CNN-Based Cross-Modal Residual Network for Image Synthesis

This study attempts to address the issue that present cross-modal image synthesis algorithms do not capture the spatial and structural information of human tissues effectively. As a consequence, the resulting photos include flaws including fuzzy edges and a poor signal-to-noise ratio. The authors offer a cross-sectional technique that combines residual modules with generative adversarial networks. The approach incorporates an enhanced residual initial module and attention mechanism into the generator network, reducing the number of parameters and improving the generator's feature learning capabilities. To boost discriminant performance, the discriminator employs a multiscale discriminator. A multilevel structural similarity loss is included in the loss function to improve picture contrast preservation. On the ADNI data set, the algorithm is compared to the mainstream algorithms. The experimental findings reveal that the synthetic PET image's MAE index has dropped while the SSIM and PSNR indexes have improved. The experimental findings suggest that the proposed model may maintain picture structural information while improving image quality in both visual and objective measures. The residue initial module and attention mechanism are employed to increase the generator's capacity for learning, while the multiscale discriminator is utilized to improve the model's discriminative performance. The enhanced method in this study can maintain the structure and contrast information of the picture, according to comparative experimental findings using the ADNI dataset. The produced picture is hence more aesthetically similar to the genuine print.

Cyclic GAN Model to Classify Breast Cancer Data for Pathological Healthcare Task

An algorithm framework based on CycleGAN and an upgraded dual-path network (DPN) is suggested to address the difficulties of uneven staining in pathological pictures and difficulty of discriminating benign from malignant cells. CycleGAN is used for color normalization in pathological pictures to tackle the problem of uneven staining. However, the resultant detection model is ineffective. By overlapping the images, the DPN uses the addition of small convolution, deconvolution, and attention mechanisms to enhance the model's ability to classify the texture features of pathological images on the BreaKHis dataset. The parameters that are taken into consideration for measuring the accuracy of the proposed model are false-positive rate, false-negative rate, recall, precision, and F1 score. Several experiments are carried out over the selected parameters, such as making comparisons between benign and malignant classification accuracy under different normalization methods, comparison of accuracy of image level and patient level using different CNN models, correlating the correctness of DPN68-A network with different deep learning models and other classification algorithms at all magnifications. The results thus obtained have proved that the proposed model DPN68-A network can effectively classify the benign and malignant breast cancer pathological images at various magnifications. The proposed model also is able to better assist the pathologists in diagnosing the patients by synthesizing the images of different magnifications in the clinical stage.

Development and validation of a structured feedback questionnaire from postgraduates on various elements of postgraduate medical curriculum

BACKGROUND

Medical Council of India, introduced the Post Graduate (PG) curriculum as 'Competency Based Medical Education' (CBME). Feedback from the end users is a vital step in curriculum evaluation. Therefore, the primary objective of this study was to develop and validate a Structured Feedback Questionnaire (SFQ) for postgraduates, encompassing all the components of the PG-CBME curriculum.

METHODS

SFQ was developed with 23 Likert based questions and four open ended questions. Content validation was done by Lawshe method. After getting institutional ethics clearance and informed consent, SFQ was administered to 121 final year PGs (response rate 100%). We performed Principal component analysis (PCA), Structural equation modeling (SEM), Chi squared test (χ2/df); goodness-of-fit index (GFI); adjusted GFI; comparative fit index (CFI) and root mean square error of approximation (RMSEA). Cronbach's alpha was done for estimating the internal consistency.

RESULTS

The validation resulted in a three-factor model comprising of "curriculum" (42.1%), "assessment" (28%), and "support" (18.5%). Chi squared test (χ2/df ratio) < 2, CFI (0.78), GFI (0.72) and RMSEA (0.09) indicated superior goodness of fit for the three-factor model for the sample data. All the extracted factors had good internal consistency of ≥0.9.

CONCLUSION

We believe that this 23 item SFQ is a valid and reliable tool which can be utilized for curriculum evaluation and thereby formulating recommendations to modify the existing curriculum wherever required, facilitating enriched program outcomes.

Combined effects of photodynamic therapy and irrigants in disinfection of root canals

In this study, the combined effects of photodynamic therapy and irrigants in eradicating common endodontic pathogens are evaluated. Roots of 80 extracted single rooted teeth are divided into 2 groups (1) mechanical flushing; (2) antibacterial irrigation. After cleaning and shaping, they are inoculated with either (A) Streptococcus mutans or (B) Enterococcus faecalis and incubated. They are again subdivided and either only irrigated or irrigated and lased. Dentin shavings are taken from root canal walls and cultured. Statistical analysis using One-Way ANOVA and Post-hoc tests are done. The combination eradicated both bacteria. Antibacterial irrigants controlled S. mutans better than PDT (p = 0.041). The combination of PDT and antibacterial irrigation proposed in this study can be used in all primary cases for thorough and reliable disinfection of root canals but may be highly effective in resistant cases like endodontic failures, as E. faecalis is prevalent in such cases.

Virtual Screening of Phytochemicals to Novel Target (HAT) Rtt109 in Pneumocystis Jirovecii using Bioinformatics Tools

INTRODUCTION

Pneumocystis jirovecii is a fungus that causes Pneumocystis pneumonia in HIV and other immunosuppressed patients. Treatment of Pneumocystis pneumonia with the currently available antifungals is challenging and associated with considerable adverse effects. There is a need to develop drugs against novel targets with minimal human toxicities. Histone Acetyl Transferase (HAT) Rtt109 is a potential therapeutic target in Pneumocystis jirovecii species. HAT is linked to transcription and is required to acetylate conserved lysine residues on histone proteins by transferring an acetyl group from acetyl CoA to form e-N-acetyl lysine. Therefore, inhibitors of HAT can be useful therapeutic options in Pneumocystis pneumonia.

AIM

To screen phytochemicals against (HAT) Rtt109 using bioinformatics tool.

MATERIALS AND METHODS

The tertiary structure of Pneumocystis jirovecii (HAT) Rtt109 was modeled by Homology Modeling. The ideal template for modeling was obtained by performing Psi BLAST of the protein sequence. Rtt109-AcCoA/Vps75 protein from Saccharomyces cerevisiae (PDB structure 3Q35) was chosen as the template. The target protein was modeled using Swiss Modeler and validated using Ramachandran plot and Errat 2. Comprehensive text mining was performed to identify phytochemical compounds with antipneumonia and fungicidal properties and these compounds were filtered based on Lipinski's Rule of 5. The chosen compounds were subjected to virtual screening against the target protein (HAT) Rtt109 using Molegro Virtual Docker 4.5. Osiris Property Explorer and Open Tox Server were used to predict ADME-T properties of the chosen phytochemicals.

RESULTS

Tertiary structure model of HAT Rtt 109 had a ProSA score of -6.57 and Errat 2 score of 87.34. Structure validation analysis by Ramachandran plot for the model revealed 97% of amino acids were in the favoured region. Of all the phytochemicals subjected to virtual screening against the target protein (HAT) Rtt109, baicalin exhibited highest binding affinity towards the target protein as indicated by the Molegro score of 130.68 and formed 16 H-bonds. The ADME-T property prediction revealed that baicalin was non-mutagenic, non-tumorigenic and had a drug likeness score of 0.87.

CONCLUSION

Baicalin has good binding with Rtt 109 in Pneumocystis jirovecii and can be considered as a novel and valuable treatment option for Pneumocystis pneumonia patients after subjecting it to invivo and invitro studies.

Comparison of the pattern, efficacy, and tolerability of self-medicated drugs in primary dysmenorrhea: a questionnaire based survey

Objective:

To compare the pattern, efficacy, and tolerability of self-medicated drugs and to assess the adequacy of their dose in primary dysmenorrhea (PD).

Materials and Methods:

A survey using a self-developed, validated, objective, and structured questionnaire as a tool was conducted among subjects with PD. Statistical analysis was carried out using Chi-square test and ANOVA with post-hoc Tuckey's test.

Results:

Out of 641 respondents, 42% were self-medicated. The pattern of drugs used was: Dicyclomine, an unknown drug, mefenamic acid, mefenamic acid + dicyclomine, and metamizole by 35%, 29%, 26%, 9%, and 1% of respondents, respectively. Mefenamic acid + dicyclomine, the combination was the most efficacious in comparison to other drugs in moderate to severe dysmenorrhea. There was better tolerability with mefenamic acid + dicyclomine group compared to other drugs. Sub-therapeutic doses were used by 86% of self-medicating respondents.

Conclusions:

The prevailing self-medication practices were inappropriate in a substantial proportion of women with inadequate knowledge regarding appropriate drug choice, therapeutic doses, and their associated side effects.