Complete Pathologic Response to Gemcitabine and Oxaliplatin Chemotherapy After Prior Therapies in a Patient With Hepatocellular Carcinoma and Peritoneal Metastases Undergoing Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy

Hepatocellular carcinoma (HCC) is often diagnosed at a late stage and frequently recurs despite curative intervention, leading to poor survival outcomes. Frontline systemic therapies include combination immunotherapy regimens and tyrosine kinase inhibitors. We report a case of a 38-year-old woman with chronic hepatitis B and C coinfection-associated non-cirrhotic HCC, which recurred in the peritoneum after initial resection of her primary tumor. Disease progression occurred on both atezolizumab/bevacizumab and lenvatinib, and she was subsequently treated with gemcitabine and oxaliplatin (GEMOX) chemotherapy and exhibited a profound clinical response on imaging with normalization of alpha fetoprotein (AFP) after several months. Following extensive multidisciplinary discussion, she underwent cytoreductive surgery (CRS) with hyperthermic intraperitoneal chemotherapy (HIPEC) that removed all visible macroscopic tumor. Her pathology demonstrated a complete pathologic response. She received two additional months of postoperative chemotherapy, and then proceeded with close monitoring off therapy. To our knowledge, this is the first reported case of a complete pathologic response to GEMOX chemotherapy in the context of CRS/HIPEC for peritoneal metastases in HCC, after progression on standard immunotherapy and tyrosine kinase inhibitor treatments. In this report, we review the current systemic treatment landscape in HCC. We highlight potential consideration of cytotoxic chemotherapy, which is less frequently utilized in current practice, in selected patients with HCC, and discuss the role of CRS/HIPEC in the management of peritoneal metastases. Further investigation regarding predictors of response to systemic treatments is strongly needed. Multidisciplinary management may ultimately prolong survival in patients with advanced HCC

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic

Operator Engagement Detection for Robot Behavior Adaptation

It has been shown that in human-robot interaction, the effectiveness of a robot varies inversely with the operator engagement in the task. Given the importance of maintaining optimal task engagement when working with a robot, it would be immensely useful to have a robotic system that can detect the level of operator engagement and modify its behavior if required. This paper presents a framework for human-robot interaction that allows inference of operator's engagement level through the analysis of his/her physiological signals, and adaptation of robot behavior as a function of the operator's engagement level. Peripheral physiological signals were measured through wearable biofeedback sensors and a control architecture inspired by Riley's original information-flow model was developed to implement such human-robot interaction. The results from affect-elicitation tasks for human participants showed that it was possible to detect engagement through physiological sensing in real-time. An open-loop teleoperation-based robotic experiment was also conducted where the recorded physiological signals were transmitted to the robot in real-time speed to demonstrate that the presented control architecture allowed the robot to adapt its behavior based on operator engagement level

12 A New Approach to Implicit Human-Robot Interaction Using Affective Cues

Abstract – It is well known that in social interactions, implicit communication between the communicators plays a significant role. It would be immensely useful to have a robotic system that is capable of such implicit communication with the operator and can modify its behavior if required. This paper presents a framework for human-robot interaction in which the operator's physiological signals were analyzed to infer his/her probable anxiety level and robot behavior was adapted as a function of the operator affective state. Peripheral physiological signals were measured through wearable biofeedback sensors and a control architecture inspired by Riley's original information-flow model was developed to implement such human-robot interaction. The target affective state chosen in this work was anxiety. The results from affect-elicitation tasks for human participants showed that it is possible to detect anxiety through physiological sensing in real-time. A robotic experiment was also conducted to demonstrate that the presented control architecture allowed the robot to adapt its behavior based on operator anxiety level

An empirical study of machine learning techniques for affect recognition in human–robot interaction

Given the importance of implicit communication in human interactions, it would be valuable to have this capability in robotic systems wherein a robot can detect the motivations and emotions of the person it is working with. Recognizing affective states from physiological cues is an effective way of implementing implicit human–robot interaction. Several machine learning techniques have been successfully employed in affect-recognition to predict the affective state of an individual given a set of physiological features. However, a systematic comparison of the strengths and weaknesses of these methods has not yet been done. In this paper, we present a comparative study of four machine learning methods—K-Nearest Neighbor, Regression Tree (RT), Bayesian Network and Support Vector Machine (SVM) as applied to the domain of affect recognition using physiological signals. The results showed that SVM gave the best classification accuracy even though all the methods performed competitively. RT gave the next best classification accuracy and was the most space and time efficient