Accurate Information Research Articles

Exploring Sparse Visual Prompt for Domain Adaptive Dense Prediction

The visual prompts have provided an efficient manner in addressing visual cross-domain problems. Previous works introduce domain prompts to tackle the classification Test-Time Adaptation (TTA) problem by placing image-level prompts on the input and fine-tuning prompts for each target domain. However, since the image-level prompts mask out continuous spatial details in the prompt-allocated region, it will suffer from inaccurate contextual information and limited domain knowledge extraction, particularly when dealing with dense prediction TTA problems. To overcome these challenges, we propose a novel Sparse Visual Domain Prompts (SVDP) approach, which applies minimal trainable parameters (e.g., 0.1%) to pixels across the entire image and reserves more spatial information of the input. To better apply SVDP in extracting domain-specific knowledge, we introduce the Domain Prompt Placement (DPP) method to adaptively allocates trainable parameters of SVDP on the pixels with large distribution shifts. Furthermore, recognizing that each target domain sample exhibits a unique domain shift, we design Domain Prompt Updating (DPU) strategy to optimize prompt parameters differently for each sample, facilitating efficient adaptation to the target domain. Extensive experiments were conducted on widely-used TTA and continual TTA benchmarks, and our proposed method achieves state-of-the-art performance in both semantic segmentation and depth estimation tasks.

Small Language Model Can Self-Correct

Generative Language Models (LMs) such as ChatGPT have exhibited remarkable performance across various downstream tasks. Nevertheless, one of their most prominent drawbacks is generating inaccurate or false information with a confident tone. Previous studies have devised sophisticated pipelines and prompts to induce large LMs to exhibit the capability for self-correction. However, large LMs are explicitly prompted to verify and modify their answers separately rather than completing all steps spontaneously like humans. Moreover, these complex prompts are extremely challenging for small LMs to follow. In this paper, we introduce the Intrinsic Self-Correction (ISC) in generative language models, aiming to correct the initial output of LMs in a self-triggered manner, even for those small LMs with 6 billion parameters. Specifically, we devise a pipeline for constructing self-correction data and propose Partial Answer Masking (PAM), aiming to endow the model with the capability for intrinsic self-correction through fine-tuning. We conduct experiments using LMs with parameters sizes ranging from 6 billion to 13 billion in two tasks, including commonsense reasoning and factual knowledge reasoning. Our experiments demonstrate that the outputs generated using ISC outperform those generated without self-correction. We believe that the output quality of even small LMs can be further improved by empowering them with the ability to intrinsic self-correct.

Enhancing Job Recommendation through LLM-Based Generative Adversarial Networks

Recommending suitable jobs to users is a critical task in online recruitment platforms. While existing job recommendation methods encounter challenges such as the low quality of users' resumes, which hampers their accuracy and practical effectiveness.With the rapid development of large language models (LLMs), utilizing the rich external knowledge encapsulated within them, as well as their powerful reasoning capabilities, is a promising way to complete users' resumes for more accurate recommendations. However, directly leveraging LLMs to enhance recommendation results is not a one-size-fits-all solution, as LLMs may suffer from fabricated generation and few-shot problems, which degrade the quality of resume completion. In this paper, we propose a novel LLM-based approach for job recommendation. To alleviate the limitation of fabricated generation for LLMs, we extract accurate and valuable information beyond users' self-description, which helps the LLMs better profile users for resume completion. Specifically, we not only extract users' explicit properties (e.g., skills, interests) from their self-description but also infer users' implicit characteristics from their behaviors for more accurate and meaningful resume completion. Nevertheless, some users still suffer from few-shot problems, which arise due to scarce interaction records, leading to limited guidance for high-quality resume generation. To address this issue, we propose aligning unpaired low-quality with high-quality generated resumes by Generative Adversarial Networks (GANs), which can refine the resume representations for better recommendation results. Extensive experiments on three large real-world recruitment datasets demonstrate the effectiveness of our proposed method.

Identifying Guarantors of War Veterans Using Robust-SEAL: A Case of the Korean War

Most countries provide veterans with various benefits to reward their sacrifice. Unfortunately, many veterans have failed to prove their status due to loss of military records. Thus, some governments allow the verification of those veterans through "buddy statements" obtained from the people who can vouch for the buddy's participation in the war. However, it is still challenging for veterans to find guarantors directly. With this background, we suggest to utilizing historical war records of combined operations to increase the pool of potential guarantors for the buddy statements. However, a combined operation network among troops can have missing edges and perturbations on attributes of the troop due to inaccurate information. In this study, we learn from some recorded interactions which might be incomplete and noisy, and predict missing linkages among the troops that might have interacted together in the war, by proposing Robust-SEAL (learning from Subgraphs, Embeddings, and Attributes for Link prediction). It combines two Graph Neural Network (GNN) architectures: robust Graph Convolutional Network which considers the uncertainty of node attributes with a probabilistic approach, and SEAL which improves the expressive power of the GNN with a labeling trick. Our proposed approach was applied to Korean War data with perturbations. For experimentations, we hid some actual interactions and found that Robust-SEAL restores missing interactions better than other GNN-based baselines.

Dr. Google vs. Dr. ChatGPT: Exploring the Use of Artificial Intelligence in Ophthalmology by Comparing the Accuracy, Safety, and Readability of Responses to Frequently Asked Patient Questions Regarding Cataracts and Cataract Surgery

ABSTRACT Purpose Patients are using online search modalities to learn about their eye health. While Google remains the most popular search engine, the use of large language models (LLMs) like ChatGPT has increased. Cataract surgery is the most common surgical procedure in the US, and there is limited data on the quality of online information that populates after searches related to cataract surgery on search engines such as Google and LLM platforms such as ChatGPT. We identified the most common patient frequently asked questions (FAQs) about cataracts and cataract surgery and evaluated the accuracy, safety, and readability of the answers to these questions provided by both Google and ChatGPT. We demonstrated the utility of ChatGPT in writing notes and creating patient education materials. Methods The top 20 FAQs related to cataracts and cataract surgery were recorded from Google. Responses to the questions provided by Google and ChatGPT were evaluated by a panel of ophthalmologists for accuracy and safety. Evaluators were also asked to distinguish between Google and LLM chatbot answers. Five validated readability indices were used to assess the readability of responses. ChatGPT was instructed to generate operative notes, post-operative instructions, and customizable patient education materials according to specific readability criteria. Results Responses to 20 patient FAQs generated by ChatGPT were significantly longer and written at a higher reading level than responses provided by Google (p < .001), with an average grade level of 14.8 (college level). Expert reviewers were correctly able to distinguish between a human-reviewed and chatbot generated response an average of 31% of the time. Google answers contained incorrect or inappropriate material 27% of the time, compared with 6% of LLM generated answers (p < .001). When expert reviewers were asked to compare the responses directly, chatbot responses were favored (66%). Conclusions When comparing the responses to patients’ cataract FAQs provided by ChatGPT and Google, practicing ophthalmologists overwhelming preferred ChatGPT responses. LLM chatbot responses were less likely to contain inaccurate information. ChatGPT represents a viable information source for eye health for patients with higher health literacy. ChatGPT may also be used by ophthalmologists to create customizable patient education materials for patients with varying health literacy.

Abstract 6474: Real-world assessment of turn-around time for actionable variant testing in NSCLC in the community setting

Abstract Introduction: Rapid turnaround times (TAT) is a key parameter when evaluating the performance of cell free (cf) nucleic acid testing in clinical oncology practice. The purpose of this study was to evaluate the impact of the real-world factors on TAT for actionable variants in NSCLC, detected using commercially available molecular diagnostic tests. These test services are run in a single, centralized laboratory site and utilized primarily by practices in the community across the United States. Methods: In this study, de-identified reference data was reviewed for a ddPCR test that evaluates hotspot mutations including Single Nucleotide Variants (SNV), Insertions and Deletions (INDEL), and gene fusions. The NGS panel additionally tests for Copy Number Amplification (CNA) mutations. Specimen processing was initiated when the specimen was entered into the Laboratory Information System (LIS) and ended when the test result was ready for release. The turnaround time evaluation excluded holidays, and weekends unless the start or end-point occurred on a non-business day. Results: In total, 35,441 specimen results were analyzed. Specimens were typically received in the laboratory within 24 hours of being shipped (88% in 24 hours). Tests by ddPCR included 30,490 (86%) results, while NGS testing accounted for 4,951 (14%) of results. The average analytic lab processing TAT for the ddPCR and NGS test ranged between 2 and 3 days (ddPCR median = 24.5 hours; NGS panel median = 69.7 hours). When no information was missing on the specimen test request form, the turn-around time for the ddPCR tests averaged 30.2 hours and NGS panel averaged 66.2 hours. The percentage of missing or inaccurate information was similar when test requests were initially received. The largest impact factor impacting initiation of specimen processing was information related annotation of the stage of cancer, followed by demographic information related to patient and specimen identifiers. Conclusion: The focus on rapid turnaround times from specimen collection to results released is critical in oncology. Site and patient information, shipping methods, technologies and workflows used in the laboratory are all part of the test process and impact the release of potentially actionable results. Our study has highlighted that rapid turnaround of results is highly feasible for molecular tests being sent out from community practices across the US to a centralized reference laboratory. Specifically, actionable variant results related to NSCLC were successfully detected in the laboratory and were ready for delivery in 2 to 3 days. Automated methods for parsing the information necessary for test requests and delivery will continue to be important in maintaining rapid test result turnaround times. Citation Format: Brittany D'Alessio, Adam Stephen, Colin Cochran, Cole Hager, Zachariah Velasco, Amanda Weaver, Leisa Jackson, Janice Riley, Alisa Tubbs, Gary A. Pestano. Real-world assessment of turn-around time for actionable variant testing in NSCLC in the community setting [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6474.

A New Climatology of Vegetation and Land Cover Information for South America

Accurate information on vegetation and land cover is crucial for numerical forecasting models in South America. This data aids in generating more realistic forecasts, serving as a tool for decision-making to reduce environmental impacts. Regular updates are necessary to ensure the data remains representative of local conditions. In this study, we assessed the suitability of ‘Catchment Land Surface Models-Fortuna 2.5’ (CLSM), Noah, and Weather Research and Forecasting (WRF) for the region. The evaluation revealed significant changes in the distribution of land cover classes. Consequently, it is crucial to adjust this parameter during model initialization. The new land cover classifications demonstrated an overall accuracy greater than 80%, providing an improved alternative. Concerning vegetation information, outdated climatic series for Leaf Area Index (LAI) and Greenness Vegetation Fraction (GVF) were observed, with notable differences between series, especially for LAI. While some land covers exhibited good performance for GVF, the Forest class showed limitations. In conclusion, updating this information in models across South America is essential to minimize errors and enhance forecast accuracy.

Abstract 3711: Procedure establishment of EGFR mutation test with circulating tumor cells of lung cancer by in vitro assay

Abstract Introduction: Non-small cell lung cancer (NSCLC) is the most common subtype, accounting for approximately 80% of lung cancer. The treatment for NSCLC has significantly improved since the introduction of targeted therapies. Epidermal growth factor receptor (EGFR) mutations are the most frequently found genetic alterations in NSCLC, and deletion of exon 19 is a representative example. Searching for this mutation may provide important clues to the use of EGFR tyrosine kinase inhibitors in NSCLC. So, the detection of EGFR mutation is important step in cancer diagnosis and determining the drug treatment strategy, but the detection of mutation with tissues has limitations such as clinical challenge to poorly accessible tumor tissues before and after treatment. The aim of this study was to explore the feasibility and utility of implementation of EGFR molecular testing from circulating tumor cells (CTC) in NSCLC. Here, we developed and optimized the procedure of mutated EGFR detection in CTC after isolation with CytoGen’s Smart BiopsyTM CTC isolator by cobas® EGFR Mutation Test. This EGFR mutation test in CTC showed the potential to overcome the limitations of tissue biopsy with high sensitivity and specificity in an in vitro assay. Method: PC-9 cells were used the positive control of EGFR mutation (EGFR exon 19 deletion). Limit of detection (LOD) was performed with PC-9 cells (200 cells to 5 cells, 6 points) spiked into blood sample (5mL, healthy donor) to mimic CTC in the blood of lung cancer patient. CTC were isolated from spiked samples using CytoGen’s Smart BiopsyTM CTC isolator, subsequently cobas® EGFR Mutation Test was carried out to detect of EGFR mutation. Results: In in vitro experiments of recovery test using cell lines spiked into blood from healthy donor, the recovery rate of CytoGen’s Smart Biopsy TM CTC isolator showed over 80%. As a result of LOD test, EGFR exon19 deletion mutation of PC-9 cells was clearly detected even in the smallest number of cells were spiked into blood (5 cells in 5mL blood) and was not detected at all in the negative controls. The result suggested that CytoGen’s Smart Biopsy TM CTC isolator can isolate CTC with high recovery rate and apply the test of oncogenic mutations including EGFR mutation in CTC. Conclusions: Confirmation of EGFR mutation is important step in the diagnosis and selection of optimal drug for cancer patients. However, tissue biopsy has several limitations such as difficulties to get tissue samples and inaccurate information due to invasive method and tumor heterogeneity. Detection of EGFR mutation in CTC might overcome and make up for limitations of tissue biopsy. In this study, we optimized the EGFR mutation detection procedure of intact live CTC isolated by CytoGen’s Smart BiopsyTM CTC isolator. Citation Format: Jihyun Lee, Hoin Kang, Min Hyung Kim, Jubi Lee, Hyoyong Kim, Soee Kim, Hae Ung Lee, Hai-Chon Lee, Jung Won Kim. Procedure establishment of EGFR mutation test with circulating tumor cells of lung cancer by in vitro assay [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3711.

Remote Sensing Extraction of Crown Planar Area and Plant Number of Papayas Using UAV Images with Very High Spatial Resolution

The efficient management of commercial orchards strongly requires accurate information on plant growing status for the implementation of necessary farming activities such as irrigation, fertilization, and pest control. Crown planar area and plant number are two very important parameters directly relating to fruit growth conditions and the final productivity of an orchard. In this study, in order to propose a novel and effective method to extract the crown planar area and number of mature and young papayas based on visible light images obtained from a DJ Phantom 4 RTK, we compared different vegetation indices (NGRDI, RGBVI, and VDVI), filter types (high- and low-pass filters), and filter convolution kernel sizes (3–51 pixels). Then, Otsu’s method was used to segment the crown planar area of the papayas, and the mean–standard deviation threshold (MSDT) method was used to identify the number of plants. Finally, the extraction accuracy of the crown planar area and number of mature and young papayas was validated. The results show that VDVI had the highest capability to separate the papayas from other ground objects. The best filter convolution kernel size was 23 pixels for the low-pass filter extraction of crown planar areas in mature and young plants. As to the plant number identification, segmentation could be set to the threshold with the highest F-score, i.e., the deviation coefficient n = 0 for single young papaya plants, n = 1 for single mature ones, and n = 1.4 for crown-connecting mature ones. Verification indicated that the average accuracy of crown planar area extraction was 93.71% for both young and mature papaya orchards and 95.54% for extracting the number of papaya plants. This set of methods can provide a reference for information extraction regarding papaya and other fruit trees with a similar crown morphology.

A Review of Crowdsourcing Update Methods for High-Definition Maps

High-definition (HD) maps serve as crucial infrastructure for autonomous driving technology, facilitating vehicles in positioning, environmental perception, and motion planning without being affected by weather changes or sensor-visibility limitations. Maintaining precision and freshness in HD maps is paramount, as delayed or inaccurate information can significantly impact the safety of autonomous vehicles. Utilizing crowdsourced data for HD map updating is widely recognized as a superior method for preserving map accuracy and freshness. Although it has garnered considerable attention from researchers, there remains a lack of comprehensive exploration into the entire process of updating HD maps through crowdsourcing. For this reason, it is imperative to review and discuss crowdsourcing techniques. This paper aims to provide an overview of the overall process of crowdsourced updates, followed by a detailed examination and comparison of existing methodologies concerning the key techniques of data collection, information extraction, and change detection. Finally, this paper addresses the challenges encountered in crowdsourced updates for HD maps.

Phase compensation of a continuous-variable quantum key distribution via temporal convolutional neural network

Abstract Although the continuous-variable quantum key distribution (CV-QKD) protocol based on a local local oscillator (LLO) can close all the security loopholes from the transmitted local oscillator (TLO), the phase noise caused by the inaccurate phase reference information limits the performance of the protocol. To reduce the residual phase noise, in this work, we propose a phase estimation and compensation method based on the temporal convolutional neural (TCN) model, where a part of phase information obtained by measuring pilot pulses is employed as the training data and input into the TCN module. With a trained TCN module, the subsequent phase drifts can be more accurately estimated, allowing for better phase compensation and lower phase noise. Numerical analysis shows that the proposed scheme can improve the transmission distance and the secret key rate of the LLO protocol.

P240: Sarcopenia of the lower limb on Magnetic Resonance Imaging in patients with Diabetes Mellitus – a systematic review

Abstract Introduction Sarcopenia is defined by low measures of muscle quantity and quality and physical performance. Accelerated sarcopenic changes in diabetic patients leads to biomechanical changes in the foot and limb loss. Aim To systematically review the literature on Magnetic Resonance Imaging (MRI) and sarcopenia in the lower limb in patients with diabetes. Methods A literature review was conducted in accordance with PRISMA guidelines. Embase and Medline databases were searched with Ovid interface, as well as Pubmed and Cochrane. The grey literature was also reviewed on ClinicalTrials.gov, WHO international clinical trials registry platform, Cochrane library and the NIHR journals library and ProQuest. The MeSH terms “sarcopenia” AND “diabetes mellitus” AND “magnetic resonance imaging” were employed in the primary search string. The articles were reviewed by two independent reviewers. Results 874 studies were screened. 20 articles were included in the final review. Methods of measurement included cross sectional area measurements, stereological muscle measurements, muscle volume estimation, adipose infiltration, signal intensity cut-off, Goutallier grading, semiquantitative 5-point scale. Reduction in cross-sectional area and volumes and greater intramuscular fat infiltration was noted in patients with diabetes Sarcopenia ++ in Diabetic polyneuropathy (DPN) compared to non-diabetic and diabetic but non-DPN counterparts. MRI findings correlate with neuropathy disability scores and physical tests. Single study using a semi-automated process to measure sarcopenia. Conclusion MRI has its merit in providing accurate and detailed information on skeletal muscle volume and assessment of fat infiltration in muscle. This is relevant to reducing complications associated with changes in the biomechanics of the foot.

Falsification of registration of organizations and registration of rights to securities in domestic criminal law

The article deals with the genesis of domestic criminal legislation on liability for distortion of accounting register information about legal entities and securities (Art. 1701 of the Criminal Code of the Russian Federation). The relevance of the topic is due to the lack of scientific research in the field of the noted encroachment. The main task is to search for and study criminal-legal norms ensuring the reliability of the mentioned information. Based on formal-legal and historical-legal methods of research it is concluded that the required criminal-legal norms in the pre-revolutionary period were absent, and there was a gap in the part of protection of the reliability of the register of securities owners, which was not filled until 2010. For the first time since the introduction of the registration of organizations in the Soviet period the submission of inaccurate information for this purpose was considered a crime (Art. 90, 226b of the Criminal Code of the RSFSR of 1922, Art. 187 of the Criminal Code of the RSFSR of 1926). At the present stage of the Criminal Code of the Russian Federation initially prohibited only the submission of false or distorted information, preventing fictitious registration in the Unified State Register of Legal Entities of business entities (part 1 of Art. 171), and only since the middle of 2010 (with an addition in 2018) and to this day the reliability of information of the accounting system of the securities market and registration data on all legal entities in the country has been put under the protection of the criminal law against falsification and distortion (Art.1701).

Digital and online tools employed by prospective teachers to access information

The proliferation of digital information technologies has underscored the heightened necessity for online information search. This study is directed towards the analysis of the strategies adopted by prospective teachers in the selection of digital content employed for accessing information, their preferences concerning the use of digital and online tools, and the rationale that underpins these preferences. To achieve this aim, the case study method was used. Employing the purposive sampling method, the study involved a participant pool of 72 teacher candidates in the 3rd and 4th grades within the faculties of education. To collect data, a personal information form, an online interview form, a focus group interview form, and a word association test were employed. The data were analyzed using the content and descriptive analysis methods. At the end of the study, the prospective teachers expressed a common tendency that when it comes to scientific research, they often prefer to search for articles whereas for more general inquiries, they have a propensity for utilizing internet searches. Most of the prospective teachers follow a step-by-step research process when accessing information. In addition, they employ both goal-oriented and general-content resources while in pursuit of scientific information. They pay attention to data reliability, accurate information, speed, and access to the Internet in the process of obtaining information. They consider the tools they use adequate and reliable. Teacher candidates establish complex conceptual networks among the array of tools they employ to access information. A comprehensive discussion concerning the outcomes of the research is also presented.

An Environmental Scan and Appraisal of Patient Online Resources for Managing Rheumatoid Arthritis Flares.

To conduct an environmental scan and appraisal of online patient resources to support rheumatoid arthritis (RA) flare self-management. We used the Google search engine (last search March 2023) using the terms "rheumatoid arthritis" and "flare management." Additional searches targeted major arthritis organizations, as well as regional, national, and international resources. Appraisal of the resources was conducted by 2 research team members and 1 patient partner to assess the understandability and actionability of the resource using the Patient Education Materials Assessment Tool (PEMAT). Resources rating ≥ 60% in both domains by either the research team or the patient partner were further considered for content review. During content review, resources were excluded if they contained product advertisements, inaccurate information, or use of noninclusive language. If content review criteria were met, resources were designated as "highly recommended" if both patient partners and researchers' PEMAT ratings were ≥ 60%. If PEMAT ratings were divergent and had a rating ≥ 60% from only 1 group of reviewers, the resource was designated "acceptable." We identified 44 resources; 12 were excluded as they did not pass the PEMAT assessment. Fourteen resources received ratings ≥ 60% on understandability and actionability from both researchers and patient partners; 10 of these were retained following content review as "highly recommended" flare resources. Of the 18 divergent PEMAT ratings, 8 resources were retained as "acceptable" following content review. There is high variability in the actionability and understandability of online RA flare materials; only 23% of resources were highly recommended by researchers and patient partners.

"There hasn't been a push to identify patients in the emergency department"-Staff perspectives on automated identification of candidates for pre-exposure prophylaxis (PrEP): A qualitative study.

Automated algorithms for identifying potential pre-exposure prophylaxis (PrEP) candidates are effective among men, yet often fail to detect cisgender women (hereafter referred to as "women") who would most benefit from PrEP. The emergency department (ED) is an opportune setting for implementing automated identification of PrEP candidates, but there are logistical and practical challenges at the individual, provider, and system level. In this study, we aimed to understand existing processes for identifying PrEP candidates and to explore determinants for incorporating automated identification of PrEP candidates within the ED, with specific considerations for ciswomen, through a focus group and individual interviews with ED staff. From May to July 2021, we conducted semi-structured qualitative interviews with 4 physicians and a focus group with 4 patient advocates working in a high-volume ED in Chicago. Transcripts were coded using Dedoose software and analyzed for common themes. In our exploratory study, we found three major themes: 1) Limited PrEP knowledge among ED staff, particularly regarding its use in women; 2) The ED does not have a standardized process for assessing HIV risk; and 3) Perspectives on and barriers/facilitators to utilizing an automated algorithm for identifying ideal PrEP candidates. Overall, ED staff had minimal understanding of the need for PrEP among women. However, participants recognized the utility of an electronic medical record (EMR)-based automated algorithm to identify PrEP candidates in the ED. Facilitators to an automated algorithm included organizational support/staff buy-in, patient trust, and dedicated support staff for follow-up/referral to PrEP care. Barriers reported by participants included time constraints, hesitancy among providers to prescribe PrEP due to follow-up concerns, and potential biases or oversight resulting from missing or inaccurate information within the EMR. Further research is needed to determine the feasibility and acceptability of an EMR-based predictive HIV risk algorithm within the ED setting.

Reducing hydrophobic drug adsorption in an in-vitro extracorporeal membrane oxygenation model

Extracorporeal membrane oxygenation (ECMO) is a life-saving cardiopulmonary bypass technology for critically ill patients with heart and lung failure. Patients treated with ECMO receive a range of drugs that are used to treat underlying diseases and critical illnesses. However, the dosing guidelines for these drugs used in ECMO patients are unclear. Mortality rate for patients on ECMO exceeds 40% partly due to inaccurate dosing information, caused in part by the adsorption of drugs in the ECMO circuit and its components. These drugs range in hydrophobicity, electrostatic interactions, and pharmacokinetics. Propofol is commonly administered to ECMO patients and is known to have high adsorption rates to the circuit components due to its hydrophobicity. To reduce adsorption onto the circuit components, we used micellar block copolymers (Poloxamer 188TM and Poloxamer 407TM) and liposomes tethered with poly(ethylene glycol) to encapsulate propofol, provide a hydrophilic shell and prevent its adsorption. Size, polydispersity index (PDI), and zeta potential of the delivery systems were characterized by dynamic light scattering, and encapsulation efficiency was characterized using High Performance Liquid Chromatography (HPLC). All delivery systems used demonstrated colloidal stability at physiological conditions for seven days, cytocompatibility with a human leukemia monocytic cell line, i.e., THP-1 cells, and did not activate the complement pathway in human plasma. We demonstrated a significant reduction in adsorption of propofol in an in-vitro ECMO model upon encapsulation in micelles and liposomes. These results show promise in reducing the adsorption of hydrophobic drugs to the ECMO circuits by encapsulation in nanoscale structures tethered with hydrophilic polymers on the surface.

The value of multimodality MR in T staging evaluation after neoadjuvant therapy for rectal cancer.

Surgery is the preferred treatment for rectal cancer, but surgical treatment alone sometimes does not achieve satisfactory results. To explore the value of multimodal Magnetic Resonance (MR) images in evaluating T staging of rectal cancer after neoadjuvant therapy and to compare and analyze with pathological results. This study retrospectively analyzed 232 patients with stage T3, T4 rectal cancer between January 1, 2017 and October 31, 2022. MR examination was performed within 3 days before surgery. Different MR sequences were used for mrT staging of rectal cancer after neoadjuvant therapy and compared with pathological pT staging. The accuracy of different MR sequences in evaluating T staging of rectal cancer was calculated, and the consistency between the two was analyzed by kappa test. The sensitivity, specificity, negative predictive value and positive predictive value of different MR sequences in evaluating rectal cancer invading mesorectal fascia after neoadjuvant therapy were calculated. A total of 232 patients with rectal cancer were included in the study. The accuracy of high-resolution T2 WI in evaluating T staging of rectal cancer after neoadjuvant therapy was 49.57%, and the Kappa value was 0.261. The accuracy of high-resolution T2WI combined with diffusion weighted imaging (DWI) in evaluating T staging of rectal cancer after neoadjuvant therapy was 61.64%, and the Kappa value was 0.411. The accuracy of high-resolution combined with DCE-MR images in evaluating T staging of rectal cancer after neoadjuvant therapy was 80.60%, and the Kappa value was 0.706. The sensitivity and specificity of high-resolution t2-weighted imaging (HR-T2WI) combined with dynamic contrast-enhancement magnetic resonance (DCE-MR) in evaluating the invasion of mesorectal fascia were 83.46% and 95.33%, respectively. Compared with HR-T2WI combined with DWI images for mrT staging of rectal cancer after neoadjuvant chemoradiotherapy (N-CRT), HR-T2WI combined with DCE-M has the highest accuracy in evaluating mrT staging of rectal cancer after neoadjuvant therapy (80.60%), and has a high consistency with pathological pT staging. It is the best sequence for T staging of rectal cancer after neoadjuvant therapy. At the same time, the sequence has high sensitivity and specificity in evaluating mesorectal fascia invasion, which can provide accurate perioperative information for the formulation of surgical plan.

Navigating Resource Challenges in Health Emergencies: The Role of Information Diffusion and Virus Spread in Demand Dynamics

The dynamics of medical resource demand during public health crises pose significant challenges to emergency supply chain management, particularly within an evolving and complex social environment. To explore this, the interactive effects of information diffusion and virus spreading on medical resource demand are investigated using a novel three-layer coevolution “information–epidemic–resource” model through Markov process simulations. The study firstly identifies eight factors influencing demand fluctuations in terms of some city characteristics, such as media exposure, consistency of public opinion, self-protection level, and restrictive protection level, while categorizing resources into individual holdings and centralized storage. Then, extensive simulations are examined to elucidate the impact of these factors. The results reveal that various city characteristics can affect fluctuation in demand for both individual holdings and centralized storage. Inaccurate media information tends to inflate fluctuations, while higher public opinion consistency can reduce it. Reinforcing self-protection decreases the demand fluctuations of individuals, and effective restrictive protections can reduce fluctuations in centralized resource storage. Moreover, an analytical simulation of various city scenarios, underpinned by statistical data from selected Chinese and German cities, demonstrates that distinct city characteristics significantly influence medical resource demand changes during epidemics. This underscores the importance of tailoring emergency medical supply strategies to the specific developmental traits of different countries and cities. This study provides valuable insights to researchers, governments, and enterprises, enhancing their preparedness and response for emergency supply chain disruptions.

An audit and analysis of electro convulsive therapy patient information sheets used in local health districts in New South Wales Australia.

Electroconvulsive Therapy (ECT) is a widely used psychiatric treatment; however, it remains contentious. It is therefore important that people are provided with accurate and balanced information before consenting to ECT. The aim of this study was to audit and analyse the content and language of ECT information sheets used in local health districts (LHDs) across the state of New South Wales Australia. Descriptive content analysis and evaluative linguistic analysis were used to investigate the information sheets, with findings then considered from a mad studies perspective. Thirteen ECT information sheets were obtained and reviewed, with the audit finding they lacked accuracy and balance. Linguistic tools were used to exaggerate positive outcomes and minimise negative effects. Despite commonalities, the structure and content of the sheets varied considerably. Findings indicate a need for co-design and co-production approaches to developing ECT information sheets. This should occur in genuine partnership with lived experience representatives based on current evidence, using neutral language, and with attention to their intent as part of processes of informed consent and decision making.