Pressurized intraperitoneal aerosol chemotherapy with cisplatin and doxorubicin or oxaliplatin for peritoneal metastasis from pancreatic adenocarcinoma and cholangiocarcinoma

Background: Systemic chemotherapy for pancreatic adenocarcinoma (PDAC) and cholangiocarcinoma (CC) with peritoneal metastases (PM) is affected by several pharmacological shortcomings and low clinical efficacy. Pressurized intraperitoneal aerosol chemotherapy (PIPAC) is expected to maximize exposure of peritoneal nodules to antiblastic agents. This study aims to evaluate safety and efficacy of PIPAC for PM of PDAC and CC origin. Methods: This is a retrospective analysis of consecutive PDAC and CC cases with PM treated with PIPAC at two European referral centers for peritoneal disease. We prospectively recorded from August 2016 to May 2019 demographic, clinical, surgical, and oncological data. We performed a feasibility and safety assessment and an efficacy analysis based on clinical and pathological regression. Results: Twenty patients with PM from PDAC (14) and CC (six) underwent 45 PIPAC administrations. Cisplatin–doxorubicin or oxaliplatin were administered to eight and 12 patients, respectively. We experienced one intraoperative complication (small bowel perforation) and 18 grade 1–2 postoperative adverse events according to Common Terminology Criteria for Adverse Events version 4.0. A pathological regression was recorded in 50% of patients (62% in the cisplatin–doxorubicin cohort and 42% in the oxaliplatin one). Median survival from the first PIPAC was 9.7 and 10.9 months for PDAC and CC, respectively. Conclusion: PIPAC resulted feasible and safe without relevant toxicity issues, with both cisplatin–doxorubicin and oxaliplatin. The pathological response observed supports the evidence of antitumoral activity. Despite the study limitations, these outcomes are encouraging, recommending PIPAC in prospective, controlled trials in the palliative setting or the first line chemotherapy for PM from PDAC and CC

P-164 Gemcitabine versus FOLFIRINOX in patients with advanced pancreatic adenocarcinoma HENT1 positive: back to the future

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Development and evaluation of an accelerometry system based on inverted pendulum to measure and analyze human balance

An accelerometry system was developed based on the inverted pendulum model and its effectiveness to measure the body's sway path and sway angle was verified in healthy adult volunteers. Sway path represents the body’s movement from its center of mass position projected to the ground surface while sway angle represents the body's orientation from the vertical. Mathematical models were developed to determine the sway displacement and sway angle from the accelerometry system. The resulting values were compared with the manual measurements obtained from a plumb bob based setup and found to correlate closely. Using the developed system, measures that analyzed the contribution of the visual, somatosensory and vestibular systems to balance were obtained. It was found that the accelerometry system followed the principle of motion of an inverted pendulum and provided information that can assist in better understanding of balance and thus it may assist clinicians in diagnosing balance dysfunctions

Fall Detection with Unobtrusive Infrared Array Sensors

As the world’s aging population grows, fall is becoming a major problem in public health. It is one of the most vital risks to the elderly. Many technology based fall detection systems have been developed in recent years with hardware ranging from wearable devices to ambience sensors and video cameras. Several machine learning based fall detection classifiers have been developed to process sensor data with various degrees of success. In this paper, we present a fall detection system using infrared array sensors with several deep learning methods, including long-short-term-memory and gated recurrent unit models. Evaluated with fall data collected in two different sets of configurations, we show that our approach gives significant improvement over existing works using the same infrared array sensor

Promotion of couples' voluntary counselling and testing for HIV through influential networks in two African capital cities

<p>Abstract</p> <p>Background</p> <p>Most new HIV infections in Africa are acquired from cohabiting heterosexual partners. Couples' Voluntary Counselling and Testing (CVCT) is an effective prevention strategy for this group. We present our experience with a community-based program for the promotion of CVCT in Kigali, Rwanda and Lusaka, Zambia.</p> <p>Methods</p> <p>Influence Network Agents (INAs) from the health, religious, non-governmental, and private sectors were trained to invite couples for CVCT. Predictors of successful promotion were identified using a multi-level hierarchical analysis.</p> <p>Results</p> <p>In 4 months, 9,900 invitations were distributed by 61 INAs, with 1,411 (14.3%) couples requesting CVCT. INAs in Rwanda distributed fewer invitations (2,680 vs. 7,220) and had higher response rates (26.9% vs. 9.6%), than INAs in Zambia. Context of the invitation event, including a discreet location such as the INA's home (OR 3.3–3.4), delivery of the invitation to both partners in the couple (OR 1.6–1.7) or to someone known to the INA (OR 1.7–1.8), and use of public endorsement (OR 1.7–1.8) were stronger predictors of success than INA or couple-level characteristics.</p> <p>Conclusion</p> <p>Predictors of successful CVCT promotion included strategies that can be easily implemented in Africa. As new resources become available for Africans with HIV, CVCT should be broadly implemented as a point of entry for prevention, care and support.</p

An overview of data fusion techniques for internet of things enabled physical activity recognition and measure

Due to importantly beneficial effects on physical and mental health and strong association with many rehabilitation programs, Physical Activity Recognition and Measure (PARM) has been widely recognised as a key paradigm for a variety of smart healthcare applications. Traditional methods for PARM relies on designing and utilising Data fusion or machine learning techniques in processing ambient and wearable sensing data for classifying types of physical activity and removing their uncertainties. Yet they mostly focus on controlled environments with the aim of increasing types of identifiable activity subjects, improved recognition accuracy and measure robustness. The emergence of the Internet of Things (IoT) enabling technology is transferring PARM studies to an open and dynamic uncontrolled ecosystem by connecting heterogeneous cost-effective wearable devices and mobile apps and various groups of users. Little is currently known about whether traditional Data fusion techniques can tackle new challenges of IoT environments and how to effectively harness and improve these technologies. In an effort to understand potential use and opportunities of Data fusion techniques in IoT enabled PARM applications, this paper will give a systematic review, critically examining PARM studies from a perspective of a novel 3D dynamic IoT based physical activity collection and validation model. It summarized traditional state-of-the-art data fusion techniques from three plane domains in the 3D dynamic IoT model: devices, persons and timeline. The paper goes on to identify some new research trends and challenges of data fusion techniques in the IoT enabled PARM studies, and discusses some key enabling techniques for tackling them