METHODS: This scoping review uses the methodological framework of Arksey and O'Malley. A comprehensive search of academic journals (English) on this topic published from 2007 to 2017 was conducted. A total of 22 studies were selected from 585 studies screened from the electronic databases.
RESULTS: First-year stroke recurrence rates are in the range of 2.2% to 25.4%. Besides that, modifiable risk factors are significantly associated with pathophysiological factors (hypertension, ankle-brachial pressure index, atherogenic dyslipidaemia, diabetes mellitus, metabolic syndrome, and atrial fibrillation) and lifestyle factors (obesity, smoking, physical inactivity, and high salt intake). Furthermore, age, previous history of cerebrovascular events, and stroke subtype are also significant influence risk factors for recurrence. A strategic secondary prevention method for recurrent stroke is health education along with managing risk factors through a combination of appropriate lifestyle intervention and pharmacological therapy.
CONCLUSION: To prevent recurrent stroke, health intervention should be geared towards changing lifestyle to embody a healthier approach to life. This is of great importance to public health and stroke survivors' quality of life.
METHODS: This is an observational retrospective study carried out in a general hospital on 117 solid tumor patients who admitted between January 2003 to December 2006. The main statistical tests used were Chi- square test and Fisher' s Exact test. The significance of the result will be when the P<0.05, while the confidence interval for this study was 95%.
RESULTS: The highest chemotherapeutic regimen was (5-FU+epirubicin+cyclophosphamide) (47, 40.2%) followed by (gemcitabine+cisplatin) (6, 5.1%) and many others. Majority of the patients receive their chemotherapy schedule of administration was one day schedule (90, 76.9%) followed by more than one day schedule (27, 23.1%).
CONCLUSION: The doses of these drugs were not high enough to produce a sufficient pharmacological effect to cause bone marrow suppression and lead to neutropenia. Besides the schedule of administration for each drug was long enough to overcome neutropenia also the high uses of granulocyte colony stimulation factor (G-CSF) which will play a major role in reducing the time and severity of neutropenia. All these factors play an important role in giving non- significant association between neutropenia onset and severity with chemotherapeutics drugs and their schedule of administration.
METHODS: Twenty volumes of interests consisting of six anterior and fourteen posterior edentulous regions were obtained from human mandibular cadavers. A CBCT system with a resolution of 80 µm (3D Accuitomo 170, J. Morita, Kyoto, Japan) and a µCT system with a resolution of 35 µm (SkyScan 1173, Kontich, Belgium) were used to scan the mandibles. Three structural parameters namely, trabecular number (Tb.N), trabecular thickness (Tb.Th), and trabecular separation (Tb.Sp) were analysed using CTAn software (v 1.11, SkyScan, Kontich, Belgium). For each system, the measurements obtained from anterior and posterior regions were tested using independent sample t-test. Subsequently, all measurements between systems were tested using paired t-test.
RESULTS: In CBCT, all parameters of the anterior and posterior mandible showed no significant differences (p > 0.05). However, µCT showed a significant different of Tb.Th (p = 0.023) between anterior and posterior region. Regardless of regions, the measurements obtained using both imaging systems were significantly different (p ≤ 0.021) for Tb.Th and Tb.N.
CONCLUSIONS: The current study demonstrated that only the variation of Tb.Th between anterior and posterior edentulous region of mandible can be detected using µCT. In addition, CBCT is less feasible than µCT in assessing trabecular bone microstructures at both regions.
OBJECTIVE: This review aims to explore the role of AI in forecasting outcomes related to chemotherapy development, cancer diagnosis, and treatment response, synthesizing current advancements and identifying critical gaps in the field.
METHODS: A comprehensive literature search was conducted across PubMed, Embase, Web of Science, and Cochrane databases up to 2023. Keywords included "Artificial Intelligence (AI)," "Machine Learning (ML)," and "Deep Learning (DL)" combined with "chemotherapy development," "cancer diagnosis," and "cancer treatment." Articles published within the last four years and written in English were included. The Prediction Model Risk of Bias Assessment tool was utilized to assess the risk of bias in the selected studies.
CONCLUSION: This review underscores the substantial impact of AI, including ML and DL, on cancer diagnosis, chemotherapy innovation, and treatment response for both solid and hematological tumors. Evidence from recent studies highlights AI's potential to reduce cancer-related mortality by optimizing diagnostic accuracy, personalizing treatment plans, and improving therapeutic outcomes. Future research should focus on addressing challenges in clinical implementation, ethical considerations, and scalability to enhance AI's integration into oncology care.