Depression and anxiety status among informal caregivers of patients with cancer treated at selected tertiary hospitals in Nepal.

Objective: Cancer is a major cause of death globally, and places a substantial burden on both patients and their caregivers. Frequent stress among caregivers often affects their mental well-being. This study was aimed at assessing anxiety and depression levels among informal caregivers of patients with cancer treated at selected tertiary hospitals in Nepal. An additional aim was to identify socio-demographic factors associated with these mental health outcomes. Methods: In this cross-sectional study, 383 informal caregivers were surveyed with the Hospital Anxiety and Depression Scale (HADS). Statistical analyses, including descriptive analysis and binary logistic regression, were conducted to explore associations with socio-demographic variables. Results: The mean age of participants was 36.1 ± 13.1 years, and 56.1% were 12-36 years old. Most participants were married (81.5%), were unemployed (66.6%), and had primary to secondary education (66.6%). The prevalence of moderate to severe anxiety (52%) and depression (45%) among caregivers was notable. Caregivers of patients treated at non-governmental hospitals were six times more likely (OR 6.3, 95% CI: [3.62-10.95], P = 0.001) to have anxiety and five times more likely (OR 5.3, 95% CI: [8.28-19.32], P = 0.001) to have depression. Conclusion: People who take care of patients with cancer in Nepal often feel substantial stress. Determining the causes of these feelings can aid in the creation of programs to support caregivers' mental health. Caring for mental well-being among caregivers is critical to achieving better cancer care and quality of life.

A performance comparison of heterostructure surface plasmon resonance biosensor for the diagnosis of novel coronavirus SARS-CoV-2.

This paper presents a performance comparison of heterostructure surface plasmon resonance (SPR) biosensors for the application of Novel Coronavirus SARS-CoV-2 diagnosis. The comparison is performed and compared with the existing literature based on the performance parameters in terms of several prisms such as BaF2, BK7, CaF2, CsF, SF6, and SiO2, several adhesion layers such as TiO2, Chromium, plasmonic metals such as Ag, Au, and two-dimensional (2D) transition metal dichalcogenides materials such as BP, Graphene, PtSe2 MoS2, MoSe2, WS2, WSe2. To study the performance of the heterostructure SPR sensor, the transfer matrix method is applied, and to analyses, the electric field intensity near the graphene-sensing layer contact, the finite-difference time-domain approach is utilized. Numerical results show that the heterostructure comprised of CaF2/TiO2/Ag/BP/Graphene/Sensing-layer has the best sensitivity and detection accuracy. The proposed sensor has an angle shift sensitivity of 390°/refractive index unit (RIU). Furthermore, the sensor achieved a detection accuracy of 0.464, a quality factor of 92.86/RIU, a figure of merit of 87.95, and a combined sensitive factor of 85.28. Furthermore, varied concentrations (0-1000 nM) of biomolecule binding interactions between ligands and analytes have been observed for the prospects of diagnosis of the SARS-CoV-2 virus. Results demonstrate that the proposed sensor is well suited for real-time and label-free detection particularly SARS-CoV-2 virus detection.

Recognition of human emotions using EEG signals: A review.

Assessment of the cognitive functions and state of clinical subjects is an important aspect of e-health care delivery, and in the development of novel human-machine interfaces. A subject can display a range of emotions that significantly influence cognition, and emotion classification through the analysis of physiological signals is a key means of detecting emotion. Electroencephalography (EEG) signals have become a common focus of such development compared to other physiological signals because EEG employs simple and subject-acceptable methods for obtaining data that can be used for emotion analysis. We have therefore reviewed published studies that have used EEG signal data to identify possible interconnections between emotion and brain activity. We then describe theoretical conceptualization of basic emotions, and interpret the prevailing techniques that have been adopted for feature extraction, selection, and classification. Finally, we have compared the outcomes of these recent studies and discussed the likely future directions and main challenges for researchers developing EEG-based emotion analysis methods.

Assessing Food Poverty, Vulnerability and Food Consumption Inequality in the Context of COVID-19: A Case of Bangladesh.

There is no denying the fact that, for a developing country like Bangladesh, the economic consequences of lockdown for containing COVID-19 pandemic can be far reaching affecting livelihoods of millions of households. Given that the share of food consumption expenditure to total expenditure is higher in the lower income groups of Bangladesh, this shock is expected to directly affect affordability of consumption of basic food items of these households. Using nationally representative household survey data of Bangladesh, and while following the Feasible Generalized Least Square method, this paper attempts to examine food poverty, food consumption inequality along with vulnerability to food poverty of households and explores the importance of different socio-demographic and environmental factors in this connection. Our estimation reflects that, greater percentage of households with young children or with elderly people are found to suffer high food vulnerability. In addition, households in environmentally endangered regions e.g. drought prone areas or river erosion affected places are more food vulnerable than those in other parts of the country. Certain occupation groups e.g. day labourer and self-employed are found to be highly vulnerable to food poverty while according to our decomposition analysis of food consumption inequality, area of residence (urban vs. rural) is expected to cause sizable inequality in food consumption. This study can therefore, help in identifying food vulnerable households for government's social protection programs and COVID-19 incentive packages, and thereby can contribute towards designing effective poverty reduction strategies.

Graphene-MoS2 with TiO2-SiO2 layers based surface plasmon resonance biosensor: Numerical development for formalin detection.

In this article, numerically a surface plasmon resonance (SPR) biosensor is developed based on Graphene-M O S 2 with TiO 2 -SiO 2 hybrid structure for the detection of formalin. Based on attenuated total reflection (ATR) method, we used angular interrogation technique to sense the presence the formalin by observing the change of "minimum reflectance with respect to SPR angle" and "maximum transmittance with respect to surface plasmon resonance frequency (SPRF)". Here, we used Chitosan as probe analyte to perform chemical reaction with formalin (formaldehyde) which is consider as target analyte. Simulation results show a negligible variation of SPRF and SPR angle for improper sensing of formalin that confirms absence of formalin whereas for proper sensing is considerably countable that confirms the presence of formalin. Thereafter, a comparison of sensitivity for different sensor structure is made. It is observed that the sensitivity without TiO2, SiO2, MoS2 and Graphene (conventional structure) is very poor and 73.67% whereas the sensitivity with graphene but without TiO2, SiO2 and MoS2 layers is 74.67% consistently better than the conventional structure. This is due to the electron loss of graphene, which is accompanying with the imaginary dielectric constant. Furthermore, the sensitivity without TiO2, SiO2 and graphene but with MoS2 layer is 79.167%. After more if both graphene and MoS2 are used and TiO2 and SiO2 layers are not used then sensitivity improves to 80.5%. This greater than before performance is due to the absorption ability and optical characteristics of graphene biomolecules and high fluorescence quenching ability of MoS2. Further again, if TiO2-SiO2 composite layer is used with the Graphene-MoS2 then the sensitivity enhances from 80.5% to 82.5%. Finally, the sensitivity for the proposed structure has been carried out, and result is 82.83%, the highest value among all the previous structures to integrate the advantages of graphene, MoS2, TiO2 and SiO2.