Excellent treatment outcomes in children younger than 18 months with stage 4 nonamplified neuroblastoma

PurposeAlthough the prognosis is generally good in patients with intermediate-risk neuroblastoma, no consensus has been reached on the ideal treatment regimen. This study analyzed treatment outcomes and toxicities in patients younger than 18 months with stage 4 MYCN nonamplified neuroblastoma.MethodsWe retrospectively analyzed 20 patients younger than 18 months newly diagnosed with stage 4 MYCN nonamplified neuroblastoma between January 2009 and December 2015. Patients received 9 cycles of chemotherapy and surgery, with or without local radiotherapy, followed by 12 cycles of differentiation therapy with 13-cis-retinoic acid. Chemotherapy consisted of alternating cycles of cisplatin, etoposide, doxorubicin, and cyclophosphamide (CEDC) and ifosfamide, carboplatin, and etoposide (ICE) regimens.ResultsThe most common primary tumor site was the abdomen (85%), and the most common metastatic sites were the lymph nodes (65%), followed by the bones (60%), liver (55%), skin (45%), and bone marrow (25%). At the end of induction therapy, 14 patients (70%) achieved complete response, with 1 achieving very good partial response, 4 achieving partial response, and 1 showing mixed response. Nine patients (45%) received local radiotherapy. At a median follow-up of 47 months (range, 17–91 months), none of these patients experienced relapse, progression, or secondary malignancy, or died. Three years after chemotherapy completion, none of the patients had experienced grade ≥3 late adverse effects.ConclusionPatients younger than 18 months with stage 4 MYCN nonamplified neuroblastoma showed excellent outcomes, without significant late adverse effects, when treated with alternating cycles of CEDC and ICE, followed by surgery and differentiation therapy

Leveraging Uncertainties in Softmax Decision-Making Models for Low-Power IoT Devices

Internet of Things (IoT) devices bring us rich sensor data, such as images capturing the environment. One prominent approach to understanding and utilizing such data is image classification which can be effectively solved by deep learning (DL). Combined with cross-entropy loss, softmax has been widely used for classification problems, despite its limitations. Many efforts have been made to enhance the performance of softmax decision-making models. However, they require complex computations and/or re-training the model, which is computationally prohibited on low-power IoT devices. In this paper, we propose a light-weight framework to enhance the performance of softmax decision-making models for DL. The proposed framework operates with a pre-trained DL model using softmax, without requiring any modification to the model. First, it computes the level of uncertainty as to the model’s prediction, with which misclassified samples are detected. Then, it makes a probabilistic control decision to enhance the decision performance of the given model. We validated the proposed framework by conducting an experiment for IoT car control. The proposed model successfully reduced the control decision errors by up to 96.77% compared to the given DL model, and that suggests the feasibility of building DL-based IoT applications with high accuracy and low complexity

Trellis Tone Modulation Multiple-Access for Peer Discovery in D2D Networks

In this paper, a new non-orthogonal multiple-access scheme, trellis tone modulation multiple-access (TTMMA), is proposed for peer discovery of distributed device-to-device (D2D) communication. The range and capacity of discovery are important performance metrics in peer discovery. The proposed trellis tone modulation uses single-tone transmission and achieves a long discovery range due to its low Peak-to-Average Power Ratio (PAPR). The TTMMA also exploits non-orthogonal resource assignment to increase the discovery capacity. For the multi-user detection of superposed multiple-access signals, a message-passing algorithm with supplementary schemes are proposed. With TTMMA and its message-passing demodulation, approximately 1.5 times the number of devices are discovered compared to the conventional frequency division multiple-access (FDMA)-based discovery

Endocrine and Metabolic Illnesses in Young Adults with Prader–Willi Syndrome

Prader–Willi syndrome (PWS) is a rare genetic disorder characterized by an insatiable appetite that leads to morbid obesity. Previous studies reported health problems in adults with PWS. However, studies on younger adults are lacking, and there are no specific studies of endocrine and metabolic illness in this age group. We performed a retrospective cohort study of 68 individuals with PWS aged 19 to 34 years at Samsung Medical Center. The prevalence of endocrine and metabolic illnesses were compared with those in an age-, sex-, and BMI-matched healthy control group. Young adults with PWS had a higher prevalence of metabolic syndrome (35.3% vs. 4.4%), type 2 diabetes mellitus (50.0% vs. 5.4%), hypertension (30.8% vs. 16.1%), dyslipidemia (38.2% vs. 14.7%), decreased bone density (26.4% vs. 0.9%), and sleep apnea (32.3% vs. 4.4%) than controls (all p < 0.05). The PWS group that maintained recombinant human growth (rhGH) treatment in adulthood had a lower probability of having a BMI ≥ 30 at the last follow-up (odds ratio = 0.106 (0.012–0.948), p = 0.045). Endocrine and metabolic illnesses in individuals with PWS may have already started in the early teens; therefore, appropriate screening and early intervention are important. Better understanding of the natural history of PWS and age-related complications will lead to better-quality medical care for individuals with PWS

Structural Determinants of Chirally Selective Transport of Amino Acids through the α‑Hemolysin Protein Nanopores of Free-Standing Planar Lipid Membranes

Despite the importance of the enantioselective transport of amino acids through transmembrane protein nanopores from fundamental and practical perspectives, little has been explored to date. Here, we study the transport of amino acids through α-hemolysin (αHL) protein pores incorporated into a free-standing lipid membrane. By measuring the transport of 13 different amino acids through the αHL pores, we discover that the molecular size of the amino acids and their capability to form hydrogen bonds with the pore surface determine the chiral selectivity. Molecular dynamics simulations corroborate our findings by revealing the enantioselective molecular-level interactions between the amino acid enantiomers and the αHL pore. Our work is the first to present the determinants for chiral selectivity using αHL protein as a molecular filter