Neutralisation of SARS-CoV-2 by anatomical embalming solutions

Teaching and learning anatomy by using human cadaveric specimens has been a foundation of medical and biomedical teaching for hundreds of years. Therefore, the majority of institutions that teach topographical anatomy rely on body donation programmes to provide specimens for both undergraduate and postgraduate teaching of gross anatomy. The COVID-19 pandemic has posed an unprecedented challenge to anatomy teaching because of the suspension of donor acceptance at most institutions. This was largely due to concerns about the potential transmissibility of the SARS-CoV-2 virus and the absence of data about the ability of embalming solutions to neutralise the virus. Twenty embalming solutions commonly used in institutions in the United Kingdom and Ireland were tested for their ability to neutralise SARS-CoV-2, using an established cytotoxicity assay. All embalming solutions tested neutralised SARS-CoV-2, with the majority of solutions being effective at high-working dilutions. These results suggest that successful embalming with the tested solutions can neutralise the SARS-CoV-2 virus, thereby facilitating the safe resumption of body donation programmes and cadaveric anatomy teaching.     

Fear of Losing Emotional Control Is Associated With Cognitive Processing Therapy Outcomes in U.S. Veterans of Afghanistan and Iraq

Evidence‐based treatments for posttraumatic stress disorder (PTSD) can reduce symptoms and improve veterans’ psychological health. Unfortunately, many veterans leave treatment before receiving maximum benefit. Fear of emotions is related to severity of PTSD, and changes in fear of emotions are correlated with changes in PTSD symptoms. This study built upon the literature linking greater fear of emotions to PTSD severity by examining whether pretreatment fear of emotions, measured by the Affect Control Scale, was associated with completion of cognitive processing therapy (CPT) and severity of posttreatment PTSD in a sample of 89 U.S. veterans who had served in Afghanistan and Iraq. About 60% of veterans completed 10 or more therapy sessions. A logistic regression on 51 of the 89 subjects that more fear of anxiety at pretreatment was associated with decreased likelihood of completing treatment, OR = 0.93, 95% CI [0.87, 1.00]. Of those veterans who completed treatment, higher fear of anger at pretreatment was negatively related to severity of PTSD posttreatment (β = ?.29, p = .037), in a model with the other predictors. Assessing veterans for fear of anxiety and anger before CPT and teaching emotion regulation skills to those in need may reduce treatment dropout.     

Healthcare use by varied highly active antiretroviral therapy (HAART) strata: HAART use, discontinuation, and naivety

OBJECTIVES: Prior reports have found a temporal association between the introduction of highly active antiretroviral therapy (HAART) and population rates of health service use among persons living with HIV. Our objective was to explore further the effect of HAART by comparing healthcare use among persons who use HAART and persons who discontinue HAART to that among HAART-naive and HIV-negative persons. METHODS: Longitudinal analyses of 1485 women with and at-risk for HIV who contributed data to the Women's Interagency HIV Study between April 1997 and March 2000. RESULTS: Compared with HAART-naive women, those using HAART had a higher probability of more than three primary care visits per 6 months [odds ratio (OR), 1.38; 95% confidence interval (CI), 1.16-1.65), a lower probability of more than one emergency room visit per 6 months (OR, 0.75; CI, 0.59-0.95), and a lower probability of more than one hospitalization per 6 months (OR, 0.67; CI, 0.51-0.88). Compared with HAART-naive women, women who had discontinued HAART had a higher frequency of primary care visits (OR, 1.57; CI, 1.26-1.97) but did not demonstrate a significant change in emergency room or hospital use. Modeling of a standardized population HIV-positive women without AIDS indicated hospitalization and emergency room use among HAART users was equivalent to that among HIV-negative women. CONCLUSIONS: HIV-positive HAART users (without AIDS) exhibited emergency room and hospitalization use patterns equivalent to those of HIV-negative women. Furthermore, the discontinuation of HAART was associated with a loss of the reduction in hospital use that was achieved with HAART.     

From the Cover: Satellites can reveal global extent of forced labor in the world’s fishing fleet

While forced labor in the world’s fishing fleet has been widely documented, its extent remains unknown. No methods previously existed for remotely identifying individual fishing vessels potentially engaged in these abuses on a global scale. By combining expertise from human rights practitioners and satellite vessel monitoring data, we show that vessels reported to use forced labor behave in systematically different ways from other vessels. We exploit this insight by using machine learning to identify high-risk vessels from among 16,000 industrial longliner, squid jigger, and trawler fishing vessels. Our model reveals that between 14% and 26% of vessels were high-risk, and also reveals patterns of where these vessels fished and which ports they visited. Between 57,000 and 100,000 individuals worked on these vessels, many of whom may have been forced labor victims. This information provides unprecedented opportunities for novel interventions to combat this humanitarian tragedy. More broadly, this research demonstrates a proof of concept for using remote sensing to detect forced labor abuses.

Forced labor in fisheries, a type of modern slavery, is increasingly recognized as a human rights crisis. The International Labor Organization (ILO) defines forced labor as “all work or service which is exacted from any person under the menace of any penalty and for which the said person has not offered himself voluntarily” (1). The ILO provides a framework of 11 forced labor risk indicators (2) that have all been documented within the fisheries sector, including indicators representative of debt-bonded labor, as well as indicators representative of servitude or slave labor such as abusive working and living conditions. In 2015, reports emerged on forced labor in Thai fisheries (3) and the role of forced labor in producing seafood imported to the United States (4). More recent reports have described the global nature of the problem (5), and there has been a call to integrate social responsibility into ocean science (6). Despite widespread condemnation and ambitious commitments, forced labor remains poorly understood in the fisheries sector. Here we show that recently available high-frequency vessel monitoring of the global industrial fishing fleet can shed new light on forced labor at a much finer resolution. We combine expertise from on-the-ground human rights practitioners and satellite vessel monitoring data for over 16,000 industrial fishing vessels to estimate 1) the number of high-risk vessels and the number of crew who may be victims working on those vessels, 2) where these vessels fish, and 3) what ports these vessels visit. This information can inform new market, policy, and enforcement interventions to combat forced labor in global fisheries. This research more generally demonstrates how remote sensing can detect forced labor abuses by observing dynamic behavior.Current estimates of forced labor in fisheries are coarse and are based on country-level statistics. Using country-level household surveys, the ILO estimated that 16 million people were victims of forced labor in 2016, with 11% of these in agriculture, forestry, or fisheries (7). The Global Slavery Index reports that the seven countries with highest slavery risk in 2018 generated 39% of global fisheries catch (3, 8), and Tickler et al. found that the United States has slavery risks of 0.2 kg per metric ton for domestic seafood and 3.1 kg per metric ton for imported seafood (9). While these studies are important for broadly understanding which countries have risk, current methods are unable to detect this problem at the level of individual fishing vessels, which will be essential for targeted interventions.We empirically examine whether vessels reported to exhibit any of the ILO indicators of forced labor behave in ways that are systematically different from other vessels, and then exploit this information using machine learning to discriminate between vessels that use forced labor from those that do not. We do so by measuring a suite of features that can be observed using satellite Automatic Identification System (AIS) vessel monitoring data made available by Global Fishing Watch (GFW) (10). There may be many behavioral correlates with forced labor that could help to differentiate between high-risk and low-risk vessels. To determine which model features to include, we first conducted a literature review of investigative journalism reports and looked for instances of forced labor case accounts that detailed specific behaviors that could be observed using vessel monitoring data. We next conducted informal phone interviews with experts from several nongovernmental organizations (NGOs) working in this field, during which we asked interviewees what observable vessel behaviors they would look for if they wanted to identify suspicious activity. The machine-learning approach we use does not assume that vessels behave in any particular way; rather, it merely uses the features identified by literature review and expert insight to exploit any observed empirical differences between vessels that use forced labor and other vessels. NGO experts and investigative journalism suggest that gaps in AIS transmission, port avoidance, transshipment, and extended time at sea may indicate the presence of forced labor (11). Certain features, like information on catch and the species being targeted, could also be helpful in discriminating between high- and low-risk behavior by providing more context on the fishing taking place. However, these data are not currently available at the vessel level on a global scale. Data on recruitment practices and vessel ownership and information on from where the crew originates could also be helpful, but, again, these data are not widely available. We arrived at a list of 27 vessel behavior and characteristic features for which we have globally available data at the vessel level (SI Appendix, Table S1, and SI Appendix).To build a predictive model for identifying high-risk vessels, we developed a training dataset that includes the behavior and characteristics of known forced-labor vessels, as well as the behavior and characteristics of other vessels. We compiled a comprehensive database of vessels that were reported to display one or more of the ILO forced labor indicators (2); these vessels are labeled as “positives.” We do not, however, know which vessels do not use forced labor (“negatives”). Rather, any vessel that we do not label as positive is “unlabeled,” and may in fact be a positive vessel that has not yet been identified or may truly be a negative vessel. This is an example of “positive-unlabeled (PU)” learning, a less straightforward problem than traditional supervised machine learning (12). We use PU learning to predict whether or not 16,261 longliner, trawler, and squid jigger fishing vessels were high-risk during each year they operated between 2012 and 2018 (“vessel-years”). We focus on this subset of vessels because they broadcasted sufficient and reliable AIS positions and because these are the only fishing gear types with documented cases of forced labor aboard vessels that broadcasted sufficient AIS data. These vessels represent 33% of the total time at sea spent by all fishing vessels operating in this time period tracked by GFW. Our PU approach leverages information from all positively labeled vessels (n = 22 unique vessels across 22 vessel-years using our baseline model assumption), but places less emphasis on unlabeled vessels given their uncertain nature (n = 16,257 unique vessels across 66,314 vessel-years using our baseline model assumption).     

Does Prior Abdominal Surgery Influence Conversion Rates and Outcomes of Laparoscopic Right Colectomy in Patients with Neoplasia?

Purpose  The study investigated the impact of prior abdominal surgery on conversions and outcomes of laparoscopic right colectomy. Methods  A consecutive series of 414 patients with cancer or adenomas who underwent a laparoscopic right colectomy from March 1996 to November 2006 were studied for surgical conversions and outcomes. Conversion was defined as an incision length > 7 cm. Results  Patients with prior abdominal surgery (n = 191) were compared with patients with no prior abdominal surgery (n = 223), and showed no significant differences in age, ASA classification, length of stay, operative time, blood loss, harvested nodes, tumor size, and specimen length. Significantly more wound infections occurred in the prior abdominal surgery group (22 vs.12, P = 0.023). Body mass index > 30 showed a three-fold increased risk of conversion. Fifteen percent of the no prior abdominal surgery patients and 17 percent of the prior abdominal surgery patients were converted (P > 0.05). Conversion was associated with a longer mean length of stay (8.8 days) relative to laparoscopically completed cases (6.3 days) regardless of prior abdominal surgery history (P < 0.0001). Conclusions  Laparoscopic right colectomy for neoplasia was not associated with a higher conversion rate or morbidity in patients with prior abdominal surgery. Prior abdominal surgery is not a contraindication to laparoscopic right colectomy. Presented at the 15th International Congress of the European Association of Endoscopic Surgery, Athens, Greece, July 4 to 7, 2007.     

Enhancing public involvement in assistive technology design research

Purpose: To appraise the application of accepted good practice guidance on public involvement in assistive technology research and to identify its impact on the research team, the public, device and trial design. Methods: Critical reflection and within-project evaluation were undertaken in a case study of the development of a functional electrical stimulation device. Individual and group interviews were undertaken with lay members of a 10 strong study user advisory group and also research team members. Results: Public involvement was seen positively by research team members, who reported a positive impact on device and study designs. The public identified positive impact on confidence, skills, self-esteem, enjoyment, contribution to improving the care of others and opportunities for further involvement in research. A negative impact concerned the challenge of engaging the public in dissemination after the study end. Conclusions: The public were able to impact significantly on the design of an assistive technology device which was made more fit for purpose. Research team attitudes to public involvement were more positive after having witnessed its potential first hand. Within-project evaluation underpins this case study which presents a much needed detailed account of public involvement in assistive technology design research to add to the existing weak evidence base.

• Implications for Rehabilitation

• The evidence base for impact of public involvement in rehabilitation technology design is in need of development.

• Public involvement in co-design of rehabilitation devices can lead to technologies that are fit for purpose.

• Rehabilitation researchers need to consider the merits of active public involvement in research.