A copper missile-triggered power coalescence and death vortex within tumor cell mitochondria for synergistic cuproptosis/phototherapy/chemotherapy.

The importance of copper homeostasis in mitochondria and copper-triggered modality of mitochondrial cell death have been confirmed. However, the existing copper-based nanoplatforms are focused on synergistic therapies while the intracellular therapeutic targets are relatively scattered. Effective integration of all targets within mitochondria to generate power coalescence remains a challenge. Herein, we developed a novel copper-based delivery system to trigger power coalescence and death vortex within tumor cell mitochondria. Specifically, a mitochondrial targeting "copper missile" loaded with curcumin (termed as Cur@CuS-TPP-HA, CCTH) was designed for cuproptosis/phototherapy/chemotherapy synergistic anti-tumor therapy. Once the CCTH NPs are shuttled to the mitochondria, near-infrared (NIR) irradiation initiates the release of copper ions and curcumin for in situ drug accumulation in cancer cell mitochondria. An excess of copper ions and curcumin can activate cuproptosis and mitochondrial apoptosis pathways, respectively. When combined, they can cause an increase in reactive oxygen species (ROS), damage to mitochondrial DNA (mt-DNA), and a decrease in energy supply, thereby leading to a "vicious circle" of mitochondrial damage that further enhances the tumor-killing efficacy. As a consequence, this "copper missile" exhibits advanced anti-tumor effects as verified through in vitro assessments and in vivo evaluations using the 4T1 breast tumor model, providing a promising approach for cuproptosis-based synergistic anti-tumor therapy.

Development and validation of a machine learning framework for improved resource allocation in the emergency department.

OBJECTIVE: The Emergency Severity Index (ESI) is the most commonly used system in over 70% of all U.S. emergency departments (ED) that uses predicted resource utilization as a means to triage [1], Mistriage, which includes both undertriage and overtriage has been a persistent issue, affecting 32.2% of total ED visits [2]. Our goal is to develop a machine learning framework that predicts patients' resource needs, thereby improving resource allocation during triage. METHODS: This retrospective study analyzed ED visits from the Medical Information Mart for Intensive Care IV, dividing the data into training (80%) and testing (20%) cohorts. We utilized data available during triage, including patient vital signs, age, gender, mode of arrival, medication history, and chief complaint. Azure AutoML was used to create different machine learning models trained to predict the 144 target columns including laboratory panels and imaging modalities as well as medications required during patients' ED visits. The 144 models' performance was evaluated using the area under the receiver operating characteristic curve (AUROC), F1 score, accuracy, precision and recall. RESULTS: A total of 391,472 ED visits were analyzed. 144 Voting ensemble models were created for each target. All frameworks achieved on average an AUC score of 0.82 and accuracy of 0.76. We gathered the feature importance for each target and observed that 'chief complaint', among others, had a high aggregate feature importance across different targets. CONCLUSION: This study shows the high accuracy in predicting resource needs for patients in the ED using a machine learning model. This can greatly improve patient flow and resource allocation in already resource limited emergency departments.

Using a Combination of Novel Research Tools to Understand Social Interaction in the Drosophila melanogaster Model for Fragile X Syndrome.

Fragile X syndrome (FXS), the most common monogenic cause of inherited intellectual disability and autism spectrum disorder, is caused by a full mutation (>200 CGG repeats) in the Fragile X Messenger Ribonucleoprotein 1 (FMR1) gene. Individuals with FXS experience various challenges related to social interaction (SI). Animal models, such as the Drosophila melanogaster model for FXS where the only ortholog of human FMR1 (dFMR1) is mutated, have played a crucial role in the understanding of FXS. The aim of this study was to investigate SI in the dFMR1B55 mutants (the groups of flies of both sexes simultaneously) using the novel Drosophila Shallow Chamber and a Python data processing pipeline based on social network analysis (SNA). In comparison with wild-type flies (w1118), SNA analysis in dFMR1B55 mutants revealed hypoactivity, fewer connections in their networks, longer interaction duration, a lower ability to transmit information efficiently, fewer alternative pathways for information transmission, a higher variability in the number of interactions they achieved, and flies tended to stay near the boundaries of the testing chamber. These observed alterations indicate the presence of characteristic strain-dependent social networks in dFMR1B55 flies, commonly referred to as the group phenotype. Finally, combining novel research tools is a valuable method for SI research in fruit flies.

Discharge process for patients experiencing homelessness in the emergency department: A thematic qualitative study.

People experiencing homelessness are more likely to utilize emergency departments than their non-homeless counterparts. However, obtaining a bed in a homeless shelter for patients can be complex. To better understand the challenges of finding a safe discharge plan for homeless patients in the emergency department, our team conducted interviews with emergency department social workers and homeless shelter case managers in the Boston area. We identified and mapped the stages in the processes performed by both parties, identifying challenges with successful placement into a shelter. Furthermore, we assembled a data dictionary of key factors considered when assessing a patient's fit for a homeless shelter. By identifying bottlenecks and areas of opportunity, this study serves as a first step in enabling homeless individuals to receive the post-discharge assistance they require.

Biomedical text readability after hypernym substitution with fine-tuned large language models.

The advent of patient access to complex medical information online has highlighted the need for simplification of biomedical text to improve patient understanding and engagement in taking ownership of their health. However, comprehension of biomedical text remains a difficult task due to the need for domain-specific expertise. We aimed to study the simplification of biomedical text via large language models (LLMs) commonly used for general natural language processing tasks involve text comprehension, summarization, generation, and prediction of new text from prompts. Specifically, we finetuned three variants of large language models to perform substitutions of complex words and word phrases in biomedical text with a related hypernym. The output of the text substitution process using LLMs was evaluated by comparing the pre- and post-substitution texts using four readability metrics and two measures of sentence complexity. A sample of 1,000 biomedical definitions in the National Library of Medicine's Unified Medical Language System (UMLS) was processed with three LLM approaches, and each showed an improvement in readability and sentence complexity after hypernym substitution. Readability scores were translated from a pre-processed collegiate reading level to a post-processed US high-school level. Comparison between the three LLMs showed that the GPT-J-6b approach had the best improvement in measures of sentence complexity. This study demonstrates the merit of hypernym substitution to improve readability of complex biomedical text for the public and highlights the use case for fine-tuning open-access large language models for biomedical natural language processing.

Entropy removal of medical diagnostics.

Shannon entropy is a core concept in machine learning and information theory, particularly in decision tree modeling. To date, no studies have extensively and quantitatively applied Shannon entropy in a systematic way to quantify the entropy of clinical situations using diagnostic variables (true and false positives and negatives, respectively). Decision tree representations of medical decision-making tools can be generated using diagnostic variables found in literature and entropy removal can be calculated for these tools. This concept of clinical entropy removal has significant potential for further use to bring forth healthcare innovation, such as quantifying the impact of clinical guidelines and value of care and applications to Emergency Medicine scenarios where diagnostic accuracy in a limited time window is paramount. This analysis was done for 623 diagnostic tools and provided unique insights into their utility. For studies that provided detailed data on medical decision-making algorithms, bootstrapped datasets were generated from source data to perform comprehensive machine learning analysis on these algorithms and their constituent steps, which revealed a novel and thorough evaluation of medical diagnostic algorithms.

Impact of medical technologies may be predicted using constructed graph bibliometrics.

Scientific research is driven by allocation of funding to different research projects based in part on the predicted scientific impact of the work. Data-driven algorithms can inform decision-making of scarce funding resources by identifying likely high-impact studies using bibliometrics. Compared to standardized citation-based metrics alone, we utilize a machine learning pipeline that analyzes high-dimensional relationships among a range of bibliometric features to improve the accuracy of predicting high-impact research. Random forest classification models were trained using 28 bibliometric features calculated from a dataset of 1,485,958 publications in medicine to retrospectively predict whether a publication would become high-impact. For each random forest model, the balanced accuracy score was above 0.95 and the area under the receiver operating characteristic curve was above 0.99. The high performance of high impact research prediction using our proposed models show that machine learning technologies are promising algorithms that can support funding decision-making for medical research.

Predictors of Mortality in Extracorporeal Membrane Oxygenation Support Patients Following Major Trauma.

INTRODUCTION: The usage of extracorporeal membrane oxygenation (ECMO) in trauma patients has increased significantly within the past decade. Despite increased research on ECMO application in trauma patients, there remains limited data on factors predicting morbidity and mortality outcome. Therefore, the primary objective of this study is to describe patient characteristics that are independently associated with mortality in ECMO therapy in trauma patients, to further guide future research. METHODS: This retrospective study was conducted using the Trauma Quality Improvement Program database from 2010 to 2019. All adult (age ≥ 16 y) trauma patients that utilized ECMO were included. A Significant differences (P < 0.05) in demographic and clinical characteristics between groups were calculated using an independent t-test for normal distributed continuous values, a Mann-Whitney U test for non-normal distributed values, and a Pearson chi-square test for categorical values. A multivariable regression model was used to identify independent predictors for mortality. A survival flow chart was constructed by using the strongest predictive value for mortality and using the optimal cut-off point calculated by the Youden index. RESULTS: Five hundred forty-two patients were included of whom 205 died. Multivariable analysis demonstrated that the female gender, ECMO within 4 h after presentation, a decreased Glasgow Coma Scale, increased age, units of blood in the first 4 h, and abbreviated injury score for external injuries were independently associated with mortality in ECMO trauma patients. It was found that an external abbreviated injury score of ≥3 had the strongest predictive value for mortality, as patients with this criterion had an overall 29.5% increased risk of death. CONCLUSIONS: There is an ongoing increasing trend in the usage of ECMO in trauma patients. This study has identified multiple factors that are individually associated with mortality. However, more research must be done on the association between mortality and noninjury characteristics like Pao2/Fio2 ratio, acute respiratory distress syndrome classification, etc. that reflect the internal state of the patient.

PlanNet: A Generative Model for Component-Based Plan Synthesis.

We propose a novel generative model named as PlanNet for component-based plan synthesis. The proposed model consists of three modules, a wave function collapse algorithm to create large-scale wireframe patterns as the embryonic forms of floor plans, and two deep neural networks to outline the plausible boundary from each squared pattern, and meanwhile estimate the potential semantic labels for the components. In this manner, we use PlanNet to generate a large-scale component-based plan dataset with 10 K examples. Given an input boundary, our method retrieves dataset plan examples with similar configurations to the input, and then transfers the space layout from a user-selected plan example to the input. Benefiting from our interactive workflow, users can recursively subdivide individual components of the plans to enrich the plan contents, thus designing more complex plans for larger scenes. Moreover, our method also adopts a random selection algorithm to make the variations on semantic labels of the plan components, aiming at enriching the 3D scenes that the output plans are suited for. To demonstrate the quality and versatility of our generative model, we conduct intensive experiments, including the analysis of plan examples and their evaluations, plan synthesis with both hard and soft boundary constraints, and 3D scenes designed with the plan subdivision on different scales. We also compare our results with the state-of-the-art floor plan synthesis methods to validate the feasibility and efficacy of the proposed generative model.

Does temperature favour the spread of Raphidiopsis raciborskii, an invasive bloom-forming cyanobacterium, by altering cellular trade-offs?

As a tropical filamentous cyanobacterium, Raphidiopsis raciborskii has attracted much attention due to its expansion and toxin production. However, the mechanisms of its expansion to temperate regions have not been studied in detail. To address the potential strategies, the physiological and metabolomic profiles of R. raciborskii FACHB 1096 isolated from a temperate lake in China were determined and measured at different temperatures (10 °C, 15 °C, 20 °C, 25 °C, and 32 °C). The results demonstrated that temperature significantly changed cell viability, chlorophyll a content, specific growth rate, Chl a fluorescence, and filamentous shape of R. raciborskii. Low temperature decreased cell viability, specific growth rate, and photosynthetic efficiency, while the proportion of akinete and carbon fixation per unit cell were significantly increased compared with high temperature (32 °C). A constructed unimodal model indicated that filament length, cell volume, and cell length/width of R. raciborskii were significantly reduced in both high and low temperature environments. Under low-temperature conditions, R. raciborskii suffered different degrees of oxidative damage and produced corresponding antioxidant substances to resist oxidative stress, suggesting that low temperature changes the metabolic level of the cells, causing the cells to gradually switch from development to defense. Metabolomic data further confirmed that temperature change induced shifts in metabolic pathways in R. raciborskii, including starch and sucrose metabolic pathways, glutathione metabolic pathways, and the pentose phosphate pathways (PPP), as well as metabolic pathways related to the tricarboxylic acid (TCA) cycle. Our results indicated that the trade-offs of R. raciborskii cells among the growth, cell size, and metabolites can be significantly regulated by temperature, with broad implications for its global expansion in temperate waterbodies.

Network Analysis of Academic Medical Center Websites in the United States.

Healthcare resources are published annually in repositories such as the AHA Annual Survey DatabaseTM. However, these data repositories are created via manual surveying techniques which are cumbersome in collection and not updated as frequently as website information of the respective hospital systems represented. Also, this resource is not widely available to patients in an easy-to-use format. Network analysis techniques have the potential to create topological maps which serve to aid in pathfinding for patients in their search for healthcare services. This study explores the topological structure of forty United States academic health center websites. Network analysis is utilized to analyze and visualize 48,686 webpages. Several elements of network structure are examined including basic network properties, and centrality measures distributions. The Louvain community detection algorithm is used to examine the extent to which these techniques allow identification of healthcare resources within networks. The results indicate that websites with related healthcare services tend to form observable clusters useful in mapping key resources within a hospital system.

Digital Impact Factor: A Quality Index for Educational Blogs and Podcasts in Emergency Medicine and Critical Care.

STUDY OBJECTIVE: Given the popularity of educational blogs and podcasts in medicine, learners and educators need tools to identify trusted and impactful sites. The Social Media Index was a multi-sourced formula to rank the effect of emergency medicine and critical care blogs. In 2022, a key data point for the Social Media Index became unavailable. This bibliometric study aimed to develop a new measure, the Digital Impact Factor, as a replacement. METHODS: The Digital Impact Factor incorporated modern measures of website authority and reach. This formula was applied to a cross-sectional study of active emergency medicine and critical care blogs and podcasts. For each website, we generated a Digital Impact Factor score based on Ahrefs Domain Rating and the follower count of the websites' pages from 8 social media platforms. A series of Spearman correlations provided evidence of association by comparing a rank-ordered list to rank lists derived from the Social Media Index over the last 5 years. The Bland-Altman analysis assessed for agreement. RESULTS: The authors identified 88 relevant websites with a median Ahrefs Domain Rating of 28 (range 0 to 71, maximum 100) and total social media followership count across 8 platforms of 1,828,557. The Domain Rating and individual social media followership scores were normalized based on the highest recorded values to yield the Digital Impact Factor (median 4.57; range 0.02 to 9.50, maximum 10). The correlation between the 2022 Digital Impact Factor and the 2021 Social Media Index was 0.94 (95% confidence interval 0.89 to 0.97; p<.001; n=41 rankings correlated), suggesting that they measure similar constructs. The Bland-Altman plot also demonstrated fair agreement between the 2 scores. CONCLUSION: The Digital Impact Factor is a measure of the relative effect of educational blogs and podcasts within emergency medicine and critical care.

Influence of ICU Surge and Capacity on COVID Mortality Across U.S. States and Regions During the COVID-19 Pandemic.

BACKGROUND: To describe the influence of COVID-19 caseload surges and overall capacity in the intensive care unit (ICU) on mortality among US population and census divisions. METHODS: A retrospective analysis of the national COVID ActNow database between January 1, 2021 until March 1, 2022. The main outcome used was COVID-19 weekly mortality rates, which were calculated and incorporated into several generalized estimation of effects models with predictor variables that included ICU bed capacity, as well as ICU capacity used by COVID cases while adjusting for ratios of vaccinations in populations, case density, and percentage of the population over the age of 65. RESULTS: Each 1% increase in general ICU capacity is correlated with approximately 5 more weekly deaths from COVID-19 per 100,000 population and each percentage increase in the number of patients with COVID-19 admitted to the ICU resulted in approximately 10 more COVID-19 deaths per week per 100,000 population. Significant differences in ability to handle caseload surges were observed across US census divisions. CONCLUSIONS: A strong association was observed between COVID-19 ICU surges, overall ICU surge, and increased mortality. Further research is needed to reveal best practices and public health measures to prevent ICU overcrowding amidst future pandemics and disaster responses.

Primary Perspectives in Meme Utilization as a Digital Driver for Medical Community Engagement and Education Mobilization: Pre-Post Study.

BACKGROUND: Memes have gone "viral," gaining increasing prominence as an effective communications strategy based on their unique ability to engage, educate, and mobilize target audiences in a call to action through a cost-efficient and culturally relevant approach. Within the medical community in particular, visual media has evolved as a means to influence clinical knowledge transfer. To this end, the GetWaivered (GW) project has leveraged memes as part of a behavioral economics toolkit to address one of the most critical public health emergencies of our time-the 20-year opioid epidemic. As part of a multidimensional digital awareness campaign to increase Drug Enforcement Administration (DEA)-X waiver course registration, GW investigated the results of meme usage in terms of impressions, website traffic, and ultimately user acquisition, as determined by web-based training enrollment and attendance outcomes. OBJECTIVE: The objective of this study was to determine the efficacy of implementing humor-based promotional content versus the traditional educational model, and how the translation of the increase in engagement would increase the participant count and website traffic for GW's remote DEA-X waiver training. METHODS: The approach to this study was based on 2 time frames (pre- and postcampaign). During April-July 2021, we developed a campaign via advertisements on Facebook, Twitter, Instagram, and the GW website to expand outreach. These memes targeted medical professionals with the ability to prescribe buprenorphine. The time frame of this campaign measured engagement metrics and compared values to preceding months (January-March 2021) for our GetWaivered website and social media pages, which translated to registrants for our remote DEA-X waiver training. RESULTS: By the end of July 2021, a total of 9598 individuals had visited the GW website. There was an average of 79.3 visitors per day, with the lowest number of daily visitors being 0 and the highest being 575. CONCLUSIONS: The use of memes may provide a medium for social media engagement (likes, comments, and shares) while influencing viewers to pursue a proposed action, such as e-training registration.

Correction: The Story of #GetMePPE and GetUsPPE.org to Mobilize Health Care Response to COVID-19 : Rapidly Deploying Digital Tools for Better Health Care.

[This corrects the article DOI: 10.2196/20469.].

Chinese medicine Yangmo decoction ameliorates intrauterine adhesion prognosis following hysteroscopic adhesiolysis. / ä¸­è¯å »è†œæ–¹æ”¹å–„å®«è ”é•œä¸‹å®«è ”ç²˜è¿žåˆ†ç¦»æœ¯åŽå®«è ”ç²˜è¿žçš„é¢„åŽ.

OBJECTIVES: Hysteroscopic adhesiolysis (HA) remains the mainstay on treatment for intrauterine adhesions (IUA). The fertility outcome of patients with moderate and severe intrauterine adhesions after HA is still far from satisfactory. Estrogen combined with progesterone is the most common treatment; however, they do not help in improving the fertility rate to the maximum because of the limitations. This retrospective, non-randomized controlled study will assess the effects of traditional Chinese medicine Yangmo decoction after HA in restoration of the endometrium and improvement of the fertility rate. METHODS: A total of 427 patients, who met the inclusion criteria, aged between 20 and 45 years and diagnosed with moderate or severe IUA underwent HA at the Third Xiangya Hospital from January to August 2021, were enrolled for this study. Participants were assigned into 2 groups: A Yangmo decoction group (n=213, patients were given Yangmo decoction consisting of Ginseng flower, Sanchi flower, Daidai flower, Snow lotus, Licorice and so on after HA), and an estrogen and progesterone group (n=214, patients were given estrogen and progesterone after HA). The following basic information was collected retrospectively for both groups, including age, parity, history of abortion, menstrual status, and times of hysteroscopic interventions. American Fertility Society (AFS) score was used by a senior surgeon and the density of opening of endometrial glands was evaluated during HA. The parameters were obtained from three-dimensional transvaginal ultrasound (3D-TVUS) preoperatively and postoperatively, to evaluate the efficacy of Yangmo decoction, estrogen, and progesterone. All patients were followed up on telephone to determine the fertility rate until 6 months from the last HA. RESULTS: Based on the basic information collected preoperatively, there were no significant differences between the groups (all P>0.05). Postoperatively, patients in the Yangmo decoction group had a better surgical success rate with a more significant AFS reduction (P<0.001), better density of opening of endometrial glands in the uterine cavity (P<0.000 1) after HA, and a better fertility rate (40.4%) in the time of 6 months after the last HA than those of the estrogen and progesterone group. CONCLUSIONS: Yangmo decoction has better therapeutic efficacy in the treatment of intrauterine adhesion after HA than the combined effect of estrogen and progesterone. Yangmo decoction helps restore the endometrium and improve the fertility rate, therefore, it can be adopted as a routine practice for IUA patients who have fertility requirements.