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Background: Immune checkpoint inhibitors (ICIs) have emerged as an effective treatment for colorectal cancer (CRC). Studies indicate that the composition of gut microbiota could potentially serve as a biomarker for predicting the clinical effectiveness of immune checkpoint inhibitors., Methods: Following PRISMA guidelines, the review was conducted after registering the protocol with PROSPERO. A comprehensive literature search was carried out across five databases: PubMed, Scopus, Web of Science, Embase, and Cochrane Library. Assessment tools from the National Institutes of Health (NIH) were used to gauge the quality of the studies., Results: A total of 5,132 papers were identified, and three studies and one conference abstract published between 2017-2022 met the inclusion criteria and were summarized in a descriptive synthesis table. These four studies were in accord with the following findings, four main phyla, Firmicutes, Bacteroidata, Actinobacteria, and Verrucomicrobiota were associated with CRC patients' clinical response toward ICIs treatment. Ruminococcaceae was predominantly related to CRC patients responding to therapy, while the Micrococcaceae family was more common among the non-responders. Bacterial taxa such as Faecalibacterium and Prevotellaceae were associated with better responses to ICIs and could be predictive biomarkers. The signature of fecal microbiota with Akkermansia muciniphila and Eubacterium rectale enrichment, and Rothia mucilaginosa depletion could independently predict better response to ICIs in patients with CRC., Conclusion: The findings have brought attention to the notable differences in terms of richness and composition of microbiota between patients who responded positively to the treatment and those who did not. Bacterial species and families, such as Faecalibacterium, Bifidobacterium, Lachnospiraceae, Akkermansia sp., Ruminococcaceae, and Prevotellaceae, have consistently surfaced as potential indicators of immunotherapeutic responses. Furthermore, this review also emphasizes the need for additional comprehensive, multi-center studies with larger sample sizes to validate reported microbiota and expand our understanding of the role of gut microbiota in CRC ICIs therapy. PROSPERO ID: CRD42021277691., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Ajab et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Precise segmentation of the nucleus is vital for computer-aided diagnosis (CAD) in cervical cytology. Automated delineation of the cervical nucleus has notorious challenges due to clumped cells, color variation, noise, and fuzzy boundaries. Due to its standout performance in medical image analysis, deep learning has gained attention from other techniques. We have proposed a deep learning model, namely C-UNet (Cervical-UNet), to segment cervical nuclei from overlapped, fuzzy, and blurred cervical cell smear images. Cross-scale features integration based on a bi-directional feature pyramid network (BiFPN) and wide context unit are used in the encoder of classic UNet architecture to learn spatial and local features. The decoder of the improved network has two inter-connected decoders that mutually optimize and integrate these features to produce segmentation masks. Each component of the proposed C-UNet is extensively evaluated to judge its effectiveness on a complex cervical cell dataset. Different data augmentation techniques were employed to enhance the proposed model's training. Experimental results have shown that the proposed model outperformed extant models, i.e., CGAN (Conditional Generative Adversarial Network), DeepLabv3, Mask-RCNN (Region-Based Convolutional Neural Network), and FCN (Fully Connected Network), on the employed dataset used in this study and ISBI-2014 (International Symposium on Biomedical Imaging 2014), ISBI-2015 datasets. The C-UNet achieved an object-level accuracy of 93%, pixel-level accuracy of 92.56%, object-level recall of 95.32%, pixel-level recall of 92.27%, Dice coefficient of 93.12%, and F1-score of 94.96% on complex cervical images dataset., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Rasheed et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Objective: There have been concerns regarding the high prevalence of vitamin D deficiency in the United Arab Emirates and the association between this prevalence and cardiovascular risk. Vitamin D deficiency is a global public health problem, with a high prevalence in the Abu Dhabi national population. This cross-sectional retrospective observational study aimed to elucidate vitamin D deficiency symptoms and contributing factors in this context., Method and Sampling: Among 12346 participants presenting for their Weqaya population comprehensive cardiovascular risk factor screening, 700 were randomly selected and telephoned, among whom 400 completed a questionnaire that elicited information on dietary intake, sun exposure, clothing, physical activity, and symptoms related to vitamin D deficiency., Results: Higher vitamin D levels were noted in participants who consumed extra servings of cod liver oil, tuna, salmon, and eggs; who wore clothes with their arms and legs exposed; and who performed daily physical activity. Symptoms of hypovitaminosis D (bone pain) were more frequently found in females. Fatigue is significantly associated with low vitamin D and younger participants. Strategies focusing on health promotion and lifestyle interventions should be implemented to address vitamin D deficiency., Competing Interests: The Seha company is the government institution and had no role in data collection or analysis or writing the manuscript and no conflict of interest The authors have declared that no competing interests exist.

Background: Emerging worldwide in the past decade, there has been a significant increase in multidrug-resistant bacteria from serious nosocomial infections, especially carbapenemase-producing Gram-negative bacilli that have emerged worldwide. The objective of this study is to investigate carbapenem resistance in Gram-negative bacilli bacteria using phenotypic detection, antimicrobial resistance profiles and genotypic characterisation methods., Methods: 200 Gram-negative bacilli isolates were collected from different clinical specimens. All clinical samples were exposed to isolation and identification of significant pathogens applying bacteriological examination and an automated Vitek-2 system. The isolates were subjected to susceptibility tests by the Vitek-2 automated system and those isolates that were resistant to beta-lactam drugs, including carbapenems, third-generation cephalosporines or cefoxitin, were selected for phenotyping using Carba plus disc system assay for detection of carbapenemase-producing isolates. These isolates were further confirmed by molecular detection. PCR was used for the detection carbapenem-resistant genes (OXA-48, IMP, NDM, VIM, and KPC)., Results: 110 (55%) of 200 Gram-negative bacilli were identified as beta-lactam-resistant isolates. The frequency of carbapenem-resistant isolates was calculated to be 30.9% (n = 34/110). A collection totalling 65/110 (59%) isolates were identified as carbapenemase producers by phenotypic method. Moreover, among the 65 carbapenemase-producing Gram-negative isolates with a positive phenotype-based result, 30 (46%), 20 (30%) and 18 (27%) isolates were positive for OXA-48, KPC and MBL enzymes, respectively, as well as the production of 27% of AmpC with porin loss. Tigecycline was the most effective antibiotic that affected 70% of MDR isolates, but high rates of resistance were detected to other tested antimicrobials. Of interest, a high incidence of MDR, XDR and PDR profiles were observed among all carbapenemase-producing isolates. 36% (24/65) of the tested isolates were MDR to 3 to 5 antimicrobial classes. 29% (17/65) of the recovered isolates were XDR to 6 to 7 antimicrobial classes. Alarmingly, 24% (16/65) of isolates displayed PDR to all the tested 8 antimicrobial classes. Genotype assay, including 53 phenotypically confirmed carbapenemase-producing isolates of Gram-negative bacilli, found 51(96%) isolates were harbouring one or more genes. The most common carbapenemase gene was bla NDM 83% (44/53) followed by bla OXA-48 75% (40/53), bla VIM 49% (26/53) and bla IMP 43% (23/53), while the gene bla KPC was least frequent 7% (4/53). 92% (46/51) of isolates were involved in the production of more than one carbapenemase gene., Conclusion: This study demonstrated the emergence of carbapenemase-producing Gram-negative pathogens implicated in healthcare-related infections. Accurate identification of carbapenem-resistant bacterial pathogens is essential for patient treatment, as well as the development of appropriate contamination control measures to limit the rapid spread of pathogens. Tigecycline exhibited potent antimicrobial activity against MDR, XDR and PDR-producing strains that establish a threatening alert which indicates the complex therapy of infections caused by these pathogens., Competing Interests: The authors have declared that no competing interests exist.

Manual offline analysis, of a scanning electron microscopy (SEM) image, is a time consuming process and requires continuous human intervention and efforts. This paper presents an image processing based method for automated offline analyses of SEM images. To this end, our strategy relies on a two-stage process, viz. texture analysis and quantification. The method involves a preprocessing step, aimed at the noise removal, in order to avoid false edges. For texture analysis, the proposed method employs a state of the art Curvelet transform followed by segmentation through a combination of entropy filtering, thresholding and mathematical morphology (MM). The quantification is carried out by the application of a box-counting algorithm, for fractal dimension (FD) calculations, with the ultimate goal of measuring the parameters, like surface area and perimeter. The perimeter is estimated indirectly by counting the boundary boxes of the filled shapes. The proposed method, when applied to a representative set of SEM images, not only showed better results in image segmentation but also exhibited a good accuracy in the calculation of surface area and perimeter. The proposed method outperforms the well-known Watershed segmentation algorithm.