BackgroundIdentification of prior mental events of suicide attempts has immense importance in suicide prevention. However, it has not been studied in Bangladesh as there was no available psychometrically valid instrument measuring it.ObjectivesWe aimed to test the psychometric properties of the interpersonal needs questionnaire (INQ-15) and acquired capability for suicide scale-fearlessness about death (ACSS-FAD) in Bangla along with the determination of the level of thwarted belongingness, perceived burdensomeness, and acquired capability for suicide.Materials and methodsWe collected data between 29 March and 14 April 2022 from 1,207 students of medical colleges and universities in Bangladesh by Google form. We assessed the psychometric properties of Bangla INQ and ACSS-FAD scales and examined factors associated with thwarted belongingness, perceived burdensomeness, and acquired capability for suicide.ResultsThe mean age of the participants was 22.82 ± 1.68 (range 18–29) years, 51% were females, 84% were graduate students, and 92% were unmarried. Both of the scales revealed acceptable levels of reliability. Confirmatory factor analysis revealed a two-factor structure of Bangla INQ after dropping three items from thwarted belongingness domain (item 9, 11, and 12) and a single factor structure for Bangla ACSS-FAD after dropping three items (item 1, 4, and 6). Perceived burdensomeness was significantly higher in females, students with a history of mental illness, family history of suicide, and the history of suicidal attempts. Fearlessness about death was significantly higher among females, non-Muslim participants, and history of suicidal attempts.ConclusionThe current study revealed psychometric properties of two suicide scales (INQ and ACSS-FAD) in Bangla that can be used in subsequent studies. Prevention strategies targeting to females, persons with psychiatric disorder, history of previous attempt(s) should be prioritized specially among the young age group.
Microfluidic devices have been widely used in mechanical, biomedical, chemical, and materials research. As a result, it is becoming increasingly important to have a cheap, fast, and reliable method for rapid microfabrication. However, prototyping of microfluidic devices typically suffers from the high initial cost, low resolution, rough surface finish, and long turn-around time. Here we present a strategic approach to closed-loop control of deterministic fabrication process based on in-situ image analysis called image-guided in-situ maskless lithography (IGIs-ML). Using the closed-loop control along with flush-flow functionality and leveraging the swelling behavior of the photocurable polymer, we demonstrate the fabrication of sub-micron high aspect ratio channels (800 nm width and >10 µm height) close to the light diffraction limit. This outperforms any reported rapid prototyping platforms which can typically reach tens of µm in channel width. Such dimensional capability is even comparable to some of the most advanced fabrication methods currently available. Dynamic image analysis simultaneously provides superior repeatability of densely packed patterns across a large area and multiple devices. A general and robust approach is established to fabricate a wide variety of microfluidic devices. This resolves the critical over-curing issues and size limitations in rapid prototyping of microfluidic devices, enabling affordable and reliable exploration of design space beyond the resolution of traditional photolithography.
The resistance of the material to a change of its color characteristics during exposure to sunlight, rubbing and washing as domestic and laundry and other various ways are referred to as color fastness of dyes or pigments. In this research, 100% cotton and blended fabrics were dyed with fluorescent pigments i.e. Shining Flu Pink-F17 and Papillion Orange-FGRN in exhaust dyeing method. The improvement of color fastness properties, i.e. color fastness to washing, rubbing, perspiration and light were observed with the treatment of using antioxidants and UV-absorbers. There were eight samples of dyed fabrics (Four samples of 100% cotton knit fabric and four samples of 60/40 cottonpolyester blended fabrics) treated with 1% (v/v) of antioxidants i.e. Gallic acid, L-Ascorbic acid and UV absorbers i.e. 2-hydroxy-4 methoxy-benzophenone, 4-4 dimethoxy-benzophenone respectively. The treatment of antioxidant L-Ascorbic acid and UV absorber 4-4 dimethoxy-benzophenone provides satisfactory improvement of fastness properties than other antioxidants and UV absorbers. The results were mainly interpreted in terms of color strength, visual assessment of evenness and fastness ratings.
Objectives. Assessment of suicide cognition would help to measure the enduring suicide risk and to predict the risk of a suicide attempt. However, no previous attempt was identified to validate the suicide cognition scale in Bangla. We aimed to assess the psychometric properties of the Brief Suicide Cognitions Scale (BSCS) in Bangla. Methods. We conducted this validation study among 529 medical and university students. We collected the responses by Google Forms with the translated version of BSCS from 20 August to 20 October 2022. We assessed internal consistency form of reliability, face validity, content validity, construct validity, concurrent validity, and discriminant validity. Results. The mean age of the respondents was 23.32 ± 1.73 years; 52.5% were males, 92% were single, 75% were undergraduate students, 40.24% were studying in medical schools, 18.53% had a chronic illness, 9.45% had a mental illness, 4.16% had a family history of suicide, and 11.15% had previous nonfatal attempts. Cronbach’s alpha was 0.84, and factor analysis revealed unidimensional construct with six items with a good model fit. The BSCS showed acceptable convergent and discriminant validity. Conclusion. This study assessed the psychometric properties of Bangla BSCS among students which found acceptable reliability and validity. Further studies could test the validation especially among clinical samples to assess the predictive validity of the instrument.
Hematopoietic stem cells (HSCs) and multipotent progenitors (MPPs) are crucial for maintaining lifelong hematopoiesis. Developing methods to distinguish stem cells from other progenitors and evaluate stem cell functions has been a central task in stem cell research. Deep learning has been demonstrated as a powerful tool in cell image analysis and classification. In this study, we explored the possibility of using deep learning to differentiate HSCs and MPPs based on their light microscopy (DIC) images. After extensive training and validation with large image data sets, we successfully develop a three-class classifier (we named it LSM model) that reliably differentiate long-term HSCs (LT-HSCs), short-term HSCs (ST-HSCs), and MPPs. Importantly, we demonstrated that our LSM model achieved its differentiating capability by learning the intrinsic morphological features from cell images. Furthermore, we showed that the performance of our LSM model was not affected by how these cells were identified and isolated, i.e., sorted by surface markers or intracellular GFP markers. Prospective identification of HSCs and MPPs in Evi1GFP transgenic mice by our LSM model suggested that the cells with the highest GFP expression were LT-HSCs, and this prediction was substantiated later by a long-term competitive reconstitution assay. Moreover, based on DIC image data sets, we also successfully built another two-class classifier that can effectively distinguish aged HSCs from young HSCs, which both express same surface markers but functionally different. This finding is of particular interest since it may provide a novel quick and efficient approach, obviating the need for a time-consuming transplantation experiment, to evaluate functional states of HSCs. Together, our study provides evidence for the first time that HSCs and MPPs can be differentiated by deep learning based on cell morphology. This novel and robust deep-learning-based platform will provide a basis for the future development of a new-generation stem cell identification and separation system. It may also provide new insight into molecular mechanism underlying self-renewal feature of stem cell.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.