Accessibility Scores Research Articles

Mechanical Thrombectomy Access Score: A Systematic Review and Modified Delphi of Global Barriers to Endovascular Therapy.

The availability of mechanical thrombectomy (MT) for acute ischemic stroke is limited, and vast disparities exist between countries. We aim to create a MT access score to measure the drivers of access to help quantify and accelerate treatment worldwide. We used a systematic review complemented by a modified Delphi method. In the first of 3 rounds, 4 independent investigators performed a systematic literature review using key search terms that drive MT access, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. In the second round, a panel of 6 anonymous international experts selected key attributes needed for scoring. In the final round, a total of 12 attributes were selected on consensus, each given a score on a 0 to 3 scale. An ultimate MT access score (range, 0-36) was proposed as a new tool to use in identifying barriers to MT access and assist in providing an initial framework for public health interventions. Of 2864 abstracts screened, 121 studies were included in the final systematic review. A total of 34 attributes that potentially drive MT access were initially identified. In the final round, 12 attributes were selected by the expert panel: public awareness, emergency medical services transportation, prehospital large vessel occlusion screening, interhospital transfer policy, emergency department protocols, stroke imaging protocols, emergency department stroke expertise or telestroke availability, interventionalists, MT-capable centers, device availability, and insurance coverage. These attributes were weighted as part of the final score of 0 to 36. The MT access score represents the first tool to quantify barriers to global MT access. Its implementation stands not just as an academic achievement but as a beacon of hope for improving stroke care and outcomes worldwide, bringing us a step closer to bridging the gap in stroke treatment disparities.

De novo design of mIDH1 inhibitors by integrating deep learning and molecular modeling

BackgroundMutations in the IDH1 gene have been shown to be an important driver in the development of acute myeloid leukemia, gliomas and certain solid tumors, which is a promising target for cancer therapy.MethodsBidirectional recurrent neural network (BRNN) and scaffold hopping methods were used to generate new compounds, which were evaluated by principal components analysis, quantitative estimate of drug-likeness, synthetic accessibility analysis and molecular docking. ADME prediction, molecular docking and molecular dynamics simulations were used to screen candidate compounds and assess their binding affinity and binding stability with mutant IDH1 (mIDH1).ResultsBRNN and scaffold hopping methods generated 3890 and 3680 new compounds, respectively. The molecules generated by the BRNN performed better in terms of molecular diversity, druggability, synthetic accessibility and docking score. From the 3890 compounds generated by the BRNN model, 10 structurally diverse drug candidates with great docking score were preserved. Molecular dynamics simulations showed that the RMSD of the four systems, M1, M2, M3 and M6, remained stable, with local flexibility and compactness similar to the positive drug. The binding free energy results indicated that compound M1 exhibited the best binding properties in all energy aspects and was the best candidate molecule among the 10 compounds.ConclusionIn present study, compounds M1, M2, M3 and M6 generated by BRNN exhibited optimal binding properties. This study is the first attempt to use deep learning to design mIDH1 inhibitors, which provides theoretical guidance for the design of mIDH1 inhibitors.

Bridging the education gap: A comprehensive analysis of travel distance and education quality based spatial accessibility of early childhood education in Metropolitan Melbourne

In Australia, Melbourne's population is growing rapidly and will soon overtake Sydney, fuelled by a COVID-19 baby boom that has increased demand for early childhood education (ECE). In response, the Victorian Government has announced transformational reforms to improve the accessibility and quality of ECE for the growing urban population. This study aims to addresses the evolution and effectiveness of ECE reforms in Melbourne from 2021 to 2036 by proposing an innovative approach that integrates spatial accessibility and educational quality into ECE evaluations. Using the distance decay function, the Floating Catchment Area (FCA) method and the Huff model, it extends traditional geographical analyses by highlighting the importance of educational quality in parental choice and providing a refined technique for simulating local demand based on educational quality and travel distance. Overall, inequalities in educational resources have been identified, with a concentration of ECE services near the CBD, despite high population density, while peripheral areas have significant service deficits. Furthermore, the projected poor accessibility scores for 2026, 2031 and 2036 highlight the urgent need for educational infrastructure development, even in areas rated as having better accessibility in 2021.

Exploring the Potential Antidiabetic Effects of Alkannin (A Naphthoquinone Compound): In Vivo and In Silico Studies

Background It is estimated that the plant has been utilized in India’s traditional medical system for close to 2,500 years. Alkanin is a key active component in Boraginaceae medicinal plant roots. Many scientists are interested in this tiny molecule due to its biological potential. Purpose To examine the hypoglycemic effects of alkannin (a naphthoquinone pigment). Methods Initially, we determined the dosage of alkannin by conducting an acute oral toxicity study per Organisation for Economic Co-operation and Development guidelines. Subsequently, streptozocin was used to induce diabetes in mice, and we chose several parameters such as body weight, food and water intake, blood glucose level, and biochemical estimation for the in vivo evaluation. The enhanced pharmacokinetic features of alkannin, such as its better cytochromes P450 metabolism profiles and higher intestinal absorption as compared to glimepiride, were validated by in silico investigations utilizing Swiss ADME (Absorption, Distribution, Metabolism, and Excretion) and AutoDock Vina. Results In particular, blood glucose levels and body weight in the alkannin-treated group drastically decreased to 138 ± 1.45 mg/dL and 27.9±0.54 g, respectively, as compared with the diabetic-treated group. Moreover, alkaline phosphatase levels dropped to 57.26 ± 3.05 U/L in the alkannin group, suggesting enhanced liver and kidney functions. Creatinine levels also decreased from 1.44 ± 0.10 mg/dL to 0.92 ± 0.09 mg/dL, and serum glutamic oxaloacetic transaminase/aspartate aminotransferase levels decreased from 64.16 ± 3.14 U/L to 47.85 ± 2.01 U/L. Additionally, diabetic controls maintained cholesterol levels between 102.1 ± 11.46 mg/dL and 93.53 ± 2.61 mg/dL. With a considerable binding score of –9.2 kcal/mol for alkannin, molecular docking demonstrated a high degree of binding efficiency and robust interaction with biological targets. The docking results revealed that alkannin exhibits drug-like properties. In addition, it satisfies the lead likeness requirement and has a lower synthetic accessibility score compared to glimepiride. Conclusion The results revealed the promising characteristics and positive outcomes of alkannin, underscoring its potential as a viable candidate for further investigation and prospective advancement as a treatment drug for diabetes.

Access to bus services: an analysis of the influence of land use types

ABSTRACT During travel, commuters are naturally influenced by built environment factors, particularly land use types. However, the existing literature overlooks this impact and neglects the application of the adjusted Gaussian two-step floating catchment area method in studying accessibility to metro. To analyse the influence of land use types on accessibility to transit and the nature of such influence, we employed the adjusted Gaussian two-step floating catchment area method in measuring accessibility and the entropy index in gauging land use diversity. We also deduced the changes in accessibility scores to capture the effects of land use types. Additionally, we examined the differences in the bus and metro accessibility scores to clarify the influence of each mode. The accessibility scores in the study area were influenced by the land use types within it, and this influence was significant in the core administrative districts. These findings will support policymakers in land use and public transport planning.

AI-Guided Design of MALDI Matrices: Exploring the Electron Transfer Chemical Space for Mass Spectrometric Analysis of Low-Molecular-Weight Compounds.

The development of matrices for Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry (MALDI MS) has traditionally relied on experimental efforts. Here, we propose a Goal-Directed artificial intelligence generative model, fueled by computational chemistry calculated data, to construct a chemical space optimized for Electron Transfer (ET) processes in MALDI analysis. We utilized a group of 30 reported ET matrices, subjected to structural enumeration and molecular properties prediction using semiempirical and ab initio calculations, to establish a comprehensive database comprising diverse structural and property data. Subsequently, employing a protocol of structural enumeration with 68 canonical SMILES of Bemis-Murcko (BM) fragments, we expanded the structural complexity of the initial library. This process generated 82753 compounds organized into 10 scaffold levels, with a p50 index from the Cyclic System Retrieval (CSR) curve of scaffolds of 50%. From the resulting enumerated library, a diverse subset of structures was selected by using the Jarvis-Patrick clustering method. These structures, along with their associated properties measured from quantum mechanics and experimental data, were used to train a Machine Learning (ML) model to predict ionization energy (Ei) values. Subsequently, a Scoring Neural Network (SNN), coupled with our Goal-Directed generative model using a Recurrent Neural Network (RNN) with Deep Learning (DL) architectures, was trained. The generative model was guided using a prior network within a Reinforcement/Transfer Learning environment. The final AI-generative model learned that structures with high unsaturation, H/C ratios under 1, and molecular weights between 100 and 300 u are favorable for ET MALDI matrices, as well as those with few aromatic rings and zero aliphatic rings. Other molecular features were also favored. The resulting AI-generated library exhibits Ei values over 8.0 eV, akin to those of reported "good" ET MALDI matrices, indicating successful design with high synthesis accessibility scores. In conclusion, our generative model provided valuable insights into the molecular features ideal for ET MALDI compounds while generating a wide range of structurally diverse molecules within a similar molecular property space. The next critical step in this process is to synthesize a selection of these generated compounds for the experimental validation and further characterization.

SMILES-based QSAR virtual screening to identify potential therapeutics for COVID-19 by targeting 3CLpro and RdRp viral proteins

The COVID-19 pandemic has prompted the medical systems of many countries to develop effective treatments to combat the high rate of infection and death caused by the disease. Within the array of proteins found in SARS-CoV-2, the 3 chymotrypsin-like protease (3CLpro) holds significance as it plays a crucial role in cleaving polyprotein peptides into distinct functional nonstructural proteins. Meanwhile, RNA-dependent RNA polymerase (RdRp) takes center stage as the key enzyme tasked with replicating the viral genomic RNA within host cells. These proteins, 3CLpro and RdRp, are deemed optimal subjects for QSAR modeling due to their pivotal functions in the viral lifecycle. In this study, SMILES-based QSAR classification models were developed for a dataset of 2377 compounds that were defined as either active or inactive against 3CLpro and RdRp. Pharmacophore (PH4) and QSAR modeling were used for the virtual screening on 60.2 million compounds including ZINC, ChEMBL, Molport, and MCULE databases to identify new potent inhibitors against 3CLpro and RdRp. Then, a filter was established based on typical molecular characteristics to identify drug-like molecules. The molecular docking was also performed to evaluate the binding affinity of 156 AND 51 potential inhibitors to 3CLpro and RdRp, respectively. Among the 15 hits identified based on molecular docking scores, M3, N2, and N4 were identified as promising inhibitors due to their good synthetic accessibility scores (3.07, 3.11, and 3.29 out of 10 for M3, N2, and N4 respectively). These compounds contain amine functional groups, which are known for their crucial role in the binding interactions between drugs and their targets. Consequently, these hits have been chosen for further biological assay studies to validate their activity. They may represent novel 3CLpro and RdRp inhibitors possessing drug-like properties suitable for COVID-19 therapy.

The Utility of Immuno-Nutritional Scores in Patients with Testicular Germ Cell Tumors

Background: Hemoglobin, Albumin, Lymphocyte, and Platelet Score (HALP) is an accessible score that is easily reproducible from routine laboratory testing while also reflecting patients’ immune-nutritional status. Along with other immuno-nutritional scores, such as the Prognostic Nutrition Index (PNI), HALP has been associated with a number of clinical and pathological features. The goal of our study was to evaluate the prognostic utility of HALP and PNI scores in testicular germ cell cancer (GCT) patients. Methods: This case-only study included 203 testicular GCT patients who were classified according to the disease stage and HALP and PNI cut-offs. Complete blood count and albumin concentration were routinely determined. Results: The values of HALP and PNI significantly differed among different clinical stages (p < 0.05). Moreover, they clearly exposed a significantly higher risk of advanced clinical stage development for those testicular GCT patients with lower values of HALP and PNI (p < 0.05). Finally, lower score levels were associated with larger tumor size (p < 0.05). Conclusion: Our investigation could provide evidence that specific immune-nutritional scores can help distinguish individuals diagnosed with testicular GCT who are more likely to be identified with advanced disease stages.

Assessing Eye Clinic Accessibility: A Study Validating and Applying the SiteWise Survey.

To validate the SiteWise survey as a reliable tool for assessing the accessibility of outpatient ophthalmology clinics for visually impaired patients and to compare accessibility between hospital-based and satellite clinics. This quality improvement study, conducted from January to December 2023, used the SiteWise survey to assess design features in seven satellite and two hospital-based clinics within the Wilmer Eye Institute network. Independent surveyors evaluated elements such as parking, sidewalks, entrances, and interior areas. Reliability was measured using Krippendorf's alpha, and accessibility scores were compared using generalized estimated equations, analyses of variance, and t tests. The SiteWise survey demonstrated high reliability with a Krippendorf's alpha of 0.99. Hospital-based clinics had higher accessibility scores (mean 78.9%) compared to satellite clinics (mean 71.3%, P < 0.05). Areas such as hallways (mean 89%) and waiting areas (mean 87%) scored highest, whereas parking lots/sidewalks (mean 61%) and stairways (mean 61%) scored lowest, indicating significant room for improvement in these areas. The SiteWise tool is reliable and effective in identifying accessibility deficiencies in outpatient ophthalmology clinics. Although indoor areas generally scored well, outdoor and transitional spaces require significant enhancements to improve accessibility for visually impaired patients. This study bridges the gap between basic research and clinical care by providing a validated tool to assess and improve the accessibility of eye care facilities, ensuring they meet the needs of visually impaired patients.

Comparative analysis of the availability of popular social networking sites

In the era of growing number of Internet users and the importance of social media, ensuring the accessibility of these platforms for all audiences has become a crucial aspect of digital inclusion. This article presents a study on the accessibility of Facebook, Instagram, X, and TikTok using Google Lighthouse and eye tracking. Two hypotheses were analyzed: H1, assuming a relationship between accessibility scores and the time to the first eye fixation on key elements of social networks, and H2, suggesting a relationship between accessibility and performance. Eye tracking research on a sample of 15 people showed that high accessibility does not always translate to ease of use, and no significant correlation between accessibility and platform performance was found.

Discovering Photoswitchable Molecules for Drug Delivery with Large Language Models and Chemist Instruction Training.

Background: As large language models continue to expand in size and diversity, their substantial potential and the relevance of their applications are increasingly being acknowledged. The rapid advancement of these models also holds profound implications for the long-term design of stimulus-responsive materials used in drug delivery. Methods: The large model used Hugging Face's Transformers package with BigBird, Gemma, and GPT NeoX architectures. Pre-training used the PubChem dataset, and fine-tuning used QM7b. Chemist instruction training was based on Direct Preference Optimization. Drug Likeness, Synthetic Accessibility, and PageRank Scores were used to filter molecules. All computational chemistry simulations were performed using ORCA and Time-Dependent Density-Functional Theory. Results: To optimize large models for extensive dataset processing and comprehensive learning akin to a chemist's intuition, the integration of deeper chemical insights is imperative. Our study initially compared the performance of BigBird, Gemma, GPT NeoX, and others, specifically focusing on the design of photoresponsive drug delivery molecules. We gathered excitation energy data through computational chemistry tools and further investigated light-driven isomerization reactions as a critical mechanism in drug delivery. Additionally, we explored the effectiveness of incorporating human feedback into reinforcement learning to imbue large models with chemical intuition, enhancing their understanding of relationships involving -N=N- groups in the photoisomerization transitions of photoresponsive molecules. Conclusions: We implemented an efficient design process based on structural knowledge and data, driven by large language model technology, to obtain a candidate dataset of specific photoswitchable molecules. However, the lack of specialized domain datasets remains a challenge for maximizing model performance.

The relationship between food insecurity, purchasing patterns and perceptions of the food environment in urban slums in Ibadan, Nigeria

BackgroundRapid urbanisation without concomitant infrastructure development has led to the creation of urban slums throughout sub-Saharan Africa. People living in urban slums are particularly vulnerable to food insecurity due to the lack of physical and economic accessibility to food. Hence, it is important to explore how vulnerable groups living in slums interact with the food environment. This study assessed the relationships between food insecurity, including restrictive coping strategies, food purchasing patterns and perceptions about the food environment among dwellers of selected urban slums in Ibadan, Nigeria.MethodsThis community-based cross-sectional study was conducted with people responsible for food procurement from 590 randomly selected households in two urban slums in Ibadan. Food insecurity and restrictive coping strategies were assessed using the Household Food Insecurity Access Scale and the Coping Strategy Index, respectively. We examined purchasing patterns of participants by assessing the procurement of household foodstuffs in different categories, as well as by vendor type. Participants’ perceptions of the food environment were derived through a five-item composite score measuring food availability, affordability and quality. Chi-square tests and logistic regression models analysed associations between food insecurity, purchasing patterns and perceptions of the food environment.ResultsThe prevalence of food insecurity in the sample was 88%, with 40.2% of the households experiencing severe food insecurity. Nearly a third (32.5%) of the households used restrictive coping strategies such as limiting the size of food portions at mealtimes, while 28.8% reduced the frequency of their daily meals. Participants purchased food multiple times a week, primarily from formal and informal food markets rather than from wholesalers and supermarkets. Only a few households grew food or had livestock (3.2%). Food insecure households had a lower perceived access to the food environment, with an approximate 10% increase in access score per one-unit decrease in food insecurity (AOR = 0.90, 95% CI: 0.84, 0.96). The most procured foods among all households were fish (72.5%), bread (60.3%), rice (56.3%), yam and cassava flours (50.2%). Food-secure households procured fruit, dairy and vegetable proteins more frequently.ConclusionFood insecurity remains a serious public health challenge in the urban slums of Ibadan. Perceptions of greater access to the food environment was associated with increasing food security. Interventions should focus on creating more robust social and financial protections, with efforts to improve livelihoods to ensure food security among urban slum-dwellers.

Inclusion of disability in primary healthcare facilities and socioeconomic inequity in Brazil.

To describe disability-related performance and inequality nationwide in Brazil, and the changes that took place between 2012 and 2019 after the introduction of Programme for Improving Primary Care Access and Quality (PMAQ). We derived scores for disability-related care and accessibility of primary healthcare facilities from PMAQ indicators collected in round 1 (2011-2013), and round 3 (2015-2019). We assessed how scores changed after the introduction of PMAQ. We used census data on per capita income of local areas to examine the disability-specific care and accessibility scores by income group. We undertook ordinary least squares regressions to examine the association between PMAQ scores and per capita income of each local area across implementation rounds. Disability-related care scores were low in round 1 (18.8, 95%CI 18.3-19.3, out of a possible 100) and improved slightly by round 3 (22.5, 95%CI 22.0-23.1). Accessibility of primary healthcare facilities was also poor in round 1 (30.3, 95%CI 29.8-30.8) but doubled by round 3 (60.8, 95%CI 60.3-61.3). There were large socioeconomic inequalities in round 1, with both scores approximately twice as high in the richest compared to the poorest group. Inequalities weakened somewhat for accessibility scores by round 3. These trends were confirmed through regression analyses, controlling for other area characteristics. Disability-related and accessibility scores also varied strongly between states in both rounds. People with disabilities are being left behind by the Brazilian healthcare system, particularly in poor areas, which will challenge the achievement of universal health coverage.

Association of Neighborhood Social Determinants of Health with Acute Kidney Injury during Hospitalization.

Acute kidney injury (AKI) is common among hospitalized patients. However, the contribution of social determinants of health (SDOH) to AKI risk remains unclear. This study evaluated the association between neighborhood measures of SDOH and AKI development and recovery during hospitalization. This is a retrospective cohort study of adults without end-stage kidney disease admitted to a large southern U.S. healthcare system from 10/2014 to 9/2017. Neighborhood SDOH measures included: 1) Socioeconomic status: Area Deprivation Index (ADI) scores, 2) Food access: Low Income Low Access (LILA) scores, 3) Rurality: Rural Urban Commuting Area (RUCA) scores, and (4) Residential segregation: dissimilarity and isolation scores. The primary study outcome was AKI based on serum creatinine (SCr)-KDIGO criteria. Our secondary outcome was lack of AKI recovery (requiring dialysis or elevated SCr at discharge). The association of SDOH measures with AKI was evaluated using generalized estimating equation models adjusted for demographics and clinical characteristics. Among 26,769 patients, 26% developed AKI during hospitalization. Compared with those who did not develop AKI, those who developed AKI were older (median 60 vs. 57 years), more commonly men (55% vs. 50%), and more commonly self-identified as Black (38% vs. 33%). Patients residing in most disadvantaged neighborhoods (highest ADI tertile) had 10% (95%CI: 1.02-1.19) greater adjusted odds of developing AKI during hospitalization than counterparts in least disadvantaged areas (lowest ADI tertile). Patients living in rural areas had 25% higher adjusted odds of lack of AKI recovery by hospital discharge (95% CI: 1.07, 1.46). Food access and residential segregation were not associated with AKI development or recovery. Hospitalized patients from the most socioeconomically disadvantaged neighborhoods and from rural areas had higher odds of developing AKI and not recovering from AKI by hospital discharge, respectively. A better understanding of the mechanisms underlying these associations is needed to inform interventions to reduce AKI risk during hospitalization among disadvantaged populations.

How does the decision to cultivate underutilized crops influence food and nutrition security in rural areas?

The neglect in the cultivation, utilization, and diverse edible indigenous crops (ICs) threatens food-nutrition security (FNS) and rural development. Here, we (i) profiled the socioeconomics, ethnobotanicals and factors influencing the decision to cultivate edible ICs among smallholder farmers and (ii) factors influencing the FNS status of the ICs farmers in rural communities in South Africa using descriptive statistics and Heckman's probit-selection model to evaluate the factors influencing the decision to cultivate ICs and FNS. In the study, the household dietary diversity score (HDDS) was 7.01 and the household's food insecurity access scores (HFIAS) was 4.40. In addition, the food security indicator shows that 40.60 % of the participants were food insecure. Taken together, these metrics shows a significant existence of food and nutrition insecurity among the participants. Additionally, the decisions to cultivate ICs among the participants was driven by the household size, access to seeds, produce market, perceived nutritional benefits of the ICs and proportion of land used for cultivation significantly affects FNS status of the participants. Overall, the cultivation of these ICs is important as their cultivation significantly contributed to the FNS of the rural communities. Policy interventions, targeted at improving the present land tenure pattern, awareness on the value of ICs and incentives for production as a means for stimulating wider acceptance, cultivation, and sustainability in South Africa was recommended.

Assessing the Effectiveness of Telepharmacy Services in Rural and Underserved Areas

This study evaluates the effectiveness of telepharmacy services in improving healthcare delivery in rural and underserved areas. Utilizing a mixed-methods design, both quantitative and qualitative data were collected over a 12-month period. The study involved 200 patients who used telepharmacy services, providing remote access to pharmacists for medication dispensing, consultations, and patient education. Quantitative data included patient surveys on medication adherence and satisfaction, along with medication records to assess dispensing errors. Qualitative data were obtained through interviews with patients and pharmacists to explore their experiences. Results indicated a significant increase in medication adherence rates from 60.0% to 85.0% post-telepharmacy implementation. Patient satisfaction scores across various domains improved notably, with mean scores for access to pharmacists, quality of consultations, and overall satisfaction showing significant enhancements (p &lt; 0.001). Additionally, there was a substantial reduction in medication errors, with dispensing errors decreasing from 15.0% to 5.0% (p &lt; 0.01) and prescription errors from 10.0% to 4.0% (p &lt; 0.05). Economic analysis revealed a decrease in healthcare costs per patient from $2000 to $1500, along with a reduction in hospitalizations per 1000 patients from 50 to 30 (p &lt; 0.01). These findings are consistent with previous studies and underscore the potential of telepharmacy as a cost-effective and efficient solution to healthcare disparities in rural and underserved regions. Despite technological and regulatory challenges, telepharmacy represents a promising approach to enhancing healthcare access, improving clinical outcomes, and achieving significant cost savings in rural healthcare systems.

Assessment of printed lung cancer surgery patient education materials in the United States

ObjectiveWell-designed patient education materials (PEMs) increase health literacy, which has been linked to better surgical patient outcomes. The quality of lung cancer surgery PEMs is unknown, however. Here we assessed printed lung cancer surgery PEMs for readability, understandability, actionability, and accessibility. MethodsVarious lung cancer programs throughout the United States were contacted for their lung cancer surgery PEMs. The readability of the received materials was calculated using 6 readability tests. Four thoracic surgeon–advanced practice practitioner dyads scored the PEMs for understandability, actionability, and accessibility using the Patient Education Material Assessment Tool and the Accessibility Assessment Tool, with the recommended minimum threshold of 70%. One-sample t tests were performed to compare each parameter against its recommended threshold. ResultsOut of 34 institutions contacted, 18 (52.9%) provided PEMs. The average reading level of the PEMs ranged from 7th grade to 11th grade, significantly exceeding the recommended 6th grade health literacy threshold (P < .01). Although mean understandability (73.7 ± 13.2%) and actionability (70.2 ± 17.8%) scores were not significantly different from the minimum threshold, and the mean accessibility score (81.8 ± 13.5%) was significantly higher than the threshold (P < .05), there was wide variation in the scores. Most PEMs scored well in organization and writing but lacked other features that can enhance patient understanding, such as visual aids and summaries. ConclusionsPEMs are written at reading levels that are too advanced for patients. Although PEMs scored well in understandability, actionability, and accessibility, analysis of individual items revealed the need for improvement, including the use of shorter sentences, more visual aids and summaries, and expansion of language translations.

Factors associated with patients’ experience of accessibility to general practice: results from a national survey in Norway

BackgroundThis study aimed to explore the influence of patient-, general practitioners (GP)-, and GP practice-level predictor variables on patient-experienced accessibility to GPs and GP practices. Additionally, we aimed to enhance our understanding of patient-experienced accessibility by analysing the free-text comments from patients who reported lowest accessibility scores to GPs and GP practices.MethodsWe performed a secondary analysis of data from a 2021-2022 national Norwegian survey on patient experiences with their GP and GP practice. We identified seven accessibility-related items including experience and acceptance of regular waiting time and for urgent appointments, time spent with the GP, waiting time in the wating-room, and getting in touch with the GP practice by telephone. A composite accessibility score was computed. Predictor variables consisted of patient’s self-reported characteristics, as well as background data about the GP and GP practice from National GP registry. The analysis included multiple linear regression of the composite accessibility score and seven accessibility items. Finally, a qualitative analysis was conducted of free-text survey comments among patients that had a score of 0 (unfavourable) on all the seven accessibility items.ResultsThe key factor for patient-experienced accessibility to general practice was seeing their own GP, showing a statistically significant positive correlation (p<0.001) across all seven accessibility items and the composite accessibility score in regression analyses. Other associations with positive experience included better self-reported health, and at the GP-level, a specialization in general medicine. Conversely, a negative experience was associated with longer time since the last GP consultation, female patients, and a higher number of GPs at the practice. Qualitative data confirmed accessibility challenges, detailing quantitative scores and highlighted that low accessibility scores were related to difficulties in seeing one’s own GP.ConclusionsThis study highlights the importance of continuity between patient and their GP in improving patients’ experiences of accessibility to general practice. Several GP and GP practice-level factors were related to patient-reported accessibility. These results can be used to inform initiatives aimed at improving accessibility to general practice.

The Levels and Factors Associated with Access to Primary Care Services among Hypertensive Patients in Muang District, Phetchabun Province, Thailand: A Cross-Sectional Study

Objectives: The objectives of this study were to assess the levels of access to primary care services and to identify factors associated with access to primary care services among hypertensive patients in Muang district, Phetchabun province, Thailand. Methods: The cross-sectional study was conducted in the primary care unit of Phetchabun Hospital from 1 May 2023 to 31 July 2023. Purposive sampling was used to select hypertensive patients to be participants. Data were collected using the questionnaire. Access to primary care services was divided into 4 dimensions (availability, accessibility, accommodation, and acceptability). The mean accessibility score was calculated and interpreted into 3 levels (low-high). Sociodemographic data were analysed as factors associated with access to primary care services using multiple logistic regression. Results: A total of 120 participants were interviewed. Male-to-female ratio was 1:2.2. The mean age (±SD) was 61.9 (±6.2) years. Overall, access to primary care was high (3.74±0.62), with accommodation receiving the highest mean accessibility score (4.06±0.66) and accessibility the lowest (3.08±1.12). We found that age ≥ 60 years (P-value 0.039, 95% confidence interval (95% CI) 1.077 – 16.383), living family (P-value 0.018, 95% CI 0.019 – 0.692), living in urban area (P-value &lt;0.001, 95% CI 10.468 - 337.271), employed status (P-value 0.001, 95% CI 2.336 - 30.388) and income &gt;10,000 Baht per month (P-value 0.037, 95% CI 1.102 – 24.642) were associated with access to primary care. Conclusions: Overall, the level of primary care access was high. Age, living status, living area, employed status, and income were significantly associated factors. Expanding primary care services to local levels such as telemedicine might help improve the level of access. The effectiveness of expanding the service should be further study.

Machine learning assisted designing of Y-series small molecule acceptors: Library generation and property prediction

The designing of new small molecule acceptors (SMAs) for organic solar cells has been a prominent area of research for many decades. It is challenging to find unique materials due to expensive experimentation. Machine learning emerges as a promising tool for rapid and cost-effective prediction of properties. In the current study, electron affinity is predicted using machine learning models. Molecular descriptors are calculated for model training. Four machine learning models are used. A reasonably high predictive capability is obtained for random forest model (r-squared values of 0.92 and 0.82 for training and test set, respectively). Furthermore, the chemical database of small molecule acceptors is generated. Generated virtual space is anticipated by exploiting the t-distributed stochastic neighbor embedding (t-SNE) method. Structure Activity Landscape Index (SALI) analysis is conducted to examine how properties change with structural variations. A minimal change in electron affinity resulted from structural modifications. Additionally, clustering analysis is performed on selected small molecule acceptors for structural grouping of SMAs. Five SMAs with lowest synthetic accessibility (SA) score are selected for density functional theory (DFT) calculations. The introduced multiple dimensional framework has potential screened the materials in short-time and efficient way.