillomavirus (HPV)–negative vs. positive tumors, although TP53, CDKN2A and CCND1 gene alterations occurred more frequently in HPV-negative tumors. HPV-positive tumors were significantly associated with immune signature-related genes compared to HPV-negative tumors. Mutations of NOTCH1 (p=0.027), CDKN2A (p < 0.001), and TP53 (p=0.038) were significantly associated with poorer overall survival. FAT1 mutations were highly enriched in cisplatin responders, and potentially targetable alterations such as PIK3CA E545K and CDKN2A R58X were noted in 14 patients (15%). CONCLUSION: We found several targetable genetic alterations, and our findings suggest that implementation of precision medicine in HNSCC is feasible. The predictive value of each targetable alteration should be assessed in a future umbrella trial using matched molecular targeted agents.
Biomarkers ; Carcinoma, Squamous Cell* ; Cisplatin ; Epithelial Cells* ; Head* ; Humans ; Korea ; Molecular Targeted Therapy ; Neck* ; Precision Medicine ; Statistics as Topic

OBJECTIVES: This study proposes a method for classifying three types of resting membrane potential signals obtained as images through diagnostic needle electromyography (EMG) using TensorFlow-Slim and Python to implement an artificial-intelligence-based image recognition scheme. METHODS: Waveform images of an abnormal resting membrane potential generated by diagnostic needle EMG were classified into three types—positive sharp waves (PSW), fibrillations (Fibs), and Others—using the TensorFlow-Slim image classification model library. A total of 4,015 raw waveform data instances were reviewed, with 8,576 waveform images subsequently collected for training. Images were learned repeatedly through a convolutional neural network. Each selected waveform image was classified into one of the aforementioned categories according to the learned results. RESULTS: The classification model, Inception v4, was used to divide waveform images into three categories (accuracy = 93.8%, precision = 99.5%, recall = 90.8%). This was done by applying the pretrained Inception v4 model to a fine-tuning method. The image recognition model was created for training using various types of image-based medical data. CONCLUSIONS: The TensorFlow-Slim library can be used to train and recognize image data, such as EMG waveforms, through simple coding rather than by applying TensorFlow. It is expected that a convolutional neural network can be applied to image data such as the waveforms of electrophysiological signals in a body based on this study.
Artificial Intelligence ; Boidae ; Classification ; Clinical Coding ; Electromyography ; Membrane Potentials ; Methods ; Needles

OBJECTIVE: Despite successful efforts to shorten the door-to-balloon time in patients with ST-segment elevation myocardial infarction (STEMI), pre-hospital delayremains a problem. We evaluated the factors related to pre-hospital delay using the Jeonbuk regional cardiovascular center database. METHODS: From 2010 to 2013, a total of 384 STEMI patients were enrolled. We analyzed the onset time, door time, and balloon time, and the patients were grouped according to pre-hospital delay (120 minutes). Clinical and socio-demographic variables were compared. RESULTS: 53.2% of patients had prolonged onset-to-door time (median 130, interquartile range [IQR] 66~242 minutes), and 68.5% of patients did not achieve <120 minute of total ischemic time (median 175, IQR 110~304 minutes). Pre-hospital delay was more frequent in patients with old age, female, no local residence, low education level, transfer via other hospital and no use of emergency squad (119). Only 20% of patients used 119, and 119 team responded in a prompt manner (call to scene time 6 min), but 41.6% of patients was transported to non-PCI-capable hospitals. Multivariate analysis revealed that transfer via other hospital [Odds ratio (OR) 2.5, 95% confidence interval (CI) 1.6-4.1, p<0.001), use of 119 (OR 0.4, 95% CI 0.2-0.6, p<0.001), age >60 years (OR 1.8, 95% CI 1.1-3.0, p=0.031) and hypertension (OR 1.9, 95% CI 1.2-2.9, p=0.047) were independent predictors of pre-hospital delay. CONCLUSIONS: The present study demonstrated a significant pre-hospital delay in the treatment of STEMI patients in the Province of Jeonbuk. Public campaigns and education are needed to raise the public awareness of STEMI and the use of 119.
Education ; Emergencies ; Emergency Medical Services ; Female ; Humans ; Hypertension ; Jeollabuk-do* ; Multivariate Analysis ; Myocardial Infarction* ; Time-to-Treatment