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Hi-C and Micro-C are the three-dimensional (3D) genome assays that use high-throughput sequencing. In the analysis, the sequenced paired-end reads are mapped to a reference genome to generate a two-dimensional contact matrix for identifying topologically associating domains (TADs), chromatin loops, and chromosomal compartments. On the other hand, the distance distribution of the paired-end mapped reads also provides insight into the 3D genome structure by highlighting global contact frequency patterns at distances indicative of loops, TADs, and compartments. This chapter presents a basic workflow for visualizing and analyzing contact distance distributions from Hi-C data. The workflow can be run on Google Colaboratory, which provides a ready-to-use Python environment accessible through a web browser. The notebook that demonstrates the workflow is available in the GitHub repository at https://github.com/rnakato/Springer_contact_distance_plot.

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BACKGROUNDS: The use of large language models (LLMs) in medicine can help physicians improve the quality and effectiveness of health care by increasing the efficiency of medical information management, patient care, medical research, and clinical decision-making. METHODS: We collected 34 frequently asked questions about glucocorticoid-induced osteoporosis (GIOP), covering topics related to the disease's clinical manifestations, pathogenesis, diagnosis, treatment, prevention, and risk factors. We also generated 25 questions based on the 2022 American College of Rheumatology Guideline for the Prevention and Treatment of Glucocorticoid-Induced Osteoporosis (2022 ACR-GIOP Guideline). Each question was posed to the LLM (ChatGPT-3.5, ChatGPT-4, and Google Gemini), and three senior orthopedic surgeons independently rated the responses generated by the LLMs. Three senior orthopedic surgeons independently rated the answers based on responses ranging between 1 and 4 points. A total score (TS) > 9 indicated 'good' responses, 6 ≤ TS ≤ 9 indicated 'moderate' responses, and TS < 6 indicated 'poor' responses. RESULTS: In response to the general questions related to GIOP and the 2022 ACR-GIOP Guidelines, Google Gemini provided more concise answers than the other LLMs. In terms of pathogenesis, ChatGPT-4 had significantly higher total scores (TSs) than ChatGPT-3.5. The TSs for answering questions related to the 2022 ACR-GIOP Guideline by ChatGPT-4 were significantly higher than those for Google Gemini. ChatGPT-3.5 and ChatGPT-4 had significantly higher self-corrected TSs than pre-corrected TSs, while Google Gemini self-corrected for responses that were not significantly different than before. CONCLUSIONS: Our study showed that Google Gemini provides more concise and intuitive responses than ChatGPT-3.5 and ChatGPT-4. ChatGPT-4 performed significantly better than ChatGPT3.5 and Google Gemini in terms of answering general questions about GIOP and the 2022 ACR-GIOP Guidelines. ChatGPT3.5 and ChatGPT-4 self-corrected better than Google Gemini.

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Objective: This study aims to understand customer perceptions of community pharmacies utilizing publicly available data from Google Maps platform. Materials and methods: Python was used to scrape data with Google Maps APIs. As a result, 17,237 reviews were collected from 512 pharmacies distributed over Riyadh city, Saudi Arabia. Logistic regression was conducted to test the relationships between multiple variables and the given score. In addition, sentiment analysis using VADER (Valence Aware Dictionary for Sentiment Reasoning) model was conducted on written reviews, followed by cross-tabulation and chi-square tests. Results: The Logistic regression model implies that a unit increase in the Pharmacy score enhances the odds of attaining a higher score by approximately 3.734 times. The Mann-Whitney U test showed that a notable and statistically significant difference between "written reviews" and "unwritten reviews" (U = 39,928,072.5, p < 0.001). The Pearson chi-square test generated a value of 2991.315 with 8 degrees of freedom, leading to a p value of 0.000. Discussion: Our study found that the willingness of reviewers to write reviews depends on their perception. This study provides a descriptive analysis of conducted sentiment analysis using VADAR. The chi-square test indicates a significant relationship between rating scores and review sentiments. Conclusion: This study offers valuable findings on customer perception of community pharmacies using a new source of data.

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New data sources and AI methods for extracting information are increasingly abundant and relevant to decision-making across societal applications. A notable example is street view imagery, available in over 100 countries, and purported to inform built environment interventions (e.g., adding sidewalks) for community health outcomes. However, biases can arise when decision-making does not account for data robustness or relies on spurious correlations. To investigate this risk, we analyzed 2.02 million Google Street View (GSV) images alongside health, demographic, and socioeconomic data from New York City. Findings demonstrate robustness challenges; built environment characteristics inferred from GSV labels at the intracity level often do not align with ground truth. Moreover, as average individual-level behavior of physical inactivity significantly mediates the impact of built environment features by census tract, intervention on features measured by GSV would be misestimated without proper model specification and consideration of this mediation mechanism. Using a causal framework accounting for these mediators, we determined that intervening by improving 10% of samples in the two lowest tertiles of physical inactivity would lead to a 4.17 (95% CI 3.84-4.55) or 17.2 (95% CI 14.4-21.3) times greater decrease in the prevalence of obesity or diabetes, respectively, compared to the same proportional intervention on the number of crosswalks by census tract. This study highlights critical issues of robustness and model specification in using emergent data sources, showing the data may not measure what is intended, and ignoring mediators can result in biased intervention effect estimates.

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Chemotherapy poses both physical and psychological challenges for patients, prompting many to seek answers independently through online resources. This study investigates German Google search behavior regarding chemotherapy-related terms using Google AdWords data from September 2018 to September 2022 to gain insights into patient concerns and needs. A total of 1461 search terms associated with "chemotherapy" were identified, representing 1,749,312 to 28,958,400 search queries. These terms were categorized into four groups based on frequency and analyzed. Queries related to "adjuvant" and "neoadjuvant" chemotherapy, as well as "immunotherapy", suggest potential confusion among patients. Breast cancer emerged as the most searched tumor type, with hair loss, its management, and dermatological issues being the most searched side effects. These findings underscore the role of search engines such as Google in facilitating access to healthcare information and provide valuable insights into patient thoughts and needs. Healthcare providers can leverage this information to deliver patient-centric care and optimize treatment outcomes.

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As mobile applications proliferate and user feedback becomes abundant, the task of identifying and resolving conflicts among application features is crucial for delivering satisfactory user experiences. This research, motivated to align application development with user preferences, introduces a novel methodology that leverages advanced Natural Language Processing techniques. The paper showcases the use of sentiment analysis using RoBERTa, topic modeling with Non-negative matrix factorization (NMF), and semantic similarity measures from Sentence-BERT. These techniques enable the identification of contradictory sentiments, the discovery of latent topics representing application features, and the clustering of related feedback instances. The approach detects conflicts by analyzing sentiment distributions within semantically similar clusters, further enhanced by incorporating antonym detection and negation handling. It employs majority voting, weighted ranking based on rating scores, and frequency analysis of feature mentions to resolve conflicts, providing actionable insights for prioritizing requirements. Comprehensive evaluations on large-scale iOS App Store and Google Play Store datasets demonstrate the approach's effectiveness, outperforming baseline methods and existing techniques. The research improves mobile application development and user experiences by aligning features with user preferences and providing interpretable conflict resolution strategies, thereby introducing a novel approach to the field of mobile application development.

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Background: Heart disease is one of the leading causes of death in the United States. Increased education and utilization of BLS by first responders have had a significant impact, but certain populations remain high risk, such as African Americans. Raising awareness among at-risk populations may lead to more bystander CPR performed, improving mortality rates. The influence of celebrity deaths and illnesses is an important driver of public awareness. Therefore, the cardiac arrests of both Bronny James and Damar Hamlin may have influenced cardiac arrest awareness. Methods: Google Trends data was pulled for the following search terms from 8/21/2022-8/14/2023: Cardiac arrest (disease), Cardiopulmonary Resuscitation (topic), Basic Life Support (topic), Myocardial Infarction (disease), Defibrillation (topic) and Automatic External Defibrillator (topic). The average relative search volume (RSV) for each search term was taken for a three-week period encompassing the week of and two weeks following the cardiac arrests of Damar Hamlin and Lebron James Jr., respectively. We used one-way ANOVA and independent sample t-tests to compare the average values of Damar Hamlin's and LeBron James Jr.'s incidents with their respective 12-month averages. Results: RSV was significantly higher surrounding Hamlin's cardiac arrest compared to James Jr.'s for Cardiopulmonary Resuscitation and Automatic External Defibrillator. RSV for Basic Life Support was increased in LeBron James Jr.'s time compared to the 12-month average and Damar Hamlin's incident. Compared to the 12-month average, Cardiac arrest, Cardiopulmonary Resuscitation, Defibrillation, and Automatic External Defibrillator during Hamlin's incident. Myocardial infarction RSV was higher during James Jr.'s incident compared to baseline. Over the long term, the search terms showed a significant increase after Damar Hamlin's incident when compared to before.RSV was significantly higher surrounding Hamlin's cardiac arrest compared to James Jr.'s for "Cardiopulmonary Resuscitation" (23.56 vs. 22.0, p < 0.00) and "Automatic External Defibrillator" (19.59 vs. 19.4, p < 0.00). RSV for "Basic Life Support" was increased in LeBron James Jr.'s time compared to the 12-month average and Damar Hamlin's incident (80.9 vs. 66.88, p = 0.04). Compared to the 12-month average, "Cardiac arrest," "Cardiopulmonary Resuscitation," "Defibrillation," and "Automatic External Defibrillator" during Hamlin's incident showed significant increases. "Myocardial infarction" RSV was higher during James Jr.'s incident compared to baseline (55 vs. 46.6, p = 0.026). Over the long term, the search terms showed a significant increase after Damar Hamlin's incident when compared to before (p < 0.05). Conclusions: Increases in the search terms for Hamlin's cardiac arrest compared to James Jr.'s cardiac arrest were associated with seeing the event live and increasing cardiac arrest awareness. Hamlins Cardiac Arrest also showed a significant increase in search terms over the long term. The increase in searches for "Basic Life Support" during James Jr.'s cardiac arrest indicates increased awareness. Also, the increase in myocardial infarction searches during both incidents could show confusion between cardiac arrest and myocardial infarction.

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In an environment, microbes often work in communities to achieve most of their essential functions, including the production of essential nutrients. Microbial biofilms are communities of microbes that attach to a nonliving or living surface by embedding themselves into a self-secreted matrix of extracellular polymeric substances. These communities work together to enhance their colonization of surfaces, produce essential nutrients, and achieve their essential functions for growth and survival. They often consist of diverse microbes including bacteria, viruses, and fungi. Biofilms play a critical role in influencing plant phenotypes and human microbial infections. Understanding how these biofilms impact plant health, human health, and the environment is important for analyzing genotype-phenotype-driven rule-of-life functions. Such fundamental knowledge can be used to precisely control the growth of biofilms on a given surface. Metagenomics is a powerful tool for analyzing biofilm genomes through function-based gene and protein sequence identification (functional metagenomics) and sequence-based function identification (sequence metagenomics). Metagenomic sequencing enables a comprehensive sampling of all genes in all organisms present within a biofilm sample. However, the complexity of biofilm metagenomic study warrants the increasing need to follow the Findability, Accessibility, Interoperability, and Reusable (FAIR) Guiding Principles for scientific data management. This will ensure that scientific findings can be more easily validated by the research community. This study proposes a dockerized, self-learning bioinformatics workflow to increase the community adoption of metagenomics toolkits in a metagenomics and meta-transcriptomics investigation. Our biofilm metagenomics workflow self-learning module includes integrated learning resources with an interactive dockerized workflow. This module will allow learners to analyze resources that are beneficial for aggregating knowledge about biofilm marker genes, proteins, and metabolic pathways as they define the composition of specific microbial communities. Cloud and dockerized technology can allow novice learners-even those with minimal knowledge in computer science-to use complicated bioinformatics tools. Our cloud-based, dockerized workflow splits biofilm microbiome metagenomics analyses into four easy-to-follow submodules. A variety of tools are built into each submodule. As students navigate these submodules, they learn about each tool used to accomplish the task. The downstream analysis is conducted using processed data obtained from online resources or raw data processed via Nextflow pipelines. This analysis takes place within Vertex AI's Jupyter notebook instance with R and Python kernels. Subsequently, results are stored and visualized in Google Cloud storage buckets, alleviating the computational burden on local resources. The result is a comprehensive tutorial that guides bioinformaticians of any skill level through the entire workflow. It enables them to comprehend and implement the necessary processes involved in this integrated workflow from start to finish. This manuscript describes the development of a resource module that is part of a learning platform named "NIGMS Sandbox for Cloud-based Learning" https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

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Clarifying vegetation changes and the driving factors can provide references for ecological restoration and sustainable social development. We analyzed vegetation distribution and trend changes in Henan Province and its basin zoning (Haihe River basin zoning, Yellow River basin zoning, Huaihe River basin zoning, Yangtze River basin zoning), with fractional vegetation cover data from 2000 to 2020 based on the Google Earth Engine platform, and by combining Theil-Sen Median trend analysis, Mann-Kendall test, and Hurst index. We also utilized factor detection and factor interaction to explore the individual and mutual influences of natural and anthropogenic factors on vegetation at different scales. The results showed that the fractional vegetation cover (FVC) in Henan Province exhibited a distribution pattern of higher coverage in the south and lower in the north during the study period, predominantly characterized by moderate to high vegetation coverage. The Yangtze River basin zoning had the highest coverage. FVC in Henan Province and its zoning exhibited a consistent pattern of fluctuating upward trends, with all areas showing significant improvement. Particularly, the Yangtze River basin zoning had the largest area of improvement. According to the Hurst index, apart from the possibility of continued improvement in the Huaihe River basin zoning, other zoning would be likely to shift from improvement to degradation in the future. Vegetation changes in Henan Province and its zoning were the result of combined effects of anthropogenic and natural factors, with the influence of these factors changing over time and the dominant factors varying by region. Anthropogenic factors such as land use/cover type and nighttime lighting had a stronger impact on vegetation than natural factors like elevation, slope, and annual mean low temperature. The interaction between factors, particularly between anthropogenic and natural factors, exhibited a nonlinear enhancing pattern.

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Real-time assessment of ecological environment quality in arid and semi-arid regions is crucial for the sustainable development of ecological environments in China. In this study, we constructed a topsoil remote sensing ecological index (TRSEI) by coupling five indicators, greenness, wetness, dryness, topsoil grain size, and heat, with the Google Earth Engine (GEE). With the index, we evaluated the ecological environment quality of Wuchuan County from 1990 to 2020, and examined the spatio-temporal variations of ecological environment quality and its driving factors by using univariate linear regression, multiple regression residual analysis, and Hurst index. Results showed that the first principal component of the TRSEI in the study area contributed over 70%, with a mean eigenvalue of 0.148, indicating the effective integration of various ecological indicators by TRSEI. The topsoil grain size index was essential for the assessment of ecological environment quality in arid and semi-arid regions. From 1990 to 2020, the fluctuation range of TRSEI in the study area was between 0.289 and 0.458, showing an overall slight deterioration trend. The ecological environment quality of cropland and de-farming region had improved, with the improved area accounting for 47.9% of the total area. The grassland, barren land, and construction land areas had deteriorated, with the deteriorated area accounting for 52.1% of the total area. In the future, 36.9% of the regions would experience continuous improvement in ecological environment quality, while 41.4% might continue to dete-riorate. Human activities were the primary driving factor for the changes in ecological environment quality in arid and semi-arid regions, accounting for 88.6% of the total area. Climate change also had a significant impact, accounting for 11.4% of the total area. The TRSEI could effectively assess the ecological environment quality of arid and semi-arid regions, providing a scientific basis for ecological conservation and construction in these areas.

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The U-Chang-Shi (Urumqi-Changji-Shihezi) urban cluster, located at the heart of Xinjiang, boasts abundant natural resources. Over the past two decades, rapid urbanization, industrialization, and climate change have significantly threatened the region's ecological livability. To comprehensively, scientifically, and objectively assess the ecological livability of this area, this study leverages the Google Earth Engine (GEE) platform and multi-source remote sensing data to develop a comprehensive evaluation metric: the Remote Sensing Ecological Livability Index (RSELI). This aims to examine the changes in the ecological livability of the U-Chang-Shi urban cluster from 2000 to 2020. The findings show that despite some annual improvements, the overall trend in ecological livability is declining, indicating that the swift pace of urbanization and industrialization has placed considerable pressure on the region's ecological environment. Land use changes, driven by urban expansion and the growth in agricultural and industrial lands, have progressively encroached upon existing green spaces and water bodies, further deteriorating the ecological environment. Additionally, the region's topographical features have influenced its ecological livability; large terrain fluctuations have made soil erosion and geological disasters common. Despite the central plains' vast rivers providing ample water resources, over exploitation and ill-conceived hydrological constructions have led to escalating water scarcity. The area near the Gurbantunggut Desert in the north, with its extremely fragile ecological environment, has long been unsuitable for habitation. This study provides a crucial scientific basis for the future development of the U-Chang-Shi urban cluster and hopes to offer theoretical support and practical guidance for the sustainable development and ecological improvement of the region.

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Large language models (LLMs) have significantly impacted various fields with their ability to understand and generate human-like text. This study explores the potential benefits and limitations of integrating LLMs, such as ChatGPT, into haematology practices. Utilizing systematic review methodologies, we analysed studies published after 1 December 2022, from databases like PubMed, Web of Science and Scopus, and assessing each for bias with the QUADAS-2 tool. We reviewed 10 studies that applied LLMs in various haematology contexts. These models demonstrated proficiency in specific tasks, such as achieving 76% diagnostic accuracy for haemoglobinopathies. However, the research highlighted inconsistencies in performance and reference accuracy, indicating variability in reliability across different uses. Additionally, the limited scope of these studies and constraints on datasets could potentially limit the generalizability of our findings. The findings suggest that, while LLMs provide notable advantages in enhancing diagnostic processes and educational resources within haematology, their integration into clinical practice requires careful consideration. Before implementing them in haematology, rigorous testing and specific adaptation are essential. This involves validating their accuracy and reliability across different scenarios. Given the field's complexity, it is also critical to continuously monitor these models and adapt them responsively.

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BACKGROUND: Large language models (LLMs) such as ChatGPT-4 and Google Gemini show potential for patient health education, but concerns about their accuracy require careful evaluation. This study evaluates the readability and accuracy of ChatGPT-4 and Google Gemini in answering questions about retinal detachment. METHODS: Comparative study analyzing responses from ChatGPT-4 and Google Gemini to 13 retinal detachment questions, categorized by difficulty levels (D1, D2, D3). Masked responses were reviewed by ten vitreoretinal specialists and rated on correctness, errors, thematic accuracy, coherence, and overall quality grading. Analysis included Flesch Readability Ease Score, word and sentence counts. RESULTS: Both Artificial Intelligence tools required college-level understanding for all difficulty levels. Google Gemini was easier to understand (p = 0.03), while ChatGPT-4 provided more correct answers for the more difficult questions (p = 0.0005) with fewer serious errors. ChatGPT-4 scored highest on most challenging questions, showing superior thematic accuracy (p = 0.003). ChatGPT-4 outperformed Google Gemini in 8 of 13 questions, with higher overall quality grades in the easiest (p = 0.03) and hardest levels (p = 0.0002), showing a lower grade as question difficulty increased. CONCLUSIONS: ChatGPT-4 and Google Gemini effectively address queries about retinal detachment, offering mostly accurate answers with few critical errors, though patients require higher education for comprehension. The implementation of AI tools may contribute to improving medical care by providing accurate and relevant healthcare information quickly.

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Purpose: To study geographic patterns of supply and demand for uveal melanoma and other ocular oncology healthcare by ocular oncology physicians in the United States. Methods: Google search interest data was obtained through trends.google.com. The combined-state density of ocular oncology physicians was calculated by dividing the number of practicing ocular oncologists in each state and its surrounding states by the state population. Relative search volume (RSV) values were divided by ocular oncology physician density to calculate the Google relative demand index (gRDI) for each state. Medicare (mRDI) and IRIS® Registry (iRDI) relative demand indices were calculated using prevalence data obtained through the Vision and Eye Health Surveillance System (VEHSS). Data from the US Census Bureau and Centers for Disease Control (CDC) databases were also utilized to analyze associations with poverty rates, percent living in urban or rural areas, vision screening rates, and ocular neoplasm rates. Results: Alabama showed the highest RSV (100), while the lowest was reported in New Mexico (20). Vermont had the highest density of combined-state ocular oncology ophthalmologists (1.85 per 100,000 residents). New Mexico had the lowest RDI (0.013 gRDI, 0.015 mRDI, 0.018 iRDI) with 32 combined-state ocular oncologists and a population of 2,114,371. Ocular neoplasm prevalence rates ranged between 1.32% and 5.40% and significantly correlated with RSV. Single-state gRDI correlated with rural status and negatively correlated with urban areas (≥50,000 individuals). Single-state ophthalmologist density correlated positively with percent living in urban areas and vision screening rates, and negatively with rural status. Conclusion: This study uncovered significant heterogeneity in the geographical distribution of ocular oncology physicians and RDI throughout the United States, highlighting potential undersupply scenarios. This may guide efforts to increase ocular oncology physician and surgeon availability in areas of need.

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OBJECTIVE: To understand post-pandemic interest in plastic surgery procedures via Joinpoint analysis of Google Trends search data. METHODS: Google Trends was used to quantify search volumes from January 2019-December 2022 for select cosmetic face and body procedures in the United States. A keyword analytic tool (Keywords Everywhere) extracted absolute search volumes (average monthly searches). Joinpoint analysis assessed search trends over time reported as monthly percentage change (MPC). RESULTS: All procedures queried, including a non-cosmetic control (cataract surgery), demonstrated expected declines at the start of the COVID-19 pandemic. Blepharoplasty, face lift, neck lift, and Botox demonstrated statistically significant increase in search volumes that remained elevated relative to pre-pandemic levels. Rhinoplasty, fillers, and abdominoplasty interest increased initially followed by return to pre-pandemic levels by the end of 2022. The remainder of search terms did not show a clear temporal associated with COVID-19 lockdowns. CONCLUSION: The "Zoom Boom" appears to be a real phenomenon reflected by sustained increase in public interest in relation to facial plastic procedures. LEVEL OF EVIDENCE: NA Laryngoscope, 2024.

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BACKGROUND: Artificial intelligence (AI) is a burgeoning new field that has increased in popularity over the past couple of years, coinciding with the public release of large language model (LLM)-driven chatbots. These chatbots, such as ChatGPT, can be engaged directly in conversation, allowing users to ask them questions or issue other commands. Since LLMs are trained on large amounts of text data, they can also answer questions reliably and factually, an ability that has allowed them to serve as a source for medical inquiries. This study seeks to assess the readability of patient education materials on cardiac catheterization across four of the most common chatbots: ChatGPT, Microsoft Copilot, Google Gemini, and Meta AI. METHODOLOGY: A set of 10 questions regarding cardiac catheterization was developed using website-based patient education materials on the topic. We then asked these questions in consecutive order to four of the most common chatbots: ChatGPT, Microsoft Copilot, Google Gemini, and Meta AI. The Flesch Reading Ease Score (FRES) was used to assess the readability score. Readability grade levels were assessed using six tools: Flesch-Kincaid Grade Level (FKGL), Gunning Fog Index (GFI), Coleman-Liau Index (CLI), Simple Measure of Gobbledygook (SMOG) Index, Automated Readability Index (ARI), and FORCAST Grade Level. RESULTS: The mean FRES across all four chatbots was 40.2, while overall mean grade levels for the four chatbots were 11.2, 13.7, 13.7, 13.3, 11.2, and 11.6 across the FKGL, GFI, CLI, SMOG, ARI, and FORCAST indices, respectively. Mean reading grade levels across the six tools were 14.8 for ChatGPT, 12.3 for Microsoft Copilot, 13.1 for Google Gemini, and 9.6 for Meta AI. Further, FRES values for the four chatbots were 31, 35.8, 36.4, and 57.7, respectively. CONCLUSIONS: This study shows that AI chatbots are capable of providing answers to medical questions regarding cardiac catheterization. However, the responses across the four chatbots had overall mean reading grade levels at the 11th-13th-grade level, depending on the tool used. This means that the materials were at the high school and even college reading level, which far exceeds the recommended sixth-grade level for patient education materials. Further, there is significant variability in the readability levels provided by different chatbots as, across all six grade-level assessments, Meta AI had the lowest scores and ChatGPT generally had the highest.

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Background Millions of individuals every day turn to the internet for assistance in understanding their hand conditions and potential treatments. While online educational resources appear abundant, there are concerns about whether resources meet the readability recommendations agreed upon by the American Medical Association (AMA) and the National Institutes of Health (NIH). Identifying educational resources that are readable for the majority of patients could improve a patient's understanding of their medical condition, subsequently improving their health outcomes. Methods The readability of the top five websites for the 10 most common hand conditions was examined using the Flesch-Kincaid (FK) analysis, comprising the FK reading ease and FK grade level. The FK reading ease score is an indicator of how difficult a text is to comprehend, while the FK grade level score is the grade level an individual reading a particular text would need to fully understand the text. Results The average FK reading ease was 56.00, which correlates with "fairly difficult (high school)". The average FK corresponded to an eighth-grade reading level, far above the sixth-grade reading level recommendation set by the AMA and NIH. Conclusion Patient education, satisfaction, and the patient-physician relationship can all be improved by providing patients with more readable educational materials. Our study shows there is an opportunity for drastic improvement in the readability of online educational materials. Guiding patients with effective search techniques, advocating for the creation of more readable materials, and having a better understanding of the health literacy barriers patients face will allow hand surgeons to provide more comprehensive care to patients.

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BACKGROUND: Tuberculosis (TB) burden and the underreporting of TB remain major health challenges in Indonesia. Interest in the internet is growing extensively, and the introduction of the TB mandatory electronic notification system in 2017 engaged the public's interest to leverage digital traces regarding TB information in Indonesia. OBJECTIVE: To quantify the correlation between Google Trends data and Indonesian TB surveillance data before and after the implementation of a mandatory TB notification system. METHODS: Google Trends searches on TB information were used. We used two sets of time series data, including before and after the launch of the TB notification system. Pearson's correlation was used to measure the correlation between TB search terms and official TB reports. RESULTS: The moving average graph showed a linear pattern of TB information with TB reports after 2017. Pearson's correlation estimated a high correlation for TB definition, TB symptoms, and official TB reports with an R-value range of 0.97 to -1.00 (p ≤ 0.05) and showed an increasing trend in TB information searching after 2016. CONCLUSION: Google Trends data can depict public interest in the TB epidemic. Validation of information-searching behavior is required to advocate the implementation of Google Trends for TB digital surveillance in Indonesia.

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Google DeepMind Technologies Limited (London, United Kingdom) recently released its new version of the biomolecular structure predictor artificial intelligence (AI) model named AlphaFold 3. Superior in accuracy and more powerful than its predecessor AlphaFold 2, this innovation has astonished the world with its capacity and speed. It takes humans years to determine the structure of various proteins and how the shape works with the receptors but AlphaFold 3 predicts the same structure in seconds. The version's utility is unimaginable in the field of drug discoveries, vaccines, enzymatic processes, and determining the rate and effect of different biological processes. AlphaFold 3 uses similar machine learning and deep learning models such as Gemini (Google DeepMind Technologies Limited). AlphaFold 3 has already established itself as a turning point in the field of computational biochemistry and drug development along with receptor modulation and biomolecular development. With the help of AlphaFold 3 and models similar to this, researchers will gain unparalleled insights into the structural dynamics of proteins and their interactions, opening up new avenues for scientists and doctors to exploit for the benefit of the patient. The integration of AI models like AlphaFold 3, bolstered by rigorous validation against high-standard research publications, is set to catalyze further innovations and offer a glimpse into the future of biomedicine.

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Background: Tele-assisted home-based palliative care (THPC) usually fulfills the desire of terminal patients to pass away at home. The overall costs of such a service deserve evaluation. Objectives: This study aims to determine health care utilization and costs for cancer patients at the end of life, stratified by THPC service. Design: Patients who received THPC were matched 1:1 based on age, gender, year of death, and propensity score with those who did not receive THPC. Setting/Subjects: A total of 773 cancer patients passed away in a regional hospital in Taiwan during the period of 2012-2020, of which 293 received THPC. Measurements: We measured the rates and costs of outpatient clinic visits, emergency department (ED) visits, hospitalizations, and intensive care unit (ICU) admissions during the last week, the last two weeks and the last month before death. In addition, we estimated the driving times and expenses required for transportation from each cancer patient's home to the hospital using Google Maps. National Health Insurance (NHI) reimbursements and out-of-pocket expenses were also calculated. Results: In comparison with patients without THPC, those who received THPC had a 50% lower likelihood of visiting the ED or being hospitalized, a more than 90% reduced chance of ICU admission, but were four times more likely to obtain their medicines from outpatient clinics. THPC patients had similar out-of-pocket expenditures, approximately half of the NHI costs, and lower rates and costs for ambulance transportation to the ED. Conclusions: THPC reduced health care costs for terminal cancer patients in the last week, the last two weeks, and the last month before death, while also increasing the likelihood of patients being able to rest and pass away at home.