Construction of machine learning-based models for cancer outcomes in low and lower-middle income countries: A scoping review

Background: The impact and utility of machine learning (ML)-based prediction tools for cancer outcomes including assistive diagnosis, risk stratification, and adjunctive decision-making have been largely described and realized in the high income and upper-middle-income countries. However, statistical projections have estimated higher cancer incidence and mortality risks in low and lower-middle-income countries (LLMICs). Therefore, this review aimed to evaluate the utilization, model construction methods, and degree of implementation of ML-based models for cancer outcomes in LLMICs. Methods: PubMed/Medline, Scopus, and Web of Science databases were searched and articles describing the use of ML-based models for cancer among local populations in LLMICs between 2002 and 2022 were included. A total of 140 articles from 22,516 citations that met the eligibility criteria were included in this study. Results: ML-based models from LLMICs were often based on traditional ML algorithms than deep or deep hybrid learning. We found that the construction of ML-based models was skewed to particular LLMICs such as India, Iran, Pakistan, and Egypt with a paucity of applications in sub-Saharan Africa. Moreover, models for breast, head and neck, and brain cancer outcomes were frequently explored. Many models were deemed suboptimal according to the Prediction model Risk of Bias Assessment tool (PROBAST) due to sample size constraints and technical flaws in ML modeling even though their performance accuracy ranged from 0.65 to 1.00. While the development and internal validation were described for all models included (n=137), only 4.4% (6/137) have been validated in independent cohorts and 0.7% (1/137) have been assessed for clinical impact and efficacy. Conclusion: Overall, the application of ML for modeling cancer outcomes in LLMICs is increasing. However, model development is largely unsatisfactory. We recommend model retraining using larger sample sizes, intensified external validation practices, and increased impact assessment studies using randomized controlled trial design

Air gasification of Malaysia agricultural waste in a fluidised bed gasifier

Hydrogen production from agricultural waste has been investigated experimentally using a bench-scale fluidised bed gasifier with 60 mm diameter and 425 mm height. During the experiments, the fuel properties and the effects of operating parameters such as gasification temperatures (800–900°C), fluidisation ratio (2.0–3.33 m/s), static bed height (10–30 mm) and equivalence ratio (0.16–0.46) were analysed. Increasing temperatures favoured hydrogen yield and composition (up to 67 mol %) but only minor effects for other parameters. As conclusion, agricultural wastes are potential candidates as an alternative renewable energy source to fossil fuels

Visual Field Abnormalities among Adolescent Boys with Hearing Impairments

The aim of this study was to compare the visual field (VF) categorizations (based on the severity of VF defects) between adolescent boys with hearing impairments and those with normal hearing. This cross-sectional study involved the evaluation of the VF of 64 adolescent boys with hearing impairments and 68 age-matched boys with normal hearing at high schools in Tehran, Iran, in 2013. All subjects had an intelligence quotient (IQ) > 70. The hearing impairments were classified based on severity and time of onset. Participants underwent a complete eye examination, and the VFs were investigated using automated perimetry with a Humphrey Visual Field Analyzer. This device was used to determine their foveal threshold (FT), mean deviation (MD), and Glaucoma Hemifield Test (GHT) results. Most (50%) of the boys with hearing impairments had profound hearing impairments. There was no significant between-group difference in age (P = 0.49) or IQ (P = 0.13). There was no between-group difference in the corrected distance visual acuity (P = 0.183). According to the FT, MD, and GHT results, the percentage of boys with abnormal VFs in the hearing impairment group was significantly greater than that in the normal hearing group: 40.6% vs. 22.1%, 59.4% vs. 19.1%, and 31.2% vs. 8.8%, respectively (P < 0.0001). The mean MD in the hearing impairment group was significantly worse than that in the normal hearing group (-0.79 ± 2.04 and -4.61 ± 6.52 dB, respectively, P < 0.0001), and the mean FT was also significantly worse (38.97 ± 1.66 vs. 35.30 ± 1.43 dB, respectively, P <0.0001). Moreover, there was a significant between-group difference in the GHT results (P < 0.0001). Thus, there were higher percentages of boys with VF abnormalities and higher mean MD, FT, and GHT results among those with hearing impairments compared to those with normal hearing. These findings emphasize the need for detailed VF assessments for patients with hearing impairments.Â

Polycaprolactone-templated reduced-graphene oxide liquid crystal nanofibers towards biomedical applications

Here, we report a facile method to generate electrically conductive nanofibers by coating and subsequently chemically reducing graphene oxide (GO) liquid crystals on a polycaprolactone (PCL) mat.</p

Applying Heckman Model for Economic Valuation of Drinking Water in Hamadan Province

Water is a socio-economic commodity which is considered as a basic human need. Limited water resources in arid and semi-arid regions and increasing demand for water has led to water shortages, reduced reliability of water supply systems, and intensified competition and conflict between different sectors of water consumption. Limited water resources have also caused economic, environmental, social and political tensions. Among the various uses of water, urban drinking water has a higher priority due to health issues, basic human needs and the possibility of social tensions due to its scarcity.Water pricing as a tool for demand management, whether through administrative decrees or market forces, is the best approach to improve water allocation and protect water resources. Considering the importance of determining the economic valuation of water in the study area and with the aim of determining the customers' willingness to pay for quality drinking water, this study was intended to estimate the economic valuation of water in urban areas of Hamadan province.One of the methods of water valuation is measuring the willingness of consumers to pay higher prices for quality water. The contingent valuation method as a standard and flexible method is widely used for measuring the willingness of consumers to pay higher prices for quality water (Khodaverdizadeh et al., 2008). Comparing the willingness to pay in urban and rural areas in Kenya, Brouwer et al. (2015) found that rural households were more sensitive to prices than urban ones, and urban households were willing to pay more for quality water than villagers. Acey et al. (2019) also examined the willingness to pay for water by Kenyan citizens. The results showed that among citizens who were younger and richer, the willingness to pay was higher. Considering the importance of determining the economic valuation of water, this study was thus intended to determine the rate of willingness to pay for quality drinking water by the households in Hamedan province using a contingent valuation method

Screening Characteristics of Bedside Ultrasonography in Confirming Endotracheal Tube Placement; a Diagnostic Accuracy Study

Introduction: Confirmation of proper endotracheal tube placement is one of the most important and lifesaving issues of tracheal intubation. The present study was aimed to evaluate the accuracy of tracheal ultrasonography by emergency residents in this regard.  Method: This was a prospective, cross sectional study for evaluating the diagnostic accuracy of ultrasonography in endotracheal tube placement confirmation compared to a combination of 4 clinical confirmation methods of chest and epigastric auscultation, direct laryngoscopy, aspiration of the tube, and pulse oximetry (as reference test).Results: 150 patients with the mean age of 58.52 ± 1.73 years were included (56.6% male). Sensitivity, specificity, positive predictive value, negative predictive value, and positive and negative likelihood ratio of tracheal ultrasonography in endotracheal tube confirmation were 96 (95% CI: 92-99), 88 (95% CI: 62-97), 98 (95% CI: 94-99), 78 (95% CI: 53-93), 64 (95% CI: 16-255), and 0.2 (95% CI: 0.1-0.6), respectively.Conclusion: The present study showed that tracheal ultrasonography by trained emergency medicine residents had excellent sensitivity (&gt;90%) and good specificity (80-90) for confirming endotracheal tube placement. Therefore, it seems that ultrasonography is a proper screening tool in determining endotracheal tube placement

DigiMOF: A Database of Metal–Organic Framework Synthesis Information Generated via Text Mining

The vastness of materials space, particularly that which is concerned with metal–organic frameworks (MOFs), creates the critical problem of performing efficient identification of promising materials for specific applications. Although high-throughput computational approaches, including the use of machine learning, have been useful in rapid screening and rational design of MOFs, they tend to neglect descriptors related to their synthesis. One way to improve the efficiency of MOF discovery is to data-mine published MOF papers to extract the materials informatics knowledge contained within journal articles. Here, by adapting the chemistry-aware natural language processing tool, ChemDataExtractor (CDE), we generated an open-source database of MOFs focused on their synthetic properties: the DigiMOF database. Using the CDE web scraping package alongside the Cambridge Structural Database (CSD) MOF subset, we automatically downloaded 43,281 unique MOF journal articles, extracted 15,501 unique MOF materials, and text-mined over 52,680 associated properties including the synthesis method, solvent, organic linker, metal precursor, and topology. Additionally, we developed an alternative data extraction technique to obtain and transform the chemical names assigned to each CSD entry in order to determine linker types for each structure in the CSD MOF subset. This data enabled us to match MOFs to a list of known linkers provided by Tokyo Chemical Industry UK Ltd. (TCI) and analyze the cost of these important chemicals. This centralized, structured database reveals the MOF synthetic data embedded within thousands of MOF publications and contains further topology, metal type, accessible surface area, largest cavity diameter, pore limiting diameter, open metal sites, and density calculations for all 3D MOFs in the CSD MOF subset. The DigiMOF database and associated software are publicly available for other researchers to rapidly search for MOFs with specific properties, conduct further analysis of alternative MOF production pathways, and create additional parsers to search for additional desirable properties

Deep learning predicts the malignant-transformation-free survival of oral potentially malignant disorders

Machine-intelligence platforms for the prediction of the probability of malignant transformation of oral potentially malignant disorders are required as adjunctive decision-making platforms in contemporary clinical practice. This study utilized time-to-event learning models to predict malignant transformation in oral leukoplakia and oral lichenoid lesions. A total of 1098 patients with oral white lesions from two institutions were included in this study. In all, 26 features available from electronic health records were used to train four learning algorithms—Cox-Time, DeepHit, DeepSurv, random survival forest (RSF)—and one standard statistical method—Cox proportional hazards model. Discriminatory performance, calibration of survival estimates, and model stability were assessed using a concordance index (c-index), integrated Brier score (IBS), and standard deviation of the averaged c-index and IBS following training cross-validation. This study found that DeepSurv (c-index: 0.95, IBS: 0.04) and RSF (c-index: 0.91, IBS: 0.03) were the two outperforming models based on discrimination and calibration following internal validation. However, DeepSurv was more stable than RSF upon cross-validation. External validation confirmed the utility of DeepSurv for discrimination (c-index—0.82 vs. 0.73) and RSF for individual survival estimates (0.18 vs. 0.03). We deployed the DeepSurv model to encourage incipient application in clinical practice. Overall, time-to-event models are successful in predicting the malignant transformation of oral leukoplakia and oral lichenoid lesions