OBJECTIVE: The aim was to present a model of CT-MRI registration used to diagnose liver cancer, specifically for improving the quality of the liver images and provide all the required information for earlier detection of the tumors. This method should concurrently address the issues of imaging procedures for liver cancer to fasten the detection of the tumor from both modalities.
METHODS: In this work, a registration scheme for fusing the CT and MRI liver images is studied. A feature point-based method with normalized cross-correlation has been utilized to aid in the diagnosis of liver cancer and provide multimodal information to physicians. Data on ten patients from an online database were obtained. For each dataset, three planar views from both modalities were interpolated and registered using feature point-based methods. The registration of algorithms was carried out by MATLAB (vR2019b, Mathworks, Natick, USA) on an Intel (R) Core (TM) i5-5200U CPU @ 2.20 GHz computer. The accuracy of the registered image is being validated qualitatively and quantitatively.
RESULTS: The results show that an accurate registration is obtained with minimal distance errors by which CT and MRI were accurately registered based on the validation of the experts. The RMSE ranges from 0.02 to 1.01 for translation, which is equivalent in magnitude to approximately 0 to 5 pixels for CT and registered image resolution.
CONCLUSION: The CT-MRI registration scheme can provide complementary information on liver cancer to physicians, thus improving the diagnosis and treatment planning process.
Methods: This was a randomized cross-sectional and hospital-based study. The standard method of microscopy was employed. Thick and thin films were prepared and viewed under a light microscope to identify and quantify malaria parasites. A well-structured and pre-tested questionnaire was used to obtain the subject's information on the demographic, socio-economic and environmental variables.
Results: A total of 380 (71.7%) participants were infected with Plasmodium falciparum with a mean parasite density of 1857.11 parasite/µL of blood. Malaria prevalence and mean parasite density were significantly higher among male compared to their female counterparts [80.3% vs 61.4% and 2026.46 vs 1619.63 parasite/µL of blood]. Similarly, age group ≤5 years had the highest malaria prevalence (92.2%) and mean parasite density (2031.66 parasite/µL of blood) than other age groups (AOR 2.281, 95% CI: 1.187-4.384, P < 0.05). The multivariate logistic analysis showed that malaria disease is significantly associated with having mother with no formal education (AOR 12.235, 95% CI: 3.253-46.021, P < 0.05), having well and river as a major source of household water supply (AOR 13.810, 95% CI: 3.012-63.314, P < 0.05 vs AOR 5.639, 95% CI: 1.455-21.853, P < 0.05) and presence of stagnant water around home (AOR 5.22, 95% CI: 2.921-9.332, P < 0.05). Furthermore, protective factors observed include ownership of mosquito bed net (AOR 0.474, 95% CI: 0.223-1.008, P < 0.05) and distance of home to hospital (AOR 0.279, 95% CI: 0.158-0.493, P < 0.05).
Conclusion: Malaria remains a serious public health problem in the study area. Adopting integrated malaria control measures including educating parents on malaria prevention and control strategies, distributing mosquito bed nets, and establishing larvae source management program is highly imperative.
Methods: We conducted a cross-sectional study involving eligible HCPs from different healthcare settings in northern Nigeria. The participants were recruited into the study using a combination of online (via Google Form) and face-to-face paper-based survey methods. The ASV knowledge of the respondents was measured using a validated anti-snake venom knowledge assessment tool (AKAT). Inadequate overall knowledge of ASV was defined as scores of 0-69.9%, and 70-100% were considered adequate overall knowledge scores. The predictors of ASV knowledge were determined using multiple logistic regression.
Results: Three hundred and thirty-one (331) eligible HCPs were included in the study analysis (310 from online and 21 from paper-based survey). Overall, an estimated 12.7% of the participants had adequate knowledge of ASV. Adequate ASV knowledge was higher among physicians compared with other HCPs (21.7%; χ2 = 8.1; p = 0.04). Those without previous training on ASV (adjusted odds ratio [aOR], 0.37; 95% confidence interval [CI], 0.18-0.73; p = 0.004) and who have not previously administered/dispensed ASV (aOR, 0.31; 95% CI, 0.15-0.63; p
Methods: A total of 174 samples of seven cephalopod species were collected from the west coast of Peninsular Malaysia. Both upper and lower beaks were extracted from the samples and the left lateral views of upper and lower beak images were acquired. Three types of traditional morphometric features were extracted namely grey histogram of oriented gradient (HOG), colour HOG, and morphological shape descriptor (MSD). In addition, deep features were extracted by using three pre-trained convolutional neural networks (CNN) models which are VGG19, InceptionV3, and Resnet50. Eight machine learning approaches were used in the classification step and compared for model performance.
Results: The results showed that the Artificial Neural Network (ANN) model achieved the best testing accuracy of 91.14%, using the deep features extracted from the VGG19 model from lower beak images. The results indicated that the deep features were more accurate than the traditional features in highlighting morphometric differences from the beak images of cephalopod species. In addition, the use of lower beaks of cephalopod species provided better results compared to the upper beaks, suggesting that the lower beaks possess more significant morphological differences between the studied cephalopod species. Future works should include more cephalopod species and sample size to enhance the identification accuracy and comprehensiveness of the developed model.
METHODS: A comprehensive search of three databases including Medline, Embase and Central was performed to identify randomized controlled trials that used oral cryotherapy for the prevention of chemotherapy-induced oral mucositis. The primary outcome was the incidence of oral mucositis for trials employing oral cryotherapy as the intervention for the prevention of oral mucositis. The meta-analysis was performed using the random-effects model and random errors of the meta-analyses were detected by trial sequential analysis.
RESULTS: A total of 14 RCTs with 1577 participants were included in the present meta-analysis. Patients treated with oral cryotherapy were associated with a significantly lower risk of developing oral mucositis of any grade (risk ratio (RR), 0.67 (95% CI: 0.56-0.81, p < 0.05)). Findings from the subgroup analyses showed that oral cryotherapy significantly reduced the risk of oral mucositis in patients undergoing bone marrow transplantation (RR 0.69, CI: 0.54-0.89, p < 0.05) as well as chemotherapy (RR 0.66, CI: 0.58-0.75, p < 0.05). Findings from the trial sequential analysis suggested that the evidence on oral cryotherapy as a preventive intervention for oral mucositis in patients with solid malignancies receiving conventional chemotherapy was conclusive.
CONCLUSION: Oral cryotherapy is effective in preventing oral mucositis in patients undergoing chemotherapy for the management of solid malignancies. The use of oral cryotherapy in preventing oral mucositis in bone marrow transplantation settings showed promising efficacy, but the evidence is not conclusive and requires more high-quality randomized controlled trials.