SCI

Recombinant adeno-associated virus (rAAV), produced from a nonpathogenic parvovirus, has become an increasing popular vector for gene therapy applications in human clinical trials. However, transduction and transgene expression of rAAVs can differ across in vitro and ex vivo cellular transduction strategies. This study compared 11 rAAV serotypes, carrying one reporter transgene cassette containing a cytomegalovirus immediate-early enhancer (eCMV) and chicken beta actin (CBA) promoter driving the expression of an enhanced green-fluorescent protein (eGFP) gene, which was transduced into four different cell types: human iPSC, iPSC-derived RPE, iPSC-derived cortical, and dissociated embryonic day 18 rat cortical neurons. Each cell type was exposed to three multiplicity of infections (MOI: 1E4, 1E5, and 1E6 vg/cell). After 24, 48, 72, and 96 h posttransduction, GFP-expressing cells were examined and compared across dosage, time, and cell type. Retinal pigmented epithelium showed highest AAV-eGFP expression and iPSC cortical the lowest. At an MOI of 1E6 vg/cell, all serotypes show measurable levels of AAV-eGFP expression; moreover, AAV7m8 and AAV6 perform best across MOI and cell type. We conclude that serotype tropism is not only capsid dependent but also cell type plays a significant role in transgene expression dynamics.

ScienceDirect

Alarm fatigue is one of the biggest problems in hospital environment nowadays, caused by excessive false physiologic monitor alarms. One of the possible reasons is due to the ineffective traditional threshold alarm system such as low blood oxygen saturation (SpO2) alarm. In this paper, we propose a robust classification procedure based on the AdaBoost algorithm with reject option that can identify and silence false SpO2 alarms, while ensuring zero misclassified clinically significant alarms. Alarms and vital signs, which are related to SpO2 such as heart rate and pulse rate, within monitoring interval are extracted into different numerical features for the classifier. We propose a variant of AdaBoost with reject option by allowing a third decision (i.e., reject) expressing doubt. Weighted outputs of each weak classifier are input to a softmax function optimizing to satisfy a desired false negative rate upper bound while minimize false positive rate and indecision rate. We evaluate the proposed classifier using a dataset collected from 100 participated children in the Children's Hospital of Philadelphia and show that the classifier can silence 23.12% of false SpO2 alarms without missing any clinically significant alarms.

Penn            IEEE

The Internet of Medical Things (IoMT) is poised to revolutionize medicine. However, medical device communication, coordination, and interoperability present challenges for IoMT applications due to safety, security, and privacy concerns. As a first step towards an IoMT middleware, we introduce OpenICE-lite. OpenICE-lite is a middleware for medical device interoperability that also provides security guarantees and allows other IoMT applications to view/analyze the data in real time. We describe two applications that currently utilize OpenICE-lite, namely (i) a critical pulmonary shunt predictor for infants during surgery; (ii) a remote pulmonary monitoring systems (RePulmo). Implementations of both systems are utilized by the Children's Hospital of Philadelphia (CHOP) as quality improvements to patient care.

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Remote physiological monitoring is increasing in popularity with the evolution of technologies in the healthcare industry. However, the current solutions for remote monitoring of blood-oxygen saturation, one of the most common continuously monitored vital signs, either have inconsistent accuracy or are not secure for transmitting over the network. In this paper, we propose RePulmo, an open-source platform for secure and accurate remote pulmonary data monitoring. RePulmo satisfies both robustness and security requirements by utilizing hospital-grade pulse oximeter devices with multiple layers of security enforcement. We describe two applications of RePulmo, namely (1) a remote pulmonary monitoring system for infants to support the Children's Hospital of Philadelphia (CHOP) clinical trial; (2) a proof-of-concept of low SpO2 smart alarm system.

Penn            IEEE

As devices in the Internet of Things (IoT) increase in number and integrate with everyday lives, large amounts of personal information will be generated. With multiple discovered vulnerabilities in current IoT networks, a malicious attacker might be able to get access to and misuse this personal data. Thus, a logger that stores this information securely would make it possible to perform forensic analysis in case of such attacks that target valuable data. We propose LogSafe, a scalable, fault-tolerant logger that leverages the use of Intel Software Guard Extensions (SGX) to store logs from IoT devices efficiently and securely. Using the security guarantees of SGX, LogSafe is designed to run on an untrusted cloud infrastructure and satisfies Confidentiality, Integrity, and Availability (CIA) security properties. We exhaustively evaluated LogSafe in order to demonstrate that it is capable of handling logs from a large number of IoT devices and at a very high data transmission rate.

Penn            IEEE

A logger in the cloud capable of keeping a secure, time-synchronized and tamper-evident log of medical device and patient information allows efficient forensic analysis in cases of adverse events or attacks on interoperable medical devices. A secure logger as such must meet requirements of confidentiality and integrity of message logs and provide tamper-detection and tamper-evidence. In this paper, we propose a design for such a cloud-based secure logger using the Intel Software Guard Extensions (SGX) and the Trusted Platform Module (TPM). The proposed logger receives medical device information from a dongle attached to a medical device. The logger relies on SGX, TPM and standard encryption to maintain a secure communication channel even on an untrusted network and operating system. We also show that the logger is resilient against different kinds of attacks such as Replay attacks, Injection attacks and Eavesdropping attacks.