Prevalence and risk factors associated with undiagnosed diabetes in India: Insights from NFHS-5 national survey.

Background: Undiagnosed diabetes is a significant public health concern in India, considering the accumulative burden of diabetes and its long-term complications. We have estimated the prevalence and factors associated with undiagnosed diabetes in India. Methods: We used data from the fifth round of the National Family Health Survey (NFHS-5, 2019-21) to estimate undiagnosed diabetes prevalence aged under 50 (15-49) years. A log-binomial model with survey-adjusted Poisson regression was used to estimate the prevalence risk ratio (PR) between undiagnosed diabetes and various factors. Multinomial logistic regression analysis was performed to examine the factors associated with diagnosed diabetes (vs. healthy) and undiagnosed diabetes (vs. healthy). All the analyses were survey-weighted and stratified by gender and reported with 95% confidence intervals. Results: The prevalence of diabetes for individuals aged 15-49 years was found to be 4.90% (4.80 to 5.00%) from the NFHS-5. Among them, the proportion of individuals with undiagnosed diabetes was 24.82% (24.07 to 25.59%), with higher among males (28.82% (26.45 to 31.30%)) than females (24.22% (23.44 to 25.01%)). The overall prevalence of undiagnosed diabetes was 1.22% (1.18 to 1.26%), with a higher prevalence among males (1.60% (1.46 to 1.76%)) than females (1.17% (1.13 to 1.21%)). Individuals who are middle-aged (45-49), have a higher body mass index (BMI), and are in a lower wealth index group, or live in the southern regions of India are at a higher risk of being undiagnosed for diabetes. Conclusion: One in every four having diabetes is undiagnosed. The study highlights the need for public health interventions to improve diabetes screening and access to health care, particularly among middle-aged individuals, and those with higher BMI, as well as addressing lifestyle and dietary factors. The findings also reveal disparities in diabetes burden among population subgroups in India, underscoring the need for targeted efforts.

Study of EEG microstates in Parkinson's disease: a potential biomarker?

The spontaneous activity of the brain is dynamic even at rest and the deviation from this normal pattern of dynamics can lead to different pathological states. EEG microstate analysis of resting-state neuronal activity in Parkinson's disease (PD) could provide insight into altered brain dynamics of patients exhibiting dementia. Resting-state EEG microstate maps were derived from 128 channel EEG data in 20 PD without dementia (PDND), 18 PD with dementia (PDD) and 20 Healthy controls (CON) using Cartool and sLORETA softwares. Microstate map parameters including global explained variance, mean duration, frequency of occurrence (TF) and time coverage were compared statistically among the groups. Eight maps that explained 72% of the topographic variance were identified and only three maps differed significantly across the groups. TF of Map1 was lower in both PDND and PDD (p < 0.001) and that of Map3 (p = 0.02) in PDND compared to control. Cortical sources showed higher activation in precuneus, cuneus and superior parietal lobe (Threshold: Log-F = 1.74, p < 0.05) with maximum activity in the precuneus region (MNI co-ordinates: - 25, - 75, - 40; Log-F = 1.9) in PDND compared to control only for Map1. Lower TF of Map1 (prototypical microstate D) may potentially serve as a biomarker for PD with or without dementia whereas higher activation of precuneus, cuneus and superior parietal lobe at resting-state could favour signal processing, lack of which could be associated with dementia in Parkinson's disorder.

Neighborhood degree sum-based molecular descriptors of fractal and Cayley tree dendrimers.

Topological index is a connection between the chemical structure and the real number that remains invariant under graph isomorphism. In structure-property and structure-activity modeling, topological indices are considered as essential molecular descriptors to predict different physicochemical properties of molecule. Dendrimers are considered to be the most significant, commercially accessible basic components in nanotechnology. In this report, some neighborhood degree sum-based molecular descriptors are obtained for the fractal tree and the Cayley tree dendrimers. Neighborhood M-polynomial yields a family of topological indices for a molecular graph in less time compared to the usual computation from their definitions. Some indices are obtained using neighborhood M-polynomial approach. In addition, some multiplicative neighborhood degree sum-based molecular descriptors are evaluated for fractal and Cayley tree dendrimers. The graphical representations of the outcomes are presented. A comparative study of the findings with some well-known degree-based indices is performed. Usefulness of the descriptors in modeling different properties and activities is discussed.

On neighborhood Zagreb index of product graphs.

The properties and activities of chemicals are strongly related to their molecular structures. Topological indices defined on these molecular structures are capable to predict those properties and activities. In this article, a new topological index named as neighborhood Zagreb index (MN ) is presented. Here the chemical importance of the MN index is investigated and it is shown that the newly introduced index is useful in predicting physico-chemical properties with high accuracy compared to some well-established and often used indices. The isomer-discrimination ability of MN is also examined. To demonstrate how the computational formula of the novel index for chemical compounds is simple and convenient, the chemical structures of favipiravir and hydroxychloroquine are used. In addition, some explicit results for this index of different product graphs such as Cartesian, tensor and wreath product are derived. Some of these results are applied to obtain the MN index of some special structures.

Molecular Descriptors of Some Chemicals that Prevent COVID-19.

BACKGROUND: Topological index is a numerical molecular descriptor that plays an important role in structure-property/structure-activity modeling. A large number of works on multiplicative degree based indices have been developed. However, no attention is paid to investigating their chemical significance. Investigation of the chemical importance of such indices is needed. The computation of topological indices for different chemical structures and networks is a current topic of interest in mathematical chemistry. OBJECTIVE: The objective of the present work is to examine the usefulness of the multiplicative degree based indices in quantitative structure property/activity relationship modeling. In addition, we intend to compute the indices for some anti-COVID-19 chemicals. MATERIALS AND METHODS: The regression analysis for octane data set is performed using MATLAB and Excel to check the predictability of the indices. The sensitivity test is conducted to examine the isomer discrimination ability. To study the indices for chemical structures preventing COVID-19, different combinatorial computation methods are utilized. RESULTS AND DISCUSSION: The regression models governing the structural dependence of different properties and activities are derived. The supremacy of the indices as useful molecular descriptors compared to some well-known and most used descriptors is established. Explicit expressions of the indices for hydroxychloroquine, remdesivir (GS-5734) and theaflavin are obtained. CONCLUSION: As the indices are shown to have remarkable efficiency in quantitative structure property/activity relationship modeling and isomer discrimination, the outcomes can predict different properties and activities of the chemicals under consideration.

Assessing cardiovascular disease risk factor screening inequalities in India using Lot Quality Assurance Sampling.

BACKGROUND: Cardiovascular diseases (CVDs) are the leading cause of mortality in India. India has rolled out Comprehensive Primary Health Care (CPHC) reforms including population based screening for hypertension and diabetes, facilitated by frontline health workers. Our study assessed blood pressure and blood sugar coverage achieved by frontline workers using Lot Quality Assurance Sampling (LQAS). METHODS: LQAS Supervision Areas were defined as catchments covered by frontline workers in primary health centres in two districts each of Uttar Pradesh and Delhi. In each Area, 19 households for each of four sampling universes (males, females, Above Poverty Line (APL) and Below Poverty Line (BPL)) were visited using probability proportional to size sampling. Following written informed consent procedures, a short questionnaire was administered to individuals aged 30 or older using tablets related to screening for diabetes and hypertension. Using the LQAS hand tally method, coverage across Supervision Areas was determined. RESULTS: A sample of 2052 individuals was surveyed, median ages ranging from 42 to 45 years. Caste affiliation, education levels, and occupation varied by location; the sample was largely married and Hindu. Awareness of and interaction with frontline health workers was reported in Uttar Pradesh and mixed in Delhi. Greater coverage of CVD risk factor screening (especially blood pressure) was seen among females, as compared to males. No clear pattern of inequality was seen by poverty status; some SAs did not have adequate BPL samples. Overall, blood pressure and blood sugar screening coverage by frontline health workers fell short of targeted coverage levels at the aggregate level, but in all sites, at least one area was crossing this threshold level. CONCLUSION: CVD screening coverage levels at this early stage are low. More emphasis may be needed on reaching males. Sex and poverty related inequalities must be addressed by more closely studying the local context and models of service delivery where the threshold of screening is being met. LQAS is a pragmatic method for measuring program inequalities, in resource-constrained settings, although possibly not for spatially segregated population sub-groups.

Is Dementia in Parkinson' Disease Related to Chronic Stress, Anxiety, and Depression?

OBJECTIVES: Stress, anxiety, and depression are known to be associated with the development of neurodegenerative disorders through interactions with the underlying pathophysiology. We hypothesized that the presence of these symptoms contributes to cognitive disturbances and dementia in Parkinson's disease (PD). The present study aimed to investigate the levels of stress, anxiety, and depression in PD patients relative to healthy individuals. MATERIALS AND METHODS: Anxiety, stress, and depression levels were assessed using standardized questionnaires in PD without dementia (PDND, n = 30), PD with dementia (PDD, n = 28), and healthy controls (HC, n = 26). Arithmetic subtraction task was used as a stressor. Galvanic skin response, heart rate and salivary cortisol, and alpha-amylase were measured during baseline and after induced stress (arithmetic task). RESULTS: Acute anxiety, acute stress, and depression levels were significantly higher in PDND compared to HC, whereas both acute and chronic anxiety, stress, and depression levels were significantly higher in PDD compared to PDND and HC. Cortisol and alpha-amylase levels were significantly higher in PDND compared to HC during both baseline and postarithmetic task. Posttask levels of cortisol were lower in PDD compared to PDND. CONCLUSION: This study concludes that higher levels of salivary cortisol and alpha-amylase at baseline and poststress task with normal levels of chronic stress and anxiety were associated with no dementia in PD. Presence of higher levels of acute, chronic anxiety, and stress along with depression with lower cortisol reactivity to stressor suggests onset of dementia in Parkinson's patients.

Deactivation of default-mode network and early suppression of decision-making areas during retrieval period by high-arousing emotions improves performance in verbal working memory task.

Emotions affect many aspects of cognition (attention, decision-making, problem solving, conflict resolution, task switching, social cognition, etc.), but the cortical areas or networks through which these effects are achieved are still debatable. In the present study, the effect of emotion on cognition was studied in healthy young individuals (n = 56). Emotions were induced using high-arousing negative, positive, and low-arousing neutral pictures from the International Affective Picture System (IAPS). Sternberg's verbal working memory task was administered at baseline and after each emotion exposure, while high-density EEG was recorded. Cortical sources were calculated using sLORETA in the 500-ms window (for every 100 ms bin) before the response and were compared with baseline. Though the number of correct responses were comparable, reaction times after emotion exposure reduced significantly. Source analysis revealed significant deactivation of default mode network (DMN) areas as well as early deactivation of decision-making areas during Sternberg's task performed after both the negative and positive emotions. This early deactivation, much before the response was made, when compared with baseline suggests that tasks performed under high-arousing emotional states may help in making decisions earlier or faster. We conclude that the exposure to high-arousing emotional stimuli improves verbal working memory by helping in directing the attentional resources toward the task, thus decreasing the decision-making time and further suppressing the DMN areas.

Deficit in specific cognitive domains associated with dementia in Parkinson's disease.

Impairment in different cognitive domains such as executive functions, language, memory and visuospatial skills occur frequently in Parkinson disease (PD) leading to significant disability and deterioration in quality of life. Heterogeneity of cognitive impairment enhances risk of developing dementia as disease progress. The objective is to explore the pattern of cognitive impairment with reference to the affected domains in PD with or without dementia relative to healthy controls. In this study, 110 PD patients and 26 healthy control were categorized into groups using Mini Mental State Examination and Clinical Dementia Rating scores as PD without dementia (PDND, n = 65; MMSE score >24; CDR = 0-1), PD with dementia (PDD, n = 45; MMSE score ≤24; CDR = 0.5-3) and healthy control (HC, n = 26; MMSE score >26; CDR = 0). Both Patients and controls underwent individual assessments of working memory, semantic memory, attention, language, executive functions, psychomotor and visuospatial skills and dementia using different cognitive function tests. Findings revealed lower scores of word memory, attention, psychomotor speed, visuospatial skills and executive functions in PDD compared to PDND. Interestingly, in PDD scores of picture memory, semantic memory and language functions were comparable with PDND. Compared to HC, PDND had no impairment in working memory, attention and executive functions, whereas PDD had lower scores in all the cognitive domains tested. Results indicate that the deficits in word memory, attention, psychomotor speed, visuospatial skills and executive functions distinguishes PDD from PDND. Impairment in specific cognitive domains may be a biomarker for predicting onset of dementia in Parkinson's disease.

The F-coindex of some graph operations.

The F-index of a graph is defined as the sum of cubes of the vertex degrees of the graph. In this paper, we introduce a new invariant which is named as F-coindex. Here, we study basic mathematical properties and the behavior of the newly introduced F-coindex under several graph operations such as union, join, Cartesian product, composition, tensor product, strong product, corona product, disjunction, symmetric difference of graphs and hence apply our results to find the F-coindex of different chemically interesting molecular graphs and nano-structures.