Two-step Selection Process Research Articles

Personalized prediction of immunotherapy response in lung cancer patients using advanced radiomics and deep learning

BackgroundLung cancer (LC) is a leading cause of cancer-related mortality, and immunotherapy (IO) has shown promise in treating advanced-stage LC. However, identifying patients likely to benefit from IO and monitoring treatment response remains challenging. This study aims to develop a predictive model for progression-free survival (PFS) in LC patients with IO based on clinical features and advanced imaging biomarkers.Materials and methodsA retrospective analysis was conducted on a cohort of 206 LC patients receiving IO treatment. Pre-treatment computed tomography images were used to extract advanced imaging biomarkers, including intratumoral and peritumoral-vasculature radiomics. Clinical features, including age, gene status, hematology, and staging, were also collected. Key radiomic and clinical features for predicting IO outcomes were identified using a two-step feature selection process, including univariate Cox regression and chi-squared test, followed by sequential forward selection. The DeepSurv model was constructed to predict PFS based on clinical and radiomic features. Model performance was evaluated using the area under the time-dependent receiver operating characteristic curve (AUC) and concordance index (C-index).ResultsCombining radiomics of intratumoral heterogeneity and peritumoral-vasculature with clinical features demonstrated a significant enhancement (p < 0.001) in predicting IO response. The proposed DeepSurv model exhibited a prediction performance with AUCs ranging from 0.76 to 0.80 and a C-index of 0.83. Furthermore, the predicted personalized PFS curves revealed a significant difference (p < 0.05) between patients with favorable and unfavorable prognoses.ConclusionsIntegrating intratumoral and peritumoral-vasculature radiomics with clinical features enabled the development of a predictive model for PFS in LC patients with IO. The proposed model’s capability to estimate individualized PFS probability and differentiate the prognosis status held promise to facilitate personalized medicine and improve patient outcomes in LC.

Drug-induced torsadogenicity prediction model: An explainable machine learning-driven quantitative structure-toxicity relationship approach

Drug-induced Torsade de Pointes (TdP), a life-threatening polymorphic ventricular tachyarrhythmia, emerges due to the cardiotoxic effects of pharmaceuticals. The need for precise mechanisms and clinical biomarkers to detect this adverse effect presents substantial challenges in drug safety assessment. In this study, we propose that analyzing the physicochemical properties of pharmaceuticals can provide valuable insights into their potential for torsadogenic cardiotoxicity. Our research centers on estimating TdP risk based on the molecular structure of drugs. We introduce a novel quantitative structure-toxicity relationship (QSTR) prediction model that leverages an in silico approach developed by adopting the 4R rule in laboratory animals. This approach eliminates the need for animal testing, saves time, and reduces cost. Our algorithm has successfully predicted the torsadogenic risks of various pharmaceutical compounds. To develop this model, we employed Support Vector Machine (SVM) and ensemble techniques, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost). We enhanced the model's predictive accuracy through a rigorous two-step feature selection process. Furthermore, we utilized the SHapley Additive exPlanations (SHAP) technique to explain the prediction of torsadogenic risk, particularly within the RF model. This study represents a significant step towards creating a robust QSTR model, which can serve as an early screening tool for assessing the torsadogenic potential of pharmaceutical candidates or existing drugs. By incorporating molecular structure-based insights, we aim to enhance drug safety evaluation and minimize the risks of drug-induced TdP, ultimately benefiting both patients and the pharmaceutical industry.

Photoactivated Chromophore for Keratitis-Corneal Cross-linking (PACK-CXL)-A Scoping Review Based on Preclinical Studies.

Photoactivated chromophore for keratitis-corneal cross-linking (PACK-CXL) stabilizes the corneal stroma and eliminates microorganisms. Numerous PACK-CXL protocols, using different energy sources and chromophores, have been applied in preclinical studies, including live animal studies, with various experimental designs and endpoints. So far, a systematic mapping of the applied protocols and consistency across studies seems lacking but is essential to guide future research. The scoping review protocol was in line with the JBI Manual for Evidence Synthesis. Electronic databases were searched (Embase, MEDLINE, Scopus, Web of Science) to identify eligible records, followed by a two-step selection process (title and abstract screening, full text screening) for record inclusion. We extracted information on (1) different PACK-CXL protocol characteristics; (2) infectious pathogens tested; (3) study designs and experimental settings; and (4) endpoints used to determine antimicrobial and tissue stabilizing effects. The information was charted in frequency maps. The searches yielded 3654 unique records, 233 of which met the inclusion criteria. With 103 heterogeneous endpoints, the researchers investigated a wide range of PACK-CXL protocols. The tested microorganisms reflected pathogens commonly associated with infectious keratitis. Bacterial solutions and infectious keratitis rabbit models were the most widely used models to study the antimicrobial effects of PACK-CXL. If preclinical PACK-CXL studies are to guide future translational research, further cross-disciplinary efforts are needed to establish, promote, and facilitate acceptance of common endpoints relevant to PACK-CXL. Systematic mapping of PACK-CXL protocols in preclinical studies guides future translational research.

Structure, acquisition, assembly, and function of the root-associated microbiomes in Japonica rice and hybrid rice

Understanding the genetic and environmental factors that structure root-associated microbiomes is necessary to inform rice (Oryza sativa L.) breeding and fertilizer. Herein, we comprehensivly explored the microbiomes along four fine-scale root compartments, bulk soil, rhizosphere, rhizoplane and endosphere, between traditional Japonica rice and improved hybrid rice acorss three spatially distant locations. Bacterial diversity decreased monotonically from external bulk soil to internal endosphere. The acquisition of endosphere symbionts is governed conjointly by a two-step selection process and an amplification selection process, which depends on the bacterial functional group. The bacterial communities are primarly affected by root compartment and planting location, followed by relatively samll but signifcant rice genotype effect. The exterior bulk soil, rhizosphere and rhizoplane communities are govrned by variable selection. In contrast, the internal endosphere counterparts are shapped by homogenizing selection, which are undiscernible between the two rice genotypes. After ruling out planting location effect, we screened 12 rice genotype-discriminatory orders that contributed an overall 97.22 % accuracy in diagnosing rice genotype. Hybrid rice is inferior to Japonica rice in carbon and nitrogen fixation, nitrogen and phosphorus biomineralization from organic sources. Collectively, we proposed a conceptual model to infer rice genotype effect on the rhizosphere microbiomes, and the subsequent requirement of external fertilizer for sustaining yield. Our findings deepen our understanding of the complex rice genotype–root microbiomes–fertilizer interactions from the prespective of microbially-mediated nutrient cycles.

A Short-Term Air Pollutant Concentration Forecasting Method Based on a Hybrid Neural Network and Metaheuristic Optimization Algorithms

In the contemporary era, global air quality has been adversely affected by technological progress, urban development, population expansion, and the proliferation of industries and power plants. Recognizing the urgency of addressing air pollution consequences, the prediction of the concentration levels of air pollutants has become crucial. This study focuses on the short-term prediction of nitrogen dioxide (NO2) and sulfur dioxide (SO2), prominent air pollutants emitted by the Kerman Combined Cycle Power Plant, from May to September 2019. The proposed method utilizes a new two-step feature selection (FS) process, a hybrid neural network (HNN), and the Coot optimization algorithm (COOT). This combination of FS and COOT selects the most relevant input features while eliminating redundant ones, leading to improved prediction accuracy. The application of HNN for training further enhances the accuracy significantly. To assess the model’s performance, two datasets, including real data from two different parts of Combined Cycle Power Plant in Kerman, Iran, from 1 May 2019 to 30 September 2019 (namely dataset A and B), are utilized. Subsequently, mean square error (MSE), mean absolute error (MAE), root mean square deviation (RMSE), and mean absolute percentage error (MAPE) were employed to obtain the accuracy of FS-HNN-COOT. Experimental results showed MSE of FS-HNN-COOT for NO2 ranged from 0.002 to 0.005, MAE from 0.016 to 0.0492, RMSE from 0.0142 to 0.0736, and MAEP from 4.21% to 8.69%. Also, MSE, MAE, RMSE, and MAPE ranged from 0.0001 to 0.0137, 0.0108 to 0.0908, 0.0137 to 0.1173, and 9.03% to 15.93%, respectively, for SO2.

NIMG-04. PRE-RADIATION PROGRESSION PREDICTION IN GLIOBLASTOMA PATIENTS

Abstract Pre-Radiation Progression (PRP) has been identified as an increase ( &amp;gt; 25%) in the size of the residual brain tumor or interval development of a new tumor after surgical resection but prior to radiation therapy. Glioblastoma (GB) patients with PRP have worse survival outcome compared to patients without PRP (non-PRP).The standardized approach to identify PRP in GB patients is to manually compare early post-operative MRI to radiation planning MRI. In this study, we investigate the efficacy of traditional radiomics and sophisticated multi-resolution fractal texture features to differentiate PRP from non-PRP. Radiation-planning T1C magnetic resonance imaging (MRI) of 13 PRP and 13 non-PRP patients is analyzed to develop the classification method. For each patient, around 600 conventional radiomics and fractal texture features are obtained from tumor tissue and entire brain areas. We propose a two-step feature selection process followed by ensemble classification. In the first step of feature selection, we apply a beta-mixture based graphical model to identify correlations between the extracted features. The features are the nodes of the graph and the edges of the graph denote the correlations among the features and adjacency to PRP node. After the first step feature selection, the 21 selected features are used in the second step feature selection based on accuracy, similarity, and stability of each feature to yield the final 12 features. Lastly, we utilize the final 12 selected features for ensemble classification of PRP from non-PRP. With leave-one-out cross validation, the predictive model has an area under curve (AUC) of 0.79 and accuracy of 80% which indicates the discriminatory function of imaging features to differentiate PRP patients from non-PRP. The experimental results show the diagnostic ability of radiation planning imaging features to predict PRP from non-PRP.

The Impact of Edema on MRI Radiomics for the Prediction of Lung Metastasis in Soft Tissue Sarcoma.

This study aimed to evaluate whether radiomic features extracted solely from the edema of soft tissue sarcomas (STS) could predict the occurrence of lung metastasis in comparison with features extracted solely from the tumoral mass. We retrospectively analyzed magnetic resonance imaging (MRI) scans of 32 STSs, including 14 with lung metastasis and 18 without. A segmentation of the tumor mass and edema was assessed for each MRI examination. A total of 107 radiomic features were extracted for each mass segmentation and 107 radiomic features for each edema segmentation. A two-step feature selection process was applied. Two predictive features for the development of lung metastasis were selected from the mass-related features, as well as two predictive features from the edema-related features. Two Random Forest models were created based on these selected features; 100 random subsampling runs were performed. Key performance metrics, including accuracy and area under the ROC curve (AUC), were calculated, and the resulting accuracies were compared. The model based on mass-related features achieved a median accuracy of 0.83 and a median AUC of 0.88, while the model based on edema-related features achieved a median accuracy of 0.75 and a median AUC of 0.79. A statistical analysis comparing the accuracies of the two models revealed no significant difference. Both models showed promise in predicting the occurrence of lung metastasis in soft tissue sarcomas. These findings suggest that radiomic analysis of edema features can provide valuable insights into the prediction of lung metastasis in soft tissue sarcomas.

What Makes Bangladeshi RMG Suppliers Resilient in Global Apparel Supply Chain Management?

The global Readymade Garments (RMG) industry heavily relies on global supply chain management that aims to seek suppliers offering affordable, high-quality products delivered quickly. However, research on the factors influencing supplier selection in the RMG sector is scarce, and existing models fail to explain the paradox of rising exports in Bangladesh despite supply chain disruptions. This research strived to pervade this knowledge gap through a mixed-method empirical study on 66 international clothing brands. The findings reveal a two-step supplier selection process involving enlistment in a qualified supplier pool and subsequent allocation of work orders within the pool. Supplier competence and competitiveness are critical for enlistment and order qualification, while supplier relationship, competitiveness, and competence influence the selection of order winners. Notably, larger companies prioritize relationships in supplier selection. International RMG buying companies should focus on supplier competence factors to mitigate supply chain risks and maintain positive public relations. For the same reason, Bangladeshi policymakers should enforce compliance and help RMG suppliers to gain competencies.

Hearing loss and COVID-19: an umbrella review.

COVID-19 can result in an extensive range of extrapulmonary, and neurological signs and symptoms such as olfactory and/or taste dysfunction, and otologic symptoms. The aim of this study was to investigate the hearing loss manifestation from COVID-19. The goal of this umbrella review was to examine hearing loss associated with COVID-19 disease. English literature published until October 15, 2022 in online databases including PubMed, Scopus, Web of Science, and Embase was considered for this purpose. Eligibility of the articles for subsequent data extraction was evaluated in a two-step selection process with consideration to an inclusion/exclusion criterion. This review followed the PRISMA protocol and the Amstar-2 checklist for quality assessment. A total of four treatment strategies were used by different studies which included oral corticosteroids, intratympanic corticosteroids, combined oral and intratympanic corticosteroids, and hyperbaric oxygen therapy. Five studies investigated corticosteroid use in the forms of oral or intratympanic injection; four studies reported (complete or partial) hearing improvements after steroid treatment, while one study stated no significant improvement in hearing function. One study reported that oral corticosteroid monotherapy alone was not effective, while vestibular symptoms were ameliorated by a combination of oral prednisone, intratympanic dexamethasone injection, and hydroxychloroquine. The findings suggest that despite being one of the rare complications of COVID-19, hearing loss can impact a patient's quality of life. The most common type reported was sensorineural hearing loss, which can be diagnosed with variable techniques.

A two-step feature selection procedure for relevant markers of Squamous Cell Lung Carcinoma using different survival models

There are potentially infinite gene expression markers for Lung Squamous Cell Carcinoma. This results in a high-dimensional data with a large number of features. The selection of relevant markers for analysis is thus, of utmost importance. In our study, we have aimed to select a subset of prominent and significant features from 31918 features of gene expressions. Analysis is then performed on the selected features using the Cox Proportional Hazards Model to know how each marker affects the survival estimates of a patient. We have employed a two-step selection process to select a subset of markers. The first step is done by L1 regularized Cox PH. Then the selected markers are screened a second time by running a univariate Cox PH model and checking for the p-value of each bio-marker via Wald inference (p<0.05). Once the final selection is made, we estimate the Hazard Ratio and Confidence intervals using Maximum Likelihood Estimates (MLE) and the Bayesian Approach with the Cox Proportional Hazards Model (CPH) and the Accelerated Failure Time Model (AFT) as an alternative. A forest plot has also been generated to show the graphical representation of the meta-analysis done in the study. With the proposed selection procedure we have managed to find a suitable subset out of a large number of variables available. The features selected have been analyzed and their validity has been confirmed by using survival models.

Active school transport in an urban environment:prevalence and perceived barriers

BackgroundActive school transport (AST) can increase children’s and adolescents’ physical activity. The proportion of children and adolescents who engage in AST has declined internationally in recent decades. This study examines the prevalence, correlates, and perceived barriers to AST in the city of Leipzig, Germany.MethodsThe study sample includes 1070 participants, 364 children and 706 adolescents, aged between 6 and 18 years, as well as their parents. The parents as well as adolescents age 10 and above completed questionnaires concerning sociodemographic variables, means of transport/AST and perceived barriers to AST. The distance between home and school was calculated as the network distance from the home to school address using the Dijkstra algorithm. Based on these data, logistic models were fitted in a two-step variable selection process, using AST as the dependent variable.ResultsApproximately half of the children (59%) and adolescents (51%) engaged in AST. The prevalence of AST exhibited a negative correlation with age (Odds Ratio (OR) = 0.94, 95% confidence interval (CI) = 0.9–0.99, p = 0.015) and did not significantly differ by gender (children: ORgirls = 1.5, CI = 0.95–2.25, p = 0.075, adolescents: ORgirls = 1.01, CI = 0.75–1.37, p = 0.924). A high socioeconomic status was positively correlated to AST on the morning trip (OR = 1.7, CI 1.3–2.21, p < 0.01) but negatively on the afternoon trip (OR = 0.7, CI = 0.53–0.9, p < 0.01) in the summer. Common barriers for children (from their parents’ perspective) and for adolescents (from their own and their parents’ perspective) were distance and a heavy load to carry. The parents of adolescents did not perceive any other specific barriers as a serious impediment. Further significant barriers perceived by the younger children’s parents were adults giving a lift on the way to other errands, no other children to walk or cycle with, and too much traffic. Too much traffic was also a significant barrier for adolescents, as were taking too much time and bad weather conditions.ConclusionsFuture interventions promoting AST in an urban environment should be guided by the identified perceived barriers.Trial registrationLIFE Child has been retrospectively registered with ClinicalTrials.gov (NCT02550236).

A Two-Step Feature Selection Radiomic Approach to Predict Molecular Outcomes in Breast Cancer.

Breast Cancer (BC) is the most common cancer among women worldwide and is characterized by intra- and inter-tumor heterogeneity that strongly contributes towards its poor prognosis. The Estrogen Receptor (ER), Progesterone Receptor (PR), Human Epidermal Growth Factor Receptor 2 (HER2), and Ki67 antigen are the most examined markers depicting BC heterogeneity and have been shown to have a strong impact on BC prognosis. Radiomics can noninvasively predict BC heterogeneity through the quantitative evaluation of medical images, such as Magnetic Resonance Imaging (MRI), which has become increasingly important in the detection and characterization of BC. However, the lack of comprehensive BC datasets in terms of molecular outcomes and MRI modalities, and the absence of a general methodology to build and compare feature selection approaches and predictive models, limit the routine use of radiomics in the BC clinical practice. In this work, a new radiomic approach based on a two-step feature selection process was proposed to build predictors for ER, PR, HER2, and Ki67 markers. An in-house dataset was used, containing 92 multiparametric MRIs of patients with histologically proven BC and all four relevant biomarkers available. Thousands of radiomic features were extracted from post-contrast and subtracted Dynamic Contrast-Enanched (DCE) MRI images, Apparent Diffusion Coefficient (ADC) maps, and T2-weighted (T2) images. The two-step feature selection approach was used to identify significant radiomic features properly and then to build the final prediction models. They showed remarkable results in terms of F1-score for all the biomarkers: 84%, 63%, 90%, and 72% for ER, HER2, Ki67, and PR, respectively. When possible, the models were validated on the TCGA/TCIA Breast Cancer dataset, returning promising results (F1-score = 88% for the ER+/ER- classification task). The developed approach efficiently characterized BC heterogeneity according to the examined molecular biomarkers.

Social stigma during COVID-19: A systematic review.

Stigmatization was reported throughout the COVID pandemic for COVID-19 patients and close contacts. The aim of this systematic review was to comprehensively examine the prevalence and impact of stigmatization during COVID-19 pandemic. English articles were searched using online databases that included PubMed, Scopus, Embase, and Web of Science up to 24 August 2022. A two-step screening and selection process was followed utilizing an inclusion and exclusion criteria and then data was extracted from eligible articles. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist was followed, and the risk of bias was assessed using the Newcastle-Ottawa Scale. Seventy-six studies were eligible for inclusion. Twenty-two studies reported the prevalence of social stigma due to COVID-19 infection with social isolation being the most commonly reported stigma. There were 20 studies that reported the majority of participants experienced stigma due to COVID-19 infection, which was as high as 100% of participants in two studies. Participants in 16 studies reported blaming from others as the second most common type of stigma, with various other types reported such as psychological pressure, verbal violence, avoidance, and labeling. The most common effect of the stigma was anxiety followed by depression, and then reduction of socialization. Findings from the present review have identified that COVID-19-related stigma studies have generally focused on its prevalence, type, and outcome. Greater awareness of this topic may assist with improving public education during pandemics such as COVID-19 as well as access to support services for individuals impacted by stigmatization.

Value of T2-weighted-based radiomics model in distinguishing Warthin tumor from pleomorphic adenoma of the parotid.

The differentiation of Warthin tumor and pleomorphic adenoma before treatment is crucial for clinical strategies. The aim of this study was to develop and test a T2-weighted-based radiomics model for differentiating pleomorphic adenoma from Warthin tumor of the parotid gland. A total of 117 patients, including 61 cases of Warthin tumor and 56 cases of pleomorphic adenoma, were retrospectively enrolled from two centers between January 2010 and June 2022. The training set included 82 cases, and the validation set included 35 cases. From T2-weighted images, 971 radiomics features were extracted. Seven radiomics features remained after a two-step selection process. We used the seven radiomics features and clinical factors through multivariable logistic regression to build radiomics and clinical models, respectively. A radiomics-clinical model was also built that combined the independent clinical predictors with the radiomics features. Through ROC curves, the three models were evaluated and compared. In the radiomics model, AUCs were 0.826 and 0.796 in training and validation sets, respectively. In the clinical model, the AUCs were 0.923 and 0.926 in the training and validation sets, respectively. Decision curve analysis revealed that the radiomics-clinical model had the best diagnostic performance for distinguishing Warthin tumor from pleomorphic adenoma of the parotid gland (AUC = 0.962 and 0.934 for the training and validation sets, respectively). The radiomics-clinical model performed well in differentiating pleomorphic adenoma from Warthin tumor of the parotid gland. • The clinical model outperformed the radiomics model in distinguishing pleomorphic adenoma from Warthin tumor of the parotid gland. • The radiomics features extracted from T2-weighted images could help differentiate pleomorphic adenoma from Warthin tumor of the parotid gland. • The radiomics-clinical model was superior to the radiomics and the clinical models for differentiating pleomorphic adenoma from Warthin tumor of the parotid gland.

Integrative review protocol for Indigenous youth participation in health equity promotion

IntroductionIndigenous youth participation in decision-making is internationally recognised as a pathway to promote health equity, decolonisation and social inclusion. Hearing Indigenous youth voices and actively involving them in decisions that...

Two-step fitness selection for intra-host variations in SARS-CoV-2.

Spontaneous mutations introduce uncertainty into coronavirus disease 2019 (COVID-19) control procedures and vaccine development. Here, we perform a spatiotemporal analysis on intra-host single-nucleotide variants (iSNVs) in 402 clinical samples from 170 affected individuals, which reveals an increase in genetic diversity over time after symptom onset in individuals. Nonsynonymous mutations are overrepresented in the pool of iSNVs but underrepresented at the single-nucleotide polymorphism (SNP) level, suggesting a two-step fitness selection process: a large number of nonsynonymous substitutions are generated in the host (positive selection), and these substitutions tend to be unfixed as SNPs in the population (negative selection). Dynamic iSNV changes in subpopulations with different gender, age, illness severity, and viral shedding time displayed a varied fitness selection process among populations. Our study highlights that iSNVs provide a mutational pool shaping the rapid global evolution of the virus.

Protease Produced by Endophytic Fungi: A Systematic Review.

The purpose of this systematic review was to identify the available literature of production, purification, and characterization of proteases by endophytic fungi. There are few complete studies that entirely exhibit the production, characterization, and purification of proteases from endophytic fungi. This study followed the PRISMA, and the search was conducted on five databases: PubMed, PMC, Science Direct, Scopus Articles, and Web of Science up until 18 May 2021, with no time or language restrictions. The methodology of the selected studies was evaluated using GRADE. Protease production, optimization, purification, and characterization were the main evaluated outcomes. Of the 5540 initially gathered studies, 15 met the inclusion criteria after a two-step selection process. Only two studies optimized the protease production using statistical design and two reported enzyme purification and characterization. The genus Penicillium and Aspergillus were the most cited among the eleven different genera of endophytic fungi evaluated in the selected articles. Six studies proved the ability of some endophytic fungi to produce fibrinolytic proteases, demonstrating that endophytic fungi can be exploited for the further production of agents used in thrombolytic therapy. However, further characterization and physicochemical studies are required to evaluate the real potential of endophytic fungi as sources of industrial enzymes.

Pattern recognition of EMG signals for low level grip force classification

Grasping of the objects is the most frequent activity performed by the human upper limb. The amputations of the upper limb results in the need for prosthetic devices. The myoelectric prosthetic devices use muscle signals and apply control techniques for identification of different levels of hand gesture and force levels. In this study; a different level force contraction experiment was performed in which Electromyography (EMGs) signals and fingertip force signals were acquired. Using this experimental data; a two-step feature selection process is applied for the designing of a pattern recognition algorithm for the classification of different force levels. The two step feature selection process consist of generalized feature ranking using ReliefF, followed by personalized feature selection using Neighborhood Component Analysis (NCA) from the shortlisted features by earlier technique. The classification algorithms applied in this study were Support Vector Machines (SVM) and Random Forest (RF). Besides feature selection; optimization of the number of muscles during classification of force levels was also performed using designed algorithm. Based on this algorithm; the maximum classification accuracy using SVM classifier and two muscle set was achieved as high as 99%. The optimal feature set consisted features such as Auto Regressive coefficients, Willison Amplitude and Slope Sign Change. The mean classification accuracy for different subjects, achieved using SVM and RF was 94.5% and 91.7% respectively.

Are different medical school admission tests associated with the outcomes of a simulation-based OSCE?

BackgroundMedical school admission procedures have the common goal to select applicants with the greatest potential of becoming successful physicians. Hamburg Medical Faculty selects medical students by grade point average (GPA) and employs a two-step selection process of a natural sciences test (HAM-Nat), in some cases followed by multiple mini-interviews (HAM-Int). Multiple mini-interviews can predict non-cognitive outcomes, while GPA has predictive validity for cognitive outcomes. The aim of our study was to explore communication skills and clinical knowledge of advanced medical students according to their respective admission procedure.MethodsIn July 2019, 146 students grouped according to their admission procedure into GPA-only (19.2 %), HAM-Nat (33.6 %), HAM-Int (30.8 %), and Waiting List (16.4 %) participated in four OSCE stations which equally assessed students’ communication skills (OSCE part 1) and clinical knowledge (OSCE part 2) in simulated patient encounters, rated by physicians with checklists. Additionally, psychosocial assessors ranked communication skills with a global rating scale (GR). The students also participated in a multiple choice (MC) exam testing clinical knowledge. Kruskal-Wallis analyses of variance of test performance and Spearman correlation of instruments were calculated.ResultsStudents from the Waiting List group performed significantly worse on the MC exam compared to GPA-only and HAM-Int (adjusted p = .029 and 0.018, respectively). No significant differences were found between the admission groups with respect to communication skills. Global Rating and OSCE part 1 (communication) correlated significantly (ρ = 0.228, p = .006) as did OSCE part 2 (clinical knowledge) and MC exam (ρ = 0.242, p = .003), indicating criterion validity. Constructs did not overlap, indicating divergent validity.ConclusionsAdvanced medical students selected for undergraduate studies by multiple mini-interviews assessing psychosocial skills showed similar communication skills compared to students admitted to medical school by other entryways. It is unclear whether these similarities are due to an effective undergraduate longitudinal communication curriculum. Assessing baseline communication skills of all medical students at entry-level may aid with this question.

Friends from the Future: A Scoping Review of Research into Robots and Computer Agents to Combat Loneliness in Older People.

Background and AimLoneliness is a common problem in older adults and contributes to poor health. This scoping review aimed to synthesize and report evidence on the effectiveness of interventions using social robots or computer agents to reduce loneliness in older adults and to explore intervention strategies.MethodsThe review adhered to the Arksey and O’Malley process for conducting scoping reviews. The SCOPUS, PUBMED, Web of Science, EMBASE, CINAHL, PsycINFO, ACM Digital Library and IEEE Xplore databases were searched in November, 2020. A two-step selection process identified eligible research. Information was extracted from papers and entered into an Excel coding sheet and summarised. Quality assessments were conducted using the Mixed Methods Appraisal Tool.ResultsTwenty-nine studies were included, of which most were of moderate to high quality. Eighteen were observational and 11 were experimental. Twenty-four used robots, four used computer agents and one study used both. The majority of results showed that robots or computer agents positively impacted at least one loneliness outcome measure. Some unintended negative consequences on social outcomes were reported, such as sadness when the robot was removed. Overall, the interventions helped to combat loneliness by acting as a direct companion (69%), a catalyst for social interaction (41%), facilitating remote communication with others (10%) and reminding users of upcoming social engagements (3%).ConclusionEvidence to date suggests that robots can help combat loneliness in older adults, but there is insufficient research on computer agents. Common strategies for reducing loneliness include direct companionship and enabling social interactions. Future research could investigate other strategies used in human interventions (eg, addressing maladaptive social cognition and improving social skills), and the effects of design features on efficacy. It is recommended that more robust experimental and mixed methods research be conducted, using a combination of validated self-report, observational, and interview measures of loneliness.