Social media have progressed drastically in building successful consumer engagement both in brand building and sustainable consumption. This paper is a review of the articles concerning the influence of social media marketing on consumer engagement in sustainable consumption practices published over the last 8 years. We follow the PRISMA technique as a methodological approach. The review investigates 70 empirical research articles published between 2014 and 2022. A total of 70% of the reviewed articles were published during the last two years. The most influential theories in this field of study are relationship marketing and consumer engagement (16 articles), social exchange (10), and sustainable consumption (8). The most commonly used methods are quantitative (in as many as 61 of the 70 reviewed articles). A careful analysis of the reviewed articles suggests that the tools that are consistently contributing to sustainable consumption are influencer marketing along with creating meaningful content with the right balance of content design, quality, and creativity, as well as the use of emojis. Consumer involvement with a brand relationship quality is key to a sustainable lifestyle. Young individuals with an entrepreneurial vision and a high drive for increased social status demonstrate the highest social media engagement in sustainable consumption. In today’s world of business, engagement in any form appears to be the buzzword . ConsumePast studies have suggested that it is consumer engagement that acts as the initiator for brands building a long-term relationship with their consumers ; often, Epstein  discusseTo understand the popularity of social media as an effective tool to build consumer engagement in the sustainable consumption environment, catching the diversity and depth of the current research in this genre, a more detailed systematic review combining the future untapped research directions along with the research questions to clarify those dimensions is of utmost need. This review tries to bridge this gap by discussing the themes that emerged along with the characteristics portrayed over the last eight years, thus paving the way for future research questions and research directions for social media marketing researchers involved in consumer engagement in the social media brand community.This work is an illustrative overview of articles on the social media brand communities involving consumer engagement with a special focus on (1) the various research methodological approaches and variables identified over this span of eight years, (2) research theories supporting previous research, and (3) future research directions along with research questions to assist social media marketing scholars in conducting fruitful and relevant research in this field. This review provides insights into several research questions:1. What are the characteristics of the recent literature on social media consumer engagement in sustainable consumption in terms of theories, contexts, and methods?2. How was consumer engagement in social media brand communities operationalized in research models ?3. What further investigations could be conducted by scholars into consumer engagement and building sustainable consumption through social media?The remainder of the paper is organized as follows. The following section presents the literature review methodology. Subsequent sections include a substantive analysis of the research studies included in this review and discussion, including limitations, future research directions, and a conclusion.The approach followed for this study is a meta-textual review further allowing the identification and extraction of the pertinent information on subjects of relevance from published research and assessing the literature . This ap(i) To assess relevant and quality articles focused on consumer engagement with a direct intervention with social media marketing.(ii) To formulate an integrative framework providing a holistic understanding of the impact of social media marketing on consumer engagement in sustainable consumption.(iii) To identify the research gaps in the literature and provide future research directions.A systematic literature review is evidence of the previous literature that accurately and reliably analyzes the quality of peer-reviewed journals following some preferred reporting items and consisting of a meta-analytical structure (PRISMA) . PRISMA The sample search strategy and identification involve three activities, namely, (a) searching appropriate keywords, (b) assessing the relevance, and (c) assessing the quality.The data search was executed using a prominent multidisciplinary database of peer-reviewed research literature, Web of Science. Li et al.  discusseThe search strings were created by regrouping the chosen keywords into three specific categories. The first category covers terms representing consumer engagement, and the second category is formulated using the influence of social media marketing. Finally, the third category investigates the sustainable consumption category. The search strings are presented in The keywords were mainly searched in the titles, abstracts, and/or keyword sections, and consequently, 7652 articles were identified from the search process. Considering the appropriateness of the journals and limiting the time according to the inclusion and exclusion criteria set, a total of 1684 articles were identified, as shown in Initial sorting of the articles’ titles and abstracts led to the exclusion of articles that did not focus on consumer engagement and social media marketing in the context of sustainable consumption. We excluded papers on the grounds of trade publications, editorial handbooks, overlapping studies in close contexts of consumer engagement, and dissertations, to ensure further homogeneity.The rationale for the sample considering research papers after 2014 is manifold. First, the study tries to acknowledge the recent trends of methodologies and to understand the recent shift in research methods and techniques. Moreover, the context of studies has significantly varied from understanding consumer engagement in brands to hospitality, to influencers and their social media activities involving consumer engagement. Hence, this present study would primarily focus on understanding these pattern shifts.Finally, 265 articles were selected for deeper reading, allowing us to discard 88 working papers. After these steps, the resulting sample consisted of 177 articles.Many a time, an article seems relevant, but it might lack quality. Hence, a consistent focus on peer-reviewed and high-quality journals was chosen along with journal ranking criteria based on the Association of Business Studies (ABS) Journal Quality Guide and only included top journals ranked as 4*, 4, and 3 to generate high-quality articles. This refinement process led to inclusion of 70 articles in this systematic review . A studyThe year-wise distribution of articles presented in To measure the progress of the impact of social media marketing on consumer engagement, we classified the empirical studies  into eitConsumer engagement in the context of social media has gained momentum across various theoretical contexts from various disciplines to showcase its effects. The study identifies 57 studies that have employed at least one theory. Here, we discuss the most applied theories in consumer engagement .This section deals with the combined interrelated theories of relationship marketing and consumer engagement, which account for 25% of the total studies (16 studies). According to Pansari and Kumar , consumeThe social exchange theory, which enlists psychology, sociology, and economics , has alsThe sustainability theory is highly visible in online consumer engagement through social media in the recent past, as consumers tend to become extremely aware of their purchases having an environmental impact. This pushes the demand for increased green sustainable brands . Kong etKatz et al.  discusseIn addition to the theories discussed, the study also explains specific behavior, which is categorized in “other theories”, which account for 25% of the studies (16 studies). For example, Liu et al.  discuss This section discusses the countries involved in the analyzed sample. The findings indicate that Europe is the biggest contributor to this study, with 31 of the 70 studies (44%). This reflects that the European countries, mainly the UK, France, Austria, Belgium, and the Netherlands, dominate the papers related to consumer engagement in sustainable consumption within the social media marketing context. Asia, surprisingly, is the second biggest contributor to this research stream , followed by the USA—19 studies (27%)—and other countries. It is noted that, unlike social science, the contexts are predominantly set in the more emerging Asian market. The feasible logic is the extensive economic and technological advancements witnessed in the Asian market in the last eight years. The study also finds that there will be ample scope for future researchers in the context of consumer engagement through social media in the South American market. Emerging markets, such as Brazil, did not showcase enough contribution in this domain of study. Hence, future research should focus on these markets. Additionally, the researchers would advise future researchers in this field to focus on cross-country consumer engagements in social media; culture would play a significant role in such studies .In this section, we will discuss the articles reviewed through the prism of the research approaches and analytical techniques adopted to assess the relationships investigated in consumer engagement research. We believe that consumer engagement studies would benefit immensely from conducting more longitudinal studies testing relationships over a period of time. Additionally, there is a lack of studies picking up the experimental method approach to understand a more trusted consumer engagement on social media platforms. Our research validates a greater scope of a mixed-method approach for conducting studies in consumer engagement.This section reviews the various independent, moderating, control, and dependent variables in consumer engagement studies influenced by social media marketing and their associated relationships that were tested to unfold certain phenomena concerning these variables see .The independent variables include cognitive and affective states , their relationship with brands, and consumer engagement  in the tune of social media marketing. The various mental states as demonstrated by consumers include perceived benefits from the brand and behavioral outcomes in building value co-creation and research integration. Consumer-related variables contribute to the understanding of engagement through interaction, advocacy, and connecting with the brand and trust. Social-media-related variables try to test the strength of attachment, having faith in social media channels, and the various follow-up techniques effectively used  to build consumer engagement. Finally, brand-related variables try to focus on building consumer appeal and brand engagement activities, developing persuasiveness and brand trust, and enriching the brand’s global identity.Our investigation of the dependent variables reveals that most of the studies focus on intentional or behavioral consumer engagement and relationship-based outcomes as well. The intentional and behavioral outcome validates consumers’ word of mouth, feedback, and recommendations along with participation in community engagements. The focus also lies in analyzing social media and brand marketers’ posts from an emotional perspective. It also judges the purchase intention of consumers. The relationship-based outcomes deal with the various engagement activities consumers and brands perform, such as frequent likes and comments of the posts along with sharing them on social media networks. There are then consumer-related variables focusing on the attitude and purchase intentions of consumers, with an overall brand experience, which finally leads to purchase decisions for consumers as well. Finally, consumer-related variables leading to sustainable consumption contribute to green buying intentions, a clear psychological state of well-being, being thought of as an environmental activist, and making a sustainable purchase decision.Our research on control variables comprises mainly brand or marketer-related and consumer-related control variables. The consumer-related variables discuss the demographic origin of the consumer along with analyzing his/her activities on the networking sites. Additionally, visual perceptions, the timing of posts, and brand familiarity with social networking sites play a crucial role. The brand/marketer-related variables focus on building a buzz about their products and services, thereby maintaining the brand community engagement along with building favorable brand attachments.Finally, the moderating variables consist of consumer-related and brand-related variables. The consumer-related variables come from culture playing a significant role in consumers’ engagement with the brand over social media, while the brand-related variables include the topic and modality of the posts in social media networks.One of the limitations is related to the extremely fast changing social media landscape. Every high-quality indexed journal approves a research paper after considerable time spent by the reviewers understanding the paper’s quality, rigor, and contribution to the research community. Hence, during that review time, further developments can occur in the field of consumer engagement under social media influence, thereby creating a gap where the present researchers fail to accommodate the most recent articles. Second, this review followed strict guidelines to ensure a stringent process of selecting journal articles ,87. HencA direction for future research is an important lookout for systematic reviews . Based oSecond, it would be interesting to find out to what extent cross-country cultural differences influence the functioning of consumer engagement in social media networks with the use of the Hofstede  model anThird, it will be interesting to understand the consumer sentiment toward social media networks in building engagement in green consumption. Researchers could deploy analytical methods to forecast future consumer engagement in social media networks, measuring constructs such as the strength of the attachment and the total revenues generated for the firm through liking and sharing of tweets. Finally, future research could be carried out to understand how consumer engagement in sustainable consumption could be stimulated in social media networks in developing countries, such as Brazil, Indonesia, and India. This is because countries such as India and Brazil are dominating social networking sites , and henThis study constitutes an attempt to assess the state of the art in the hyper-dynamic field of social media consumer engagement in sustainable consumption. We analyzed research articles that examined the role of social media networks in engaging consumers to become attracted to a sustainable brand or product. We believe that this review will enable the scholarly community to initiate and conduct relevant research in this vital emerging research area.According to our results, the investigated research area is gaining a rapidly increasing interest in the scientific community, as evidenced by the number of studies published. A total of 49 articles included in this review were published during the last two years. Twelve appeared in 2020 and only nine in the period of 2014–2019.Most of the reviewed studies have been published in Europe (44%), followed by Asia (35%), and the USA (27%).The most influential theories in this field of study are relationship marketing and consumer engagement (16 articles), social exchange (10), and sustainable consumption (8).The most commonly used methods are quantitative (in as many as 61 of the 70 reviewed articles). The prevalent data analysis techniques are SEM (28 studies), CFA/EFA (23), and various regression models (16).A careful analysis of the reviewed articles suggests that the tools that are consistently contributing to sustainable consumption are influencer marketing  along wiThe review led to the conclusion that consistent and disciplined consumer involvement  with a sThis systematic review is a work that draws attention to the consumer segments, which are prone to adopting new technologies . Young i


Major depressive disorder is a leading cause of disability and suicide worldwide. Consecutive rounds of conventional interventions are ineffective in a significant sub-group of patients whose disorder is classified as treatment-resistant depression. Significant progress in managing this severe form of depression has been achieved through the use of deep brain stimulation of the medial forebrain bundle (MFB). The beneficial effect of such stimulation appears strong, safe, and enduring. The proposed neural substrate for this promising clinical finding includes midbrain dopamine neurons and a subset of their cortical afferents. Here, we aim to broaden the discussion of the candidate circuitry by exploring potential implications of a new “convergence” model of brain reward circuitry in rodents. We chart the evolution of the new model from its predecessors, which held that midbrain dopamine neurons constituted an obligatory stage of the final common path for reward seeking. In contrast, the new model includes a directly activated, non-dopaminergic pathway whose output ultimately converges with that of the dopaminergic neurons. On the basis of the new model and the relative ineffectiveness of dopamine agonists in the treatment of depression, we ask whether non-dopaminergic circuitry may contribute to the clinical efficacy of deep brain stimulation of the MFB. Major depressive disorder is a heterogeneous chronic mental disorder that leads to high rates of functional impairment and is involved in at least half of completed suicides . EpidemiAn estimated 3 out of 10 individuals diagnosed with depression suffer from symptoms that do not improve after consecutive rounds of treatment . These pGiven the estimated 280 million adults who suffer from depression  and the In this white paper, we expand on our recent theory article  to exploBehavioral neuroscientists use animal models to study psychopathology, including depression. Proponents of animal models argue that they facilitate the study of brain–behavior relations under conditions of experimental control over extended periods of time ,18. CritNotwithstanding the difficulties in establishing direct causal relationships, animal models are useful in generating hypotheses that should be tested and scrutinized in humans . In the A powerful means for studying motivation is provided by the intracranial self-stimulation (ICSS) phenomenon. Interestingly ICSS has been recognized since the 1980s as a valid model for studying depression in animals . HoweverThe study of the neurochemical correlates of depression accelerated in the 1950s with the serendipitous discoveries that a variant of an anti-tuberculosis agent  and a drug developed as an antihistamine  have antidepressant properties ,30. BaseResearchers have paid significant attention to the role of another monoamine, dopamine, in depression. An extensive literature suggests that the dopamine system may be implicated in the pathophysiology of depression, and the mechanism subserving several interventions, including deep brain stimulation ,33,34. TThe rapid mood improvement induced by psychostimulants is a main reason for the continued interest in their role in treating depression . Thus, aThe first report of ICSS was roughly contemporaneous to the first pharmacological findings concerning antidepressant action. Olds and Milner showed that animals will work tirelessly to trigger electrical stimulation of various deep brain loci . Their dA rat working for rewarding electrical stimulation of the MFB provides a mirror image of depression. Whereas a depressed patient will struggle to summon the motivation to pursue previously rewarding activities , thePapers co-authored by Jaak Panksepp and pioneers in the use of deep brain stimulation to relieve treatment-resistant depression propose a fundamental link between the neural mechanisms underlying the clinically beneficial effect in humans and intracranial self-stimulation in rodents ,50,51. MWe discern three phases in how the role of dopaminergic transmission in brain stimulation reward has been understood. In the first two phases, a small core of experts assessed available data with caution and nuance that tended to diminish as the distance between an observer and the primary data increased. We hope that this commentary can contribute to forestalling a similar trend should the recently emerged third phase attract the attention of our colleagues.The first phase followed in the wake of a momentous neuroanatomical advance: the use of histochemical fluorescence to provide the first visualization of central neural projections on the basis of their neurochemistry ,53. The The second phase of conceptualization of the role of dopaminergic axons in MFB self-stimulation incorporates the results of psychophysical studies that estimated the refractory periods, conduction velocity, and behaviorally relevant direction of conduction of the fibers directly responsible for the rewarding effect ,63,64,65The psychophysical characterization of the directly stimulated neurons subserving MFB self-stimulation opposes the hypothesis that direct activation of the dopaminergic axons is the sole  cause of the rewarding effect. If so, how can the extensive pharmacological data implicating dopaminergic neurotransmission in ICSS be accommodated? The “series-circuit” model  was propAccording to the series-circuit model, the directly stimulated neurons subserving MFB self-stimulation should be sought among the mono- or poly-synaptic ,74,75 inThe importance of identifying the directly stimulated neurons subserving electrical self-stimulation of the MFB was further eclipsed by the demonstration that rodents will work vigorously for direct, specific, optogenetic activation of midbrain dopamine neurons. Even to experts, there are striking similarities between the behavior of rodents working for electrical MFB stimulation and optical stimulation of midbrain dopamine neurons, an observation that fits the series-circuit model neatly. In this view, the rewarding optical stimulation achieves directly what the electrical stimulation achieves indirectly: excitation of the midbrain dopamine neurons.Despite the intuitive appeal of the series-circuit model and its acceptance beyond the circle of initial proponents, discrepant findings have long been noted. Huston and colleagues demonstrated that rats can learn to perform simple movements to earn MFB stimulation, despite extensive damage to the forebrain terminal fields of the midbrain dopamine neurons ,80. The The evidence most devastating to the series-circuit model arises from the recent measurement of operant performance as a function of both the strength and cost of reward . The resinput to the dopamine neurons.Manipulations of dopaminergic neurotransmission have consistently displaced the reward mountain along the cost axis and have only rarely been accompanied by shifts along the stimulation-strength axis ,88,89,90both the stimulation strength and cost axes. Shifts along the strength axis require that a non-linear reward-growth function be positioned at or beyond the output of the dopamine neurons. However, positioning a non-linear reward-growth function downstream from the dopamine neurons falsifies the predictions of the series-circuit model in the case of electrical MFB stimulation. A non-linear growth function at or beyond the output of the dopamine neurons predicts shifts of the reward mountain along the stimulation-strength axis in response to drugs that perturb dopaminergic neurotransmission. This is not what was seen in 27 of 32 rats tested in 4 studies [The key evidence that falsifies the series-circuit model was provided by a reward-mountain study in which direct, specific, optogenetic activation of midbrain dopamine neurons was substituted for electrical MFB stimulation . Adminis studies ,88,89,90A new model of the brain circuitry subserving the reward-seeking behavior was proposed to reconcile the data from the electrical and optical self-stimulation studies . In thisFollowing the receipt of highly rewarding electrical MFB stimulation, rats show powerfully enhanced motivation to obtain additional stimulation trains ,45,47. TAt the phenomenological level, the priming effect would appear well suited to counteract the motivation anhedonia typical of depression. If so, finding the directly activated neurons subserving this effect in rodents would raise important questions for research aimed at determining the neural mechanisms responsible for the beneficial effect of MFB stimulation in relieving treatment-resistant depression. Does a homologous pathway exist in humans and pass sufficiently close to the clinically effective stimulation site to be activated by the antidepressant stimulation? If these questions are answered in the affirmative and the neurotransmitter(s) released by the neurons constituting this pathway are identified in rodents, this could point the way towards a new pharmacological tool for treating depression.The convergence model brings to mind images of MFB circuitry  much morThe notion that signals from the myelinated axons subserving MFB self-stimulation reach the behavioral final-common path for reward pursuit in parallel to those from dopamine neurons  invites On the basis of data acquired in human patients by means of diffusion-weighted MRI and supplemented by neuroanatomical staining of post-mortem tissue, Coenen and colleagues have proposed that a principal substrate for the antidepressant effect of MFB stimulation in humans is composed of myelinated fibers arising in the orbitofrontal cortex and terminating on dopaminergic somata in the VTA . We undeBased on the third-phase view of the role of dopaminergic neurons in MFB self-stimulation, we appeal to our colleagues to entertain the possibility that a reward-related pathway that parallels the midbrain dopaminergic projections is activated by the clinically effective stimulation and contributes to the clinically beneficial effect.We appeal further for additional neuroanatomical studies of the region of the human MFB where electrical brain stimulation relieves depressive symptoms. Although we know of no studies demonstrating myelination of dopaminergic axons in rodents, some axons arising in the SNc of the squirrel monkey are myelinated . Do any In addition to its implications for identifying the cellular substrates of the antidepressant effect of MFB stimulation, the third-phase view also sends a more general message, one concerning convergent causation . The motor patterns that constitute goal-seeking actions are highly stereotyped and are subject to multiple influences. The fact that an animal performs such actions does not tell us which subset of these multiple influences is responsible. Although there is a striking behavioral resemblance between rats working for electrical MFB stimulation and optical stimulation of midbrain dopamine neurons, the research that gave rise to the convergence model implies that partially different causal chains link the stimulation to the behaviors. In this view, both dopaminergic and non-dopaminergic pathways, operating partially in parallel, subserve ICSS of the MFB. Given the important role of appetitive motivation in ICSS and depression, both pathways should be considered as candidate neural substrates for the relief of treatment-resistant depression by electrical MFB stimulation.


Suboptimal complementary feeding contributes to undernutrition in children aged 6–23 months in low- and middle-income countries like Bangladesh. Multifaceted interventions have been taken to improve complementary feeding practices, but there is limited evidence about the effect of home visits by community health workers (CHWs) on complementary feeding practices of the caregivers of children.We collated data from the baseline and the corresponding follow-up survey done as part of an evaluation of Bangladesh Maternal, Infant and Young Child Nutrition (MIYCN) programme. We collected data on complementary feeding practices using a 24-h recall questionnaire. Age-appropriate dietary diversity, minimum meal frequency, and minimum acceptable diet were assessed based on recommended food consumption as per child's age. To understand the effect of CHWs' visits on complementary feeding practices, we performed Generalized Estimating Equation (GEE) procedure for dealing with correlated data and adjusted other covariates.A total of 758 and 745 caregivers of children aged 6–23 months participated in the baseline and follow-up survey, respectively. Complementary feeding practices were improved in 1 year of programme implementation; dietary diversity increased from 46 to 54%, minimum meal frequency from 82 to 91%, and minimum acceptable diet from 41 to 53%. Caregivers of children who had received the visit of CHWs in the last 12 months prior to the day of the follow-up survey were more likely  to maintain dietary diversity in their children's feeding practices than the caregivers who had not received a CHW visit in the last 12 months. The likelihood of maintaining a minimum acceptable diet in feeding practices was 1.57 times higher  among the caregivers who were exposed to the visits of the CHWs in the last 12 months compared to the caregivers who were not exposed to the CHW's visits in the last 12 months.Integration of promotional activities of complementary feeding practices into the mainstream nutrition programme can be instrumental in optimizing complementary feeding practices among the caregivers of the under-five children. However, home visits by CHWs should be prioritized in such an integrated programme. These volunteer community health workers (CHWs) are the first point of contact for the community people receiving healthcare services from BRAC. BRAC is distributing Pushtikona-5 (a brand name of BRAC's MNP) sachets containing Iron, vitamin A, vitamin C, folic acid, and zinc through their Shasthya Shebikas in the community at a subsidized price. Although BRAC started distributing Pushtikona-5 as part of their mainstream HNP in 2010, the MIYCN Phase II programme emphasized on its promotional activities, such as training for the health workers to promote and supply the product to them, to reduce anemia among the children aged 6–59 months. The training was not limited to promoting the product; it also covered the contents on how to counsel the caregivers to improve complementary feeding to the children.BRAC, a non-governmental organization, has implemented the Maternal, Infant and Young Child Nutrition (MIYCN) Phase II programme in Bangladesh in partnership with the Global Alliance for Improved Nutrition (GAIN), Renata Pharmaceutical Company (manufacturer of MNP) and Social Marketing Company (SMC) . BRAC haThe icddr,b- a Bangladesh-based international health research institute was commissioned to evaluate this MIYCN programme, and it conducted cross-sectional surveys as part of periodic assessment of the programme. A detailed description of the evaluation and BRAC programme is published elsewhere , 12, 13.Although there are some evidences that nutrition education intervention for caregivers of the children can improve their complementary feeding practices , there iWe used data from two cross-sectional surveys (baseline and the corresponding follow-up survey after 1 year) that were conducted as part of an evaluation of the Bangladesh MIYCN programme. Although during the baseline and the corresponding follow-up survey, data were collected from caregivers of children aged 6–59 months, we extracted data on caregivers of children aged 6–23 months for this study.Caregivers of children aged 6–23 months were the study population in this study. A caregiver was defined as the child's biological mother or the person who took care or looked after and gave the child most meals on most days.In the evaluation of MIYCN programme, we estimated sample size considering a 50% prevalence of MNP coverage, a precision of ±10% and a design effect of 2. Thus, our estimated sample size for caregivers of 6–59 months children was 192 households per district.We performed a two-stage sampling procedure for selecting study participants at the household level. In the first stage, we selected communities or primary sampling units (PSUs) using systematic random sampling, with an equal selection probability for each community or PSU.Systematic samples of 16 PSUs were drawn from a complete list of the targeted communities or PSUs, which were sorted by district and by sub-district within the programme district to reach the minimum estimated sample size. This ensured that all of the target communities had an equal chance of being selected for the sample. In the second stage, a physical map-segment sample approach was exercised to segment the selected communities or PSUs. Then, the Expanded Programme on Immunization method was applied by spinning a bottle/pen placed in the center of the segment, counting the households along that route and picking the fifth household. The selection of households depended on the inclusion criteria .Shasthya Shebika, were sampled from the 10 districts in each survey at the first stage of sampling. To reach the representative sample size, 12 eligible households with at least a child of 6–59 months were selected. In this study, we used data of households with children aged 6–23 months and so, at first, we excluded the PSUs where there were no children aged 6–23 months. Thus, 2 PSUs were excluded from the baseline survey. Finally, from the remaining PSUs (158 at baseline and 160 at follow-up survey), we selected the households of children aged 6–23 months . The baseline and the corresponding follow-up survey were done in 10 districts of Bangladesh in 2014 and 2015 respectively. A total of 160 communities or PSUs where BRAC's MIYCN programme was being implemented with the involvement of 3 months .Complementary feeding practice for children aged 6–23 months was the outcome of interest. We measured dietary diversity, minimum meal frequency, and minimum acceptable diet to assess complementary feeding practices. Dietary diversity was assessed based on the consumption of ≥4 food-groups of the children, and minimum meal frequency and minimum acceptable diet were assessed based on the recommended food consumption, by the World Health Organization as per child's age . Table 1Demographic and socioeconomic variables were also assessed. We measured the proportion of poor, middle and rich households by wealth index.We defined caregiver's exposure to home visit by CHW as receiving at least one visit by a caregiver from the CHW of BRAC during 1 year of the MIYCN programme intervention. Along with other interventions of health, population and nutrition programme of BRAC, interventions under MIYCN programme were also being implemented with the network of CHWs. Therefore, caregivers who received the visit of CHWs in the last 12 months prior to the day of survey were considered to be exposed to the visit of CHW.t-test were performed to see the changes in the different characteristics between baseline and follow-up period. To understand the effect of a home visit by CHWs on complementary feeding practices, exposure variable (receiving the visit of CHWs in the last 12 months) was kept, along with other potential covariates and confounding factors including child's sex and age, number of household members, household's wealth status and parental education for adjusting in the multiple regression model. We performed Generalized Estimating Equation (GEE) using binary logistic regression models for dealing with correlated data because the data were obtained from different communities or PSUs. We checked multicollinearity among independents variables and found the average variance inflation factor 1.06 indicating negligible collinearity. Data analysis was performed using the statistical software package STATA, version 13 .Descriptive analyses were carried out to assess the characteristics, prevalence and patterns of the study sample and nature of variables. The probability weighting technique was applied to estimate the proportion and mean of the variables of interest. Bivariate analysis including adjusted Wald test and The study protocol was reviewed and approved by the Institutional Review Board (IRB) of icddr,b which comprises a Research Review Committee (RRC) and an Ethical Review Committee (ERC). Before conducting the interviews, well-informed written consents were taken from the women who were surveyed.A total of 758 and 745 caregivers of children aged 6–23 months participated in the baseline and follow-up surveys respectively. We did not find any significant differences among participants in the baseline and follow-up survey in terms of their household size, their children's age and sex, their education and religious status. However, there was a significant difference in the mean age of the caregivers, from baseline (24.7 ± 5.2) to follow-up survey (25.6 ± 6.1) and the percentage of poor and rich households based on wealth index between baseline  and the corresponding follow-up survey  .Results showed that complementary feeding practices were improved in 1 year of MIYCN programme implementation . About 4To understand the effect of home visits by CHW on complementary feeding practices, we applied the Generalized Estimating Equation procedure. After adjusting the covariates, results from the multivariable GEE model showed that, during the follow-up survey, dietary diversity and minimum acceptable diet were significantly associated with the CHWs' visits in the last 12 months . CaregivResults of this study clearly demonstrated the positive effect of CHWs visits on complementary feeding practices when infant and young child nutrition program is integrated into a mainstream nutrition programme. This finding corresponds with the evidences generated from other study that also suggests that a mainstream maternal and child care programme could be a viable avenue to improve acceptance and utilization of other services pertaining to child nutrition . OverallAlthough this study was not intended to identify the factors associated with home visit of CHW for promoting complementary feeding practices among the caregivers of under-five children, the findings of the study reinforce the necessity of home visit by CHW. Therefore, while designing a programme aiming at promoting complementary feeding practices it is imperative to take the aspects into account that can increase home visit by CHW. Training is one of the vital aspects that can improve self-efficacy and self-confidence of a CHW and thus, encourages her or him to visit the households of the caregivers of children.When a CHW works in a mainstream nutrition programme integrated with promotional programme of complementary feeding practices, the CHW is very likely to receive intensive training under different components of the programme. The application of knowledge and skills that the CHW acquire from the training arranged under different components of the programme also can result in improving complementary feeding practices of the caregivers of children served by them .Results of this study showed that the caregivers of the under-five children who were exposed to the home visits by CHWs maintained more complementary feeding practices compared to the caregivers who were not exposed. It indicates that counseling by the CHW during home visit works. Nevertheless, the caregivers of the children are more likely to listen to those CHWs who are acceptable to them. Evidence shows that acceptancy of the CHWs depends on how they are selected, trained and supervised . AnotherAlthough complementary feeding practices among the caregivers of children have been improved during the intervention period, this result should be interpreted cautiously since there might have secular trend of improving complementary feeding practices among the caregivers in the intervention areas. It could otherwise overestimate the effect of home visit by CHW on complementary feeding practices.This study was cross-sectional in design and, so, it could not show any causal relationship of home visit by the CHW with complementary feeding practices. Data were collected from 10 districts of Bangladesh, so it may not necessarily be representative of other districts of the country where BRAC's MIYCN programme was not implemented. In addition, the individuals in the baseline and follow-up samples were not the same. Therefore, the results may not represent the actual effect of home visits by the CHW on complementary feeding practices among the caregivers of the children.Positive association of home visits by CHWs with complementary feeding practices generated an evidence-base for the integration of complementary feeding promotional programme into the mainstream maternal, neonatal and child health programme for achieving comprehensive outcomes, like optimal IYCF practices among caregivers of under-five children. Integration of promotional activities of complementary feeding practices with the mainstream nutrition programme can be instrumental in optimizing complementary feeding practices. However, home visit by CHWs should be prioritized in such an integrated programme.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Institutional Review Board (IRB) of icddr,b. The patients/participants provided their written informed consent to participate in this study.MR contributed to conceptualization of the study and drafted the manuscript. MR and MT analyzed data. HS acquired funding for this study. MT, HS, and TA reviewed and edited the manuscript. All authors critically revised the manuscript, agree to be fully accountable for ensuring the integrity and accuracy of the work, read, and approved the final manuscript.


The checkrein deformity is characterized by flexion contracture of the interphalangeal joint and extension contracture of the metatarsophalangeal joint. It is a rare condition occurring after lower extremity trauma, especially a malleolar fracture. Little is known about the possible cause and therapeutic strategy. This unique case presents a 20-year-old male patient with a diagnosis of the checkrein deformity secondary to open reduction and internal fixation of a Lauge–Hansen pronation external rotation stage IV malleolar fracture. After performing a detailed physical examination, radiographic evaluation, and ultrasonography, open exploration was performed to remove the hardware and correct the deformity with sole tenolysis of the flexor hallucis longus (FHL). In the 4-month follow-up, no recurrence of the checkrein deformity was observed. This deformity was caused by FHL adhesion. Interosseous membrane injury and fibular fracture together with local hematomas increases the risk of FHL adhesion. Open exploration and tenolysis of the FHL are feasible options to correct the checkrein deformity. The checkrein deformity of the hallux is characterized by flexion contracture of the interphalangeal (IP) joint and extension contracture of the metatarsophalangeal (MP) joint . It is uA 20-year-old male patient  was first admitted to our department with diagnosis of Lauge–Hansen PER stage IV malleolar fracture . ORIF wiIn contrast to fixed claw toes caused by the IP joint lesions, checkrein deformity may be exaggerated or ameliorated by ankle and foot activity. This rare complication might be caused by intrinsic muscle dysfunction, tendon entrapment, or tethering induced by fracture callous formation or Volkmann contracture . It coulAnatomically, the FHL originates from the posterior surface of the fibula in the deep posterior compartment, passes through the medial retromalleolar region, and inserts into the hallux. An invariable intertendinous connection is observed between the FHL and flexor digitorum longus (FDL) at the master knot of Henry .Different surgical methods are available for correcting checkrein deformity, including FHL to extensor hallucis longus (EHL) transfer , lengtheIn contrast to the most common supination-external rotation type, the category of Lauge–Hansen PER stage IV appeared as medial mortise widening with deltoid ligament rupture, distal tibiofibular syndesmosis diastasis with interosseous membrane injury, fibular fracture above the syndesmosis, and posterior inferior tibiofibular ligament injury. Therefore, ORIF using a combination of medial and lateral approaches was usually performed in the primary operation with the aim of decreasing posttraumatic arthritis.However, the checkrein deformity occurred 3 months postoperatively. In similar case reports, Leitschuh et al.  describeThe soft tissue in patients with a Lauge–Hansen PER stage IV fracture might be damaged to varying degrees. Herein, we speculated that interosseous membrane injury and fibular fracture increased the risk of adhesion of the FHL muscle belly to adjacent tissues in our patient. Scarred fibroses following local hematoma might be an additional cause. Preoperative radiographic evaluation and ultrasonography provided useful information to confirm FHL adhesion. A possible adhesion site was located at the fibula in the deep posterior compartment. In addition, the tendinous interconnections between the FHL and FDL tendons might account for the deformity of both the hallux and the second toe. Therefore, sole tenolysis of the FHL sufficed to correct the deformity and improve the motion of the IP and MP joints of both the hallux and second toe with no need to explore FDL. Lengthening of the tendon was unnecessary for FHL adhesion and might even result in compromised foot function.This patient suffered from posttraumatic adhesion of the FHL in the deep posterior compartment, resulting in a checkrein deformity of the hallux and second toe. A thorough evaluation of the history, physical examination, ultrasonography, and radiographic evaluation could provide detailed information to confirm the pathology of FHL. The sole tenolysis of the FHL sufficed to correct the deformity of the hallux and second toe due to the intertendinous connection of the FHL and FDL.


The COVID-19 pandemic highlighted the fragility of the human response to pandemics on a global scale. First, the airborne transmission of the virus has led to a significant increase in infections. Weak medical systems ineffectively isolate the source of the disease. Second, close contact increases the probability of cross-infection and puts understaffed health workers at risk. In addition, the lack of detection devices limits tracking the health status of people and diagnosing cases in real time. Finally, the strict protection policy has led to a sharp decline in transportation efficiency and shortages of supplies. These factors make COVID-19 have an unprecedented impact on human society.After 2 years of fighting COVID-19, humans are lessening the virus’s influence with robotics and artificial intelligence (AI). Robots make up for the lack of professionals. It has unique advantages in improving work efficiency and blocking virus transmission. For example, temperature detection robots have tracked health status in public places. Telemedicine robots can reduce close interactions between caregivers and infected patients. The COVID-19 throat swab intelligent sampling robot has achieved a single sampling success rate of more than 95%, improving the standardization of biological sample Research Topic and ensuring specimen quality. In this Research Topic, we aim to collect current research around robotics and AI in the pandemic to overcome the challenges preventing wide-scale robotic deployment. Our purpose is to address the feasibility and impact of robotics in COVID-19 diagnosis and detection.This Research Topic contains nine research articles that present the newest research achievements of robotic applications: four papers focus on detection methods and devices; three papers are on the diagnosis of symptoms or viruses; and the remaining two papers study the delivery of healthcare resources.Zhou et al.) proposes a cough recognition network (CRN) based on the CNN model and a Mel-spectrogram. CRN can achieve excellent performance in cough recognition compared to other methods. The results of generalization tests show that CRN adapts to cough monitoring in complex scenes in daily life. It is expected to be a potential solution for disease management during the COVID-19 pandemic and to reduce health care workers’ exposure.The first paper (Jumlongku) presents the effectiveness of a new method of using robot-assisted surgery to cut the skull of a cadaver. Compared with the oscillating saw method, the noise level is reduced. It also limits the release of aerosol particles to levels similar to those of ordinary autopsy procedures. This method reduces fine infectious dust particles, especially during the COVID-19 pandemic.The second paper  proposes an autonomous robotic solution enabling POCUS scanning of COVID-19 patients’ lungs for diagnosis and staging. Under a CT scan, an algorithm is developed to estimate the optimal position and orientation of a US probe on a patient to image target points in the lungs. Without CT data, a deep learning algorithm can predict the 3D landmark positions of a ribcage given a torso surface model. The results demonstrate the preliminary feasibility of the system and its potential for offering a solution to help mitigate the spread of COVID-19 in vulnerable environments.The third paper  presents the design of a robotic UVGI platform. It can be deployed alongside human workers and operate autonomously within cramped rooms. The results show that UVGI can effectively inactivate germs on commonly touched surfaces in radiology suites. Despite the short irradiation period, UVGI can inactivate microbes with more complex cell structures, thus indicating a high likelihood of effectiveness against COVID-19.The fourth paper (Jumlongkul and Chutivongse) proposes an AMBU ventilator consisting of an automated AMBU bag ventilator, a negative pressure head box, and a transporting capsule. It allows the initial setting of the preliminary flow rate, rhythm, and volume of oxygen during the intubation period to protect medical colleagues from potential erosol infection. It is expected to block airborne transmission of COVID-19 viruses during the procedure.The fifth paper  proposes a robot-assisted system that automatically scans tissue to enable isolation between patients and sonographers during the COVID-19 pandemic. This system automatically scans the tissue using a dexterous robot arm that holds the US probe and assesses the quality of the acquired US images in real time. According to the quality assessment algorithm, this system successfully maintains US image quality and is fast enough for use in a robotic control loop.The sixth paper (Cheng and Tavakoli) reviewed applications requiring physiological organ motion compensation in the medical telerobotic system. Physical distancing restrictions cause significant effects on the delivery of physical healthcare procedures worldwide to limit the spread and transmission of the novel coronavirus. Medical telerobotic systems can play a positive role in the provision of telemedicine. This paper focuses on control-theoretic approaches to outline possible future directions of telerobotic systems in COVID-19 healthcare.The seventh paper (Grimshaw and Oyekan) proposes a cable-driven parallel manipulator (CDPM) to balance an unstable load, a ball plate system. It consists of eight cables attached to the end effector plate. The hardware includes a reinforcement-learning trained neural network controller and nested PID controller to output the desired platform response. During the COVID-19 pandemic, this robotic device can reduce workers’ exposure.The eighth paper  surveys the available army doctrine of healthcare missions. The proposed framework adopts metrics of spacing error, separation distance, and string stability to compare the performance of autonomous convoys. According to hierarchical decision-making, this paper argues for using nonlinear battlefield techniques for delivering healthcare logistics to remote pandemic outbreak areas.The ninth paper (These nine articles are strictly selected based on many submissions. We hope that the discussion of recent advances in this area can promote an understanding of how robotics and AI can be used to diagnose and detect COVID-19 and provide a framework for how we can integrate smart systems to reduce pressure on healthcare systems during current and future pandemics.


The complexity of coronary artery disease is currently on the rise . As a rePercutaneous mechanical circulatory support devices (pMCS) have been often used to assist PCI in high-risk patients, especially with poor ejection fraction (EF), despite the lack of confirmative data. The first device to be widely used was the intra-aortic balloon pump (IABP) but its routine use was downgraded following the result of the IABP-SHOCK II trial in cardiogenic shock . On the The presence of multivessel disease (MVD) is often one of the main issues related to the concept of CHIP. Whereas ischemia guidance for PCI in MVD has reached a certain level of consensus in stable coronary artery disease  the sameA tailored approach to ischemia-reperfusion injury is needed. The scarcity of new therapeutic options to prevent reperfusion injury reflects the complexity of this process. In the last decades, several different therapies have been studied, but a clear effective strategy is yet to be reported . From a Lastly, there is increasing evidence that new-generation drug-eluting stents (DESs) with thinner struts are associated with better outcomes . ReductiComplex coronary intervention is more and more becoming a tailoring art. Complexity dwells either in the presenting anatomy, in the knowledge of technologies, or the clinical history of the patient. Frequently, the treating physician, beyond appropriately balancing the risk and benefits of interventions, is forced to guarantee excellent results while facing a growing demand for minimally invasive treatments. In this optic Machine Learning (ML) artificial intelligence has delivered impressive results, and we expect its application in this setting to increase in the next few years . One que


The various existing measures to quantify upper limb use from wrist-worn inertial measurement units can be grouped into three categories: 1) Thresholded activity counting, 2) Gross movement score and 3) machine learning. However, there is currently no direct comparison of all these measures on a single dataset. While machine learning is a promising approach to detecting upper limb use, there is currently no knowledge of the information used by machine learning measures and the data-related factors that influence their performance. The current study conducted a direct comparison of the 1) thresholded activity counting measures, 2) gross movement score,3) a hybrid activity counting and gross movement score measure (introduced in this study), and 4) machine learning measures for detecting upper-limb use, using previously collected data. Two additional analyses were also performed to understand the nature of the information used by machine learning measures and the influence of data on the performance of machine learning measures. The intra-subject random forest machine learning measure detected upper limb use more accurately than all other measures, confirming previous observations in the literature. Among the non-machine learning  algorithms, the hybrid activity counting and gross movement score measure performed better than the other measures. Further analysis of the random forest measure revealed that this measure used information about the forearm’s orientation and amount of movement to detect upper limb use. The performance of machine learning measures was influenced by the types of movements and the proportion of functional data in the training/testing datasets. The study outcomes show that machine learning measures perform better than traditional measures and shed some light on how these methods detect upper-limb use. However, in the absence of annotated data for training machine learning measures, the hybrid activity counting and gross movement score measure presents a reasonable alternative. We believe this paper presents a step towards understanding and optimizing measures for upper limb use assessment using wearable sensors. The accurate evaluation of the real-world impact of a neurorehabilitation intervention is crucial to gauge its true value. Thus, there is a growing interest in quantifying how much and how well patients use their affected upper limb(s) outside of therapy; here, ‘how much’ refers to the amount of use of the upper-limb, and ‘how well’ refers to the movement quality during use. The shortcomings of current questionnaire-based assessments of upper limb use in daily life  have ledupper limb use—a binary construct indicating the presence or absence of a voluntary, meaningful movement or posture  in the form of wristbands , David , Laput , which mA comparison of the performance of these different upper limb use measures has also recently appeared in the literature , Subash. Lum et versus non-functional movements and the types of tasks in a dataset can significantly impact a model’s training and testing accuracy. Furthermore, there has also been little work on the interpretability of machine learning methods used for upper limb use assessment. The current methods are black boxes that use a set of handcrafted features based on the authors’ intuition and experience about the nature of the movements of interest. Understanding the relative importance of the features could improve the interpretability of these methods. Comparing classifiers with a reduced number of features can indicate which features are more important than the others. Analyzing the relationship between important features and interpretable features (such as arm orientation) can lend insight into the basis for the classification.Machine learning methods are data-driven approaches. The nature of the training data will impact the performance and generalizability of their result. However, this influence of training data on upper limb use detection has not been investigated in the current literature. Factors such as the relative amounts of functional status quo in upper limb use assessment.This study aims to answer the following research questions to push forward the Q1. What is the best measure or algorithm for measuring upper-limb use, among the existing and the new measures introduced in this study?Q2. What information about a subject’s movement do machine learning algorithms rely on to measure upper-limb use?a priori to measure upper-limb use, how does the nature of the training and validation datasets affect a machine learning algorithm’s performance?Q3. Given that machine learning algorithms need to be trained and validated The additional focus on machine learning measures (Q2 and Q3) is due to the increasing interest in these measures, and the current lack of understanding of these measures in this application.This study uses an annotated dataset collected from our previous study , which cData from a previous study   no prior history of upper limb movement problems due to neurological conditions; 2) no current difficulty in upper-limb movements; 3) age between 25 and 70 years; and 4) willingness to give informed consent. Subjects who had pain while moving the upper limb and/or allergy to the plastic material used for the IMU-watch casing and straps were excluded from the study.Participants performed various functional tasks , a weighted Support Vector Machine (SVM) with a Radial Basis Function kernel, and a three-layer Multi-Layer Perceptron (MLP) were trained on the features and ground truth. The model parameters for the RF and SVM  were chosen by performing a grid search in a nested cross-validation approach. In nested cross-validation, the model parameters were optimized using every fold in the training set as the validation set . The parIntra-subject: Stratified 5-fold cross-validation was used to implement the intra-subject model. To account for the variability in performance due to the different random splits, the intra-subject models were generated by iterating through the train-validate-test process 10 times.Inter-subject: Leave-one-out cross-validation was used to implement the inter-subject model.All classifiers were implemented using the Scikit-Learn package .To get a handle on the features used by the random forest method, the Gini importance index was computed for the 11 features. The index represents the importance of a feature relative to the other features used in training the model. This analysis was carried out only for the random forest because of the availability of the Gini importance score, and because the random forest performed the best among all methods. To further verify feature importance, three reduced models were trained and validated on the dataset again: 1) mean of The Spearman correlation of the mean and variance features with the variables used by TAC and GM methods  was also computed. This was done to understand the physical significance of the features.The nature of the dataset used for developing a machine learning method is a crucial factor in determining performance, arguably as important as choosing a classifier itself. The training dataset used must be a representative sample of the activities and tasks typically performed by a subject in daily life; failing this can drastically affect performance. The training data set must contain sufficient information about the different types of movements of interest to ensure the measure generalizes well to real-world data.The variability in patients’ ability to use their affected limbs resulted in different proportions of functional and non-functional movements in their datasets. To demonstrate the effect of the different proportions, the sensitivities and specificities of the intra-subject random forest models were fit to the percentage of functional data present in the dataset using a linear model, and the slope of this model was tested.The current study investigated the effect of presence/absence of selected tasks in training and testing datasets by comparing the performance of the classifier if a certain task is 1) present in both testing and training sets, 2) absent from training set but present in testing set, 3) present in training set but absent from testing set, and 4) absent from both training and testing sets. The tasks chosen for this analysis were opening a bottle, drinking from a cup, and walking ; walking tasks were excluded from the patient data since only two patients had performed them. Additionally, the data segments in between tasks marked as unknown tasks were excluded from this analysis.The performance of the different UL use measures across different subjects (and different iterations for intra-subject models) are presented as the ‘sensitivity’ vs. ‘1-specificity’ plots. The measures were also compared using the Youden index , a measuThe Youden index for the ideal classifier is one and is 0 for a random classifier.1) Comparison of the Youden indices of different types of measures was done by grouping them into three categories: traditional , inter-subject machine learning, and intra-subject machine learning measures.2) Comparisons of the Youden indices of reduced and full models using only the random forest inter- and intra-subject models.3) Comparison of the sensitivities and specificities of all combinations of presence/absence of a task using only the random forest intra-subject models.The results from the different analyses were compared using a one-way ANOVA, and t-tests with Bonferroni correction were performed to examine pairwise differences. The following tests were performed:https://github.com/biorehab/upper-limb-use-assessment.The full dataset used in this study and the code for the analysis are available at Five patients with mild-to-moderate hemiparesis (mean age of 35.4 ± 13.21 years) and ten right-handed unimpaired controls (mean age of 23.3 ± 3.21 years) participated in the study. The demographic details of the patients are listed in p < 0.0001).The performance of the different measures computed across different subjects (and different iterations for intra-subject models) are presented as the ‘sensitivity’ vs. ‘1-specificity’ plots shown in. The thresholded activity counting measures had high sensitivity but low specificity with a median Youden index of around 0.07. The GM measure had low sensitivity but high specificity and a median Youden index of 0.16. Thus, confirming that thresholded activity counting measures overestimate, while GM underestimates upper limb use. The amount of activity that the TAC measures pick up is dependent on its threshold. The TAC measures demonstrated in this study have a low threshold of zero that they detect all movements, including non-functional movements, and therefore overestimate use. While the pitch thresholds in the GM algorithm results in a reasonable threshold for functional movements, its threshold for the total change in yaw and pitch may be too restrictive which results in the underestimation of use. GMAC was found to be a reasonable compromise between thresholded activity counting and GM, resulting in a median Youden index of 0.3.The machine learning-based upper limb use measures perform better than the traditional measures; intra-subject measures have the best performance . The RF p > 0.1) in detection performance of the different machine learning algorithms.Furthermore, we observed that features from the IMU’s gyroscope , i.e., using only the mean accelerations achieved similar performance to the full intra-subject model . However, the acceleration variances were required to achieve similar performance in the inter-subject models for patients; models using just mean of p < 0.05). The SVM and MLP methods showed similar trends.The Gini importance index for the mean and variance of What do the mean and variance features convey? The Spearman correlation coefficient shown in  between versus non-functional), and the second is the effect of the tasks present in the dataset.There are two data-related aspects that can impact performance. The first is the effect of the proportion of the two classes of movements  There aren't significant changes in the sensitivity under the different conditions for any of the tasks.2) Specificities for the functional tasks opening a bottle and drinking from a cup do not change much under the different conditions. However, there is a drop in specificity under 3) There is a significant drop in the specificity for walking tasks under obtained .1) ThereThe results obtained for the patient data at the individual level were varied. Each patient performed different and fewer tasks, and so, meaningful conclusions about the tasks in the dataset could not be made.The current study compared the sensitivity and specificity of existing measures for quantifying upper limb use—a fundamental construct in assessing upper limb functioning . The resAlthough the improved performance in upper limb use assessment through machine learning methods is valuable in practice, the black-box nature of these methods obscures their mechanism of operation. Traditional measures (TAC and GM), despite their poor performance, offer an intuitive explanation of their classification since they employ interpretable quantities such as counts, forearm pitch, and yaw. Previous work by Lum et al. employed models using 11 accelerometer features and did not attempt to understand the roles of these different features and their physical significance. Understanding the most relevant and important movement features can guide the optimal selection of sensors for measurements and further improve measure performance. The current study shows that the random forest method employs a combination of arm orientation (mean acceleration features) and the amount of movement (variance of acceleration) to estimate upper limb use. Either GM or TAC does not measure up to the random forest method as they do not use all the relevant information and have relatively simple decision rules. However, an advantage of these traditional methods is that they do not require any training, unlike the machine learning methods. The hybrid GMAC measure performed very closely to the inter-subject machine learning models, indicating that it might still be useful in the absence of training datasets required for employing machine learning methods. Additionally, GMAC can be computed with only accelerometers since it only employs pitch and activity counts. However, the detection ability of the intra-subject models is unmatched owing to the differences between subjects, especially in those with hemiparesis.x-axis) was an important feature in classifying movements. In another dataset where only tabletop tasks are included, the pitch may not play as significant a role, and activity counts could be the determinant in distinguishing between functional movements and rest.Most functional movements were performed on top of a table in the current dataset, while walking formed a significant portion of the non-functional movements class. Therefore, it is no surprise that the pitch of the arm  how well represented the movements of this particular task are in that of the other tasks in the dataset, and 2) how much the proportion of the functional class is affected by the presence or absence of a particular task in the dataset. Tasks that have similar movement patterns and similar proportions of functional movements to other tasks can be removed from the dataset without losing performance. But tasks with unique movement patterns and different proportions of functional movements must be included in the training dataset to ensure good performance for a machine learning method. It can be safely concluded that the choice of tasks for the training dataset has an integral part in determining the performance of machine learning-based upper limb use measures. Machine learning methods should ideally be trained on a sample representative of daily life behavior, consisting of similar tasks and proportions of functional and non-functional movements. The variability across subjects in everyday tasks and the amount of functional activity compels the use of intra-subject models for classifying upper limb movements. However, training and deploying these intra-subject models in practice is cumbersome, primarily because of the arduous process of manual ground truth labeling. With a much larger dataset with more participants, it is possible that inter-subject model performance may improve. In which case, pre-trained, generalized models can be deployed without tuning for individual subjects.This work compared upper limb use measures that employed just two wrist-worn IMU sensors. Continuous and objective assessment of upper limb use can be done in an unobtrusive manner using wrist-worn sensors. The use of additional sensors to filter out walking movements improves performance but is likely to impact usability and adherence . One of The focus of this work is only on upper limb use, a construct that is useful in evaluating other important usage constructs. Our previous work  expoundsThe relatively small dataset size used in this current study is its main shortcoming. The dataset contained 10 unimpaired and five stroke survivors with hemiparesis carrying out a set of daily activities; stroke survivors did not perform some of the tasks due to difficulty in performing them. There were also variations in the percentages of functional and non-functional movements within the dataset. Nevertheless, the agreement of the results with that of 1) Developing a large, open, annotated dataset with unimpaired and people with impaired movement abilities, performing a wide range of daily activities wearing different types of sensors (at least wrist worn IMUs). This can stimulate work on developing and validating optimal classifiers with high accuracy and reliability. The proposed dataset must have a good sample of the types of movements expected from patients in daily life and a good proportion of functional and non-functional movements of interest.2) Automatic annotation of functional/non-functional movements from video recordings using an RGBD camera must be explored to eliminate the cumbersome manual labelling process. Recent work has shown that data from multiple IMUs can detect different functional primitives of complex upper limb movements . Thus, iThe use of machine learning-based measures for quantifying upper limb use seems to be the way forward. Future work in this space must focus on improving the performance of machine learning methods to deploy them in clinical practice efficiently. We propose the following two activities that are worth pursuing:We believe that exploring these two avenues will help make sensor-based upper limb use detection highly accurate and efficient for routine clinical use.This paper presented a detailed comparison of existing measures to quantify upper limb use and confirms previous findings that an intra-subject random forest measure outperforms others. Among the traditional measures, the hybrid GMAC measure, introduced in this study, outperforms all other traditional measures. Thus, the GMAC is an appropriate alternative in the absence of data for training machine learning measures. The current work also sheds light on the random forest measure, demonstrating that it uses a combination of arm orientation and amount of movement to detect upper limb use. The importance of data-related factors, such as class proportion and types of tasks, on the performance of machine learning measures, was also demonstrated. We strongly believe that this study is a step towards understanding and optimizing measures for upper limb use assessment using wearable sensors.


Object detection is a computer vision task that involves localisation and classification of objects in an image. Video data implicitly introduces several challenges, such as blur, occlusion and defocus, making video object detection more challenging in comparison to still image object detection, which is performed on individual and independent images. This paper tackles these challenges by proposing an attention-heavy framework for video object detection that aggregates the disentangled features extracted from individual frames. The proposed framework is a two-stage object detector based on the Faster R-CNN architecture. The disentanglement head integrates scale, spatial and task-aware attention and applies it to the features extracted by the backbone network across all the frames. Subsequently, the aggregation head incorporates temporal attention and improves detection in the target frame by aggregating the features of the support frames. These include the features extracted from the disentanglement network along with the temporal features. We evaluate the proposed framework using the ImageNet VID dataset and achieve a mean Average Precision (mAP) of 49.8 and 52.5 using the backbones of ResNet-50 and ResNet-101, respectively. The improvement in performance over the individual baseline methods validates the efficacy of the proposed approach. Computer Vision is an application of Artificial Intelligence (AI) focused on implementing human-like cognition and visual processing in computer systems. It has extensive applications, including autonomous driving , surveilMotion Blur: Occurs due to the rapid or sudden movement of the objects, resulting in the object being blurry and losing its characteristics.Defocus: Occurs when the camera is not able to focus on the object in motion or when the imaging system itself is being moved. This results in unclear and out-of-focus frames.Occlusion: Occurs when the object is hidden behind other objects or elements in the environment. Occlusion results in a significant loss of information.Illumination Variance: Variation in the intensity of light can cause the object to have significantly different characteristics, such as losing colour information under a shadow.Scale Variance: The perceived size of the object changes as it moves towards or away from the camera and also when the camera zooms or moves with respect to the object.Spatial Variance: When the camera angle changes, it introduces different viewpoints, rotations or locations of the object, which may result in significantly different characteristics at different viewpoints.The simple application of still-image object detectors is sub-optimal in challenging environments ,13. FurtThe challenges of blurriness, camera defocus, occlusion and illumination variance can be overcome by leveraging information from the adjacent frames. Therefore, one of the keys to efficient video object detection is effectively leveraging the temporal information across video frames. Recent works have devised various feature aggregation methods ,16,17 thTo validate this hypothesis, inspired by the unified attention head introduced in , we diseThe use of attention also alleviates the challenge of processing large amounts of video data, as it focuses only on the relevant parts of the image.We highlight an important problem that naive attention-based feature aggregation is sub-optimal for video object detection. As a remedy, we follow the spirits of  in stillThe proposed approach can be effortlessly integrated into any video object detection method to improve performance.Upon integrating our disentanglement head prior to feature aggregation in recent video object detection methods, we observe consistent and significant performance improvements. When our module is adopted into the recent state-of-the-art method TROI , we achiTo summarize, the primary contributions of this paper are as follows:The rest of the paper is organized as follows: In Initial approaches to video object detection used handcrafted features, these included methods such as a histogram of oriented gradients (HOG) , scale-iOptical flow algorithms detect object motion by assigning a velocity vector to each pixel , and wheOptical flow methods use the strong features from a key frame to strengthen the features in weaker frames. The challenge of selecting the key frame was addressed by Deep Feature Flow  (DFF). IThe Flow-guided Feature Aggregation  (FGFA) aOptical flow methods leverage the temporal context between only two frames and do not completely utilize the context present in the entire video data; an efficient object detector must be capable of utilizing the full contextual information across all frames. A convolutional LSTM  is a typLu et al.  propose A shortcoming in association LSTM is that the temporal context is limited to adjacent frames and, therefore, it uses only short-term motion information. This lack of long-term motion information was addressed by spatial-temporal memory network  (STMN). Another challenge with video data is the amount of information to be processed, which requires extensive computation. Unlike the previously discussed methods, attention-based methods aim to reduce the amount of computation by focusing only on certain parts of the data while ignoring the rest . In the Memory-Enhanced Global–Local Aggregation  (MEGA) iThe Progressive Sparse Local Attention  (PSLA) mThe tracking algorithms process spatial and temporal information that can be leveraged for improving object detection. In contrast to optical flow methods that predict the trajectory of an object using a flow field, tracking-based methods are more accurate and have a longer trajectory. The cascading tracking detector  (CaTDet)The Detect and Track  (D&T) apInspired by the human visual system, attention mechanisms are employed in computer vision tasks to emphasize the most important regions of an image while disregarding the other regions. Attention can be regarded as a dynamic mechanism that assesses the importance of the features and adapts the weights accordingly . This seCarion et al.  introducWang et al. tackle the challenge of scale variance by using spatial attention to refine multi-scale features . The autThe proposed approach is a two-stage object detection framework based on the Faster R-CNN architecture. The object detection framework is implemented using openmmlab , an openThe backbone comprises a network that acts as a base feature extractor for object detection. It takes an image as the input and outputs the corresponding feature maps, which are then propagated to the subsequent modules for further processing. In this work, the backbone is implemented using ResNet . ResiduaL different levels of a feature pyramid, C is the number of channels. The general form of applying attention is shown in Equation  is a convolutional-based neural network that performs classification and regression. The classifier determines whether a proposal contains the object, whereas the regressor is used to determine the coordinates of the object in the proposal. Anchors of varying size and scale are first drawn over the input image; the RPN then determines whether the anchor contains an object. Varying thresholds are used to filter out anchors with low Intersection over Union (IoU); the remaining high-quality anchors are passed on to the subsequent stage for further processing.The head is used for specific task outputs, such as bounding box or mask prediction. Here, we use Sequence-Level Semantics Aggregation (SELSA)  for videThe traditional RoI align  utilizesIn this section, we briefly discuss the datasets and the metrics we use for training and evaluating the proposed object detection framework. We use the ImageNet VID  from ImaPrecision: Ratio of correctly predicted bounding boxes to all the predicted samples. It measures the model’s ability to identify only relevant objects.Recall: Ratio of correctly predicted bounding boxes to all ground truth bounding boxes. It measures the prediction of all relevant cases.Intersection over Union (IoU): Measure of the overlap between the predicted bounding box and the ground truth. The IoU threshold is used to classify detection; only if the overlapping area is greater than a threshold t, the detection is regarded as correct.Average Precision (AP): Interprets the precision–recall curve as the weighted mean of precisions at each threshold, with the increase in recall from the preceding threshold used as the weight.N is the number of classes.50 is the mAP at IoU = 0.5 and mAP75 is the mAP at IoU = 0.75.Mean Average Precision (mAP): Evaluates the accuracy of object detectors by averaging the average precision across all classes as shown below, where The performance of the object detection models is evaluated using Mean Average Precision (mAP). The calculation of the mAP, along with its associated concepts, are discussed below:We experiment with ResNet-50 and ResNet-101 as the backbone network and carry out ablation studies and parameter optimisation using the smaller ResNet-50 model. The configured parameters are then used for training the ResNet-101 model. The backbone extracts the features from the frames, which are then processed by a sequence of attention blocks, and the output of the final task-attention mechanism is given to the region proposal network. The RPN consists of 12 anchors from 4 scales and 3 aspect ratios. The selected proposals are further processed by the Temporal RoI align operator, which replaces the traditional RoI align operator. The final classification and bounding box regression are implemented using SELSA. As part of the pipeline, the data is augmented through methods such as padding, flipping, resizing and normalisation. The backbone network is initialised with the ImageNet pre-trained weights. We train all the models with identical configurations, and each model is trained for seven epochs. The models were trained using 4 NVIDIA A100 GPUs with a batch size of 4 and SGD as the optimizer. The learning rate is initially set to 0.005 and is stepped down by 10 at the fourth and sixth epochs.In this section, we evaluate and discuss the performance of the proposed object detection framework using different backbones. First, we conduct several ablation studies by varying different parameters to determine the optimal configuration. All the other constraints were constant, and ResNet-50 was used as the backbone. The best results are highlighted in bold.An attention block consists of scale, spatial and task-aware attention mechanisms. The depth of the attention head can be increased by stacking the attention blocks in conjunction. We train the effect of varying the number of attention blocks, and the results are presented in The most similar ROI align extracts the most similar ROI features from the support frame feature maps for the target frame proposals. A similarity map for each support frame is computed, and the top K most similar points are projected. Using these points, the most similar ROI features are extracted from the support frame feature maps. The effect of the number of similar points is presented in The temporal attention block is used for aggregating the features extracted from the target frame feature map and support frames feature maps. The input in the form of ROI features is split into N groups for N temporal attention blocks, and each block generates an attention map. The output across all the temporal attention blocks is concatenated to obtain the temporal ROI features. The performance of varying the temporal attention is presented in We evaluate the performance of the proposed framework against the corresponding SELSA and Temporal RoI models as the baseline. The results using ResNet-50 are presented in In The result from detection is visualised and compared in We also emphasize and present the performance of the model under challenging conditions of video object detection discussed previously. From In In this work, we first raise an important problem of naive attention-based feature aggregation that hinders the upper bound in existing state-of-the-art video object detection methods. To mitigate this challenging problem, motivated by , we dise


The aim of this study was to evaluate the effect of broccoli leaf powder (BLP) incorporation on the technological properties, sensory quality and volatile organic compounds (VOCs) of durum wheat pasta. Incorporation of BLP increased cooking loss; however, all pasta samples were found to be in the acceptable range of 8 g/100 g. The addition of BLP decreased optimal cooking time and water absorption but increased the swelling index. Firmness and total shearing force decreased with increased BLP content. The obtained pasta was greener than the control, with a higher content of minerals, and an increasing tendency with respect to protein was observed. The VOC profile of enriched pasta was richer and contained compounds typical of broccoli , affecting its aroma. The sensory evaluation results indicate that the addition of BLP did not affect the overall acceptance of pasta. Up to 5% BLP content afforded an interesting, more nutritious pasta without compromising its technological and sensory quality. The Western diet is rich in highly processed products, which can contribute to the development of noncommunicable diseases. Consumers are becoming increasingly aware of the importance of diet with respect to the maintenance of a healthy lifestyle and the development of noncommunicable diseases. Nevertheless, the consumption of vegetables does not meet the dietary guidelines very often ,2. ThereThe fruit and vegetable processing industry is one of the largest producers of by-products . Many byTherefore, we attempted to incorporate broccoli leaf powder (BLP) into another type of product obtained from different ingredients and processed differently, namely durum pasta. Compared to bakery goods, lower temperatures are applied for pasta production, which can potentially preserve the bioactive and aroma compounds in fortified pasta products. The aim of this study was to evaluate the effect of BLP on the technological properties, sensory quality and volatile organic compounds (VOCs) of durum wheat pasta.In this study, three formulations were compared, including control  and increased yellowness  and greenness . No statistically significant difference in terms of greenness was noted between B2.5 and B5 pasta, although B5 seemed to be greener  of pasta samples decreased with increased BLP content, which is in agreement with other studies on the fortification of semolina-based products with plant-based ingredients . In terms of procedure, a list of all descriptors of smell, appearance, taste and texture was prepared . All attAssessment of the products was carried out by a previously sensory panel (6 persons), who had been trained and monitored according to ISO guidelines [PN-EN ISO 8586: 2014]. Assessments were carried out in a sensory laboratory room, which fulfils the requirements of the ISO standards [PN-EN ISO 8589:2010]. A computerised sensory program, FIZZ , was used for analysis and graphical presentation of the collected results.VOCs were extracted using the SPME method described in , with slCompounds were tentatively identified by comparison of the mass spectra with the NIST/EPA/NIH Mass Spectral Library  for records with a matching score above 70% and confirmed by comparing the Kovats retention indices. For the detected compounds, the odour descriptions were assigned based on the online database .® SPSS™ Statistics 27  and STATISTICA version 13.3 software . The significance of differences between the samples was set at a p-value < 0.05. To identify the differences between the VOC distribution between the BLP and pasta products, as well as the differences between pasta products based on VOC and sensory analyses, orthogonal partial least squares discriminant analysis (OPLS-DA) was used. To assess the contribution of each variable to the model and group separation, variable importance in projection (VIP) scores were measured. Multivariate analysis was performed using SIMCA 16 software .All chemical and technological analyses were performed in triplicate. The results were subjected to a one-way analysis of variance (ANOVA) with Fisher’s LSD as a post hoc test using IBMIn this study, the suitability of BLP as an ingredient in the manufacture of fortified wheat pasta was investigated based on analysis of the technological, nutritional and sensory properties of the developed product. The obtained results indicate that BLP can be successfully used as an additive in pasta products. Incorporation of BLP enhanced the mineral content of pasta and improved its appearance without compromising the cooking, texture and sensory features. Addition of up to 5% of BLP into pasta products was found not to deteriorate the quality of durum pasta nor its sensory quality, which was rated as high. However, as an increase in cabbage-like aroma and the content of VOCs was noted with increased BLP concentration, the concentration of BLP additive cannot be too high.In summary, BLP-enriched pasta can be used to increase vegetable consumption in the general public without substantial changes in the daily diet. Moreover, the utilization of broccoli leaves in new product development could facilitate the management of vegetable processing waste.


Endometriosis is a common benign gynaecological disease that affects pelvic structures and causes adhesions. Endometriosis outside the pelvis exists but is rarer. Deep endometriosis may affect organs such as the urinary bladder, ureters, bowel and sacral roots. Adenomyosis  frequently co-exists with deep endometriosis. Over the past decades, multiple attempts have been made to describe the anatomical extent of endometriosis. Out of approximately 20 classification systems suggested and published so far, three have gained widespread acceptance. These are the rASRM (American Society of Reproductive Medicine) classification, the Endometriosis Fertility Index (EFI) and the Enzian classification. Ideally, a classification system should be useful both for describing disease extent based on surgical findings and results of imaging methods .To highlight the advantages and disadvantages of the three classification systems.This is a narrative review based on selected publications and experience of the authors. We discuss the current literature on the use of the rASRM, EFI and Enzian classification systems for describing disease extent with imaging methods and for prediction of fertility, surgical complexity, and risk of surgical complications. We underline the need for one universally acceptable terminology to describe the extent of endometriosis.A useful classification system for endometriosis should describe the sites and extent of the disease, be related to surgical complexity and to disease-associated symptoms, including subfertility and should satisfy needs of both, imaging specialists for pre-operative classification and surgeons. The need for such a system is obvious and is provided by the #Enzian classification. Future research is necessary to test its validity. Therefore, one single terminology to describe the location and extent of endometriosis is needed, and this terminology should be the same for imaging experts and surgeons.Since the very first description of endometriosis in 1860 , several attempts have been made to classify and describe the anatomical extent of endometriosis and adhesions caused by it. The classification systems have changed over the years, but none has provided a clinically useful system that describes both peritoneal, ovarian, and deep endometriosis (DE). Due to the inadequacy of the existing systems, leading experts suggested that one should use a combination of the three most popular systems, i.e., the revised American Society for Reproductive Medicine (rASRM) classification, the Enzian classification and the endometriosis fertility index (EFI) . This isToday, ovarian endometriomas and DE can be detected by ultrasound or magnetic resonance imaging (MRI), . AdhesioThe American Fertility Society (AFS) first presented their classification system for endometriosis and pelvic adhesions in 1979 . After rAttempts have been made to use TVS or MRI to stage endometriosis preoperatively using the rASRM classification. In a retrospective study including 204 women, Leonardi et al.  found thVercellini et al.  analysedCan the rASRM stage predict the complexity of surgery and surgical complications? In a retrospective study including 401 patients undergoing surgery for various stages of endometriosis, only women with rASRM stage 4 disease experienced Clavien-Dindo (CD) grade II and III complications  . SimilarIn 2010, the Endometriosis Fertility Index (EFI) was suggested. It was designed by Adamson and Pasta . The EFILike the rASRM score, the EFI is primarily used to describe impaired fertility. Its clinical applicability and value to predict spontaneous pregnancy rates in endometriosis patients following endometriosis surgery has been confirmed in several studies . Tubal fTomasetti et al. (2019) found that the EFI could be calculated based on clinical information and TVS results. This required evaluation of tubal patency with hystero-salpingo-contrast- sonography (HyCoSy). Adding information from a diagnostic laparoscopy improved agreement with the final EFI (end-of-surgery EFI score) by only 10% (absolute percentage). This was considered a limited improvement when weighed against the invasiveness of a surgical procedure. Future studies are needed to confirm these findings and to test their applicability in everyday clinical practice. A prerequisite for assigning an EFI score before surgery is expert ultrasonography.Stimulated by the fact that the commonly used rASRM classification does not adequately describe DE, the Austrian-German-Swiss Scientific Endometriosis Foundation (Stiftung Endometrioseforschung/SEF) created and published the Enzian classification of endometriosis in 2003. The aim was to better describe and stage DE and to add missing information to the r-ASRM classification . The EnzDi Paola et al.  and BurlHudelist et al.  evaluateTwo studies have examined the correlation between the Enzian classification of DE  and the severity of preoperative pain symptoms. Both found a correlation . SeveralA major criticism of the Enzian classification has been its focus on retroperitoneal, deep infiltrating disease. To overcome this, the #Enzian classification  was creaA comparison between the rASRM, EFI, Enzian and #Enzian classification systems is provided in Until recently, the Enzian classification was predominantly used to describe DE. It is a commonly used method of staging DE in internationally recognised centres of expertise . A greatThe ideal classification system for endometriosis should describe the sites and extent of the disease, be related to surgical complexity and to disease- associated symptoms, including subfertility. Most importantly, it should be possible to use the classification system both by imaging specialists for pre-operative classification and by surgeons. The need for such a system is obvious and is provided by the #Enzian classification. The validation of this system regarding the above aspects should be the focus of future research.