Pure separation and sorting of microparticles from complex fluids are essential for biochemical analyses and clinical diagnostics. However, conventional techniques require highly complex and expensive labeling processes for high purity separation. In this study, we present a simple and label-free method for separating microparticles with high purity using the elasto-inertial characteristic of a non-Newtonian fluid in microchannel flow. At the inlet, particle-containing sample flow was pushed toward the side walls by introducing sheath fluid from the center inlet. Particles of 1 μm and 5 μm in diameter, which were suspended in viscoelastic fluid, were successfully separated in the outlet channels: larger particles were notably focused on the centerline of the channel at the outlet, while smaller particles continued flowing along the side walls with minimal lateral migration towards the centerline. The same technique was further applied to separate platelets from diluted whole blood. Through cytometric analysis, we obtained a purity of collected platelets of close to 99.9%. Conclusively, our microparticle separation technique using elasto-inertial forces in non-Newtonian fluid is an effective method for separating and collecting microparticles on the basis of size differences with high purity.

Clinicopathologic characteristics of acral melanoma diagnosed at an advanced stage and resulting in a low survival rate are not significantly different between Koreans and other Asians. Interestingly, based on our study, long-term physical stress or pressure strength can influence the incidence and spreading pattern of acral melanoma in a particular manner. Acral melanoma occurs on more physically stressed sites with the long axis along natural creases on the sole. A further prospective investigation, especially of in situ lesions, regarding location-based differences in incidence, progress, and survival is necessary to better understand the pathophysiologic characteristics of acral melanoma.

No abstract

Inverse treatment planning in radiation therapy is formulated as solving optimization problems. The objective function and constraints consist of multiple terms designed for different clinical and practical considerations. Weighting factors of these terms are needed to define the optimization problem. While a treatment planning optimization engine can solve the optimization problem with given weights, adjusting the weights to yield a high-quality plan is typically performed by a human planner. Yet the weight-tuning task is labor intensive, time consuming, and it critically affects the final plan quality. An automatic weight-tuning approach is strongly desired. The procedure of weight adjustment to improve the plan quality is essentially a decision-making problem. Motivated by the tremendous success in deep learning for decision making with human-level intelligence, we propose a novel framework to adjust the weights in a human-like manner. This study uses inverse treatment planning in high-dose-rate brachytherapy (HDRBT) for cervical cancer as an example. We develop a weight-tuning policy network (WTPN) that observes dose volume histograms of a plan and outputs an action to adjust organ weighting factors, similar to the behaviors of a human planner. We train the WTPN via end-to-end deep reinforcement learning. Experience replay is performed with the epsilon greedy algorithm. After training is completed, we apply the trained WTPN to guide treatment planning of five testing patient cases. It is found that the trained WTPN successfully learns the treatment planning goals and is able to guide the weight tuning process. On average, the quality score of plans generated under the WTPN's guidance is improved by ~8.5% compared to the initial plan with arbitrarily set weights, and by 10.7% compared to the plans generated by human planners. To our knowledge, this is the first time that a tool is developed to adjust organ weights for the treatment planning optimization problem in a human-like fashion based on intelligence learnt from a training process. This is different from existing strategies based on pre-defined rules. The study demonstrates potential feasibility to develop intelligent treatment planning approaches via deep reinforcement learning.

PURPOSE Biomarkers on the basis of tumor-infiltrating lymphocytes (TIL) are potentially valuable in predicting the effectiveness of immune checkpoint inhibitors (ICI). However, clinical application remains challenging because of methodologic limitations and laborious process involved in spatial analysis of TIL distribution in whole-slide images (WSI). METHODS We have developed an artificial intelligence (AI)–powered WSI analyzer of TIL in the tumor microenvironment that can define three immune phenotypes (IPs): inflamed, immune-excluded, and immune-desert. These IPs were correlated with tumor response to ICI and survival in two independent cohorts of patients with advanced non–small-cell lung cancer (NSCLC). RESULTS Inflamed IP correlated with enrichment in local immune cytolytic activity, higher response rate, and prolonged progression-free survival compared with patients with immune-excluded or immune-desert phenotypes. At the WSI level, there was significant positive correlation between tumor proportion score (TPS) as determined by the AI model and control TPS analyzed by pathologists ( P < .001). Overall, 44.0% of tumors were inflamed, 37.1% were immune-excluded, and 18.9% were immune-desert. Incidence of inflamed IP in patients with programmed death ligand-1 TPS at < 1%, 1%-49%, and ≥ 50% was 31.7%, 42.5%, and 56.8%, respectively. Median progression-free survival and overall survival were, respectively, 4.1 months and 24.8 months with inflamed IP, 2.2 months and 14.0 months with immune-excluded IP, and 2.4 months and 10.6 months with immune-desert IP. CONCLUSION The AI-powered spatial analysis of TIL correlated with tumor response and progression-free survival of ICI in advanced NSCLC. This is potentially a supplementary biomarker to TPS as determined by a pathologist.

Rosacea is a chronic inflammatory dermatosis affecting the face and eyes. An association between systemic comorbidities and rosacea has been reported, but the link to enteral microbiota is uncertain. We aimed to investigate the link between rosacea and enteral microbiota.

BACKGROUND:Because of the growing number of actionable biomarkers in non-small cell lung cancer (NSCLC), sufficient tissue availability for testing is becoming a greater challenge. Liquid biopsy offers a potential solution by complementing standard tissue-based methods. In this study, the authors analyzed the concordance of actionable genomic alterations sequenced from circulating tumor DNA (ctDNA; Guardant360) and tissue (Oncomine Focus Assay). METHODS: From September 2015 to May 2018, 421 paired plasma and tissue samples from patients with advanced NSCLC who had previously undergone tissue testing by standard methods were collected. Both types of samples were available for 287 patients (262 in cohort 1 [treatment-naive] and 25 in cohort 2 [treatment failure]), and only 1 sample type was available for 134 patients (50 in cohort 3 [plasma only] and 84 in cohort 4 [tissue only]). RESULTS: In cohort 1, 198 samples (77.6%) showed concordance between tissue and plasma next-generation sequencing (NGS). Among the discordant cases, plasma testing detected additional genomic alterations in 11 patients (4.2%). In 50 patients without tissue-based NGS results (cohort 3), the ctDNA-based test detected genomic alterations in 20 samples (40.0%). The median allele frequency (AF) of mutations identified with ctDNA-based NGS (0.74%) was lower than that identified with the tissue-based NGS test (13.90%). Clinical responses to matched targeted therapy occurred, regardless of the ctDNA AF. Upfront ctDNA-based testing identified 60.4% of patients with genomic alterations. In addition, ctDNA-based testing uncovered 12.0% more actionable alterations when it was performed after tissue-based NGS testing. CONCLUSIONS: The results indicate that a ctDNA-based test identifies additional patients with actionable genomic alterations and could, therefore, be used to complement traditional tissue-based testing for NSCLC.

This prospective study was performed to determine the efficacy and safety of temozolomide (TMZ) plus thalidomide during and after radiation therapy (RT) in pediatric patients with newly diagnosed diffuse pontine glioma (DPG). Seventeen patients with pediatric DPG were enrolled between November 2004 and March 2008. The median age was eight years (range, 3-16 years); seven patients were male and ten were female. With the exception of one glioblastoma case, which was diagnosed via open biopsy, all diagnoses were established using neuroradiological studies. The authors used the Korean Society for Pediatric Neuro-Oncology (KSPNO)-A053 protocol. The mean follow-up period was 12 months (range, 8.5-25 months). Five patients were withdrawn from the study. The rates of response to treatment and survival were analyzed in 12 patients. Ten out of the 12 patients showed a partial response (PR), whereas one patient exhibited stable disease (SD) and another patient had progressive disease (PD). The tumor control rate was 92% (11/12) and the response rate was 83% (10/12). The median progression-free survival (PFS) of the 12 patients was 7.2 months (95% confidence interval (CI), 3.6-10.7). Six-month and twelve-month PFS were 58.3 and 16.7%, respectively. Overall survival (OS) was 12.7 months (95% CI, 10.4-15.1). One and two-year survival were 58.3 and 25%, respectively. The main adverse effect was hematological toxicity, with four patients exhibiting grade 3 or 4 toxicity. All patients tolerated the regimen well enough to continue the adjuvant chemotherapy. No Pneumocystis jiroveci pneumonia was noted. The TMZ plus thalidomide regimen was safe and tolerated well enough to be administered on an outpatient basis. Larger studies are required to demonstrate the efficacy of this regimen.