An urgent need for COP27: confronting converging crises.

The last 12 months have provided further evidence of the potential for cascading ecological and socio-political crises that were warned of 12 months ago. Then a consensus statement from the Regional Action on Climate Change Symposium warned: "the Earth's climatic, ecological, and human systems are converging towards a crisis that threatens to engulf global civilization within the lifetimes of children now living." Since then, the consequences of a broad set of extreme climate events (notably droughts, floods, and fires) have been compounded by interaction with impacts from multiple pandemics (including COVID-19 and cholera) and the Russia-Ukraine war. As a result, new connections are becoming visible between climate change and human health, large vulnerable populations are experiencing food crises, climate refugees are on the move, and the risks of water, food, and climate disruption have been visibly converging and compounding. Many vulnerable populations now face serious challenges to adapt. In light of these trends, this year, RACC identifies a range of measures to be taken at global and regional levels to bolster the resilience of these populations in the face of such emerging crises. In particular, at all scales, there is a need for globally available local data, reliable analytic techniques, community capacity to plan adaptation strategies, and the resources (scientific, technical, cultural, and economic) to implement them. To date, the rate of growth of the support for climate change resilience lags behind the rapid growth of cascading and converging risks. As an urgent message to COP27, it is proposed that the time is now right to devote much greater emphasis, global funding, and support to the increasing adaptation needs of vulnerable populations.

Patient- and Ventilator-Specific Modeling to Drive the Use and Development of 3D Printed Devices for Rapid Ventilator Splitting During the COVID-19 Pandemic

In the early days of the COVID-19 pandemic, there was a pressing need for an expansion of the ventilator capacity in response to the COVID19 pandemic. Reserved for dire situations, ventilator splitting is complex, and has previously been limited to patients with similar pulmonary compliances and tidal volume requirements. To address this need, we developed a system to enable rapid and efficacious splitting between two or more patients with varying lung compliances and tidal volume requirements. We present here a computational framework to both drive device design and inform patient-specific device tuning. By creating a patient- and ventilator-specific airflow model, we were able to identify pressure-controlled splitting as preferable to volume-controlled as well create a simulation-guided framework to identify the optimal airflow resistor for a given patient pairing. In this work, we present the computational model, validation of the model against benchtop test lungs and standard-of-care ventilators, and the methods that enabled simulation of over 200 million patient scenarios using 800,000 compute hours in a 72 h period. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Automatic classification between COVID-19 pneumonia, lung cancer and normal lung tissues on chest CT Scans

Coronavirus sickness (COVID-19) may be a pandemic sickness, that has already caused thousands of casualties and infected many countless individuals worldwide. Whereas most of the individuals infected with the COVID-19 intimate with delicate to moderate respiratory disease, some developed deadly respiratory illness. Any technological tool sanctioning screening of the COVID-19 infection with high accuracy will be crucially useful to the attention professionals. The usage of chest CT scan pictures for classifying and diagnosing COVID-19 respiratory illness has shown an excellent range of exactness and accuracy quite the other tool that lessens the number of deaths within the severe cases. This paper presents a proposed model of convolutional neural network (CNN) with a large multi-national dataset that is able to classify covid-19 pneumonia;lung cancer and the normal lung tissues from chest computed tomography (CT) scans with a classification accuracy of 94.05%.

Giving Psychology Trainees a Voice During the COVID-19 Pandemic: Trainee Mental Health, Perceived Safety, and Support

The impact of infectious disease outbreaks on mental health among health care workers is wellestablished. Minimal research has focused on health care trainees' well-being, especially during unprecedented events such as the 2019 coronavirus pandemic (COVID-19). Trainees are vulnerable to inherent power and resource differentials, which may exacerbate stress. The present study used a mixed methods approach to examine mental health symptoms, perceived safety, and ongoing and desired support among a national sample of psychology interns, psychology intern and postdoctoral trainees during the COVID-19 pandemic (N = 400). Participants reported clinically elevated anxiety and depressive symptoms. Participants working on-site who felt that their health or safety was at risk reported more anxiety symptoms. Most common workplace safety concerns included inadequate protection against risk and face-to-face patient care requirements. Trainees desired more support, better communication, more remote work and telehealth options, and flexibility in training requirements. Themes also emerged related to supervisor pressure and disregard of trainees' concerns. Results have significant implications for the training environment and quality of patient care. Increased support of psychology trainees is vital during the current and potential future public health crises. © 2020 American Psychological Association.