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Yanling Jin,1,* Nitika Sanger,2,* Ieta Shams,3,* Candice Luo,4,* Hamnah Shahid,5,* Guowei Li,1,* Meha Bhatt,1 Laura Zielinski,6 Bianca Bantoto,7 Mei Wang,1 Luciana PF Abbade,8 Ikunna Nwosu,4 Alvin Leenus,1 Lawrence Mbuagbaw,1 Muhammad Maaz,1 Yaping Chang,1 Guangwen Sun,1 Mitchell AH Levine,1,9 Jonathan D Adachi1,9 Lehana Thabane,1,9 Zainab Samaan1,10 1Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, ON, Canada; 2Department of Medical Science, Medical Sciences Graduate Program, McMaster University, Hamilton, ON, Canada; 3Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, ON, Canada; 4Faculty of Health Sciences, Bachelors of Health Sciences, McMaster University, Hamilton, ON, Canada; 5Department of Arts and Science, McMaster University, Hamilton, ON, Canada; 6Department of Neuroscience, McMaster Integrative Neuroscience Discovery and Study, McMaster University, Hamilton, ON, Canada; 7Department of Science, Honours Integrated Sciences Program, McMaster University, Hamilton, ON, Canada; 8Department of Dermatology and Radiotherapy, Botucatu Medical School, Universidade Estadual Paulista, UNESP, São Paulo, Brazil; 9St. Joseph’s Healthcare Hamilton, Hamilton, ON, Canada; 10Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada *These authors contributed equally to this work Purpose: Reporting guidelines (eg, Consolidated Standards of Reporting Trials [CONSORT] statement) are intended to improve reporting standards and enhance the transparency and reproducibility of research findings. Despite accessibility of such guidelines, researchers are not required to adhere to them. Our goal was to determine the current status of reporting quality in the medical literature and examine whether adherence of reporting guidelines has improved since the inception of reporting guidelines. Materials and methods: Eight reporting guidelines, such as CONSORT, Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), STrengthening the Reporting of OBservational studies in Epidemiology (STROBE), Quality of Reporting of Meta-analysis (QUOROM), STAndards for Reporting of Diagnostic accuracy (STARD), Animal Research: Reporting In Vivo Experiments (ARRIVE), Consolidated Health Economic Evaluation Reporting Standards (CHEERS), and Meta-analysis of Observational Studies in Epidemiology (MOOSE) were examined. Our inclusion criteria included reviews published between January 1996 to September 2016 which investigated the adherence to reporting guidelines in the literature that addressed clinical trials, systematic reviews, observational studies, meta-analysis, diagnostic accuracy, economic evaluations, and preclinical animal studies that were in English. All reviews were found on Web of Science, Excerpta Medical Database (EMBASE), MEDLINE, and Cumulative Index to Nursing and Allied Health Literature (CINAHL). Results: Among the general searching of 26,819 studies by using the designed searching method, 124 studies were included post screening. We found that 87.9% of the included studies reported suboptimal adherence to reporting guidelines. Factors associated with poor adherence included non-pharmacological interventions, year of publication, and trials concluding with significant results. Improved adherence was associated with better study designs such as allocation concealment, random sequence, large sample sizes, adequately powered studies, multiple authorships, and being published in journals endorsing guidelines. Conclusion: We conclude that the level of adherence to reporting guidelines remains suboptimal. Endorsement of reporting guidelines by journals is important and recommended. Keywords: guidelines, adherence, review, CONSORT

Simple Summary: Worldwide gastrointestinal (GI) malignancies account for about 25% of the global cancer incidence. For some malignancies, screening programs, such as routine colon cancer screenings, have largely aided in the early diagnosis of those at risk. However, even after diagnosis, many GI malignancies lack robust biomarkers to serve as definitive staging and prognostic tools to aid in clinical decision-making. Radiomics uses high-throughput data to extract various features from medical images with the potential to aid personalized precision medicine. Machine learning is a technique for analyzing and predicting by learning from sample data, finding patterns in it, and applying it to new data. We reviewed the fundamental concepts of radiomics such as imaging data acquisition, lesion segmentation, feature design, and interpretation specific to GI cancer studies and assessed the clinical applications of radiomics and machine learning in diagnosis, staging, evaluation of tumor prognosis, and treatment response. Gastrointestinal (GI) cancers, consisting of a wide spectrum of pathologies, have become a prominent health issue globally. Despite medical imaging playing a crucial role in the clinical workflow of cancers, standard evaluation of different imaging modalities may provide limited information. Accurate tumor detection, characterization, and monitoring remain a challenge. Progress in quantitative imaging analysis techniques resulted in "radiomics", a promising methodical tool that helps to personalize diagnosis and treatment optimization. Radiomics, a sub-field of computer vision analysis, is a bourgeoning area of interest, especially in this era of precision medicine. In the field of oncology, radiomics has been described as a tool to aid in the diagnosis, classification, and categorization of malignancies and to predict outcomes using various endpoints. In addition, machine learning is a technique for analyzing and predicting by learning from sample data, finding patterns in it, and applying it to new data. Machine learning has been increasingly applied in this field, where it is being studied in image diagnosis. This review assesses the current landscape of radiomics and methodological processes in GI cancers (including gastric, colorectal, liver, pancreatic, neuroendocrine, GI stromal, and rectal cancers). We explain in a stepwise fashion the process from data acquisition and curation to segmentation and feature extraction. Furthermore, the applications of radiomics for diagnosis, staging, assessment of tumor prognosis and treatment response according to different GI cancer types are explored. Finally, we discussed the existing challenges and limitations of radiomics in abdominal cancers and investigate future opportunities. [ABSTRACT FROM AUTHOR]

Skin cancer is a significant health concern worldwide, and early and accurate diagnosis plays a crucial role in improving patient outcomes. In recent years, deep learning models have shown remarkable success in various computer vision tasks, including image classification. In this research study, we introduce an approach for skin cancer classification using vision transformer, a state-of-the-art deep learning architecture that has demonstrated exceptional performance in diverse image analysis tasks. The study utilizes the HAM10000 dataset; a publicly available dataset comprising 10,015 skin lesion images classified into two categories: benign (6705 images) and malignant (3310 images). This dataset consists of high-resolution images captured using dermatoscopes and carefully annotated by expert dermatologists. Preprocessing techniques, such as normalization and augmentation, are applied to enhance the robustness and generalization of the model. The vision transformer architecture is adapted to the skin cancer classification task. The model leverages the self-attention mechanism to capture intricate spatial dependencies and long-range dependencies within the images, enabling it to effectively learn relevant features for accurate classification. Segment Anything Model (SAM) is employed to segment the cancerous areas from the images; achieving an IOU of 96.01% and Dice coefficient of 98.14% and then various pretrained models are used for classification using vision transformer architecture. Extensive experiments and evaluations are conducted to assess the performance of our approach. The results demonstrate the superiority of the vision transformer model over traditional deep learning architectures in skin cancer classification in general with some exceptions. Upon experimenting on six different models, ViT-Google, ViT-MAE, ViT-ResNet50, ViT-VAN, ViT-BEiT, and ViT-DiT, we found out that the ML approach achieves 96.15% accuracy using Google’s ViT patch-32 model with a low false negative ratio on the test dataset, showcasing its potential as an effective tool for aiding dermatologists in the diagnosis of skin cancer.

Ovarian cancer is the second most fatal gynecological cancer. For the last decade or so significant use of non-circulating and circulating biomarkers has been highlighted. However, the study of such biomarkers at nanovesicle technology such as exosomes, proteomic and genomics studies could further contribute to better identification of anomalous protein and networks which could act as potential targets for biomarker and immunotherapy development. This review provides an overview of the circulating and non-circulating biomarkers with the aim of addressing the current challenges and potential biomarkers that could lead to early ovarian cancer diagnosis and better management. By means of this review we also lay a hypothesis that characterization of exosomal protein, nucleic acid content from body fluids (serum, plasma, urine, etc.) can decode the secret of disease and potentially improve diagnostic sensitivity which could further lead to more effective screening and early detection of the disease.

Transarterial embolization with chemotherapy and radiation is well-documented forms of treatment for liver cancers but reflux of embolic particles to nontarget tissues can result in unintended consequences such as gastrointestinal ulceration. Traditionally, operators have used coil embolization of hepatoenteric collaterals to prevent reflux. Antireflux microcatheters that contain expandable baskets (Surefire) or inflatable balloons have recently been developed as tools to avoid these side effects. We describe cases where antireflux catheters were used instead of coil embolization. Using antireflux catheters, we eliminated particle reflux into nontarget vessels. We also review the literature on antireflux catheters involved in preventing reflux during chemo- and radioembolization.

This Research was conducted to investigate the effect of fertilizer management on growth and yield performance of aromatic fine rice varieties. The experiment consisted of two factors were aromatic fine rice and fertilizer management. There were four varieties namely Kalizira, Kataribhog, Tulshimala and BRRI Dhan34 with four fertilizer treatments recommended dose of fertilizers (T1), cowdung @ 10 tha-1 (T2), 50% of recommended dose of fertilizers + 50% cowdung(T3), 75% of recommended dose of fertilizers + 50% cowdung (T4). The result showed that BRRI Dhan 34 significantly superior for effective tillers number/ hill (18.46), panicles length (26.67cm), grains number/ panicle (146.30), harvest index (40.73 %), grain protein content (6.23%), grain yield (2.79 tha-1), straw yield (4.06 tha-1), and biological yield (6.85 tha-1). Among fertilizer management, the highest effective tillers number/hill (16.20), grains number/ panicle (142.45, panicles length (26.66 cm),1000 grain weight (13.75), grain protein content (10.2%), biological yield (6.30), were obtained from T3 treatment. Based on interaction effect showed that the highest effective tillers number/hill (18.36), grains number/ panicle (155.33), panicles length (26.73 cm), grain protein content (10.80%), biological yield (7.85) was found with BRRI Dhan 34 combined with T3 treatment. However, grain yield increased with the increase in nitrogen levels. Together tillers number/hill, grains number/panicle, grain protein content, harvest index, grain yield was the main responsible yield contributing characters to improve the yield quality of aromatic fine rice.

Zinc sulfide (ZnS) nanoparticles were fabricated using the chemical precipitation method. The X-ray diffraction (XRD), Raman spectroscopy, and scanning electron microscopy (SEM) techniques were used to investigate the structural parameters of the formed ZnS. The hexagonal crystal structure of the Zn and ZnS phases was formed. The average crystallite size of the ZnS phase is 10.3 nm, which is much smaller than that of the Zn phase (54.5 nm). Several frequencies and phonon modes were detected in the Raman scattering spectrum belonging to the ZnS nanoparticles. The synthesized ZnS nanoparticles were used as catalysts to eliminate the Congo red (CR) dye, with different concentrations, from synthetic wastewater. The impact of the CR dye concentration and shaking period on the adsorption of CR was thoroughly investigated, and various adsorption kinetic models were tested. After 3 h of shaking, the adsorption efficiency reached 26.01% for 40 mg/L CR dye and 27.84% for 20 mg/L CR dye. The adsorption capacities of the CR dye in the presence of ZnS are 16% and 9% for 40 and 20 mg/L, respectively. Based on the correlation factor, the intraparticle diffusion kinetic model was considered the best of the tested models.