Synoptic Reporting by Summarizing Cancer Pathology Reports using Large Language Models.

Background: Synoptic reporting, the documenting of clinical information in a structured manner, is known to improve patient care by reducing errors, increasing readability, interoperability, and report completeness. Despite its advantages, manually synthesizing synoptic reports from narrative reports is expensive and error prone when the number of structured fields are many. While the recent revolutionary developments in Large Language Models (LLMs) have significantly advanced natural language processing, their potential for innovations in medicine is yet to be fully evaluated. Objectives: In this study, we explore the strengths and challenges of utilizing the state-of-the-art language models in the automatic synthesis of synoptic reports. Materials and Methods: We use a corpus of 7,774 cancer related, narrative pathology reports, which have annotated reference synoptic reports from Mayo Clinic EHR. Using these annotations as a reference, we reconfigure the state-of-the-art large language models, such as LLAMA-2, to generate the synoptic reports. Our annotated reference synoptic reports contain 22 unique data elements. To evaluate the accuracy of the reports generated by the LLMs, we use several metrics including the BERT F1 Score and verify our results by manual validation. Results: We show that using fine-tuned LLAMA-2 models, we can obtain BERT Score F1 of 0.86 or higher across all data elements and BERT F1 scores of 0.94 or higher on over 50% (11 of 22) of the questions. The BERT F1 scores translate to average accuracies of 76% and as high as 81% for short clinical reports. Conclusions: We demonstrate successful automatic synoptic report generation by fine-tuning large language models.

Hot spots and trends in microbial disease research on cultural heritage: a bibliometric analysis.

This study is to understand and analyze the development history, research hotspots, and research trends in the study of microbial diseases of cultural heritage through bibliometric analyses in order to fill the current gap of no literature review in this research field and to make certain contributions to the research in this field and the protection of cultural heritage. Bibliometric and visual analyses of the literature on cultural heritage microbial diseases in the Web of Science (WoS) core collection were carried out using VOSviewer and R-bibliometrix, choosing the two main literature types of papers and reviews. The emphasis was placed on analyzing and summarizing core research strengths, hotspots, and trends. Six hundred sixty-seven documents (573 articles and 94 reviews) were retrieved. αIn the WoS core collection, the first literature on cultural heritage microbial disease research was published in January 2000, and the annual number of publications from 2000 to 2009 did not exceed one; the annual number of publications from 2010 onwards increased rapidly, and after 2018, the number of publications per year exceeded 60, reaching 94 in 2020, which indicates that cultural heritage microbial disease research is booming. Our research showed that Italy, the USA, and China were the leading research countries, and Univ Milan was the institution with the most publications. International Biodeterioration &Biodegradation was the most published and co-cited journal, and Gu JD was the most prolific author. The research hotspots in the study of microbial diseases of cultural heritage mainly include biological degradation of cultural heritage; identification of diseased microorganisms and disease mechanisms; cultural heritage microbial disease prevention and control methods; monitoring, prevention, and control of diseased microorganisms in indoor air; antibacterial agents, especially essential oils, nanoparticles, and other safe and efficient antibacterial products research and development; and exploration of the mechanisms of biofilm protection of cultural heritage on cultural heritage surfaces. Monitoring and identifying cultural heritage microbial communities, identifying disease mechanisms, and researching safe and efficient bacteriostatic products such as essential oils and nanoparticles will be the main research directions in the field of cultural heritage microbial disease prevention and control in the future.

Age-related trajectories of the development of social cognition.

Age-related trajectories of intrinsic functional connectivity (iFC), which represent the interconnections between discrete regions of the human brain, for processes related to social cognition (SC) provide evidence for social development through neural imaging and can guide clinical interventions when such development is atypical. However, due to the lack of studies investigating brain development over a wide range of ages, the neural mechanisms of SC remain poorly understood, although considerable behavior-related evidence is available. The present study mapped vortex-wise iFC features between SC networks and the entire cerebral cortex by using common functional networks, creating the corresponding age-related trajectories. Three networks [moral cognition, theory of mind (ToM), and empathy] were selected as representative SC networks. The Enhanced Nathan Kline Institute-Rockland Sample (NKI-RS, N = 316, ages 8-83 years old) was employed delineate iFC characteristics and construct trajectories. The results showed that the SC networks display unique and overlapping iFC profiles. The iFC of the empathy network, an age-sensitive network, with dorsal attention network was found to exhibit a linear increasing pattern, that of the ventral attention network was observed to exhibit a linear decreasing pattern, and that of the somatomotor and dorsal attention networks was noted to exhibit a quadric-concave iFC pattern. Additionally, a sex-specific effect was observed for the empathy network as it exhibits linear and quadric sex-based differences in iFC with the frontoparietal and vision networks, respectively. The iFC of the ToM network with the ventral attention network exhibits a pronounced quadric-convex (inverted U-shape) trajectory. No linear or quadratic trajectories were noted in the iFC of the moral cognition network. These findings indicate that SC networks exhibit iFC with both low-level (somatomotor, vision) and high-level (attention and control) networks along specific developmental trajectories. The age-related trajectories determined in this study advance our understanding of the neural mechanisms of SC, providing valuable references for identification and intervention in cases of development of atypical SC.

Discovery of novel amide derivatives as potent quorum sensing inhibitors of Pseudomonas aeruginosa.

With the increasing reports of antibiotic resistance in this species, Pseudomonas aeruginosa is a common human pathogen with important implications for public health. Bacterial quorum sensing (QS) systems are potentially broad and versatile targets for developing new antimicrobial compounds. While previous reports have demonstrated that certain amide compounds can inhibit bacterial growth, there are few reports on the specific inhibitory effects of these compounds on bacterial quorum sensing systems. In this study, thirty-one amide derivatives were synthesized. The results of the biological activity assessment indicated that A9 and B6 could significantly inhibit the expression of lasB, rhlA, and pqsA, effectively reducing several virulence factors regulated by the QS systems of PAO1. Additionally, compound A9 attenuated the pathogenicity of PAO1 to Galleria mellonella larvae. Meanwhile, RT-qPCR, SPR, and molecular docking studies were conducted to explore the mechanism of these compounds, which suggests that compound A9 inhibited the QS systems by binding with LasR and PqsR, especially PqsR. In conclusion, amide derivatives A9 and B6 exhibit promising potential for further development as novel QS inhibitors in P. aeruginosa.

Best acupuncture method for mammary gland hyperplasia: Evaluation of randomized controlled trials and Bayesian network meta-analysis.

Objective: To evaluate the effectiveness of different acupuncture treatments for mammary gland hyperplasia (MGH) using a network meta-analysis. Methods: Several databases were searched without language restrictions from 2000 to February 2023, including PubMed, Embase, Web of Science, Cochrane Library, China Science and Technology Journal Database, China Biology Medicine Database, Wanfang Database, China National Knowledge Infrastructure Database, and other professional websites and gray literature. Inclusion criteria were adult women diagnosed with MGH; intervention measures included acupuncture and related therapies; the control group was treated with simple drugs; and the research type was a randomized controlled trial (RCT). The primary outcomes were treatment effectiveness and estradiol and progesterone levels. Secondary outcomes were breast lump size and visual analog scale (VAS) score of breast pain. Exclusion criteria were studies unrelated to MGH, incorrect study populations, control measures or interventions, incomplete data, non-RCTs, case reports, and animal experiments. Cochrane tools were used to assess the risk of bias. The R software (x64 version 4.2.1), Review Manager 5.3 software and STATA 16.0 software were used for data analysis. Results: Following a rigorous screening process, data extraction, and quality assessment, 48 eligible RCTs encompassing 4,500 patients with MGH and 16 interventions were included. The results indicated that acupuncture, alone or in combination with traditional Chinese or Western medicine, had better therapeutic effects than conventional therapy. In terms of effectiveness, warm needle acupuncture was the best choice (94.6%). Bloodletting pricking was the most effective method (85.7%) for lowering progesterone levels. Bloodletting pricking was the most effective method (98.3%) for lowering estradiol levels. Manual acupuncture combined with traditional Chinese medicine was the most effective (74.5%) treatment to improve the size of the breast lump. Warm needle acupuncture was the most effective (69.8%) in improving the VAS score. Conclusion: Acupuncture therapy was more effective in treating MGH than drug therapy alone, and warm needle acupuncture and bloodletting pricking were the two best options. However, larger sample sizes and high-quality RCTs are required.

Metal-Graphene Hybrid Terahertz Metasurfaces for Circulating Tumor DNA Detection Based on Dual Signal Amplification.

Terahertz (THz) spectroscopy has impressive capability for label-free biosensing, but its utility in clinical laboratories is rarely reported due to often unsatisfactory detection performances. Here, we fabricated metal-graphene hybrid THz metasurfaces (MSs) for the sensitive and enzyme-free detection of circulating tumor DNA (ctDNA) in pancreatic cancer plasma samples. The feasibility and mechanism of the enhanced effects of a graphene bridge across the MS and amplified by gold nanoparticles (AuNPs) were investigated experimentally and theoretically. The AuNPs serve to boost charge injection in the graphene film and result in producing a remarkable change in the graded transmissivity index to THz radiation of the MS resonators. Assay design utilizes this feature and a cascade hybridization chain reaction initiated on magnetic beads in the presence of target ctDNA to achieve dual signal amplification (chemical and optical). In addition to demonstrating subfemtomolar detection sensitivity and single-nucleotide mismatch selectivity, the proposed method showed remarkable capability to discriminate between pancreatic cancer patients and healthy individuals by recognizing and quantifying targeted ctDNAs. The introduction of graphene to the metasurface produces an improved sensitivity of 2 orders of magnitude for ctDNA detection. This is the first study to report the combined application of graphene and AuNPs in biosensing by THz spectroscopic resonators and provides a combined identification scheme to detect and discriminate different biological analytes, including nucleic acids, proteins, and various biomarkers.

Assessing the Seasonality of Lab Tests Among Patients with Alzheimer's Disease and Related Dementias in OneFlorida Data Trust.

About 1 in 9 older adults over 65 has Alzheimer's disease (AD), many of whom also have multiple other chronic conditions such as hypertension and diabetes, necessitating careful monitoring through laboratory tests. Understanding the patterns of laboratory tests in this population aids our understanding and management of these chronic conditions along with AD. In this study, we used an unimodal cosinor model to assess the seasonality of lab tests using electronic health record (EHR) data from 34,303 AD patients from the OneFlorida+ Clinical Research Consortium. We observed significant seasonal fluctuations-higher in winter in lab tests such as glucose, neutrophils per 100 white blood cells (WBC), and WBC. Notably, certain leukocyte types like eosinophils, lymphocytes, and monocytes are elevated during summer, likely reflecting seasonal respiratory diseases and allergens. Seasonality is more pronounced in older patients and varies by gender. Our findings suggest that recognizing these patterns and adjusting reference intervals for seasonality would allow healthcare providers to enhance diagnostic precision, tailor care, and potentially improve patient outcomes.

Quality of Answers of Generative Large Language Models Versus Peer Users for Interpreting Laboratory Test Results for Lay Patients: Evaluation Study.

BACKGROUND: Although patients have easy access to their electronic health records and laboratory test result data through patient portals, laboratory test results are often confusing and hard to understand. Many patients turn to web-based forums or question-and-answer (Q&A) sites to seek advice from their peers. The quality of answers from social Q&A sites on health-related questions varies significantly, and not all responses are accurate or reliable. Large language models (LLMs) such as ChatGPT have opened a promising avenue for patients to have their questions answered. OBJECTIVE: We aimed to assess the feasibility of using LLMs to generate relevant, accurate, helpful, and unharmful responses to laboratory test-related questions asked by patients and identify potential issues that can be mitigated using augmentation approaches. METHODS: We collected laboratory test result-related Q&A data from Yahoo! Answers and selected 53 Q&A pairs for this study. Using the LangChain framework and ChatGPT web portal, we generated responses to the 53 questions from 5 LLMs: GPT-4, GPT-3.5, LLaMA 2, MedAlpaca, and ORCA_mini. We assessed the similarity of their answers using standard Q&A similarity-based evaluation metrics, including Recall-Oriented Understudy for Gisting Evaluation, Bilingual Evaluation Understudy, Metric for Evaluation of Translation With Explicit Ordering, and Bidirectional Encoder Representations from Transformers Score. We used an LLM-based evaluator to judge whether a target model had higher quality in terms of relevance, correctness, helpfulness, and safety than the baseline model. We performed a manual evaluation with medical experts for all the responses to 7 selected questions on the same 4 aspects. RESULTS: Regarding the similarity of the responses from 4 LLMs; the GPT-4 output was used as the reference answer, the responses from GPT-3.5 were the most similar, followed by those from LLaMA 2, ORCA_mini, and MedAlpaca. Human answers from Yahoo data were scored the lowest and, thus, as the least similar to GPT-4-generated answers. The results of the win rate and medical expert evaluation both showed that GPT-4's responses achieved better scores than all the other LLM responses and human responses on all 4 aspects (relevance, correctness, helpfulness, and safety). LLM responses occasionally also suffered from lack of interpretation in one's medical context, incorrect statements, and lack of references. CONCLUSIONS: By evaluating LLMs in generating responses to patients' laboratory test result-related questions, we found that, compared to other 4 LLMs and human answers from a Q&A website, GPT-4's responses were more accurate, helpful, relevant, and safer. There were cases in which GPT-4 responses were inaccurate and not individualized. We identified a number of ways to improve the quality of LLM responses, including prompt engineering, prompt augmentation, retrieval-augmented generation, and response evaluation.

Quantum imaging of biological organisms through spatial and polarization entanglement.

Quantum imaging holds potential benefits over classical imaging but has faced challenges such as poor signal-to-noise ratios, low resolvable pixel counts, difficulty in imaging biological organisms, and inability to quantify full birefringence properties. Here, we introduce quantum imaging by coincidence from entanglement (ICE), using spatially and polarization-entangled photon pairs to overcome these challenges. With spatial entanglement, ICE offers higher signal-to-noise ratios, greater resolvable pixel counts, and the ability to image biological organisms. With polarization entanglement, ICE provides quantitative quantum birefringence imaging capability, where both the phase retardation and the principal refractive index axis angle of an object can be remotely and instantly quantified without changing the polarization states of the photons incident on the object. Furthermore, ICE enables 25 times greater suppression of stray light than classical imaging. ICE has the potential to pave the way for quantum imaging in diverse fields, such as life sciences and remote sensing.

Quality of Answers of Generative Large Language Models vs Peer Patients for Interpreting Lab Test Results for Lay Patients: Evaluation Study.

Background: Even though patients have easy access to their electronic health records and lab test results data through patient portals, lab results are often confusing and hard to understand. Many patients turn to online forums or question and answering (Q&A) sites to seek advice from their peers. However, the quality of answers from social Q&A on health-related questions varies significantly, and not all the responses are accurate or reliable. Large language models (LLMs) such as ChatGPT have opened a promising avenue for patients to get their questions answered. Objective: We aim to assess the feasibility of using LLMs to generate relevant, accurate, helpful, and unharmful responses to lab test-related questions asked by patients and to identify potential issues that can be mitigated with augmentation approaches. Methods: We first collected lab test results related question and answer data from Yahoo! Answers and selected 53 Q&A pairs for this study. Using the LangChain framework and ChatGPT web portal, we generated responses to the 53 questions from four LLMs including GPT-4, Meta LLaMA 2, MedAlpaca, and ORCA_mini. We first assessed the similarity of their answers using standard QA similarity-based evaluation metrics including ROUGE, BLEU, METEOR, BERTScore. We also utilized an LLM-based evaluator to judge whether a target model has higher quality in terms of relevance, correctness, helpfulness, and safety than the baseline model. Finally, we performed a manual evaluation with medical experts for all the responses of seven selected questions on the same four aspects. Results: Regarding the similarity of the responses from 4 LLMs, where GPT-4 output was used as the reference answer, the responses from LLaMa 2 are the most similar ones, followed by LLaMa 2, ORCA_mini, and MedAlpaca. Human answers from Yahoo data were scored lowest and thus least similar to GPT-4-generated answers. The results of Win Rate and medical expert evaluation both showed that GPT-4's responses achieved better scores than all the other LLM responses and human responses on all the four aspects (relevance, correctness, helpfulness, and safety). However, LLM responses occasionally also suffer from lack of interpretation in one's medical context, incorrect statements, and lack of references. Conclusions: By evaluating LLMs in generating responses to patients' lab test results related questions, we find that compared to other three LLMs and human answer from the Q&A website, GPT-4's responses are more accurate, helpful, relevant, and safer. However, there are cases that GPT-4 responses are inaccurate and not individualized. We identified a number of ways to improve the quality of LLM responses including prompt engineering, prompt augmentation, retrieval augmented generation, and response evaluation.

Rapid and sensitive detection of Staphylococcus aureus using a THz metamaterial biosensor based on aptamer-functionalized Fe3O4@Au nanocomposites.

Staphylococcus aureus (S. aureus) poses a serious threat to global public health, necessitating the establishment of rapid and simple tools for its accurate identification. Herein, we developed a terahertz (THz) metamaterial biosensor based on aptamer-functionalized Fe3O4@Au nanocomposites for quantitative S. aureus assays in different clinical samples. Fe3O4@Au@Cys@Apt has the dual advantages of magnetism and a high refractive index in the THz range and was used to rapidly separate and enrich target bacteria in a complex environmental solution. Furthermore, conjugation to the nanocomposites significantly increased the resonance frequency shift of the THz metamaterial after target loading. Our results showed that the shifts in the metamaterial resonance frequency were linearly related to S. aureus concentrations ranging from 1.0 × 103 to 1.0 × 107 CFU/mL, with a detection limit of 4.78 × 102 CFU/mL. The biosensor was further applied to S. aureus detection in spiked human urine and blood with satisfactory recoveries (82.4-109.6%). Our approach also demonstrated strong concordance with traditional plate counting (R2 = 0.99306) while significantly lowering the analysis time from 24 h to <1 h. In conclusion, the proposed biosensor can not only perform culture-free and extraction-free detection of target bacteria but can also be easily extended to the determination of other pathogenic bacteria, rendering it suitable for various bacteria-related disease diagnoses.

Adherence Promotion With Tailored Motivational Messages: Proof of Concept and Message Preferences in Older Adults.

This study examined the feasibility of using tailored text messages to promote adherence to longitudinal protocols and determined what facets of text message tone influence motivation. Forty-three older adults (Mage = 73.21, SD = 5.37) were recruited to engage in video-game-based cognitive training for 10 consecutive days. Participants received encouraging text messages each morning that matched their highest or lowest ranking reasons for participating in the study, after which they rated how effective each message was in motivating them to play the games that day. After 10 days, participants rated all possible messages and participated in semi-structured interviews to elicit their preferences for these messages. Results showed that messages matching participants' reasons for participating were more motivating than mismatched messages. Further, participants preferred messages that were personalized (i.e., use second person voice) and in formal tones. Messages consistent with these preferences were also rated as more motivating. These findings establish the feasibility of using message tailoring to promote adherence to longitudinal protocols and the relevance of tailoring messages to be personal and formal.

Enriching Real-world Data with Social Determinants of Health for Health Outcomes and Health Equity: Successes, Challenges, and Opportunities.

OBJECTIVE: To summarize the recent methods and applications that leverage real-world data such as electronic health records (EHRs) with social determinants of health (SDoH) for public and population health and health equity and identify successes, challenges, and possible solutions. METHODS: In this opinion review, grounded on a social-ecological-model-based conceptual framework, we surveyed data sources and recent informatics approaches that enable leveraging SDoH along with real-world data to support public health and clinical health applications including helping design public health intervention, enhancing risk stratification, and enabling the prediction of unmet social needs. RESULTS: Besides summarizing data sources, we identified gaps in capturing SDoH data in existing EHR systems and opportunities to leverage informatics approaches to collect SDoH information either from structured and unstructured EHR data or through linking with public surveys and environmental data. We also surveyed recently developed ontologies for standardizing SDoH information and approaches that incorporate SDoH for disease risk stratification, public health crisis prediction, and development of tailored interventions. CONCLUSIONS: To enable effective public health and clinical applications using real-world data with SDoH, it is necessary to develop both non-technical solutions involving incentives, policies, and training as well as technical solutions such as novel social risk management tools that are integrated into clinical workflow. Ultimately, SDoH-powered social risk management, disease risk prediction, and development of SDoH tailored interventions for disease prevention and management have the potential to improve population health, reduce disparities, and improve health equity.

A Novel Optical Fiber Terahertz Biosensor Based on Anti-Resonance for The Rapid and Nondestructive Detection of Tumor Cells.

The sensitive and accurate detection of tumor cells is essential for successful cancer therapy and improving cancer survival rates. However, current tumor cell detection technologies have some limitations for clinical applications due to their complexity, low specificity, and high cost. Herein, we describe the design of a terahertz anti-resonance hollow core fiber (THz AR-HCF) biosensor that can be used for tumor cell detection. Through simulation and experimental comparisons, the low-loss property of the THz AR-HCF was verified, and the most suitable fiber out of multiple THz AR-HCFs was selected for biosensing applications. By measuring different cell numbers and different types of tumor cells, a good linear relationship between THz transmittance and the numbers of cells between 10 and 106 was found. Meanwhile, different types of tumor cells can be distinguished by comparing THz transmission spectra, indicating that the biosensor has high sensitivity and specificity for tumor cell detection. The biosensor only required a small amount of sample (as low as 100 µL), and it enables label-free and nondestructive quantitative detection. Our flow cytometry results showed that the cell viability was as high as 98.5 ± 0.26% after the whole assay process, and there was no statistically significant difference compared with the negative control. This study demonstrates that the proposed THz AR-HCF biosensor has great potential for the highly sensitive, label-free, and nondestructive detection of circulating tumor cells in clinical samples.

Big advocacy, little recognition: the hidden work of Black patients in precision medicine.

As cost-effective next-generation genome sequencing rapidly develops, calls for greater inclusion of Black people in genomic research, policy, and practice are necessary for effective translation of genomic science into precision population health and medicine. Employing a community-based participatory mixed methods research design, we developed a semi-structured survey that was disseminated to three cancer advocacy organizations. Of the 81 survey respondents 49 (60%) self-identified as Black, and 26 (32%) indicated a prior breast cancer diagnosis. Black participants' expressed concerns about genetic testing were evenly distributed between concerns that could be addressed through genetic counseling (24%) and concerns about subsequent use of their genetic data (27%). Patient advocates contributed to contextualization of respondent concerns in terms of community experiences. Although genetic counseling services and policies governing genomic data use are not always accessible to many Black communities, advocates on our research team provided a bridge to discussion of the intersection between respondent concerns and the roles advocates play in filling gaps in access to genetic counseling and data governance. Concerns expressed by Black patients underscore a shared need among all patients for access to education, inclusion in research, and assurances regarding the use and handling of genetic data. Black cancer patients have joined in patient-led efforts to overcome systemic inequities in cancer care to improve their health outcomes through representation. Often their efforts are overshadowed by a relentless burden of continued health disparities. Future research should support their hidden work as a means to reduce barriers and improve representation in genomic databases.

Effects of Straw Mulching on Soil Properties and Enzyme Activities of Camellia oleifera-Cassia Intercropping Agroforestry Systems.

In order to explore the influences of rice straw mulching on soil fertility in agroforestry systems, the soil C and N contents and enzyme activities were investigated in a C. oleifera-cassia intercropping ecosystem in Central Southern China. Three straw mulching application treatments were set up in this study, in 2021, namely, straw powder mulching (SPM), straw segment mulching (SSM), and non-straw mulching as the control (CK). Soil samples were collected from three soil depths (0-10 cm,10-20 cm, and 20-40 cm) in each treatment on the 90th-day after the treatments. The soil organic carbon (SOC), total nitrogen (TN), microbial carbon (MBC), soil enzyme activities (including acid phosphatase (ACP), urease (UE), cellulase (CL), and peroxidase (POD)), and soil water content (SWC) were determined. The results showed that the SOC significantly increased due to the mulching application in SPM and SSM, in the topsoil of 0-10 cm when compared to the CK. The SWC, SOC, TN, and MBC increased by 0.8 and 56.5, 3.5 and 37.5, 21.3 and 61.6, and 5.8% and 76.8% in the SPM and SSM treatments compared to the CK, respectively. The soil enzyme activities of ACP, UE, CE, and POD increased significantly due to straw mulching compared with CK throughout all soil layers. The soil enzyme activities of CL and POD were significantly higher in SSM than in SPM across the soil depth except for ACP. The enzyme activities of ACP were 14,190, 12,732, and 6490 U/g in SSM, SPM, and control, respectively. This indicated that mulching application enhanced the enzyme activity of ACP. Mulching had no significant effects on UE and CL, while the POD decreased significantly in the order of SPM > SSM > CK across all soil layers, being, on average, 6.64% and 3.14% higher in SSM and SPM, respectively, compared to the CK plots. The SOC and MBC were the key nutrient factors affecting the soil enzyme activities at the study site. The results from this study provided Important scientific insights for improving soil physicochemical properties during the management of the C. oleifera intercropping system and for the development of the C. oleifera industry.

Phytochemistry and biological activities of corynanthe alkaloids.

Medicinal plants constitute a source for designing clinically useful drugs targeting diseases through various mechanisms. Plant secondary metabolites can be used as lead compounds of drugs. Corynanthe alkaloids are highly abundant natural bioactive substances of various core structures possessing important properties such as nerve excitation and antimalarial and analgesic effects. In this review, we summarize and review the state-of-the-art corynanthe-type alkaloid research focusing on phytochemistry, pharmacology, and structural chemistry. Approximately 120 articles reporting 231 alkaloids classified into simple corynanthe, yohimbine, oxindole corynanthe, mavacurane, sarpagine, akuammiline, strychnos, and ajmaline-type groups were compiled. Relevant biological properties discussed include antiviral, antibacterial, anti-inflammatory, antimalarial, muscle-relaxant, vasorelaxant, and analgesic activities and activities affecting the main nervous and cardiac systems, as well as NF-κB inhibitory and Na+-glucose cotransporter inhibitory properties. This review provides insights and a reference for future studies, thus paving the way for the discovery of drugs based on corynanthe alkaloids.

Towards Interpretable Multimodal Predictive Models for Early Mortality Prediction of Hemorrhagic Stroke Patients.

The increasing death rate over the past eight years due to stroke has prompted clinicians to look for data-driven decision aids. Recently, deep-learning-based prediction models trained with fine-grained electronic health record (EHR) data have shown superior promise for health outcome prediction. However, the use of EHR-based deep learning models for hemorrhagic stroke outcome prediction has not been extensively explored. This paper proposes an ensemble deep learning framework to predict early mortality among ICU patients with hemorrhagic stroke. The proposed ensemble model achieved an accuracy of 83%, which was higher than the fusion model and other baseline models (logistic regression, decision tree, random forest, and XGBoost). Moreover, we used SHAP values for interpretation of the ensemble model to identify important features for the prediction. In addition, this paper follows the MINIMAR (MINimum Information for Medical AI Reporting) standard, presenting an important step towards building trust among the AI system and clinicians.

Prediction of Outcomes After Heart Transplantation in Pediatric Patients Using National Registry Data: Evaluation of Machine Learning Approaches.

BACKGROUND: The prediction of posttransplant health outcomes for pediatric heart transplantation is critical for risk stratification and high-quality posttransplant care. OBJECTIVE: The purpose of this study was to examine the use of machine learning (ML) models to predict rejection and mortality for pediatric heart transplant recipients. METHODS: Various ML models were used to predict rejection and mortality at 1, 3, and 5 years after transplantation in pediatric heart transplant recipients using United Network for Organ Sharing data from 1987 to 2019. The variables used for predicting posttransplant outcomes included donor and recipient as well as medical and social factors. We evaluated 7 ML models-extreme gradient boosting (XGBoost), logistic regression, support vector machine, random forest (RF), stochastic gradient descent, multilayer perceptron, and adaptive boosting (AdaBoost)-as well as a deep learning model with 2 hidden layers with 100 neurons and a rectified linear unit (ReLU) activation function followed by batch normalization for each and a classification head with a softmax activation function. We used 10-fold cross-validation to evaluate model performance. Shapley additive explanations (SHAP) values were calculated to estimate the importance of each variable for prediction. RESULTS: RF and AdaBoost models were the best-performing algorithms for different prediction windows across outcomes. RF outperformed other ML algorithms in predicting 5 of the 6 outcomes (area under the receiver operating characteristic curve [AUROC] 0.664 and 0.706 for 1-year and 3-year rejection, respectively, and AUROC 0.697, 0.758, and 0.763 for 1-year, 3-year, and 5-year mortality, respectively). AdaBoost achieved the best performance for prediction of 5-year rejection (AUROC 0.705). CONCLUSIONS: This study demonstrates the comparative utility of ML approaches for modeling posttransplant health outcomes using registry data. ML approaches can identify unique risk factors and their complex relationship with outcomes, thereby identifying patients considered to be at risk and informing the transplant community about the potential of these innovative approaches to improve pediatric care after heart transplantation. Future studies are required to translate the information derived from prediction models to optimize counseling, clinical care, and decision-making within pediatric organ transplant centers.