Addisu Getie, Melaku Bimerew, Mihretie Gedfew
et al.
Introduction: Cognitive impairment is a medical condition caused by neurodegeneration, marked by a gradual decline in neurological, motor, psychological, and cognitive domain functions, as well as daily activities. It primarily affects individuals with conditions such as Alzheimer's disease, stroke, HIV/AIDS, diabetes mellitus, cancer, epilepsy, dementia, and other chronic illnesses, as well as older adults. While some individual studies have explored the effects of cognitive impairment, there is a lack of nationwide research to provide a comprehensive understanding of its burden among individuals with chronic diseases. Objective: To assess the pooled prevalence of cognitive impairment and its associated factors among individuals with chronic diseases in Ethiopia. Methods: Several databases were examined to find available articles. The data were extracted and sorted in Microsoft Excel before being exported to STATA/MP 17.0 for analysis. A random-effects Der Simonian-Laird model with a 95 % confidence interval was used to pool the data. Cochrane I2 statistics and Egger's test were used to evaluate heterogeneity and publication bias, respectively. To determine the cause of heterogeneity, subgroup analysis was performed. A log-odds ratio was utilized to illustrate the association between cognitive impairment and its associated factors. P-values less than 0.05 were considered statistically significant. Result: This study included 22 individual articles comprising a total of 6818 participants. The overall prevalence of cognitive impairment among individuals with chronic diseases was 44.43 % (95 % CI: 37.76–51.10). Studies conducted in Addis Ababa reported a higher prevalence of 50.89 % (95 % CI: 34.59–67.19). Similarly, research focusing on older adults indicated the highest prevalence, at 57.58 % (95 % CI: 28.78–86.39). Participants who are unable to read and write were 3.82 times more likely to experience cognitive impairment compared to those who had completed primary education (AOR = 3.82; 95 % CI: 2.97–4.91). Conclusion: This review found a high prevalence of cognitive impairment among Ethiopians with chronic diseases, especially in older adults and those in Addis Ababa. Illiteracy significantly increased the risk. These findings highlight the need for targeted cognitive screening and integration of cognitive care into chronic disease management.
Eric Lyimo, Neema B. Kulaya, Lembris Njotto
et al.
Abstract Background Malaria, which affects over half of the world’s population, is controlled through clinical interventions and vector control strategies. However, these efforts are threatened by resistance to anti-malarial drugs and insecticides, as well as affected by environmental, ecological, and climatic changes. This study examined changes in malaria prevalence and related factors based on data from 18 cross-sectional surveys conducted in two villages in northeastern Tanzania. Methods From 2003 to 2021, annual cross-sectional malariometric surveys were conducted in two study villages, Mkokola (lowland) and Kwamasimba (highland), samples collected to determine Plasmodium falciparum infection and human exposure to malaria vector Anopheles. Pearson's chi-squared test was used for comparing proportions, logistic and linear regressions test were used analyse associations. Generalized Estimating Equations (GEE) was used to analyse the relationship between malaria prevalence and climatic variables. Results Malaria prevalence in Kwamasimba and Mkokola dropped from ~ 25% and ~ 80% to 0% and 1%, respectively, between 2003 and 2011, reaching 0% in both villages by 2014. This decline was associated with increased bed net use and reduced exposure to Anopheles bites. However, between 2018 and 2021, prevalence resurged, with Kwamasimba reaching 2003–2004 levels despite high bed net use. Between 2003 and 2021 there was an increasing trend in average monthly maximum temperatures (R2 = 0.1253 and 0.2005), and precipitation (R2 = 0.125 and 0.110) as well as minimum relative humidity (R2 = 0.141 and 0.1162) in Kwamasimba and Mkokola villages, respectively, while maximum relative humidity slightly decreased. Furthermore, during 2003–2011, malaria prevalence was positively associated with temperature, maximum temperature, and relative humidity, while precipitation showed a negative association (Estimate:− 0.0005, p < 0.001). Between 2012–2021, all climatic factors, including temperature (Estimate: 0.0256, p < 0.001), maximum temperature (Estimate: 0.0121, p < 0.001), relative humidity (Estimate: 0.00829, p < 0.001), and precipitation (Estimate: 0.000105, p < 0.001), showed positive associations. Conclusion From 2003 to 2014, malaria prevalence declined in two Tanzanian villages but resurged after 2018, particularly in highland Kwamasimba. Most likely, vector dynamics affected by changing climatic conditions drove this resurgence, emphasizing the need for adaptive, climate-informed malaria control strategies.
Arctic medicine. Tropical medicine, Infectious and parasitic diseases
Arwin Nemani, Schahryar Kananian, Annabelle Starck
et al.
Abstract Background Refugees and asylum seekers encounter numerous post-migration living difficulties (PMLDs) that can substantially affect their mental health. However, the role of PMLDs remains insufficiently explored, particularly in clinical refugee populations. This study aimed to identify subgroups based on patterns of PMLD by examining their relationship with depressive symptoms and determining which stressors function as key bridges. Methods This study reports a secondary analysis of baseline data from the ReTreat trial. Data were collected from 141 refugees and asylum seekers enrolled in a multicentre randomized controlled trial of a culturally adapted CBT program in Germany. Participants completed measures of depressive symptoms (PHQ-9) and post-migration stressors (27-item checklist). Latent Profile Analysis (LPA) was used to identify distinct burden profiles. Exploratory Factor Analysis (EFA) examined the dimensionality of PMLDs. Network analysis was conducted to investigate symptom–stressor connectivity. Results Three latent profiles emerged: Class 1 showed elevated distress across all domains; Class 2 was characterized by family separation and homesickness; and Class 3 exhibited minimal post-migration stress. EFA of PMLDS supported a four-factor solution: institutional/legal stressors, structural hardship, health/service access, and emotional/family-related strain. Depressive symptoms differed significantly across profiles, with highest scores in the high burden group (Class 1). Network analysis identified institutional/legal and emotional/family-related stressors as central bridge nodes linking PMLDs to depressive symptoms. Conclusions PMLDs are multidimensional and heterogeneously distributed among forcibly displaced individuals. Legal insecurity and emotional strain are particularly influential in connecting environmental hardship to depressive symptoms. Trial registration This study uses baseline data from a registered randomized controlled trial (DRKS00021536).
Special situations and conditions, Medical emergencies. Critical care. Intensive care. First aid
In the following paper, we will study a charged scalar field under an electromagnetic external field taking into account the spatial confining of the system. We shall use the Coleman-Weinberg method in one-loop approximation to obtain the effective potential of the model in the proper time representation. Through generalized Matsubara formalism, we applied several kinds of boundary conditions on the frontier of the system. The regularization of the model is performed by a scheme independent of the external electromagnetic applied field. The model presents phase transition and we carry out its analysis by the free energy density functional of the bosonic system. The findings show magnetic catalysis, electric catalysis, and inverse electric catalysis phenomena, all of them depending on the thickness of the system.
These are the lecture notes based on [dD23] for the (upcoming) lecture "T-systems with a special emphasis on sparse moment problems and sparse Positivstellensätze" in the summer semester 2024 at the University of Konstanz. The main purpose of this lecture is to prove the sparse Positiv- and Nichtnegativstellensätze of Samuel Karlin (1963) and to apply them to the algebraic setting. That means given finitely many monomials, e.g. $1, x^2, x^3, x^6, x^7, x^9,$ how do all linear combinations of these look like which are strictly positive or non-negative on some interval $[a,b]$ or $[0,\infty)$, e.g. describe and even write down all $f(x) = a_0 + a_1 x^2 + a_2 x^3 + a_3 x^6 + a_4 x^7 + a_5 x^9$ with $f(x)>0$ or $f(x)\geq 0$ on $[a,b]$ or $[0,\infty)$, respectively. To do this we introduce the theoretical framework in which this question can be answered: T-systems. We study these T-systems to arrive at Karlin's Positiv- and Nichtnegativstellensatz but we also do not hide the limitations of the T-systems approach. The main limitation is the Curtis$-$Mairhuber$-$Sieklucki Theorem which essentially states that every T-system is only one-dimensional and hence we can only apply these results to the univariate polynomial case. This can also be understood as a lesson or even a warning that this approach has been investigated and found to fail, i.e., learning about these results and limitations shall save students and researchers from following old footpaths which lead to a dead end. We took great care finding the correct historical references where the results appeared first but are perfectly aware that like people before we not always succeed.
In the context of rail transit operations, real-time passenger flow prediction is essential; however, most models primarily focus on normal conditions, with limited research addressing incident situations. There are several intrinsic challenges associated with prediction during incidents, such as a lack of interpretability and data scarcity. To address these challenges, we propose a two-stage method that separates predictions under normal conditions and the causal effects of incidents. First, a normal prediction model is trained using data from normal situations. Next, the synthetic control method is employed to identify the causal effects of incidents, combined with placebo tests to determine significant levels of these effects. The significant effects are then utilized to train a causal effect prediction model, which can forecast the impact of incidents based on features of the incidents and passenger flows. During the prediction phase, the results from both the normal situation model and the causal effect prediction model are integrated to generate final passenger flow predictions during incidents. Our approach is validated using real-world data, demonstrating improved accuracy. Furthermore, the two-stage methodology enhances interpretability. By analyzing the causal effect prediction model, we can identify key influencing factors related to the effects of incidents and gain insights into their underlying mechanisms. Our work can assist subway system managers in estimating passenger flow affected by incidents and enable them to take proactive measures. Additionally, it can deepen researchers' understanding of the impact of incidents on subway passenger flows.
In the case (4/3)<p<4, and assuming a pair of locally finite positive Borel measures on the real line have no common point masses, we prove variants of two conjectures of T. Hytönen and E. Vuorinen from 2018 on two weight testing theorems for the Hilbert transform on weighted L^{p} spaces, but with extended energy side conditions. Namely, assuming the extended energy conditions, the two weight norm inequality holds (1) if and only if the global quadratic interval testing conditions hold, (2) if and only if the local quadratic interval testing, the quadratic Muckenhoupt, and the quadratic weak boundedness conditions all hold. We also give a slight improvement of the second conjecture in this setting by replacing the quadratic Muckenhoupt conditions with two smaller conditions.
Laurie M. Heller, Benjamin Elizalde, Bhiksha Raj
et al.
Machine Listening, as usually formalized, attempts to perform a task that is, from our perspective, fundamentally human-performable, and performed by humans. Current automated models of Machine Listening vary from purely data-driven approaches to approaches imitating human systems. In recent years, the most promising approaches have been hybrid in that they have used data-driven approaches informed by models of the perceptual, cognitive, and semantic processes of the human system. Not only does the guidance provided by models of human perception and domain knowledge enable better, and more generalizable Machine Listening, in the converse, the lessons learned from these models may be used to verify or improve our models of human perception themselves. This paper summarizes advances in the development of such hybrid approaches, ranging from Machine Listening models that are informed by models of peripheral (human) auditory processes, to those that employ or derive semantic information encoded in relations between sounds. The research described herein was presented in a special session on "Synergy between human and machine approaches to sound/scene recognition and processing" at the 2023 ICASSP meeting.
Susanna Gordleeva, Yuliya A. Tsybina, Mikhail I. Krivonosov
et al.
Mammalian brains operate in a very special surrounding: to survive they have to react quickly and effectively to the pool of stimuli patterns previously recognized as danger. Many learning tasks often encountered by living organisms involve a specific set-up centered around a relatively small set of patterns presented in a particular environment. For example, at a party, people recognize friends immediately, without deep analysis, just by seeing a fragment of their clothes. This set-up with reduced "ontology" is referred to as a "situation". Situations are usually local in space and time. In this work, we propose that neuron-astrocyte networks provide a network topology that is effectively adapted to accommodate situation-based memory. In order to illustrate this, we numerically simulate and analyze a well-established model of a neuron-astrocyte network, which is subjected to stimuli conforming to the situation-driven environment. Three pools of stimuli patterns are considered: external patterns, patterns from the situation associative pool regularly presented to the network and learned by the network, and patterns already learned and remembered by astrocytes. Patterns from the external world are added to and removed from the associative pool. Then we show that astrocytes are structurally necessary for an effective function in such a learning and testing set-up. To demonstrate this we present a novel neuromorphic model for short-term memory implemented by a two-net spiking neural-astrocytic network. Our results show that such a system tested on synthesized data with selective astrocyte-induced modulation of neuronal activity provides an enhancement of retrieval quality in comparison to standard spiking neural networks trained via Hebbian plasticity only. We argue that the proposed set-up may offer a new way to analyze, model, and understand neuromorphic artificial intelligence systems.
Aortic aneurysms in pregnancy are rare but often fatal due to the natural physiologic changes of pregnancy and comorbidities specific to pregnancy, which increase the risk for aortic dissection and rupture. These physiologic changes are most pronounced in the third trimester and during the peripartum period, when approximately one third of dissections occur. In patients with known aortic aneurysms or conditions that make them prone to aortic aneurysms, preconception counseling can make pregnancy safer and more manageable. Aortic aneurysms diagnosed during pregnancy are usually due to underlying connective tissue diseases or aortopathies that have not been previously diagnosed. These women require multidisciplinary care including but not limited to obstetrics and gynecology, maternal fetal medicine, neonatology, cardiology, cardiothoracic surgery, cardiothoracic anesthesia, and genetics. Decisions include screening for dissection, when to proceed with surgical management, the best mode and timing for delivery, postpartum care, and contraception.
Upcoming vehicles introduce functions at the level of conditional automation where a driver no longer must supervise the system but must be able to take over the driving function when the system request it. This leads to the situation that the driver does not concentrate on the road but is reading mails for example. In this case, the driver is not able to take over the driving function immediately because she must first orient herself in the current traffic situation. In an urban scenario a situation that an automated vehicle is not able to steer further can arise quickly. To find suitable handover situations, data from traffic infrastructure systems, vehicles, and drivers is fused in a cloud-based situation to provide the hole traffic environment as base for the decision when the driving function should be transferred best and possibly even before a critical situation arises
Frailty refers to a loss of physiologic function that makes a person susceptible to disability from minor stresses. The frailty syndrome is a collection of symptoms primarily due to the aging-related loss and dysfunction of skeletal muscle and bone, that place older adults at increased levels of risk for disability, dependency, falls, injury, hospitalization, need for long term care, and mortality. Frailty is thought distinguishable from disability and comorbidity. Frailty always implies multisystem dysfunction. Severity of frailty range subclinical to a clinical stage to impending death. It is Important to recognize and treat the frailty syndrome before occurrence of any adverse outcomes. Fried's Phenotype screening tool identifies a person as being frail.