3D Bioprinting: A Systematic Review for Future Research Direction.

Purpose: 3D bioprinting is capable of rapidly producing small-scale human-based tissue models, or organoids, for pathology modeling, diagnostics, and drug development. With the use of 3D bioprinting technology, 3D functional complex tissue can be created by combining biocompatible materials, cells, and growth factor. In today's world, 3D bioprinting may be the best solution for meeting the demand for organ transplantation. It is essential to examine the existing literature with the objective to identify the future trend in terms of application of 3D bioprinting, different bioprinting techniques, and selected tissues by the researchers, it is very important to examine the existing literature. To find trends in 3D bioprinting research, this work conducted an systematic literature review of 3D bioprinting. Methodology: This literature provides a thorough study and analysis of research articles on bioprinting from 2000 to 2022 that were extracted from the Scopus database. The articles selected for analysis were classified according to the year of publication, articles and publishers, nation, authors who are working in bioprinting area, universities, biomaterial used, and targeted applications. Findings: The top nations, universities, journals, publishers, and writers in this field were picked out after analyzing research publications on bioprinting. During this study, the research themes and research trends were also identified. Furthermore, it has been observed that there is a need for additional research in this domain for the development of bioink and their properties that can guide practitioners and researchers while selecting appropriate combinations of biomaterials to obtain bioink suitable for mimicking human tissue. Significance of the Research: This research includes research findings, recommendations, and observations for bioprinting researchers and practitioners. This article lists significant research gaps, future research directions, and potential application areas for bioprinting. Novelty: The review conducted here is mainly focused on the process of collecting, organizing, capturing, evaluating, and analyzing data to give a deeper understanding of bioprinting and to identify potential future research trends.

Molecular morphology & interactions, functional properties, rheology and in vitro digestibility of ultrasonically modified pearl millet and sorghum starches.

The present research investigated to study the effect of ultrasound treatment on isolated pearl millet starch (PMS) and sorghum starch (SS). Ultrasonication was applied to PMS and SS for 10, 15, and 20 min. Ultrasonically modified pearl millet and sorghum starches evaluated for their techno-functionality, pasting profile, morphology, in vitro starch digestibility, XRD, and molecular interactions. Ultrasound treatment increased water and oil absorption capacity, swelling power, and solubility with treatment time. For ultrasonicated PMS and SS, a significant increase (p < 0.05) in paste clarity (PC) (70.05 % and 67.23 %), freeze-thawing stability (FTS), gel consistency (GC) (25.05 mm and 32.95 mm), and in vitro starch digestibility were observed (57.70 g/100 g and 50.29 g/100 g), whereas no significant changes were recorded for the color values after the ultrasound treatment. Variations in pasting property were also observed in ultrasonicated starches with treatment duration. SEM images confirmed ultrasonication mainly forms pores and indentations on starch granule surface. FTIR spectra and X-ray diffractogram for ultrasonicated starches revealed a slight decrease in the peak intensity and A-type X-ray pattern with lower relative crystallinity (RC) than the native starches. G' > G″ value, indicating the elastic behavior and lower tan δ value, depicting viscous behavior and high gel strength.

A Comprehensive Analysis of Deep Learning-Based Approaches for Prediction and Prognosis of Infectious Diseases.

Artificial intelligence is the most powerful and promising tool for the present analytic technologies. It can provide real-time insights into disease spread and predict new pandemic epicenters by processing massive amount of data. The main aim of the paper is to detect and classify multiple infectious diseases using deep learning models. The work is conducted by using 29,252 images of COVID-19, Middle East Respiratory Syndrome Coronavirus, Pneumonia, normal, Severe Acute Respiratory Syndrome, tuberculosis, viral pneumonia, and lung opacity which has been collected from various disease datasets. These datasets are used to train the deep learning models such as EfficientNetB0, EfficientNetB1, EfficientNetB2, EfficientNetB3, NASNetLarge, DenseNet169, ResNet152V2, and InceptionResNetV2. The images have been initially graphically represented using exploratory data analysis to study the pixel intensity and find anomalies by extracting the color channels in an RGB histogram. Later, the dataset has been pre-processed to remove noisy signals using image augmentation and contrast enhancement techniques. Further, feature extraction techniques such as morphological values of contour features and Otsu thresholding have been applied to extract the feature. The models have been evaluated on the basis of various parameters, and it has been discovered that during the testing phase, the InceptionResNetV2 model generated the highest accuracy of 88%, best loss value of 0.399, and root mean square error of 0.63.

Stress in Caregivers of Stroke Patients During Rehabilitation: An Observational Study.

INTRODUCTION: Stroke causes a high burden of death and disability all over the world. The majority of stroke survivors continue to have difficulties, and their families must shoulder a considerable portion of the expenditures of ongoing rehabilitation and long-term care. In India, stroke rehabilitation is still underachieved due to various reasons leading to delay or incomplete recovery of the patients thus adding up more burden on the caregivers. Thus, studying the caregiver burden of stroke rehabilitation will help policymakers tackle this issue faced by our lower economically challenged citizens. OBJECTIVES: The objective is to measure the perceived burden on caregivers during stroke rehabilitation. METHODS AND MATERIALS: The observational study was conducted by interviewing the stroke survivors' caregivers and visiting the physiotherapy OPD using the caregiver burden scale/questionnaire. RESULTS: The study had 76 caregivers, 51.32% were women and 48.68% were men. The average age for caregivers was 42 years and 55 years for patients. The average duration of giving care was six months. The perceived caregiver burden score was low (mean-19.61) suggesting that not all assistance is associated with stress. The correlation of each burden measure with Modified Rankin Scale for disability is significantly correlated (r=0.7, P<0.0001). Further investigation revealed that caregivers had considerably higher levels of stress when the patient needed to exercise, walk or use the restroom. A low yearly income, a higher secondary education, and a small number of family members were shown to be connected with individuals who scored the highest on stress. CONCLUSION: Based on this study, we conclude that people with low income residing in nuclear families require more support for caregiving during rehabilitation. We recommend that health and welfare policy measures be developed to lessen caregiver burden in order to improve caregivers' post-stroke experiences.

People's Perspective on Out-of-Pocket Expenditure for Healthcare: A Qualitative Study From Pune, India.

Background Out-Of-Pocket Expenditure (OOPE) directly reflects the burden of health expenses that households bear. Despite the availability of social security schemes providing healthcare benefits, a high proportion of Indian households are still incurring OOPE. In order to recognize the reasons behind OOPE, a comprehensive understanding of people's attitudes and behavior is needed. Methodology By purposive sampling, 16 in-depth interviews were conducted using an interview guide in the catchment area of urban and rural health centers of a tertiary healthcare hospital. Interviews were conducted in Marathi and Hindi and were audio tape-recorded after taking informed consent. The interviews were transcribed and translated into English, followed by a thematic analysis. Results Although most participants knew that government hospitals provide facilities and experienced doctors, inconvenience and unsatisfactory quality deter them from utilizing government facilities. A few had experiences with government schemes; almost all concur that the formality and procedure of claiming insurance are cumbersome and all have had bad experiences. Cost of medications and consultation accounted for the majority of the healthcare expenditures. While some participants had benefitted from insurance, few regretted not enrolling in one. Conclusion The awareness regarding government schemes was derisory. Government-financed health insurance schemes and their utilization are crucial to reducing OOPE. Efforts should be made to increase accessibility to public healthcare services. Nevertheless, there is potential to redress the barriers to improve scheme utilization.

Spectrum of histopathological changes and its quantification using a scoring system in patients with gastroesophageal reflux disease.

Aim: The aim of this study was to evaluate the role of histopathological and histomorphometric features in oesophageal biopsy of patients presenting with symptoms of Gastroesophageal Reflux Disease (GERD). Material and Methods: Present study included 42 patients and 12 controls. Complete clinical evaluation followed by endoscopic examination of the patients was done and multipleoesophageal biopsies were taken. Biopsies were processed routinely and stained with Hematoxylin and Eosin and examined for any changes related to GERD. Morphometric assessment was done by using Leitz optical micrometer. The histological scoring was done based on the parameters: basal cell hyperplasia, stromal papillae elongation, cells with irregular nuclear contour (CINC), eosinophilic infiltrate, gastric and intestinal metaplasia. A numerical score was assigned to each parameter and sum of these scores represented the total score. Statistics: The statistical analysis was done using graph pad prism, Medcalc software and Windows MS office. P value and mean standard deviation (SD) was calculated. Results: The endoscopic findings of all the controls and 83.33% of patients were normal. Only 16.67% of patients had reflux associated changes of varying grades on endoscopy. Oesophageal biopsy of all patients had changes related to GERD on histology. Immunohistochemistry confirmed that cells with irregular nuclear contour were T- lymphocytes. The mean (SD) histological scoring of control and patients were 1.75 (0.62) and 5.66 (1.31) respectively. The difference was considered to be statistically significant (P < 0.001). Thus, it was suggested that a cut-off of histological score > 3 can be used to indicate GERD. Conclusion: Patients with gastroesophageal reflux symptoms can have normal endoscopic findings but can be diagnosed on the basis of histological changes in the squamous epithelium. Scoring of the histopathological parameters along with the cut-off value can give a definitive diagnosis of GERD.

Extracellular Vesicle-Derived DNA vs. CfDNA as a Biomarker for the Detection of Colon Cancer.

Liquid biopsy has emerged as a promising non-invasive way to diagnose tumor and monitor its progression. Different types of liquid biopsies have different advantages and limitations. In the present research, we compared the use of two types of liquid biopsy, extracellular vesicle-derived DNA (EV-DNA) and cell-free DNA (cfDNA) for identifying tumor mutations in patients with colon carcinoma. METHOD: DNA was extracted from the tumor tissue of 33 patients diagnosed with colon carcinoma. Targeted NGS panel, based on the hotspots panel, was used to identify tumor mutations. Pre-surgery serum and plasma were taken from the patients in which mutation was found in the tumor tissue. Extracellular vesicles were isolated from the serum followed by the extraction of EV-DNA. CfDNA was extracted from the plasma. The mutations found in the tumor were used to detect the circulating tumor DNA using ultra-deep sequencing. We compared the sensitivity of mutation detection and allele frequency obtained in EV-DNA and cfDNA. RESULTS: The sensitivity of mutation detection in EV-DNA and cfDNA was 61.90% and 66.67%, respectively. We obtained almost identical sensitivity of mutation detection in EV-DNA and cfDNA in each of the four stages of colon carcinoma. The total DNA concentration and number mutant copies were higher in cfDNA vs. EV-DNA (p value = 0.002 and 0.003, respectively). CONCLUSION: Both cfDNA and EV-DNA can serve as tumor biomarkers. The use of EV-DNA did not lead to improved sensitivity or better detection of tumor DNA in the circulation.

Artificial intelligence-based classification of schizophrenia: A high density electroencephalographic and support vector machine study.

BACKGROUND: Interview-based schizophrenia (SCZ) diagnostic methods are not completely valid. Moreover, SCZ-the disease entity is very heterogeneous. Supervised-Machine-Learning (sML) application of Artificial-Intelligence holds a tremendous promise in solving these issues. AIMS: To sML-based discriminating validity of resting-state electroencephalographic (EEG) quantitative features in classifying SCZ from healthy and, positive (PS) and negative symptom (NS) subgroups, using a high-density recording. SETTINGS AND DESIGN: Data collected at a tertiary care mental-health institute using a cross-sectional study design and analyzed at a premier Engineering Institute. MATERIALS AND METHODS: Data of 38-SCZ patients and 20-healthy controls were retrieved. The positive-negative subgroup classification was done using Positive and Negative Syndrome Scale operational-criteria. EEG was recorded using 256-channel high-density equipment. Eight priori regions-of-interest were selected. Six-level wavelet decomposition and Kernel-Support Vector Machine (SVM) method were used for feature extraction and data classification. STATISTICAL ANALYSIS: Mann-Whitney test was used for comparison of machine learning-features. Accuracy, sensitivity, specificity, and area under receiver operating characteristics-curve were measured as discriminatory indices of classifications. RESULTS: Accuracy of classifying SCZ from healthy and PS from NS SCZ, were 78.95% and 89.29%, respectively. While beta and gamma frequency related features most accurately classified SCZ from healthy controls, delta and theta frequency related features most accurately classified positive from negative SCZ. Inferior frontal gyrus features most accurately contributed to both the classificatory instances. CONCLUSIONS: SVM-based classification and sub-classification of SCZ using EEG data is optimal and might help in improving the "validity" and reducing the "heterogeneity" in the diagnosis of SCZ. These results might only be generalized to acute and moderately ill male SCZ patients.

An ovarian spheroid based tumor model that represents vascularized tumors and enables the investigation of nanomedicine therapeutics.

The failure of cancer therapies in clinical settings is often attributed to the lack of a relevant tumor model and pathological heterogeneity across tumor types in the clinic. The objective of this study was to develop a robust in vivo tumor model that better represents clinical tumors for the evaluation of anti-cancer therapies. We successfully developed a simple mouse tumor model based on 3D cell culture by injecting a single spheroid and compared it to a tumor model routinely used by injecting cell suspension from 2D monolayer cell culture. We further characterized both tumors with cellular markers for the presence of myofibroblasts, pericytes, endothelial cells and extracellular matrix to understand the role of the tumor microenvironment. We further investigated the effect of chemotherapy (doxorubicin), nanomedicine (Doxil®), biological therapy (Avastin®) and their combination. Our results showed that the substantial blood vasculature in the 3D spheroid model enhances the delivery of Doxil® by 2.5-fold as compared to the 2D model. Taken together, our data suggest that the 3D tumors created by simple subcutaneous spheroid injection represents a robust and more vascular murine tumor model which is a clinically relevant platform to test anti-cancer therapy in solid tumors.