The early identification of individuals at risk of fear of falling after stroke is crucial in order to individualise preventive actions and interventions. The aim of this study was to identify the incidence of, and baseline factors in acute stroke that are associated with fear of falling at 6 months after stroke. Fear of falling was assessed by one question, which was answered by 279 of 452 eligible individuals. Univariable and multivariable logistic regression analyses were performed to determine the factors that were associated with fear of falling. The dependent variable was fear of falling at 6 months after stroke. The independent variables were related to function, activity and participation, including personal and environmental factors. Fear of falling was reported by 117 (41.9%) individuals. Poor postural control in acute stroke, measured using the modified version of the Postural Assessment Scale for Stroke Patients , and being physically inactive prior to the stroke, measured using the Saltin-Grimby Physical Activity Scale , were found to be associated with fear of falling at 6 months after stroke. The findings in this study are useful in clinical practice to optimise rehabilitation after stroke. Individuals with fear of falling worry about the physical harm and consequent lasting functional disability which could occur as a result of falling2. They are also concerned about the social embarrassment and indignity associated with falling2. Fear of falling may lead to the avoidance of engaging in day-to-day activities4 as well as adoption of overly cautious behaviours6, in addition to reduced physical function, isolation and poorer quality of life9.Fear of falling is a psychological condition associated with excessive worrying about losing one’s balance11. Individuals with stroke are more likely to experience fear of falling compared with those without a stroke14. As stroke onset is acute, cognitive and bodily changes occur instantly, leaving people highly vulnerable to fear of falling7. Due to the differences between the development and progress of fear of falling in different health conditions, research in this area should focus on fear of falling in specific disease conditions, such as stroke.One of the leading causes of disability worldwide is stroke15, 59% after hospital discharge16 and 48% of community-dwelling people17 report fear of falling after stroke. At 6 months after stroke, when individuals are likely to have adapted their behaviour to reduce falling to some extent, more than 50% of the individuals reported fear of falling after stroke19. A small qualitative study showed that individuals after stroke continue to report fear of falling as late as 7 years after stroke and require physical and cognitive adjustment to combat their fear of falling20. Due to its widespread prevalence and long-lasting consequences, it is important to address the issue of fear of falling in individuals with stroke.Previous studies show that up to 51% of inpatients18. A previous study from the same cohort as in the current study, has shown that female sex, the use of a walking aid and poor postural control are risk factors for fear of falling in the acute phase, when assessed within one to seven days after stroke22. Findings from a few studies in the chronic phase of stroke (more than 6 months after stroke onset)22 show that greater fear of falling was associated with higher age, history of past falls, lower functional mobility, reduced activity, increased anxiety or depression, impaired postural control, high sitting time and worse stroke severity25.The early identification of individuals at risk of fear of falling is crucial in order to prevent its occurrence and optimise the rehabilitation after stroke. In the late subacute phase of stroke, fear of falling was found to be associated with low muscle strength in the lower limbs26 have documented fear of falling in the late subacute phase of stroke, at three to 6 months after stroke onset22. One study found that fear of falling was associated with low muscle strength in the lower limbs in the late subacute phase of stroke18. However, these studies have small sample sizes (n = 28–50)26 or deal specifically with the association of fear of falling with factors affecting quality of life26. The data relating to the risk factors for fear of falling during the late subacute phase of stroke are therefore sparse22, although individuals have frequently mentioned fear of falling as a major concern in this phase27.A few studiesThe aim of this study was to identify the incidence of fear of falling, and the factors prior to a stroke and in the acute phase after a stroke that are associated with fear of falling at 6 months after a stroke. Our hypothesis was that fear of falling at 6 months after a stroke was associated particularly with female sex and poor postural control, along with factors such as previous fall(s) at the stroke unit, the use of a walking aid and self-reported physical inactivity.The Fall Study of Gothenburg’ 15. In FallsGOT, a consecutive sample of 504 individuals 18 years or older, with a clinical diagnosis of stroke and admitted to the stroke unit at Sahlgrenska University Hospital/Östra in Gothenburg between 1 October 2014 and 30 June 2016 was studied. The cohort did not include individuals who were judged as suitable to receive thrombolysis or thrombectomy, as they were referred to another stroke unit in the city. The exclusion criteria were: unwillingness to participate in the study, those patients undergoing palliative care or those with severe dementia. The participants in the FallsGOT cohort have been described in detail in previous studies28. All the participants in the FallsGOT cohort who were still alive 6 months after a stroke were invited to participate via a postal questionnaire. The sample size analyses for the study cohort  have previously been presented15.This prospective, longitudinal cohort study is a six-month follow-up of ‘29.All the assessments were made by the staff at the stroke unit. Data on age, sex, height, weight, location, type of stroke and length of stay at the hospital were collected from medical records. The subtyping of ischemic strokes based on presenting symptoms and signs was performed using the Oxford classification system30. The definition was given to the members of the stroke unit prior to the data collection stage. Stroke severity was assessed using the National Institute of Health Stroke Scale (NIHSS)31. The NIHSS scores were classified into mild (0–4 points), moderate (5–15 points) and severe stroke (16–42 points)32. Cognitive function was assessed by the occupational therapist using the Montreal Cognitive Assessment (MoCA) scale, which is an 11-item ordinal scale with a total score ranging from 0 to 30, which was performed for all inpatients with stroke sometime during the care period33. A higher score on the MoCA scale indicates better cognitive function and a score of 26 or higher indicates normal cognitive function34. Fear of falling during the acute phase of stroke was assessed by asking ‘Are you afraid of falling?’ with yes and no as possible answers.Information about falls was collected by the nurses at the stroke unit according to clinical routine. A fall was defined according to the World Health Organisation (WHO) as ‘an event which results in a person coming to rest inadvertently on the ground or floor or other lower level’36. It has been shown to have good intra-rater and inter-rater reliability39, as well as good responsiveness40. The SwePASS has also been found to be a moderate predictor of falls during the first year after stroke28, a strong predictor of falls during the stay at the stroke unit15 and within the first 12 months after stroke onset28 as well as of recurrent falls within the first year post-stroke41. The SwePASS scores were categorised as poor (0–24 points), moderate (25–30) or good (31–36) postural control based on previous evidence on its ability to predict falls15. Self-perceived physical activity prior to the stroke event was estimated using the Saltin-Grimby Physical Activity Level Scale (SGPALS)42. The SGPALS is a one-item self-reported outcome measurement including four response categories. A higher score represents a higher self-reported physical activity level. An SGPALS score of 1 was regarded as physically inactive and scores between 2 and 4 were regarded as light to high physical activity.The standardised assessment of the individual’s activity capacity of postural control was performed by the physiotherapists at the stroke unit using the Modified Version of the Postural Assessment Scale for Stroke Patients (SwePASS). In SwePASS, postural control refers to the ability to maintain a given posture in lying, sitting and standing and to ensure equilibrium in changes of position related to different levels of support and timing. The SwePASS is a 12-item ordinal clinical scale with four response categories per item and a total score ranging from 0 to 36All assessments, apart from the MoCA, were made as soon as possible after hospital admission, and always within four days. The MoCA was performed at any time during the stay at the stroke unit, according to clinical routine. Details regarding the individual’s current smoking status and information on whether or not the individual used a walking aid while at the stroke unit were collected. Systolic blood pressure was measured in supine position after at least 5 min of rest. Information regarding the individual’s current smoking status and comorbidities was collected by reviewing the participants’ medical records.The dependent variable, fear of falling at 6 months after stroke, was assessed using a questionnaire containing the question, ‘Are you afraid of falling?’ with “yes” and “no” as possible answers. The questionnaire was sent by post at the same time as the request for participation. All the participants received the questionnaire within a time span of 14 days before or after completing 6 months after stroke. Everyone who answered this questionnaire was included in the current study. The inclusion process of the study is shown in Fig. 43.The statistical analyses were performed using the IBM Statistical Package for Social Sciences software, version 24. Descriptive statistics, such as means, medians, standard deviations and interquartile ranges, were presented. Logistic regression was used to identify the factors that were associated with the dependent variable, presence of fear of falling at 6 months after stroke. The independent variables that were analysed were: age; sex; height; weight; location of stroke; previous fall(s) at the stroke unit; length of stay at the stroke unit; use of a walking aid; being a smoker/non-smoker; stroke severity using the NIHSS score; postural control using the SwePASS score and self-reported physical activity level using the SGPALS. Multicollinearity between the independent variables was determined using Spearman’s rank correlation, with correlation coefficients of ≥0.7 indicating multicollinearityp-values of < 0.1 were included in the multivariable logistic regression model. In the multivariable regression model, a p-value of < 0.05 was considered to be statistically significant. The goodness-of-fit for the multivariable logistic model was tested using the Hosmer–Lemeshow test and the improvement in fit was verified using Cox and Snell as well as Nagelkerke pseudo R2 values. The results of the logistic regression models were presented as odds ratios (OR), 95% confidence intervals (CI) and p-values. The area under the receiver operating characteristic (ROC) curve was presented for each variable and for the final prediction model. An area of 70–79% under the curve was regarded as acceptable, 80–89% was regarded as excellent and 90–100% was regarded as outstanding diagnostic accuracy44.Univariable logistic regression was performed first, after which variables with Ethical approval was obtained from the Swedish Ethical Review Authority, Gothenburg, Sweden . Written informed consent was obtained from all participants or next of kin prior to their inclusion in the study. All methods were performed in accordance with the relevant guidelines and regulations of the Swedish Ethical Review Authority. The FallsGOT study was listed in ClinicalTrials.gov with trial registration number NCT02264470.A total of 279 individuals from the FallsGOT cohort responded to the questionnaire and were included in this study. The demographic and baseline characteristics of the study group are presented in Table No multi-collinearity was found, except between sex and weight where male sex was correlated with higher weight (correlation coefficient = 0.70). The results of the univariable and the multivariable logistic regression analyses of the independent variables against the presence of fear of falling as the dependent variable are shown in Table The final model was obtained using multivariable logistic regression analysis, where poor postural control (a SwePASS score of ≤ 24) and being physically inactive (SGPALS score = 1) remained as statistically significant predictors of fear of falling. Individuals with poor postural control after an acute stroke had a 2.6 times higher odds ratio for fear of falling at 6 months after stroke compared with those with good postural control (a SwePASS score of 31–36). Additionally, individuals that reported being physically inactive prior to stroke had a two times higher odds ratio for fear of falling 6 months after stroke compared with those that reported a light to high physical activity level. The accuracy of the final model was estimated to be 71% using the area under the ROC curve. Table In a sample of 279 individuals, we identified the incidence of fear of falling using a single question and determined the factors prior to and in the acute phase after a stroke that are associated with fear of falling at 6 months after stroke. The results showed that 42% of the individuals experience fear of falling at 6 months after stroke. We found that poor postural control and self-reported physical inactivity prior to the stroke were the predictors of fear of falling at 6 months after a stroke event. Our hypothesis that poor postural control and self-reported physical inactivity were associated with fear of falling was confirmed. However, our hypothesis that female sex, the use of a walking aid and fall(s) at the stroke unit were associated with fear of falling was rejected. The multivariable model built as a part of this study had acceptable diagnostic accuracy. The probability of fear of falling varies greatly depending on the presence or absence of risk factors. In physically inactive individuals with poor or moderate postural control just over 13 of 20 reported fear of falling compared with eight of 20 in physically active individuals with good postural control. To our knowledge, this is the first study with a relatively large sample size that shows the association between fear of falling at 6 months after stroke using assessments performed in the acute phase after a stroke.26. When compared with another study performed in the same cohort of individuals, fear of falling at 6 months after stroke was slightly reduced in the current study (42%) compared with the acute phase of stroke (51%)21.The proportion of individuals who reported fear of falling (42%) in the current study is in line with previous research conducted in the late subacute phase of stroke where the proportion was between 32% and 50%21. This finding also corroborates a study performed in the chronic phase of stroke which showed that people with fear of falling have poorer postural control when standing and walking23. Poor postural control has also recently been shown to be associated with falls in the first year after stroke35. Considering these findings together, it can be assumed that postural control is one of the strongest predictors of both risk of falling and fear of falling after stroke35. Given that the time taken for a SwePASS assessment is approximately 8 min38, measuring postural control in the acute phase after a stroke is probably the fastest and most efficient method for predicting both fear of falling and risk of falling after stroke.Our finding that poor postural control is one of the significant predictors of fear of falling is in line with a previous study with the same cohort as the current study, which showed that poor postural control was a significant predictor of fear of falling in the acute phase of stroke25. The validity of our finding is also strengthened by a research finding on reverse association, where physical activity such as walking has been shown to reduce fear of falling in individuals after stroke45. It is possible that individuals with less prior physical activity are less confident about their walking ability, have poorer muscle strength, reduced co-ordination, lower postural equilibrium, longer reaction times and consequently more fear of falling after stroke. Furthermore, the finding is in agreement with systematic reviews that have found that physical activity is likely to reduce the risk and fear of falling to a moderate degree in older adults47. Given that physical inactivity is a risk factor for stroke48, it is important to provide targeted interventions to promote physical activity prior to stroke in order to reduce the incidence of fear of falling after stroke, as well as stroke itself.The finding that self-reported physical inactivity is a predictor of fear of falling matches research in the chronic phase of stroke, where factors such as higher sitting time, less functional mobility, reduced activity and low participation, all of which lead to physical inactivity, were found to be associated with greater fear of falling21. Our study found that this holds true at 6 months after stroke, and that female sex was a predictor of fear of falling in the univariable analysis, but not in the multivariable analysis. Similarly, another study conducted at 12 months after stroke also showed that female sex was a predictor of fear of falling in the univariable analysis49. We also found that those who did not use a walking aid at the stroke unit reported more fear of falling at 6 months after stroke, in contrast to the results of a previous study performed during the acute phase of a stroke21. The reasons for the differences in associations between fear of falling and female sex as well as using a walking aid, at the different time points are unclear. The association between fear of falling and a history of previous falls has been quantified in the chronic phase of stroke24, and has been described qualitatively in the subacute phase of stroke7, but the current study was not able to find any such association at 6 months after stroke.In the acute phase after a stroke, females reported fear of falling more frequently than men45, cognitive behavioural therapy50 and balance training50, could be used to reduce fear of falling in individuals with stroke. Based on the findings in the current study, further research to gather evidence with a view to developing interventions that are effective in reducing fear of falling after stroke is warranted.The results of this study may help us to identify the individuals at risk of fear of falling at 6 months after stroke. As the high-risk individuals could be identified as early as during the first week after hospital admission after stroke, subsequent early assessments and interventions may be provided to them to reduce fear of falling, thereby increasing quality of life. Interventions such as physical exercise in the form of walkingThe strength of this study is the relatively large sample of individuals with stroke. We believe that these results could be applied to individuals in the acute and subacute phase, mainly after a mild stroke. The predictors identified in this study were easy to measure and therefore, less time consuming to collect. This study, however, is not without limitations. Of the 452 individuals still alive 6 months after stroke, only 279 were included in the current study, which might have caused a selection bias. We have no information on fear of falling in 38% of individuals who did not respond to the questionnaire. A further selection bias might have been caused by not including those who underwent a thrombolysis or thrombectomy. The question about fear of falling has not been tested for reliability or validity. There could also be additional predictors of fear of falling, which has not been considered in this study. Further research could also include qualitative approach for a deeper understanding of the predictors of fear of falling. As in all observational studies, the cause and effect relationship between the dependent and independent variables cannot be determined. The clinical implication of this study is that the identification of individuals at risk of fear of falling at 6 months after stroke can be achieved by one single question about physical activity level prior to stroke along with an easily performed assessment of postural control in the acute phase of stroke.The findings in this study show that physical inactivity prior to stroke and poor postural control in the acute phase after a stroke and are associated with fear of falling 6 months after stroke, which could be addressed using population-based targeted interventions prior to stroke and in the individualised rehabilitation plans early after stroke.


In the case of a terrorist attack by a “dirty bomb”, blast injuries, external irradiation and the incorporation of radioactivity are to be expected. Departing from information about the radiological attack scenario with cesium-137 in the U.S. National Scenario Planning Guide, we estimated the radiological doses absorbed. Similar calculations were performed for a smaller plume size and a detonation in a subway. For conditions as described in the U.S. scenario, the committed effective dose amounted to a maximum of 848 mSv, even for very unfavorable conditions. Red bone marrow equivalent doses are insufficient to induce acute radiation sickness (ARS). In the case of a smaller plume size, the ARS threshold may be exceeded in some cases. In a subway bombing, doses are much higher and the occurrence of ARS should be expected. The health hazards from a dirty bomb attack will depend on the location and the explosive device. The derived Haddon matrix indicates that preparing for such an event includes education of all the medical staff about radiation effects, the time lines of radiation damages and the treatment priorities. Further determinants of the outcome include rapid evacuation even from difficult locations, the availability of a specific triage tool to rapidly identify victims at risk for ARS, the availability of an antidote stockpile and dedicated hospital beds to treat seriously irradiated victims.The online version contains supplementary material available at 10.1186/s40779-021-00349-w. Risk is often defined as a product of damage size and the probability of occurrence . The proThe threat of terrorists using chemical, biological or radiological materials alone or in combination with explosive devices is of great concern (CBRN terrorism). Radiological terrorism by using a radiological dispersal device  is a significant threat, as the construction is not much more complicated than building a conventional bomb. At the difference of an improvised nuclear device (IND) using nuclear fission as a source of energy release, there is no need to master nuclear technology. Moreover, in contrast to chemicals or biologics, radionuclides are perfectly stable against the heat generated by the detonation.Up to now, a “dirty bomb” was never detonated, but several cases point out that attacks had been planned and prepared . The majIn previous studies, we focused on the medical countermeasures that should be put in place to be prepared in case of a dirty bomb attack (e.g. antidote stockpiling and screening capacities for radionuclide incorporation) and their optimal mix to maximize the benefits for the victims , 21. In 3 − 5 802 × 103 psi) are generated and the flow of the high-pressure reaction products leads to an energy transfer to the ambient non-reacted material at a high propagation velocity . The front of high pressure progresses outwards, and directly behind this shock front travels the high velocity blast wind. At a defined location in the vicinity of the explosion point, the positive blast wave is immediately followed by the negative pressure or suction of the wave. The Friedlander wave is an idealized pressure wave form and in a real setting more complex profiles may be expected, in particular, if the wave is reflected on surfaces , ta, ta83], As absorbed radiological doses depend on many variables difficult to predict for concrete scenarios, we performed a sensitivity analysis and varied the surface characteristics of the ground , particle size distribution and the time till the evacuation of the victims.To simulate a situation as observed in the Oslo bombing , we assuIn order to calculate the radiological doses absorbed in a subway bombing, we assumed a homogeneous distribution of radioactivity in the volume of a cuboid calculated from the measures of a train wagon of the type Siemens C2 (length 115 m × width 2.90 m × height 3.60 m)   or from 141 mSv up to 848 mSv (breathable fraction 100%). The equivalent dose absorbed by the red bone marrow within the first 10 days will nevertheless not exceed 61 mSv (breathable fraction 100%), so that even in the case of an evacuation delay of 3 h the occurrence of an acute radiation syndrome is not to be expected . In addition, the ground surface on which activity is deposited is much smaller resulting in higher surface activities (6.90 Ci/m2 vs. 0.46 Ci/m2 on the ground for the Oslo bombing model). This leads to committed effective doses of several thousand to ten of thousand mSv . As even for low fractions of breathable particles (e.g. 10%) doses from internal contamination exceed doses from external irradiation, the total dose absorbed  will sharply increase up to the time point of complete activity deposition on the ground to increase at a slower pace thereafter Fig. . TherefoThe radiological dose absorbed by victims in the proximity of the detonation point is determined primarily by factors related to the construction of the explosive device . The location of the detonation is of particular importance as shown by the marked differences between an open space and a confined space detonation, as it heavily affects the distribution of radioactivity. Our calculations support the common view that the detonation of a dirty bomb in an open space will rather not be suited to induce an acute radiation syndrome, although in particular circumstances it cannot be fully excluded . The medical challenge will rather be the treatment of victims with blast/mechanical trauma and taking in charge a large number of victims contaminated more or less with radioactivity with some of them needing decorporation therapy to reduce long term health effects. Identifying the patients actually needing decorporation treatment and rapidly initiating it in case of a large-scale scenario may reveal difficult. In the case of a bombing in a confined space like a subway, radiological doses should be expected to be substantially higher and in an order of magnitude suited to cause deterministic radiation injuries, i.e. acute radiation sickness, in addition to blast injuries and stochastic radiation damages.In order to prevent an attack with a dirty bomb, or at least to reduce the probability of its occurrence, it is necessary to limit and strictly control the availability of radioactive materials and all hazardous substances and components that might be used for the construction of explosive devices. Moreover, intelligence gatherings and observation of potential perpetrators of malevolent acts are mandatory. These activities are outside the scope of the mission of rescue and medical emergency services.After detonation, uninjured people who are able to walk should move away from the attack site for general safety reasons. In the event of radioactivity release, that will probably be unknown at this very early time point, the distance to the hypocenter is enhanced and thus external radiation intensity and the risk of contamination are reduced. An important factor with impact on the radiological dose absorbed by the victims who are unable to walk, or trapped in the area near the detonation point, is the time needed for evacuation. Besides medical reasons, fast evacuation and clearing of the target area is a must for safety reasons, as the possibility of a “second hit” must always be considered . TherefoThe time needed for the evacuation of victims will depend on the location of the bombing and the concrete situation. Evacuation from a subway system in the underground will probably be more time consuming than on the surface and will also heavily depend on the occurrence of possible structural collapses. Evacuation time is however a factor that can be influenced by mentally and materially preparing and training the rescuers and members of the medical emergency system in order to cope with this kind of disaster.Following the first triage at the casualty collection area and a second triage at hospital admission, and after completion of urgent life-saving measures if needed, all victims should be assessed for radiation damages. For this purpose, specific triage systems based on the occurrence time of prodromal symptoms and laboratory findings (e.g. time course of blood lymphocytes) should be used. The IT-based H-module developed at the Bundeswehr Institute of Radiobiology is a modern tool for this purpose that permits to assess the probability to develop an acute radiation syndrome and thus to take a decision on an adequate patient orientation and the need of an early administration of growth factors . This seMoreover, all victims should be considered to have potentially incorporated radioactivity with the danger of long-term health effects, although probably only a small fraction of them will actually need decorporation therapy, at least in the case of an open space bombing. The Radio-Nuclear Working Group of the WHO assumed that about 1% of potentially contaminated victims might actually require treatment . The ideIn the case of a bombing in a confined space with higher air activity concentrations, radionuclide incorporation by inhalation will be much more important, so that the fraction of victims actually needing decorporation treatment is expected to be higher. It was shown that the protective efficacy of Prussian Blue is limited (14 days treatment started after 6 h: reduction of the dose by 24%) and less than for example by Ca (DTPA) injection for plutonium incorporation (for similar timelines protective efficacy 42%) . This isThe quality of care of the victims at medical facilities  will depend on the level of preparedness for a dirty bomb attack. Education of the medical and paramedical personnel is of paramount importance, although there is no need to be a specialist in CBRN or radiobiology. Understanding the basics of health effects induced by ionizing radiation (deterministic vs. stochastic damages) and the timelines (prodromi—latency—manifestation phase of an acute radiation sickness) is a pre-requisite for a sound medical assessment and decision making. The ability to correctly identify patients who are at risk for the occurrence of an acute radiation sickness and forward them in time to specialized institutions for treatment, and the awareness that decorporation therapy should be initiated early after a suspected radioactivity incorporation, should be the goal of basic medical NR-protection education.The great importance of the training of medical staff is impressively demonstrated by the events in Fukushima in 2011. Evacuated people and even children from the region were turned away from hospitals because it was feared they could be radioactive and pollute other people . The stiThe availability of specific resources, like antidotes  in the required amounts and technical screening equipment for radioactivity measurement, is not the responsibility of individual medical treatment facilities, but of the authorities responsible for disaster preparedness. This requires a long-term planning and a lack of such resources cannot be reasonably compensated once the disaster has occurred.A prerequisite for professional management of a complex and rare emergency situation is first of all the establishment of an incident command system with a fast flow of information to create a good situational awareness among the operative leadership . In the Just following the acute phase, rescue vehicles and medical facilities having transported or admitted contaminated patients will have to be screened and decontaminated quickly in order to maintain the medical services to the community at the usual level. In addition, the location of the bombing will have to be screened and decontaminated to avoid, as far as possible, disruptions of the economic activity in the area. Moreover, in the long run after a dirty bomb attack, at least a part of the victims will have to be followed up for health impairments and probably many more, victims as well as rescuers, for psychological problems.Our calculations and the conclusions we have drawn in the discussion permit to construct a Haddon matrix. It is a tool that was initially developed in the 1970s to analyze traffic accidents and to develop preventive strategies . The fraEven if considering only a single radionuclide, our results show that scenarios of a dirty bomb attack may greatly differ leading to always complex but differing challenges for rescuers and emergency medical services as well as hospitals. Deterministic radiation damages will probably not be the core issue after open space detonations, but it may be a critical issue after bombing in a closed space like a subway. Offering screening for internal radioactive contamination and rapid decorporation treatment to a large number of potentially contaminated victims require adequate equipment and stockpiling as well as well functioning logistics. The Haddon matrix seems to be a well-suited instrument to analyze dirty bomb scenarios and be of help to optimize preparedness. The most important factor is probably the education of all professionals involved in coping with such a disaster , as awareness and a sound judgement regarding the situation is the prerequisite for good decisions and the best use of resources.Additional file 1: Fig. S1. Formulae used to calculate the radiological doses for a cylindrical plume (open space bombing) or cubic distribution volume . DRF: dose rate factors for ground shine (gs) or cloud shine  for the effective dose or the red bone marrow equivalent dose. Tdep: time until complete particle deposition on the ground; Tevac: evacuation time from the scene. Calculations are done for 5 µm and 100 µm particles (differences in the sedimentation velocities) and thereafter summed up to the total dose.Additional file 2: Fig. S2. Exterior and interior view of a Siemens C2 subway train as used for our calculations.Additional file 3: Table S1. Committed effective dose and total equivalent doses absorbed over 50 years by organs in a victim of a “dirty bomb” attack staying for 30 min in the vicinity of the detonation point depending on the distribution of particle sizes (5 µm or 100 µm). Table S2. Committed effective dose and equivalent dose absorbed in the first 10 days  by the red bone marrow (RBM) in a victim of a “dirty bomb” attack in the vicinity of the detonation point depending on the height of the plume, the distribution of particle sizes (5 µm or 100 µm) and the evacuation time. Table S3. Impact of the evacuation time on the committed effective dose and equivalent dose absorbed in the first 10 days by the red bone marrow (RBM) in a victim of a “dirty bomb” attack in the vicinity of the detonation point depending on the distribution of particle sizes (5 µm or 100 µm). Table S4. Committed effective dose and equivalent dose absorbed in the first 10 days by the red bone marrow (RBM) in a victim of a “dirty bomb” attack in the vicinity of the detonation point depending on the activity in the bomb, the distribution of particle sizes (5 µm or 100 µm) and the evacuation time. Table S5. Committed effective dose and equivalent dose absorbed in the first 10 days by the red bone marrow (RBM) in a victim of a “dirty bomb” attack in the vicinity of the detonation point depending on the activity of the bomb, the distribution of particle sizes (5 µm or 100 µm) and the evacuation time. Table S6. Committed effective dose and equivalent dose absorbed within the first 10 days by the red bone marrow (RBM) in a victim of a “dirty bomb” attack in a subway depending on the evacuation time and the distribution of particle sizes (5 µm or 100 µm).


Adaptive habitat construction is a process by which individuals alter their environment so as to increase their (inclusive) fitness. Such alterations are a subset of the myriad ways that individuals condition their environment. We present an individual‐based model of habitat construction to explore what factors might favor selection when the benefits of environmental alterations are shared by individuals of the same species. Our results confirm the predictions of inclusive fitness and group selection theory and expectations based on previous models that construction will be more favored when its benefits are more likely to be directed to self or near kin. We found that temporal variation had no effect on the evolution of construction. For spatial heterogeneity, construction was disfavored when the spatial pattern of movement did not match the spatial pattern of environmental heterogeneity, especially when there was spatial heterogeneity in the optimal amount of construction. Under those conditions, very strong selection was necessary to favor genetic differentiation of construction propensity among demes. We put forth a constitutive theory for the evolution of adaptive habitat construction that unifies our model with previous verbal and quantitative models into a formal conceptual framework. Adaptive habitat construction is a process by which individuals alter their environment so as to increase their (inclusive) fitness. We show that the conditions for such adaptations are narrow such that most effects of organisms on their environment are non‐adaptive. Very often those alterations affect the future fitness of those organisms and others around them. It is therefore unsurprising that natural selection would shape those alterations so as to increase the fitness of the organisms. If those alterations decrease the fitness of the organism, selection typically should act to minimize those effects.  construction activities have been claimed to be adaptations that have come about through natural selection . These factors probe the strength of diffuse selection on groups of potentially related individuals, that is, kin selection. In the initial simulations, the environment was uniform and unstructured; both the baseline and optimal environments were the same in all demes and dispersal was equally likely between all demes.Because construction is costly, it should be favored when the benefits of habitat construction are enjoyed either by the individuals that bore the costs or their close relatives ; such benefit sharing is a form of inclusive fitness through group selection  increased the environmental value away from the baseline, and subsequent decay moved it back toward the baseline. The optimal environment was also the same for all demes and 10 units greater than the baseline environment. The number of demes was 16, 32, 64, 128, or 256, and the respective carrying capacity for each deme was 32, 16, 8, 4, or 2; thus, the total metapopulation size (512) was the same for all simulations.i from 1 to 50; Figure iθ) in a linearly increasing fashion along the array from approximately −10 arbitrary units at one end of the gradient to about +10 units at the other; the environments in adjacent demes differed by 0.4 units [iθ = 0.4(i – 25.5)]. Each deme also had an optimal environment (iθ*) that was either 10 units above the baseline  decayed back toward its baseline state. The decay between the end of generation t – 1 and the start of generation t (ΔitE) wasiE(t−1) is the environment in deme i at the end of the generation t − 1 and δ is the rate of decay. This produced an environment of iE(t−1) + ΔitE before construction. For simulations that explored the effects of decay rate, δ varied from 10% to 100%; otherwise, δ was fixed at 50%.Between generations, the environment in each deme  by an individual was a logistic function of its construction propensity:where ith deme in generation t (ΔitH) was a saturating function of the sum of the construction of all iN individuals in the deme:Figure  inset. TThe maximal amount of construction in a single generation was 5.0 units; the mean optimum environment was 10 units greater than the baseline environment Figure .ith deme at the time of selection was the environment at the end of the previous generation plus the changes due to decay and construction:t (itE). The environment in the The form of habitat construction that we model is “unresponsive” as the amount of construction performed by an individual is based solely on its genotype. In contrast, “responsive” construction would entail an individual assessing the state of the environment first, then doing only that amount of construction necessary to reach the optimal state. We used a different form of restraint in our model; construction was limited due to the two saturating functions  was determined at birth by five unlinked diploid trait loci. The loci contributed additively to the trait, which for simplicity was a scalar with units of equivalent magnitude as the environment: 2.5i at time t  (first term) minus the cost of construction (second term):f is a function (see below) that accounts for a decrease in fitness due to the difference between the current environment and the optimum environment . Because we set units of trait values to be of equivalent magnitude to environmental units, Topt,it directly equals itS without need of a transformation. For all simulations, ω = 4. For the structured environment, the length of the spatial gradient across all demes was approximately 2.5 times the width of the within‐deme selection function (2ω). Habitat construction was costly; γ was the per‐unit construction cost, which was multiplied by the construction trait as defined in Equation (γ) chosen was based on the percentage decrease in total fitness  for individuals that expressed the optimum phenotype, so an individual that contributed the maximal construction would experience a 1% decrease in fitness. Although in the simulations this cost function allowed for the possibility of negative fitness values, such negative values simply meant that an individual had a 0% probability of survival.Life history events occurred in one of two sequences Figure : (1) birt Figure  and ω deEquation . The cosf term in Equation ; the difference (denominator) also equals 10 for these simulations. This function (itf) equals 1.0 when the environment in the ith deme at the time of selection (itS) equals the optimum environment in that deme (iθ*), falls linearly with the absolute value of the difference between itS and iθ*, and reaches a minimum of 1 − φ when itS is at the baseline (iθ) in the center of the gradient  to be greater than the optimum (iθ*), which would cause a similar reduction in fitness.For simulations with unstructured environments: t Figure . Selecti2.6itS), or the optimal environment (iθ*). That variation also had two spatial patterns: either it was independent among demes, that is, each deme experienced a different pattern of variation, or all demes experienced the same pattern of variation. Variation in the environment was added at the time of construction. Random variation within each deme was simulated as a sequence of independent zero‐mean Gaussian random deviates (itz) with a standard deviation of τ that was scaled to be a percentage of the initial difference between the baseline and optimal environments (10 units for these simulations). If each deme experienced a different pattern of variation, the environment in the ith deme at the time of selection was as follows:In some simulations with the unstructured environment, there was also random variation added to one of two aspects of that environment, either the selective environment experienced by individuals , depending on whether that variation was independent or correlated among demes. Regardless of the existence of any extrinsically imposed temporal variation, the dynamic of construction and decay always produced autocorrelated temporal variation within each deme. The propensity to disperse was fixed (non‐evolving), dispersal probabilities were identical for all individuals, and dispersal per se had no cost – survival during dispersal was 100%.For the stepping‐stone pattern of dispersal, the dispersal probability was determined using a zero‐mean Gaussian random number, which in turn determined the number of demes through which an individual moved; the integer part of the random number determined the number of demes moved and the sign determined the direction of movement , with each parent producing a haploid gamete of unlinked alleles. Each pair then produced one offspring. This process was repeated until the carrying capacity of that deme was reached. This procedure assumes soft selection within each deme because population size (after reproduction) was determined independently of the outcome of selection; because individuals within a deme compete to produce successful offspring, such a procedure will weakly oppose kin selection by increasing kin competition when the deme size is very small Wade, . The modWhen new offspring were generated, each allele at each locus mutated with a probability of 10%. In general, lower mutation rates simply lengthen the time scale over which evolution happens without affecting the eventual outcome, for the kinds of models considered here  of each individual was equal to 2.5  was reached. Each parameter combination was replicated 20 times; the results shown are the means and standard errors of those replicates.itE), the mean of the construction propensity of individuals (itA), and the mean fitness (ttW). The parameters were measured by first averaging among individuals within demes, and then averaging among demes. For structured environments, for each of these parameters we also examined the slope along the gradient. Slopes were calculated as a linear regression on the deme averages. For construction propensity, the slope was a measure of genetic differentiation among demes. All slopes were standardized relative to the slope of the baseline environment  to return the demes to full size before parameters were calculated. For unstructured environments, evolutionary outcomes were assessed by examining the mean of the total construction within demes  were averaged across all demes. This average was divided by 10, so a value of 1.0 indicates that habitat construction moved the average environment to match the optimum at the midpoint of the environmental gradient, which was always 10 units higher than the baseline; no construction would result in a value of 0. . For the parallel optimum, a slope of 1 indicates that habitat construction resulted in an environment that matched the slope of the optimal environment across the gradient; for the single optimum, a slope of 0 indicates that habitat construction caused the environment to match the slope of the optimal environment across the gradient.For total construction, the environmental values  occurred for small population sizes (N = 4) at fast rates of decay  at a decay rate of 40%.  For intermediate‐to‐high decay rates, fitness declined with increasing population size for the smaller deme sizes  with a matching pattern of dispersal . If dispersal occurred before selection (“move first”), the greatest propensity for construction , there was almost no construction (mean itE/10) at high decay rates, with a peak at 20% , the amount of construction Figure  and the Dispersal rates had the predicted effect on construction for the “move first” life history pattern, with less construction as rates increased, especially at smaller population sizes Figure . Again,. That isThese results were used to set the parameters for the next sets of simulation that explored the effects of environmental structure and patterns of heterogeneity.3.2For the unstructured environment, we looked at the effects of random environmental variation, using parameters that resulted in construction at or close to the optimal amount in the absence of temporal variation  and one in which the optimal amount decreased along the gradient (single optimum). In these simulations, the pattern of dispersal was a stepping‐stone, thus matching the gradient pattern of heterogeneity in the baseline environment. As with unstructured environments, a structured gradient generally favors construction at lower dispersal rates and when selection happens before dispersal Figure . These eThe difference between the parallel optimum and single optimum scenarios is that the latter requires genetic differentiation in the amount of construction undertaken along the gradient to achieve maximum fitness, with the greatest amount of construction at the left‐hand end of the gradient and little to no construction at the right‐hand end Figure . For the3.4Dispersal pattern and environmental structure can reinforce or oppose each other. A stepping‐stone dispersal pattern reinforces a structured environmental gradient in that individuals that move are most likely to land in an environment very similar to the one departed from. In contrast, an island (non‐structured) dispersal pattern matches an unstructured environment because traveling a greater distance does not result in traversing a greater environmental space. We tested the effects of a mismatch in these factors by exploring the effects of dispersal rates of an island dispersal pattern in a structured environment. Overall, the level of adaptation was less than for the stepping‐stone dispersal pattern , for the type of unresponsive construction explored here. For a discussion of responsive versus unresponsive construction in our models, see Scheiner et al. . Given tOn the other hand, habitat construction can create a positive feedback that maintains itself. Once construction exists and trait values evolve to that constructed optimum, joint selection on the trait and the propensity for construction will reinforce each other. Kylafis and Loreau , using a4.4A constitutive theory is a set of propositions that serve as guidelines or rules for building models within a defined domain ; Formal analysis (lead); Methodology ; Software (lead); Writing – original draft ; Writing – review & editing . Michael Barfield: Conceptualization (supporting); Formal analysis (supporting); Methodology ; Software (supporting); Writing – original draft ; Writing – review & editing . Robert D. Holt: Conceptualization ; Formal analysis (supporting); Methodology ; Software (supporting); Writing – original draft ; Writing – review & editing .


Observing a metastatic malignant melanoma and its primary lesion at the same time is rare. The histopathological detection of any unusual pigmented lesion is critical. Observing a metastatic malignant melanoma and its primary lesion at the same time is rare. The histopathological detection of any unusual pigmented lesion is critical. Any unusual pigmented lesion after surgical removal should be microscopically investigated. In the case of metastatic melanoma of parotid with no specific primary origin, patient’s history is helpful. Malignant melanoma is a rare type of cancer in salivary glands, most of which are likely to be metastatic. Malignant melanoma of parotid glands occurs almost invariably as metastases from a primary tumor that is located in the skin or the mucous membranes of the head and neck regions.2A 19‐year‐old woman with pain and swelling in her right parotid gland was referred to Shohada Hospital (Tehran‐Iran) with no underlying diseases. She has complained of a mobile mass on the parotid region for a duration of 3 months. In her clinical examination, the oral mucosa and overlying skin were observed as intact with normal color. The patient's magnetic resonance imaging (MRI) indicated a high signal of a lobular‐contoured, as a 4 × 2.5 cm solid mass lesion in the superior lobe of her right parotid gland Figure . MultinuTo discover the primary origin of the recent melanoma lesion, a comprehensive past medical history was taken from the study case. As a result, we found that she had a history of congenital scalp pigmented nevus with no pathological consultation or any report of metastasis after 2 years Figure . Conside3Melanoma is the most serious skin cancer with a high mortality rate that reminds the necessity of performing the histopathological analysis of any pigmented lesion after biopsy, as well as the detection of the history of pigmented lesions.Generally, malignant melanomas of the parotid gland are metastatic and have maxillofacial skin origin.According to a research by Greene and Bernier,Addressing the study by Prayson et al., 12 cases were reported who were all diagnosed with malignant melanomas of parotid with cutaneous origins of head and neck areas, except one that had an unknown origin. It is interesting that neither none of them were bilateral nor MRI was used for localizing their tumors.4Melanomas of the head and neck areas can be metastatic to local lymph nodes and parotid glands. Considering the high mortality rate of this malignancy, all pigmented lesions with unusual features, large size, irregular pigmentation, unknown duration, or recent enlargement should be submitted for microscopic examinations. Furthermore, recording the exact past medical history of the affected patients is necessary. In the case of any malignancy, neglecting the marginal safety in the surgical operations of pigmented lesions can increase the probability of the metastasis.The authors declare that they have no conflict of interest.Author 1: Acquisition of data and diagnosis of the lesion. Author 2: Study supervision and diagnosis of the lesion. Author 3: Initiate the preparation of this case report for scientific publication and surgery of case. Author 4: Study search and design, analysis, and interpretation of data.Shohada_e_Tajrish Educational Hospital, Shahid Beheshti University of Medical Sciences, Oral and Maxillofacial Pathology Department approved this report.The authors of this article have obtained all required consent forms from the patient. Her consent has been given for the images and other clinical information to be reported in this article excluding her name.


Delivery and sampling nanoliter volumes of liquid can benefit new invasive surgical procedures. However, the dead volume and difficulty in generating constant pressure flow limits the use of small tubes such as capillaries. This work demonstrates sub‐millimeter microfluidic chips assembled directly on the tip of a bundle of two hydrophobic coated 100 µm capillaries to deliver nanoliter droplets in liquid environments. Droplets are created in a specially designed nanopipette and propelled by gas through the capillary to the microfluidic chip where a passive valve mechanism separates liquid from gas, allowing their delivery. By adjusting the driving pressure and microfluidic geometry, both partial and full delivery of 10 nanoliter droplets with 0.4 nanoliter maximum error, as well as sampling from the environment are demonstrated. This system will enable drug delivery and sampling with minimally invasive probes, facilitating continuous liquid biopsy for disease monitoring and in vivo drug screening. A microfluidic mount on capillaries bundle for precise nanolitre droplet delivery and sampling is presented. A sub‐millimetric microfluidic chip made by two‐photon polymerization is mounted on a dual capillary assembly. The chip has many passive valves to let only liquid flow to the outside. This allows nanoliter precision delivery and sampling of liquid droplets. However, biopsy can pose risks, cause discomfort and certain side effects to the patient's health. Therefore, alternative diagnostic techniques, which can complement the gold standard of biopsy for diagnosis of diseases are being pursued. One such alternative is liquid biopsy. Liquid biopsy is a technique that has garnered substantial attention over the past decade. It is defined as the characterization of tumours by analyzing biomarkers that are circulating in the blood or in other bodily fluids. The liquid biopsy technique allows for a more comprehensive sampling of the tumours that may be present and the technique can reduce the need for conducting difficult and risky biopsies from organs such as the brain. With the increasing demand for targeted therapy and cell‐based intervention, there is also a growing interest in the development of new microtools that can be used for in situ, in vivo, in vitro applications. Thus, combining the liquid biopsy technique with microtool technologies for targeted therapy and cell‐based intervention is clinically advantageous. With less invasive sub‐millimetric tools at the micron scale for liquid biopsy, certain negative effects could be reduced, while facilitating new interventions and introducing the possibility of long‐term tethered devices or implants for local monitoring of diseases. Indeed, combining sampling with a capacity to deliver small drug liquid volume could facilitate local destruction of a targeted tissue/cells followed by liquid biopsy sampling of the intracellular chemical composition.Biopsy is defined as the removal of a small quantity of biological tissue for histopathological examination, and is the gold standard for the diagnosis of many diseases.1 For However, with size reduction, the biopsy sample volume needs to be reduced, and thus delivering and sampling nanoliter volumes at the tip of sub‐millimeter slender tools are challenging. Indeed, the conventional approach of a simple tube is limited as dead volume (which corresponds to the inner volume of the tube necessary to fill from one end to get liquid at the other end) implies large samples volumes. Moreover, bending perturbation changes the overall volume and reduces delivery and sampling precision. Indeed, passive valves use the change of wetting conditions to stop liquid/gas interface movement and therefore withstand a pressure difference as illustrated in Figure Following from the digital microfluidic solutions in ref. , we prop9 IndeFigure  structured Surface enhanced Raman scattering (SERs) arrays and optical waveguides, force sensors, or pneumatic actuators as in our previous work. In this work, we chose to assemble the structure after printing to simplify the fabrication process while providing a more robust fixation resilient to shock and vibration. Such a fabrication process has been demonstrated for optical resonator at the tip of optical fiber. 2PP structures were also demonstrated as the micro nozzle of a microfluidic output.To create such passive valves, we fabricate a sub‐millimeter microfluidic chip that can be assembled at the tip of the capillary bundle as shown in Figure 10 str In addition, the gas droplet formation in water‐based media is also a well‐known mechanism. However, the creation of liquid droplets in a gas medium inside microfluidic channels remains challenging, as the gas cannot prevent the contact between the liquid and the wall. This subsequently leads to a change in the wall wetting condition as the droplet passes in the channel, which causes a non‐continuous pressure change associated with a local energy minimum due to hysteresis of the interface line, rendering the repeatable creation of small liquid droplets in a classical microfluidic T junction difficult.Much research in microfluidics has focused on the realization of droplets in two phase flow. For example, use of immiscible liquids, such as aqueous droplets in oil flow, has been proposed to realise droplet based microfluidics for performing small scale chemical reactions.17 In Therefore, we have also demonstrated in this article a way to produce repeatable 10 nanoliter liquid droplets inside a capillary tube through the use of a hydrophobic coating, ensuring the integrity of the droplet during the propulsion.The article is organised as follows: We first propose a model to comment on the dynamics of a liquid droplet inside a hydrophobic tube. Secondly, we detail the fabrication of the microfluidic 2PP structure and its assembly with the capillary bundle. We also illustrate the connection of this bundle to a pneumatic circuit in order to propel and create the liquid droplet inside a capillary. Thirdly, we record the volume of multiple droplets and comment on the repeatability of the droplet formation, as well as the droplet speed evolution with the driving pressure. Fourthly, we recorded the droplet volume variation while passing different microfluidic structures attached at the capillary bundle tip for different driving pressure and vacuum values. Finally, we demonstrate how the input and output command can be regulated to realise liquid sampling without having to send a droplet to the microfluidic structure.22.1 While hydrophilic material simplifies the wetting of the chip, it makes it harder to dry as surfaces tend to remain wet to minimise the overall system energy. This is generally not a problem as fully wet conditions are usually desired in microfluidicsFor most microfluidic applications, hydrophilic materials or coatings are chosen. This enables a smooth filling of the chip, preventing gas bubble entrapment, and allows a capillary pumping solution.19 WhiFigure However, a droplet of liquid propelled in a dry hydrophilic tube will experience size reduction as part of the liquid can remain attached on the tube surface as illustrated in γ the surface tension, ρ the liquid density, and g the acceleration of gravity. For water, Lc = 2.7 mm so droplets remain easily attached to all the tube section for tube diameters below this value.A second requirement to guarantee the droplet integrity is a sufficiently small tube so the capillary forces are dominant compared to gravity. This can be easily checked by using the capillary length defined by :r being the curvature radius of the interface, note rc and ra as in Figure Figure For two droplets, this loss is double and the minimum pressure for motion is double as well. The pressure loss increases linearly with the droplet numbers thus it is possible for any driving pressure to create enough droplets to block a tube permanently. It is therefore essential to control the created droplet number in order to convey them in a tube in a controllable fashion. In this work we develop methods to limit to a single droplet.We can therefore derive a model of a moving droplet in a tube with the following assumption :Full wetting of the tube surface and no slip condition at solid/gas and solid/liquid boundaries. However, this assumption doesn't apply to higher droplet speed as shown in Figure −1.Laminar flow. In a tube (circular section) the flow transitions from laminar to turbulent for a Reynolds number (Re) above 2300. In our experiment maximum Re had values less than 15 in both water and air by considering a 100 micron diameter tube and a maximum speed of 1 m sParabolic flow distribution. This is a direct consequence of the laminar flow in a tube. However, this hypothesis is not valid near the gas/liquid interface which imposes constant flow profiles. We decided to make this assumption nonetheless to approximate the drag exerted from the tube wall to the inside fluid.Neglect gravity impact. The droplet volumes were around 10 pL, the weight was therefore equivalent to the force generated by 0.1 mbar pressure on one of the droplet sides. As our pressure control precision is 1 mbar, we decided to neglect gravity impact.Neglect Inertia. As no acceleration regime was noticeable experimentallyPin and Pout in Figure r the capillary diameter, Pr and Pa the pressure, respectively, at the back and front of the droplet. This can be linked to the driving pressure ΔP = Pin − Pout by taking account of the pressure loss due to the friction between the tube and the gas. The friction force from the fluid on a tube section is given by the expression :μ the fluid viscosity, r is radius, v the speed profile, n an axis defined by the normal to the surface, and v the function that dictates the parabolic flow speed with the distance to the tube center such as v(r′) = 2vd(1 − r′2/r2), with vd being the droplet speed. This expression simplifies toThe droplet movement is controlled by the pressure at each end of the tube, denoted as L being the overall tube length.Therefore the force on the droplet can be corrected from the pressure loss of the gas :Fd :The same derivation can be done inside the droplet , to obtain the friction from the capillary to the droplet, :γ being the surface tension of the liquid gas interface, θa and θr respectively the advancing and receding contact angle.The forces induced by both triple contact lines  can be expressed by20:(8n being the number of droplets.As we neglect inertia (and thus the transition) regime and by considering the drag force from the wall on the droplet, a force balance gives :2.2n‐octadecyltrichlorosilane was used to form a hydrophobic self‐assembled monolayer (SAM) on the glass surface. This treatment procedure was adapted from a hydrophobic SAM static surface process, wherein this work we continuously pumped the organosilane coating solution through the 100 µm glass capillaries (TSP100170 CM‐scientific). The identical treatment made on a flat glass surface exhibited an advancing contact angle of (119.7 ± 0.9)° and a receding contact angle of (96.9 ± 3.9)°, with a statistically significant difference (p < 0.01) determined. The resulting contact angle hysteresis is (22.8 ± 4.0)°. These measurements were made on a custom‐made contact angle measurement system following a specific protocol for hydrophobic surface. Pictures of the drops from which the contact angles were measured, along with a full description of the measurement procedure, are given in Figure The first necessary step was to make the 100 µm capillary hydrophobic. To do so, we utilised an organosilane based treatment, whereby  process,21 whePin) and was controlled by a computer. The 2PP structure was fitted inside the capillary. This structure reduced the diameter of the capillary from 100 to 50 µm, therefore providing a passive valve to stop the liquid/gas front and allowed a repeatable droplet volume creation. This structure was printed on a glass substrate, then picked up from the plate with the capillary and pushed inside the tube at approximately 1.5 mm with a 70 µm tungsten filament.The coated capillaries were then assembled to form the delivery/sampling fiber. The overall set‐up, as well as the pneumatic connections, are presented in Figure As the depth at which the structure is pushed controls the volume of the droplets, different liquid volume for the micropipette can be selected during the fabrication. The precision at which the valve is placed determines the absolute precision of the pipette. In the reported micropipette the position could be adjusted with one micrometer precision thus with a 0.1 nL accuracy. We call this assembly the nanopipette as it produces 10 nL droplets.The pressure controller was set to 0 (corresponding to atmospheric pressure). The syringe advanced the liquid front just to the front of the capillary opening.) it remained wet from previous droplets and the liquid overfilled the tube.The syringe advanced the liquid front so the capillary filled with liquid. As the 2PP passive valve was not hydrophobic (contact angle around 65°14) itThe syringe retracted the liquid front. As the capillary was hydrophobic the liquid was removed more quickly than in the containing tube until being stopped by the 2PP passive valve. The main difficulty for this was that the passive valve worked better in stopping a gas front as it is hydrophilic and remains wet.The liquid/gas front in the main tube detached from the capillary opening leaving liquid filled in the capillary before the passive valve, thus forming a droplet. The gas pressure was then raised to open the passive valve and propelled the droplet in the capillary.By combining the control of the syringe and the pressure, droplets could be formed in a repeatable way in the tube as shown in  The speed of the gas flow as well as the amount of remaining liquid on the surface increases the risk of such second droplet occurrence. To overcome this problem the structure of the passive valve was optimised to prevent a remaining liquid pocket. A computer model view of the passive valve is presented in Figure A significant challenge was to guarantee that the droplet did not split in two while leaving the nanopipette, as the presence of a second droplet greatly impacts the speed as mentioned in Equation  and meas23 TheAt the other end, we assembled the glass tube with a similar hydrophobic capillary to form a two capillary bundle with the end aligned on the same plane. This bundle was fixed using a cyanoacrylate‐based glue. The capillary of the created bundle connected to the nanopipette is the input capillary whereas the other one is the output capillary.The 2PP microfluidic structure was then printed on glass and assembled directly on the capillary bundle under a microscope. The alignment was achieved using a manual stage (Thorlab‐ Pt3 stages). More details on this process are available in Figure Figure Two designs of the 2PP microfluidic structure were investigated for this article. The first one  in order to get a negative pressure (compared to the environment) contribution to the droplet propulsion. This allowed for control of the droplet pressure difference with the environment while maintaining a constant driving pressure ΔP = Pin − Pout as stated in Equation  and thus a larger critical pressure for the droplet to move. Indeed, the main reason behind changing the static SAM procedure from the literature to a flowing procedure, coupled with substantial solvent rinsing steps at the end of the coating procedure, was to limit the formation of inhomogeneous areas of significant polycondensation causing the organosilane to form organically modified silicate particles, which could restrict or even block the flow through the capillary. However, despite the adapted SAM coating procedure, it should be expected that the roughness and thickness of the SAM coating along the length of the lumen of the capillary may still display some inhomogeneity and this may be increasing the contact angle hysteresis.The measured minimum pressure difference to start moving the droplet is 1.17 times higher than the one predicted using the contact angle measurement on a flat surface and Equation . This di2.4P = Pin − Pout was set to a constant value of 85 mbar. Variation of droplet volume between the nanopipette and the capillary output were measured. This value indicated the amount of liquid transferred between the microfluidic structure and the environment. Positive value corresponded to flow from the environment to the droplet (sampling behavior) and negative to flow from the droplet to the environment (delivery).The two different microfluidic structures (A and B) were mounted successively on a two capillary bundle with a nanopipette producing a 13 pL droplet in the input capillary. The driving pressure ΔFigure Pin and Pout pairs set to generate an 85 mbar driving pressure. To ease the notation, we consider that 0 mbar pressure corresponds to the environment pressure and that negative pressure corresponds to pressure below this reference. In design A, for pressure less than the pair Pin = 41 mbar, Pout = −44 mbar, pressure inside the droplet is lower than 0 mbar and the liquid from the environment flowed in the droplet as it passed in the microfluidic structure. This corresponds to the sampling regime because part of the environment is captured in the droplet and conveyed back to the capillary exit.Pin = 44 mbar, Pout = −41 mbar. However, the end of the droplet was blocked at the microfluidic structure entrance as the diameter of the section was reduced. For easy visualization picture of the experiment and Video 1 are performed with black ink whereas deionised water was used for the measurement.For pressures above this value, the droplet pressure was higher than the environment and a part of the droplet was released, this corresponds to the delivery regime. For both delivery and sampling, Figure Pin was set to 0 mbar while Pout was decreased until the pressure difference was enough for the environment liquid to force through the passive valve and fill the microfluidic part . A large sampling droplet formed in the output capillary until Pout was set back to zero. Then Pin was raised until the inner passive valve break  and the droplet on the output capillary could be conveyed to the capillary exit. These steps are illustrated along experimental pictures on Figure By varying the pressure with time, design B can also sample liquid without the use of a droplet to activate the passive valve. For this 33.1A first interesting result is the increase of the droplet volume as it was propelled inside a single capillary as shown on Figure  This transition is quantified by the capillary number (Ca) which define as :v the droplet speed. At 22 mbar the droplet speed is around 1 mms−1 which corresponds to Ca = 1.2 × 10−4. This corresponds to values in the literature where the transition is known to happen for Ca between 10−4 and 10−5. Measurements specific to hydrophobic surface recorded the stabilisation of the contact angle hysteresis for Ca value above 3 × 10−4.The speed value for pressure between 20 and 23 mbar did not follow the clear trend that seems to emerge for higher pressure values. Indeed, as the speed increases, the mechanism of wetting changes from a thermally activated regime  to a hydrodynamic regime.24 Thi; however, for a continuous flow and with smaller impact on the flow rate (5% differences reported in the literature). The slipping of the droplet could be also explained by gas entrapment between the droplet and the surface that would occur at relatively small speed because of the hydrophobic surface and remain stable due to the small dimension of the tube which increases such stability.For higher speed values, a clear trend was observed. However, the speed increase with pressure is roughly two times higher than the one explained in the model describes by Equation . While t27; ho for important capillary number (Ca ≈ 1). In this case, a thin air layer forms between the droplet and the capillary This reduces drastically the droplet friction on the surface and lead to homogenous liquid speed inside the droplet. A model  have also been reported on hydrophillic capillary30 for3.2θa or the receding one cos θr using the Laplace pressure as shown in Equation (−1 (we were limited to 500 fps in this study). Moreover, a precise measurement of the dynamic contact angle is difficult and requires a specific experimental design with a moving solid in order to get a static image of the triple line The droplet speed could be linked to cos θa − cos θr by Equation /2. Indeed, this transition happens when the average is close to zero and therefore when the pressure in the droplet is the same as the environment. However, even if this approximation is sufficient to predict overall interaction of the microfluidic structure with the environment for one droplet, a closer look at the high‐speed Video 5 shows that the droplet slows and stops in the microfluidic. This speed variation must have an effect on the pressure loss as well as the contact angle and therefore the actual Pdrop may be varying during its passage in the microfluidic chip impacting as well on the delivery/sampling flow rate with time.Nonetheless, the characterization with varying Design B, with an embedded passive valve was designed to exploit this phenomenon. A passive valve forced the droplet to stay in the microfluidic structure until it is fully delivered. This design is therefore closer to the application as it allows for the delivery of the same volume over a wide pressure range which would be the case for an in‐vivo environment where the pressure is not expected to be constant.The sampling process illustrated in Figure Additional passive valve integration inspired from the nanopipette design and integrated in the microfluidic structure or the capillary would allow repeatable sampled volume with an automatic pressure cycle. This would render the liquid biopsy fully automatic and more repeatable for a given value set during the microfluidic fabrication. Moreover, the associated control mechanism would only require sequential command of the pump and no precise timing on the applied pressure would be required4−1 were possible. We also demonstrated that the relation between the driving pressure and the speed could be modelled as a first approximation by a linear curve for speeds between 5 × 10−3 m s−1 and 0.6 ms−1.In this article, we first presented a method to create a nanoliter droplet in a hydrophobic capillary. This method allowed the repeatable creation of 10.05 nL liquid droplets with only a 2% repeatability error on the volume. Secondly, we studied the gas propulsion of these droplets in the hydrophobic capillary, demonstrating that speeds up to 0.6 msFinally, we proposed a sub‐millimeter slender probe integrating a microfluidic structure at the tip capable of both delivering this nanoliter droplet in a liquid environment as well as sampling a droplet from the environment. This method benefits from the advantage of droplet based microfluidics, such as the absence of dead volume limitations on the precision, allowing for the manipulation of small samples in the nanoliter range.These results stand as a proof of concept of a potential minimally invasive drug delivery and sampling probe. Such minimally invasive tools could reduce the side effects associated with biopsy. Moreover, the small impact on the targeted organ due to the nanoliter sampling would allow biopsy to be repeated in time. Such time resolved biopsy would enable both in vivo study of organs under different stresses and the monitoring of the progress of diseases such as cancer using an implantable system relying on the small footprint of the probe. Automation of the process is also a key challenge to guarantee the repeatability of the delivery and sampling for practical applications. For this, future work should focus on the development of a syringe pump to fill the micropipette feedback with visual tracking of the liquid front.However, many challenges need to be addressed to reach this goal. To this end, our future work will focus on extending the proof of concept to biological challenges such as the use of biocompatible material and surface treatment to guarantee omniphobic contact with biological fluids.32 AutInvestigation on the passive valve design could also allow for adjustment on demand of the micropipette droplet size. Indeed, more complex designs such as succession of passive valve (for a discrete volume selection) or reducing section (for a continuous volume selection with the pressure) could be made. Different microfluidic designs at the tip of the structure will also be investigated in order to control more precisely the sampled volume and to develop cleaning procedures using flush fluid droplet to prevent or limit bio‐fouling or obstruction of the capillary valve holes.Finally, this work showed that the dynamics of long droplets in hydrophobic channels still need to be resolved. Therefore, further experiments with higher frame rate should be performed to characterize the contact between the moving droplet and the hydrophobic capillary walls.5n‐octadecyltrichlorosilane , which had been passed through a 0.22 µm syringe filter, dissolved in toluene (10 mL). To the solution, hydrochloric acid (HCl)  was added, affording droplets of acid within the toluene solution. Before coating, the internal lumen of the capillary was flushed through with acetone, 2‐propanol, deionised water, ethanol, deionised water and then toluene, in that order, before the n‐octadecyltrichlorosilane coating solution was pumped through the capillary for 5 h, avoiding the uptake of the droplets of hydrochloric acid at the bottom of the solution vial. After 5 h, the capillary tube was flushed through sequentially with toluene, ethanol, ethanol‐deionised water (1:1), deionised water, and finally air. By applying this procedure to a planar clean glass substrate, water contact angles of >115° were measured, thus a hydrophobic coating was inferred to have formed on the internal lumen surface of the capillary when this procedure was used.The hydrophobic coating solution consisted of Nanoscribe technology was used to print both capillary valves of the nanopipette and microfluidic structure. IP‐Dip was placed on ITO‐coated glass and both structures were printed at 50% laser power using x25 objective. The structures were developed 10 min in propylene glycol monomethyl ether acetate (PGMEA) then placed for 2 h in a fresh PGMEA bath for further development. They were then rinsed in 2‐propanol for 2 min and then dried in a stream of air.mean ± propagated error) and the difference between the two groups was compared with Welch's t‐test.The statistical calculations (mean and standard deviation) for the advancing and receding contact angles as well as the droplet volume change were performed using Microsoft Excel. The advancing and receding contact angle measurement data are expressed as the , secured funding, and supervised all steps of the project.The authors declare no conflict of interest.Supporting InformationClick here for additional data file.


The main objective of this study is to identify the socioeconomic, meteorological, and geographical factors associated with the severity of COVID‐19 pandemic in India. The severity is measured by the cumulative severity ratio (CSR)—the ratio of the cumulative COVID‐related deaths to the deaths in a pre‐pandemic year—its first difference and COVID infection cases. We have found significant interstate heterogeneity in the pandemic development and have contrasted the trends of the COVID‐19 severities between Maharashtra, which had the largest number of COVID deaths and cases, and the other states. Drawing upon random‐effects models and Tobit models for the weekly and monthly panel data sets of 32 states/union territories, we have found that the factors associated with the COVID severity include income, gender, multi‐morbidity, urbanization, lockdown and unlock phases, weather including temperature and rainfall, and the retail price of wheat. Brief observations from a policy perspective are made toward the end. Section 2Despite a surge in the studies on COVID‐19 in economics or social sciences, they are mostly about the impact or the consequences of COVID‐19 pandemic. The empirical literature on the socioeconomic factors associated with the COVID‐19 pandemic or infections is still scarce in India or in other developing countries. This section provides a selective review of the emerging literature of the correlates of the COVID‐19 pandemic in India.In an earlier contribution, Joe et al.  conducteAs reviewed by Das et al. , recent It is evident that socioeconomic factors influence the COVID‐19 pandemic and infections, but virtually no studies have considered them in India,Acharya and Porwal  have conOur study builds on the existing literature on the correlates of the COVID‐19 in India in some important ways. First, our study extends the analysis to October 31, 2020, and thus captures the surge in the COVID pandemic. We use a measure of COVID‐19 severity, namely the cumulative severity ratio (CSR). CSR takes COVID‐related deaths over a period since the occurrence of the first death relative to deaths in a pre‐pandemic year over the same duration. The first difference of CSR is taken to capture a flow measure of the pandemic based on the new COVID‐related deaths in comparison with the deaths in a pre‐pandemic year. It helps monitor the progression of the pandemic—whether it is intensifying, weakening, or unchanged. We use panel models that allow the use of time‐invariant fixed effects.33.1A new indicator “relative severity” proposed by the World Bank illustrates the unequal distribution and progression of COVID‐19 deaths across states , with the cutoff date October 31, 2020. For the DSR, the denominator used in the ratio is the total number of deaths in each region in 2017/365.The COVID‐19 data are collated from Ministry of Health and Family Welfare, India. The data on past mortality patterns are based on the state‐wise number of registered deaths in 2017 from the Ministry of Health and Family Welfare, Government of India. For the purpose of deriving CSR, the number of reported deaths in 2017 is scaled down from annual estimates to the length of the pandemic in each state, calculated as the number of days since the first death in the state till the data point  for the panel of 75 economies and have found that while lockdown measures lead to reductions in Rt, gathering bans are more effective than workplace and school closures. How these policies are effective in India remains uncertain, which would justify our focus on different phases.Unlock 1.0 (June 1–30) was the first phase of the reopening in stages, with an economic focus where shopping malls, hotels, and restaurants reopened. In Unlock 2.0 (July 1–31), the lockdown measures were restricted only to the contaminated zones, and some inter‐ and intrastate travels were permitted. Further relaxation of restrictions  occurred in Unlock 3.0, while Maharashtra and Tamil Nadu imposed a lockdown (August 1–31). Unlock 4.0 (September 1–30) was characterized by permissions of gathering at marriages/funerals, while wearing face masks became compulsory in public places and Unlock 5.0 (October 1–31) by opening cinemas and a gradual restarting of onsite teaching at schools at the discretion of state governments. How these government policies effectively influenced the COVID‐19 infection cases or fatalities is debatable and essentially an empirical question. Some authors have constructed the panel data across different countries and have estimated the effects of government policies on the COVID‐19 infections. For instance, Chen et al.  estimateWe observe in Figures Andhra Pradesh saw a rise in CSR from early July. Its CSR became the second highest roughly at around 103% next to Maharashtra on September 18. DSR in Andhra Pradesh was the second highest in late July to early October with its peak nearly 110% in late August. DSR has declined since then. Uttar Pradesh has seen a rise in CSR from July to October. Other states in the graphs, namely Madhya Pradesh, Rajasthan, Assam, and Kerala, have experienced a gradual increase in CSR, but the pandemics measured by CSR or DSR were not as severe as those of the states mentioned earlier. We observe a large variation in levels of the severity across different states.We see large variations in other states/union territories not highlighted in Figures Figures 3.3Given the trends of severity ratios we observe in Figures T (or the panel unit‐root tests cannot be conducted). For the monthly panel we use the level of CSR, its first difference, or the log of cases as a dependent variable.Table 44.1To find the explanation for the regional variation in the severity of COVID‐19 pandemic, we use a panel of 32 states/union territories for which the data on various variables are available covering the period from March 13, 2020, to October 31, 2020. As we have noted, we have organized the data as weekly or monthly panels where all the variables on daily basis are averaged for each week or month. Because of the missing observations of a few variables, our estimation is based on 1,041 observations for the weekly panel (32 states times 32.5 weeks on average) and 223 observations for the monthly panel (32 states times 7.0 weeks).Our econometric models are motivated by the emerging empirical literature on the correlates of the COVID‐19 pandemic we reviewed in Section More specifically, we regress a dependent variable, either the CSR, the first difference of CSR (DCSR), or confirmed COVID‐19 infection cases on a number of explanatory variables. CSR captures the overall development of the COVID‐19 pandemic, while its first difference denote how the severity progresses over time. We also use the number of the infection cases given that a surge in the confirmed cases is closely associated with the COVID‐19 pandemic with some lags.We have selected the explanatory variables, while constrained by the data availability, to reflect the growing empirical literature on the correlates of COVID‐19 pandemics or infections. The time‐variant explanatory variables are weather variables, namely temperature and rainfall as well as the lagged commodity price (retail wheat price). We have also used a number of time‐invariant variables such as the log of per capita income, urbanization, presence of more than one morbidity condition among those above 60 years, and the sex ratio . The model also includes a few phase dummies as defined in the previous section.We estimate the following equation. We have taken the logarithm of most of the explanatory variables to capture the relative effect, or the elasticity of each factor.i stands for states (from 1 to 32) and t for weeks from March 13 to October 31, 2020 (from 1 to 34) for the weekly panel data and March to October (from 1 to 8) for the monthly panel data. A dependent variable is DCSRit (the first difference of CSR) or logCovidCasesit (the log of the daily infection cases—averaged over a week)—both of which are I(0)—for the weekly panel data and CSRit, DCSRit, or logCovidCasesit (the log of infection cases averaged over a month) for the monthly panel as in Equation In Equation PerCapitaIncomei (PCI) denotes income at state levels that is measured by per capita net state domestic product .Multimorbidityabove60i).Urbanizationi, the share of the population living in urban areas. The idea is that a higher population density and urbanization would increase more interactions among people and raise both CSR and its first difference. Furthermore, we have inserted SexRatioi , as it is well documented that, while COVID‐19 infection rates are broadly similar between men and women, men are more likely to suffer from severe illness or die as a result of COVID infections in China . A consistent result has been found for DSR as well. However, the sign is reversed in Table A lagged retail price of wheat is negatively correlated with DCSR or CSR, and the estimates are statistically significant in all the cases except Columns 3 or 4 (RE model applied to the monthly panel). Columns 1 and 2 based on the weekly data and 7 and 8 based on the monthly data show a similar level of parameter estimates. A 1% increase in wheat price is associated with a 0.17%–0.19% decrease in changes in CSR, while the estimated coefficient of the Tobit model suggests that a 1% price increase is correlated with a 4.5%–4.6% decrease in CSR (in levels). The results overall suggest a negative correlation between wheat prices and CSR, which could be due to shift to cheaper and more nutritious cereals. However, once we take the second or third lags, the parameter estimates are negative but not statistically significant.We have controlled for temperature and rainfall to reflect the empirical literature on the correlates of the COVID‐19 infection and pandemic. While the estimated coefficient of temperature is positive and that of rainfall is negative in all the cases, we only find a positive and statistically significant estimate for temperature in Columns 5 and 6 (Tobit for CSR) and a negative and significant estimate for rainfall in Columns 7 and 8 (random‐effects model for DCSR). We refrain from inferring any associations between weather conditions and the pandemic once time and state effects are accounted for. Table Table On the contrary, the state with a higher share of the elderly with morbidity conditions tends to have lower COVID‐19 cases (where a 1% increase of the former is associated with a 0.28% decrease in the cases), while the unconditional correlation between the two variables is positive. It is conjectured that while morbidity conditions among the elderly can lead to fatalities once they are infected, they may not influence the probability of being infected at the population level. Urbanization is not significantly associated with the number of cases . As noted earlier, the sex ratio is positive and significant, implying that the states with more females per 1,000 males tend to have more infection cases (an increase of one woman per 100 men is associated with a 0.03% increase in the cases). As in Table On the effect of weather, both temperature and rainfalls have a positive and significant coefficient estimate, that is, hot and rainy weather conditions are correlated with higher COVID‐19 infection rates. A 1% increase in temperature is associated with a 0.13%–0.17% increase in the number of cases on average, other factors held constant. In contrast, a 1 mm increase in rainfall is associated with a 0.03%–0.06% increase in the cases. Phase or month dummy variables show that the number of cases tends to be larger and larger in subsequent months or phases. State dummy variables show, after controlling for covariates , that Maharashtra, Andhra Pradesh, Gujarat, Kerala, and Tamil Nadu are the states that exhibit a higher number of infection cases than other states.6This study has provided econometric results on the socioeconomic, meteorological, and geographical correlates of the severity of COVID‐19 pandemic in India. We have used the measures of excess mortality called CSR and its first difference (DCSR) up to October 31, 2020. The log of COVID‐19 cases has also been estimated as a rapid increase in the cases implies a pandemic. The study has adopted a random‐effects model and a random‐effects Tobit model, the latter of which considers the fact that some states did not record COVID‐19‐oriented deaths in early phases. The factors associated with the severer pandemic reflected in a large CSR or DCSR include higher income at the state level, a higher share among the elderly with multi‐morbidity conditions, urbanization, a lower share of females in the population, lower local retail prices of wheat, and lockdown and unlock phases. On the contrary, the correlates of a higher number of infection cases—which are different from the above factors—include lower income, a lower share of the elderly with multi‐morbidity conditions, a higher share of females in the population, lower wheat prices, as well as hotter and/or rainier climatic conditions.The positive association between the severity measures and per capita income implies that higher incomes are associated with higher mortalities. The underlying mechanisms include greater economic activity, more travel and intermixing, and, consequently, higher exposure to the infection and higher risk of dying if denied medical assistance. A negative association between the infection cases and per capita income is puzzling, but it could be the case that the state with greater economic activities did not necessarily have a matching capacity for testing.A more or less expected result is the positive association between CSR (or DCSR) and urbanization. Although there has been a large‐scale reverse migration from urban areas to villages, indications are that large segments are forced to return to small towns and cities with flickering signs of economic revival.The negative association of both CSR and DCSR with the sex ratio, the number of women per 1,000 men, means that the state with a higher share of women  tends to have a lower severity ratio or its increase (or a milder pandemic) after controlling for state fixed effects. This is consistent with Joe et al.  who arguA few limitations are briefly noted. First, as we have used a state, rather than a household, as the unit of analysis, we could not capture variation in COVID‐19 fatalities within states. Second, our analysis is limited by the lack of data on health capacity and infrastructure for measuring the response to the COVID pandemic. A third limitation is that we are unable to assess the impact of return migration on the villages/small towns to which they belong.Finally, we will make a few points about COVID‐related policiesAnother major concern is that the response to COVID‐19 infection depends on the immune system of the individuals. Although we did not include the variables on nutrition or health status at individual levels due to data constraints, we have shown that the development of the COVID‐19 pandemic is correlated with changes in wheat prices. The underlying mechanism connecting these two variables is unclear, but there exists a possibility that the change in wheat prices influences the nutritional conditions of the vulnerable groups in the population through changes in the availability of staple foods or substitution from wheat to other staple foods. Specifically, individuals with poor nutritional status are likely to have a weak immune system. A significant proportion of women in the age group 15–49 years, for example, are undernourished, and this makes them more vulnerable to COVID‐19 morbidity and mortality. As risks of chronic diseases accumulate over a life span, the old (60 years and above) tend to be more vulnerable to diabetes and cardiovascular diseases, and thus exposes them to higher risks of COVID‐19 morbidity and mortality. Amid elevated risks to lives and livelihoods, there is also a surge in hunger and food deprivation in both rural and urban areas. Besides, disruption of healthcare services is inimical to nutritional health (Joe et al., As our results show that some states were slower to recover from the pandemic than others, a perceptive comment by Horton  merits sIn brief, the rapid surge of the corona pandemic calls for extraordinary measures. While some are identified here, their implementation is daunting.