Perceptions of climate change and associated health impacts among communities in Johor River Basin, Malaysia

INTRODUCTION: As climate change is threatening every region of the world, extreme weather events resultant of global warming is occurring at increasing rate and scale in Malaysia. Weather-related disasters such as flood and haze pose critical challenges to the infrastructure and raise public health concerns in the country, especially when main economic sectors rely heavily on climate variability. Public perception on environmental issues is crucial for development of pro-environmental policies. Among studies conducted to understand public awareness regarding global warming, reports of perception on the health impacts were very limited. Taking this limitation into account, this study was designed to examine the perception on the health impacts of climate change among the diverse communities living in the Johor River Basin. MATERIALS AND METHODS: The cross-sectional study was conducted through cloud-data-based digital questionnaires completed by randomly selected residents in the Johor River Basin (n=647). Data was analysed with descriptive statistics using SPSS 27 (IBM\uae) Software. Comparisons between indigenous and non-indigenous communities were performed using Chi square analysis. RESULTS: Respondents in this study consisted of indigenous people (n=79) and non-indigenous people (n=568). Indigenous respondents generally perceived more frequent occurrence of extreme weather events in the next 20 years, even for the phenomena unfamiliar in Malaysian settings. All respondents showed similar concerns for health impacts of global warming, although the non-indigenous respondents perceived the risk further into the future (25 years) compared to the indigenous respondents who perceived current or imminent (<10 years) risks. Intense concerns for self, children, family members and community were shown by nearly all indigenous respondents (97-99%), while the non-indigenous people in this study expressed stronger concerns at country level and for future generations. During the last haze episode, most indigenous respondents (85%) did not notice any change in air quality nor discomfort among family members, in contrast 70% of the nonindigenous respondents claimed to have suffered from breathing problems themselves as well as others in the family. All respondents were concerned about air quality in their surroundings, indigenous people were concerned for the near future (<10 years), and non-indigenous people were concerned for the next 25 years. CONCLUSION: In this study, respondents were generally concerned about the health impacts of unimpeded global warming. There was significant difference in perceptions between indigenous and non-indigenous respondents. The findings were useful, complemented with further studies, to improve understanding of public awareness and to help develop relevant education programmes accessible for wider audience

A Paclitaxel-Eluting Stent for the Prevention of Coronary Restenosis

Background Intimal hyperplasia and resulting restenosis limit the efficacy of coronary stenting. We studied a coronary stent coated with the antiproliferative agent paclitaxel as a means of preventing restenosis. Methods We conducted a multicenter, randomized, controlled, triple-blind study to evaluate the ability of a paclitaxel-eluting stent to inhibit restenosis. At three centers, 177 patients with discrete coronary lesions (<15 mm in length, 2.25 to 3.5 mm in diameter) underwent implantation of paclitaxel-eluting stents (low dose, 1.3 µg per square millimeter, or high dose, 3.1 µg per square millimeter) or control stents. Antiplatelet therapies included aspirin with ticlopidine (120 patients), clopidogrel (18 patients), or cilostazol (37 patients). Clinical follow-up was performed at one month and four to six months, and angiographic follow-up at four to six months. Results Technical success was achieved in 99 percent of the patients (176 of 177). At follow-up, the high-dose group, as compared with the control group, had significantly better results for the degree of stenosis (mean [±SD], 14±21 percent vs. 39±27 percent; P<0.001), late loss of luminal diameter (0.29±0.72 mm vs. 1.04±0.83 mm, P<0.001), and restenosis of more than 50 percent (4 percent vs. 27 percent, P<0.001). Intravascular ultrasound analysis demonstrated a dose-dependent reduction in the volume of intimal hyperplasia (31, 18, and 13 mm3, in the high-dose, low-dose, and control groups, respectively). There was a higher rate of major cardiac events in patients receiving cilostazol than in those receiving ticlopidine or clopidogrel. Among patients receiving ticlopidine or clopidogrel, event-free survival was 98 percent and 100 percent in the high-dose and control groups, respectively, at one month, and 96 percent in both at four to six months. Conclusions Paclitaxel-eluting stents used with conventional antiplatelet therapy effectively inhibit restenosis and neointimal hyperplasia, with a safety profile similar to that of standard stents.published_or_final_versio

Angioplasty and stenting to treat stenosis in the large supra-aortic vessels supplying ischemic areas of the brain

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Artificial intelligence and deep learning in ophthalmology

Artificial intelligence (AI) based on deep learning (DL) has sparked tremendous global interest in recent years. DL has been widely adopted in image recognition, speech recognition and natural language processing, but is only beginning to impact on healthcare. In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography and visual fields, achieving robust classification performance in the detection of diabetic retinopathy and retinopathy of prematurity, the glaucoma-like disc, macular oedema and age-related macular degeneration. DL in ocular imaging may be used in conjunction with telemedicine as a possible solution to screen, diagnose and monitor major eye diseases for patients in primary care and community settings. Nonetheless, there are also potential challenges with DL application in ophthalmology, including clinical and technical challenges, explainability of the algorithm results, medicolegal issues, and physician and patient acceptance of the AI 'black-box' algorithms. DL could potentially revolutionise how ophthalmology is practised in the future. This review provides a summary of the state-of-the-art DL systems described for ophthalmic applications, potential challenges in clinical deployment and the path forward

Genomic landscape of lung adenocarcinoma in East Asians

Lung cancer is the world’s leading cause of cancer death and shows strong ancestry disparities. By sequencing and assembling a large genomic and transcriptomic dataset of lung adenocarcinoma (LUAD) in individuals of East Asian ancestry (EAS; n = 305), we found that East Asian LUADs had more stable genomes characterized by fewer mutations and fewer copy number alterations than LUADs from individuals of European ancestry. This difference is much stronger in smokers as compared to nonsmokers. Transcriptomic clustering identified a new EAS-specific LUAD subgroup with a less complex genomic profile and upregulated immune-related genes, allowing the possibility of immunotherapy-based approaches. Integrative analysis across clinical and molecular features showed the importance of molecular phenotypes in patient prognostic stratification. EAS LUADs had better prediction accuracy than those of European ancestry, potentially due to their less complex genomic architecture. This study elucidated a comprehensive genomic landscape of EAS LUADs and highlighted important ancestry differences between the two cohorts

Acceptance and Perception of Artificial Intelligence Usability in Eye Care (APPRAISE) for Ophthalmologists: A Multinational Perspective

Background: Many artificial intelligence (AI) studies have focused on development of AI models, novel techniques, and reporting guidelines. However, little is understood about clinicians' perspectives of AI applications in medical fields including ophthalmology, particularly in light of recent regulatory guidelines. The aim for this study was to evaluate the perspectives of ophthalmologists regarding AI in 4 major eye conditions: diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD) and cataract. Methods: This was a multi-national survey of ophthalmologists between March 1st, 2020 to February 29th, 2021 disseminated via the major global ophthalmology societies. The survey was designed based on microsystem, mesosystem and macrosystem questions, and the software as a medical device (SaMD) regulatory framework chaired by the Food and Drug Administration (FDA). Factors associated with AI adoption for ophthalmology analyzed with multivariable logistic regression random forest machine learning. Results: One thousand one hundred seventy-six ophthalmologists from 70 countries participated with a response rate ranging from 78.8 to 85.8% per question. Ophthalmologists were more willing to use AI as clinical assistive tools (88.1%, n = 890/1,010) especially those with over 20 years' experience (OR 3.70, 95% CI: 1.10–12.5, p = 0.035), as compared to clinical decision support tools (78.8%, n = 796/1,010) or diagnostic tools (64.5%, n = 651). A majority of Ophthalmologists felt that AI is most relevant to DR (78.2%), followed by glaucoma (70.7%), AMD (66.8%), and cataract (51.4%) detection. Many participants were confident their roles will not be replaced (68.2%, n = 632/927), and felt COVID-19 catalyzed willingness to adopt AI (80.9%, n = 750/927). Common barriers to implementation include medical liability from errors (72.5%, n = 672/927) whereas enablers include improving access (94.5%, n = 876/927). Machine learning modeling predicted acceptance from participant demographics with moderate to high accuracy, and area under the receiver operating curves of 0.63–0.83. Conclusion: Ophthalmologists are receptive to adopting AI as assistive tools for DR, glaucoma, and AMD. Furthermore, ML is a useful method that can be applied to evaluate predictive factors on clinical qualitative questionnaires. This study outlines actionable insights for future research and facilitation interventions to drive adoption and operationalization of AI tools for Ophthalmology

Machine learning for genetic prediction of psychiatric disorders: a systematic review

Machine learning methods have been employed to make predictions in psychiatry from genotypes, with the potential to bring improved prediction of outcomes in psychiatric genetics; however, their current performance is unclear. We aim to systematically review machine learning methods for predicting psychiatric disorders from genetics alone and evaluate their discrimination, bias and implementation. Medline, PsycInfo, Web of Science and Scopus were searched for terms relating to genetics, psychiatric disorders and machine learning, including neural networks, random forests, support vector machines and boosting, on 10 September 2019. Following PRISMA guidelines, articles were screened for inclusion independently by two authors, extracted, and assessed for risk of bias. Overall, 63 full texts were assessed from a pool of 652 abstracts. Data were extracted for 77 models of schizophrenia, bipolar, autism or anorexia across 13 studies. Performance of machine learning methods was highly varied (0.48–0.95 AUC) and differed between schizophrenia (0.54–0.95 AUC), bipolar (0.48–0.65 AUC), autism (0.52–0.81 AUC) and anorexia (0.62–0.69 AUC). This is likely due to the high risk of bias identified in the study designs and analysis for reported results. Choices for predictor selection, hyperparameter search and validation methodology, and viewing of the test set during training were common causes of high risk of bias in analysis. Key steps in model development and validation were frequently not performed or unreported. Comparison of discrimination across studies was constrained by heterogeneity of predictors, outcome and measurement, in addition to sample overlap within and across studies. Given widespread high risk of bias and the small number of studies identified, it is important to ensure established analysis methods are adopted. We emphasise best practices in methodology and reporting for improving future studies