Anti-lung cancer synergy of low-dose doxorubicin and PD-L1 blocker co-delivered via mild photothermia-responsive black phosphorus.

We have previously identified a latent interaction mechanism between non-small cell lung cancer cells (NSCLCC) and their associated macrophages (TAM) mediated by mutual paracrine activation of the HMGB1/RAGE/NF-κB signaling. Activation of this mechanism results in TAM stimulation and PD-L1 upregulation in the NSCLCC. In the present work, we found that free DOX at a low concentration that does not cause DNA damage could activate the HMGB1/RAGE/NF-κB/PD-L1 pathway byinducing oxidative stress. It was thus proposed that a combination of low-dose DOX and a PD-L1 blocker delivered in the NSCLC tumor would achieve synergistic TAM stimulation and thereby synergetic anti-tumor potency. To prove this idea, DOX and BMS-202 (a PD-L1 blocker) were loaded to black phosphorus (BP) nanoparticles after dosage titration to yield the BMS-202/DOX@BP composites that rapidly disintegrated and released drug cargo upon mild photothermal heating at 40 °C. In vitro experiments then demonstrated that low-dose DOX and BMS-202 delivered via BMS-202/DOX@BP under mild photothermia displayed enhanced tumor cell toxicity with a potent synergism only in the presence of TAM. This enhanced synergism was due to an anti-tumor M1-like TAM phenotype that was synergistically induced by low dose DOX plus BMS-202 only in the presence of the tumor cells, indicating the damaged tumor cells to be the cardinal contributor to the M1-like TAM stimulation. In vivo, BMS-202/DOX@BP under mild photothermia exhibited targeted delivery to NSCLC graft tumors in mice and synergistic anti-tumor efficacy of delivered DOX and BMS-202. In conclusion, low-dose DOX in combination with a PD-L1 blocker is an effective strategy to turn TAM against their host tumor cells exploiting the HMGB1/RAGE/NF-κB/PD-L1 pathway. The synergetic actions involved highlight the value of TAM and the significance of modulating tumor cell-TAM cross-talk in tumor therapy. Photothermia-responsive BP provides an efficient platform to translate this strategy into targeted, efficacious tumor therapy.

Comparison of the specific energies of sinusoidal VCS cutter rings and CCS cutter rings in breaking rock-like materials based on the FEM.

Disc cutters are essential for full-section hard-rock tunnel boring machines. The performance of these devices directly affects tunnel engineering costs and duration. This paper proposes a sinusoidal variable cross-section (VCS) cutter ring and design method and establishes a digital model. Rock-like materials are simulated with a finite element model, and the model validity is verified via rock simulation mechanics tests. A disc cutter rolling rock simulation model for a linear cutting machine is also established, and simulation tests are performed for single- and three-cutter rolling using sinusoidal VCSs and constant cross-section (CCS) cutter models, respectively. The stress and energy changes for the cutters and rock-like material damage area were compared via simulation, confirming that some sinusoidal VCS cutter rings do less work on rock-like materials and cause larger crushing areas under the same engineering parameters; therefore, these cutter rings have smaller specific energies. The sinusoidal VCS cutter ring performance is 7% greater than that of CCS on average under single-cutter simulation, and the intermediate cutter performance of the intermediate cutter is 9% greater than that of CCS on average under three-cutter simulation. Thus, sinusoidal VCS cutter rings offer improved rock damage performance, and further research and application of this technology will improve the working efficiency of tunnel boring machines.

Potassium affects the association between dietary intake of vitamin C and NAFLD among adults in the United States.

INTRODUCTION: Although the association between nonalcoholic fatty liver disease (NAFLD) and vitamin C has been well studied, the effects of dietary potassium intake on this relationship are still unclear. Thus, this study aimed to determine the effects of dietary potassium intake on the association between vitamin C and NAFLD. METHODS: We performed a cross-sectional learn about with 9443 contributors the usage of 2007-2018 NHANES data. Multiple logistic regression evaluation has been utilized to check out the affiliation of dietary vitamin C intake with NAFLD and advanced hepatic fibrosis (AHF). Subsequently, we plotted a smoothed match curve to visualize the association. Especially, the analysis of AHF was conducted among the NAFLD population. In addition, stratified evaluation used to be developed primarily based on demographic variables to verify the steadiness of the results. Effect amendment by way of dietary potassium intake used to be assessed via interplay checks between vitamin C and NAFLD in the multivariable linear regression. RESULTS: In this cross-sectional study, we found that vitamin C was negatively related to NAFLD and AHF. The relationship between vitamin C and NAFLD was different in the low, middle and high potassium intake groups. Furthermore, potassium intake significantly modified the negative relationship between vitamin C and NAFLD in most of the models. CONCLUSION: Our research showed that potassium and vitamin C have an interactive effect in reducing NAFLD, which may have great importance for clinical medication.

A Principal Component Regression-Based Electrophysiological Study of Patients with Severe Infections.

Objective: Severe infections can lead to neuromyopathy in critically ill patients, resulting in limb weakness and difficulty in weaning from a ventilator. This study aims to assess the electrophysiological test results in patients with severe infection and their correlation with severity scores (APACHE II and SOFA). Methods: Thirty-one patients with severe infection in the EICU were prospectively studied. Factor analysis and principal component regression were applied to develop linear models of electrophysiological diagnostic outcomes with APACHE II and SOFA scores for the entire patient cohort, the younger group (age<55) cohort, and the older group (age>55) cohort of patients with severe infections, respectively. Results: Among patients with a severe infection in the EICU, the proportion of patients without critical neuromyopathy with more than 50% F-wave presence in the median, ulnar, and tibial nerves (64.9%, 56.8%, 48.6%, respectively) was significantly higher than in the group with critical neuromyopathy (52.1%, 35.4%, 29.2%, respectively.), and the proportion of patients with critical neuromyopathy who did not elicit the three types of F wave was significantly higher in the cohort of patients with critical neuromyopathy (40.5%, 32.4%, 35.1%, respectively.) were significantly higher than in the cohort of patients without critical illness (18.8%, 12.5%, 20.8%, respectively). In addition, on average, patients with critical neuromyopathy had a much lower CMAP for the median nerve (wrist, elbow) (2.4, 1.88, respectively) (4.3, 3.9, respectively in undiagnosed cohort), ulnar nerve (wrist, elbow) (2.4, 1.88, respectively) (5.65, 5.4, respectively in undiagnosed cohort), and tibial nerve(ankle, popliteal fossa) (2.7, 1.57, respectively)(6.55, 5.3, respectively in undiagnosed cohort) nerves than patients without critical neuromyopathy, and showed more non-elicitation, which was not seen in the cohort of patients without critical neuromyopathy. The CMAP returned to normal in the cohort of patients without critical neuromyopathy. Therefore, with respect to our selected electrophysiological parameters, the two patient groups showed significant differences in terms of the specific values and statistical analysis (Table 1). Through factor analysis and principal component regression, we found that CMAP and F-wave were highly correlated with APACHE II and SOFA scores, and the correlation between the electrophysiological wave spectrum and the two scores was further quantified by principal component regression. Conclusion: Electrophysiological spectroscopy can serve as an early warning for the development of neuromuscular disease in EICU patients. Abnormal electrophysiological diagnosis prior to actual neuromuscular abnormalities and its subsequent return to normal can help identify high-risk patients and implement early interventions.

Repurposing non-pharmacological interventions for Alzheimer's disease through link prediction on biomedical literature.

Non-pharmaceutical interventions (NPI) have great potential to improve cognitive function but limited investigation to discover NPI repurposing for Alzheimer's Disease (AD). This is the first study to develop an innovative framework to extract and represent NPI information from biomedical literature in a knowledge graph (KG), and train link prediction models to repurpose novel NPIs for AD prevention. We constructed a comprehensive KG, called ADInt, by extracting NPI information from biomedical literature. We used the previously-created SuppKG and NPI lexicon to identify NPI entities. Four KG embedding models (i.e., TransE, RotatE, DistMult and ComplEX) and two novel graph convolutional network models (i.e., R-GCN and CompGCN) were trained and compared to learn the representation of ADInt. Models were evaluated and compared on two test sets (time slice and clinical trial ground truth) and the best performing model was used to predict novel NPIs for AD. Discovery patterns were applied to generate mechanistic pathways for high scoring candidates. The ADInt has 162,212 nodes and 1,017,284 edges. R-GCN performed best in time slice (MR = 5.2054, Hits@10 = 0.8496) and clinical trial ground truth (MR = 3.4996, Hits@10 = 0.9192) test sets. After evaluation by domain experts, 10 novel dietary supplements and 10 complementary and integrative health were proposed from the score table calculated by R-GCN. Among proposed novel NPIs, we found plausible mechanistic pathways for photodynamic therapy and Choerospondias axillaris to prevent AD, and validated psychotherapy and manual therapy techniques using real-world data analysis. The proposed framework shows potential for discovering new NPIs for AD prevention and understanding their mechanistic pathways.

Process management program to prevent falls in hospitalized patients with neuropsychiatric disorders: a quality improvement program.

BACKGROUND: Falls were among the most common adverse nursing events. The incidence of falls in patients with neuropsychiatric disorders was high, and the occurrence of falls not only caused physical and psychological harm to patients but also led to medical disputes. Therefore, interventions for falls prevention were essential, but evaluations of the intervention process were lacking. METHODS: In this study, a process management program to prevent falls based on the "structure-process-outcome" quality evaluation model was designed and applied to the clinical practice of falls prevention in hospitalized patients with neuropsychiatric disorders. The process quality evaluation checklist to prevent falls was used to supervise the implementation effect of intervention measures to prevent falls, identify the problems in the intervention measures, and make continuous improvements, to reduce the incidence of falls in such hospitalized patients as the final index. RESULTS: The incidence of inpatient falls decreased from 0.199‰ (0.199 per 1000 patient-days) to 0.101‰ (0.101 per 1000 patient-days) before and after the implementation of the process management program for 12 months, 24 months, and 36 months, respectively, and the difference was statistically significant (P<0.05). The probability of falls was reduced by 49% after 36 months of monitoring. Furthermore, the proportion of patients at high risk of falls exhibited a downward trend. CONCLUSION: This quality improvement program was feasible and effective at reducing falls in hospitalized patients with neuropsychiatric disorders. Therefore, attention should be given to monitoring process quality in the management of falls.

A Deep Learning Approach for Automated Extraction of Functional Status and New York Heart Association Class for Heart Failure Patients During Clinical Encounters.

Introduction: Serial functional status assessments are critical to heart failure (HF) management but are often described narratively in documentation, limiting their use in quality improvement or patient selection for clinical trials. We developed and validated a deep learning-based natural language processing (NLP) strategy to extract functional status assessments from unstructured clinical notes. Methods: We identified 26,577 HF patients across outpatient services at Yale New Haven Hospital (YNHH), Greenwich Hospital (GH), and Northeast Medical Group (NMG) (mean age 76.1 years; 52.0% women). We used expert annotated notes from YNHH for model development/internal testing and from GH and NMG for external validation. The primary outcomes were NLP models to detect (a) explicit New York Heart Association (NYHA) classification, (b) HF symptoms during activity or rest, and (c) functional status assessment frequency. Results: Among 3,000 expert-annotated notes, 13.6% mentioned NYHA class, and 26.5% described HF symptoms. The model to detect NYHA classes achieved a class-weighted AUROC of 0.99 (95% CI: 0.98-1.00) at YNHH, 0.98 (0.96-1.00) at NMG, and 0.98 (0.92-1.00) at GH. The activity-related HF symptom model achieved an AUROC of 0.94 (0.89-0.98) at YNHH, 0.94 (0.91-0.97) at NMG, and 0.95 (0.92-0.99) at GH. Deploying the NYHA model among 166,655 unannotated notes from YNHH identified 21,528 (12.9%) with NYHA mentions and 17,642 encounters (10.5%) classifiable into functional status groups based on activity-related symptoms. Conclusions: We developed and validated an NLP approach to extract NYHA classification and activity-related HF symptoms from clinical notes, enhancing the ability to track optimal care and identify trial-eligible patients.

Culture-free detection of ß-lactamase-Producing bacteria in urinary tract infections using a paper sensor.

Developing simple, inexpensive, fast, sensitive, and specific probes for antibiotic-resistant bacteria is crucial for the management of urinary tract infections (UTIs). We here propose a paper-based sensor for the rapid detection of ß-lactamase-producing bacteria in the urine samples of UTI patients. By conjugating a strongly electronegative group -N+(CH3)3 with the core structures of cephalosporin and carbapenem antibiotics, two visual probes were achieved to respectively target the extended-spectrum/AmpC ß-lactamases (ESBL/AmpC) and carbapenemase, the two most prevalent factors causing antibiotic resistance. By integrating these probes into a portable paper sensor, we confirmed 10 and 8 cases out of 30 clinical urine samples as ESBL/AmpC- and carbapenemase-positive, respectively, demonstrating 100% clinical sensitivity and specificity. This paper sensor can be easily conducted on-site, without resorting to bacterial culture, providing a solution to the challenge of rapid detection of ß-lactamase-producing bacteria, particularly in resource-limited settings.

Large language models for biomedicine: foundations, opportunities, challenges, and best practices.

OBJECTIVES: Generative large language models (LLMs) are a subset of transformers-based neural network architecture models. LLMs have successfully leveraged a combination of an increased number of parameters, improvements in computational efficiency, and large pre-training datasets to perform a wide spectrum of natural language processing (NLP) tasks. Using a few examples (few-shot) or no examples (zero-shot) for prompt-tuning has enabled LLMs to achieve state-of-the-art performance in a broad range of NLP applications. This article by the American Medical Informatics Association (AMIA) NLP Working Group characterizes the opportunities, challenges, and best practices for our community to leverage and advance the integration of LLMs in downstream NLP applications effectively. This can be accomplished through a variety of approaches, including augmented prompting, instruction prompt tuning, and reinforcement learning from human feedback (RLHF). TARGET AUDIENCE: Our focus is on making LLMs accessible to the broader biomedical informatics community, including clinicians and researchers who may be unfamiliar with NLP. Additionally, NLP practitioners may gain insight from the described best practices. SCOPE: We focus on 3 broad categories of NLP tasks, namely natural language understanding, natural language inferencing, and natural language generation. We review the emerging trends in prompt tuning, instruction fine-tuning, and evaluation metrics used for LLMs while drawing attention to several issues that impact biomedical NLP applications, including falsehoods in generated text (confabulation/hallucinations), toxicity, and dataset contamination leading to overfitting. We also review potential approaches to address some of these current challenges in LLMs, such as chain of thought prompting, and the phenomena of emergent capabilities observed in LLMs that can be leveraged to address complex NLP challenge in biomedical applications.

Abundance of Modifications in Mature miRNAs Revealed by LC-MS/MS Method Coupled with a Two-Step Hybridization Purification Strategy.

Accurate detection of endogenous miRNA modifications, such as N6-methyladenosine (m6A), 7-methylguanosine (m7G), and 5-methylcytidine (m5C), poses significant challenges, resulting in considerable uncertainty regarding their presence in mature miRNAs. In this study, we demonstrate for the first time that liquid chromatography coupled with a tandem mass spectrometry (LC-MS/MS) nucleoside analysis method is a practical tool for quantitatively analyzing human miRNA modifications. The newly designed liquid-solid two-step hybridization (LSTH) strategy enhances specificity for miRNA purification, while LC-MS/MS offers robust capability in recognizing modifications and sufficient sensitivity with detection limits ranging from attomoles to low femtomoles. Therefore, it provides a more reliable approach compared to existing techniques for revealing modifications in endogenous miRNAs. With this approach, we characterized m6A, m7G, and m5C modifications in miR-21-5p, Let-7a/e-5p, and miR-10a-5p isolated from cultured cells and observed unexpectedly low abundance (<1% at each site) of these modifications.

Amanitin-induced variable cytotoxicity in various cell lines is mediated by the different expression levels of OATP1B3.

Amanita phalloides is one of the deadliest mushrooms worldwide, causing most fatal cases of mushroom poisoning. Among the poisonous substances of Amanita phalloides, amanitins are the most lethal toxins to humans. Currently, there are no specific antidotes available for managing amanitin poisoning and treatments are lack of efficacy. Amanitin mainly causes severe injuries to specific organs, such as the liver, stomach, and kidney, whereas the lung, heart, and brain are hardly affected. However, the molecular mechanism of this phenomenon remains not understood. To explore the possible mechanism of organ specificity of amanitin-induced toxicity, eight human cell lines derived from different organs were exposed to α, ß, and γ-amanitin at concentrations ranging from 0.3 to 100 µM. We found that the cytotoxicity of amanitin differs greatly in various cell lines, among which liver-derived HepG2, stomach-derived BGC-823, and kidney-derived HEK-293 cells are most sensitive. Further mechanistic study revealed that the variable cytotoxicity is mainly dependent on the different expression levels of the organic anion transporting polypeptide 1B3 (OATP1B3), which facilitates the internalization of amanitin into cells. Besides, knockdown of OATP1B3 in HepG2 cells prevented α-amanitin-induced cytotoxicity. These results indicated that OATP1B3 may be a crucial therapeutic target against amanitin-induced organ failure.

Gate Voltage- and Bias Voltage-Tunable Staggered-Gap to Broken-Gap Transition Based on WSe2/Ta2NiSe5 Heterostructure for Multimode Optoelectronic Logic Gate.

Two-dimensional (2D) materials with superior properties exhibit tremendous potential in developing next-generation electronic and optoelectronic devices. Integrating various functions into one device is highly expected as that endows 2D materials great promise for more Moore and more-than-Moore device applications. Here, we construct a WSe2/Ta2NiSe5 heterostructure by stacking the p-type WSe2 and the n-type narrow gap Ta2NiSe5 with the aim to achieve a multifunction optoelectronic device. Owing to the large interface potential barrier, the heterostructure device reveals a prominent diode feature with a large rectify ratio (7.6 × 104) and a low dark current (10-12 A). Especially, gate voltage- and bias voltage-tunable staggered-gap to broken-gap transition is achieved on the heterostructure device, which enables gate voltage-tunable forward and reverse rectifying features. As results, the heterostructure device exhibits superior self-powered photodetection properties, including a high detectivity of 1.08 × 1010 Jones and a fast response time of 91 µs. Additionally, the intrinsic structural anisotropy of Ta2NiSe5 endows the heterostructure device with strong polarization-sensitive photodetection and high-resolution polarization imaging. Based on these characteristics, a multimode optoelectronic logic gate is realized on the heterostructure via synergistically modulating the light on/off, polarization angle, gate voltage, and bias voltage. This work shed light on the future development of constructing high-performance multifunctional optoelectronic devices.

Mechanically Tunable Transmittance Convection Shield for Dynamic Radiative Cooling.

Radiative cooling is the process to dissipate heat to the outer space through an atmospheric window (8-13 µm), which has great potential for energy savings in buildings. However, the traditional "static" spectral characteristics of radiative cooling materials may result in overcooling during the cold season or at night, necessitating the development of dynamic spectral radiative cooling for enhanced energy saving potential. In this study, we showcase the realization of dynamic radiative cooling by modulating the heat transfer process using a tunable transmittance convection shield (TTCS). The transmittance of the TTCS in both solar spectrum and atmospheric window can be dynamically adjusted within ranges of 28.8-72.9 and 27.0-80.5%, with modulation capabilities of ΔTsolar = 44.1% and ΔT8-13 µm = 53.5%, respectively. Field measurements demonstrate that through the modulation, the steady-state temperature of the TTCS architecture is 0.3 °C lower than that of a traditional radiative cooling architecture during the daytime and 3.3 °C higher at nighttime, indicating that the modulation strategy can effectively address the overcooling issue, offering an efficient way of energy saving through dynamic radiative cooling.

Construction of S100 family members prognosis prediction model and analysis of immune microenvironment landscape at single-cell level in pancreatic adenocarcinoma: a tumor marker prognostic study.

Pancreatic adenocarcinoma characterized by a mere 10% five-year survival rate, poses a formidable challenge due to its specific anatomical location, making tumor tissue acquisition difficult. This limitation underscores the critical need for novel biomarkers to stratify this patient population. Accordingly, this study aimed to construct a prognosis prediction model centered on S100 family members. Leveraging six S100 genes and their corresponding coefficients, an S100 score was calculated to predict survival outcomes. The present study provided comprehensive internal and external validation along with power evaluation results, substantiating the efficacy of the proposed model. Additionally, the study explored the S100-driven potential mechanisms underlying malignant progression. By comparing immune cell infiltration proportions in distinct patient groups with varying prognoses, the research identified differences driven by S100 expression. Furthermore, the analysis explored significant ligand-receptor pairs between malignant cells and immune cells influenced by S100 genes, uncovering crucial insights. Notably, the study identified a novel biomarker capable of predicting the sensitivity of neoadjuvant chemotherapy, offering promising avenues for further research and clinical application.

[Effects of thinning on accumulation of soil microbial residue carbon of Picea asperata plantations in sub-alpine region of western Sichuan, China]. / 间伐对川西亚高山云杉人工林土壤微生物残体碳积累的影响.

Microbial residues are an important component of soil organic carbon (SOC). It is unclear how long-term thinning affects the accumulation characteristics of microbial residue carbon (C). We analyzed the differences in soil physicochemical properties, microbial communities, extracellular enzyme activities, and microbial residue C in topsoil (0-10 cm) and subsoil (20-30 cm) in Picea asperata plantation of non-thinned (control, 4950 trees·hm-2) and thinned for 14 years (1160 trees·hm-2) stands, aiming to reveal the regulatory mechanism of thinning on microbial residue C accumulation. The results showed that thinning significantly increased SOC content, total nitrogen content, available phosphorus content, the proportion of particulate organic C, soil water content, C-cycle hydrolase, and acid phosphatase activities, but significantly reduced the proportion of mineral-associated organic C. Thinning significantly affected the content of fungal and microbial residue C, and the contribution of microbial residue C to SOC, and these effects were independent of soil layer. The content of fungal and microbial residue C was 25.0% and 24.5% higher under thinning treatments. However, thinning significantly decreased the contribution of microbial residue C to SOC by 12.3%, indicating an increase in the proportion of plant-derived C in SOC. Stepwise regression analysis showed that total nitrogen and soil water content were key factors influencing fungal and micro-bial residue C accumulation. In summary, thinning promoted microbial residue C sequestration by altering soil pro-perties and changed the composition of SOC sources.

A scoping review of fair machine learning techniques when using real-world data.

OBJECTIVE: The integration of artificial intelligence (AI) and machine learning (ML) in health care to aid clinical decisions is widespread. However, as AI and ML take important roles in health care, there are concerns about AI and ML associated fairness and bias. That is, an AI tool may have a disparate impact, with its benefits and drawbacks unevenly distributed across societal strata and subpopulations, potentially exacerbating existing health inequities. Thus, the objectives of this scoping review were to summarize existing literature and identify gaps in the topic of tackling algorithmic bias and optimizing fairness in AI/ML models using real-world data (RWD) in health care domains. METHODS: We conducted a thorough review of techniques for assessing and optimizing AI/ML model fairness in health care when using RWD in health care domains. The focus lies on appraising different quantification metrics for accessing fairness, publicly accessible datasets for ML fairness research, and bias mitigation approaches. RESULTS: We identified 11 papers that are focused on optimizing model fairness in health care applications. The current research on mitigating bias issues in RWD is limited, both in terms of disease variety and health care applications, as well as the accessibility of public datasets for ML fairness research. Existing studies often indicate positive outcomes when using pre-processing techniques to address algorithmic bias. There remain unresolved questions within the field that require further research, which includes pinpointing the root causes of bias in ML models, broadening fairness research in AI/ML with the use of RWD and exploring its implications in healthcare settings, and evaluating and addressing bias in multi-modal data. CONCLUSION: This paper provides useful reference material and insights to researchers regarding AI/ML fairness in real-world health care data and reveals the gaps in the field. Fair AI/ML in health care is a burgeoning field that requires a heightened research focus to cover diverse applications and different types of RWD.

Clinical evaluation of resorbable polylactic acid (PLA) intracanal posts for primary incisor restoration. Randomized controlled clinical trial.

This randomized, controlled clinical trial compares the clinical performance of glass-fibre and resorbable polylactic acid (PLA) intracanal posts used to restore carious primary incisors in young patients. The study sample includes 180 primary upper central incisors of 90 children aged 3 to 4 years. All patients were randomly divided into two equal groups of 45 children who received PLA and glass-fibre (GFP) intracanal posts. The clinical assessment of incisor restorations was carried out immediately upon completion and at months 3, 6 and 12 according to the following criteria: anatomical form, marginal adaptation, surface roughness, marginal pigmentation, colour match, secondary caries and contact point. The Gingival Index (GI), the Bleeding Index (Cowell modification; mBI), and bite force (BF) were measured. At the 3-month follow-up, the occlusal BF of patients who received PLA posts was higher than the baseline; the GI and mBI scores were lower, by contrast (p < 0.05). This tendency was even more pronounced 6 and 12 months after the restoration. The incidence of side effects or symptoms (apical inflammation, cervical fracture, loosening of the crown) after the PLA posts was significantly lower than after the GFP (p < 0.05). No statistically significant differences were present between the two groups with respect to colour matching, anatomical form, marginal adaptation, marginal pigmentation, surface roughness, occlusal contact and secondary caries. Based on the results, applying PLA intracanal posts and cyanoacrylate to residual anterior crowns in young children can improve their gingival health, reduce side effects, and increase the likelihood of successful restoration.

COVID-19 vaccination and the risk of autoimmune diseases: a Mendelian randomization study.

Background: In recent times, reports have emerged suggesting that a variety of autoimmune disorders may arise after the coronavirus disease 2019 (COVID-19) vaccination. However, causality and underlying mechanisms remain unclear. Methods: We collected summary statistics of COVID-19 vaccination and 31 autoimmune diseases from genome-wide association studies (GWAS) as exposure and outcome, respectively. Random-effects inverse variance weighting (IVW), MR Egger, weighted median, simple mode, and weighted mode were used as analytical methods through Mendelian randomization (MR), and heterogeneity and sensitivity analysis were performed. Results: We selected 72 instrumental variables for exposure (p < 5 × 10-6; r2 < 0.001, genetic distance = 10,000 kb), and MR analyses showed that COVID-19 vaccination was causally associated with an increased risk of multiple sclerosis (MS) (IVW, OR: 1.53, 95% CI: 1.065-2.197, p = 0.026) and ulcerative colitis (UC) (IVW, OR: 1.00, 95% CI: 1.000-1.003, p = 0.039). If exposure was refined (p < 5 × 10-8; r2 < 0.001, genetic distance = 10,000 kb), the associations became negative. No causality was found for the remaining outcomes. These results were robust to sensitivity and heterogeneity analyses. Conclusion: Our study provided potential evidence for the impact of COVID-19 vaccination on the risk of MS and UC occurrence, but it lacks sufficient robustness, which could provide a new idea for public health policy.

Developing deep learning-based strategies to predict the risk of hepatocellular carcinoma among patients with nonalcoholic fatty liver disease from electronic health records.

OBJECTIVE: The accuracy of deep learning models for many disease prediction problems is affected by time-varying covariates, rare incidence, covariate imbalance and delayed diagnosis when using structured electronic health records data. The situation is further exasperated when predicting the risk of one disease on condition of another disease, such as the hepatocellular carcinoma risk among patients with nonalcoholic fatty liver disease due to slow, chronic progression, the scarce of data with both disease conditions and the sex bias of the diseases. The goal of this study is to investigate the extent to which the aforementioned issues influence deep learning performance, and then devised strategies to tackle these challenges. These strategies were applied to improve hepatocellular carcinoma risk prediction among patients with nonalcoholic fatty liver disease. METHODS: We evaluated two representative deep learning models in the task of predicting the occurrence of hepatocellular carcinoma in a cohort of patients with nonalcoholic fatty liver disease (n = 220,838) from a national EHR database. The disease prediction task was carefully formulated as a classification problem while taking censorship and the length of follow-up into consideration. RESULTS: We developed a novel backward masking scheme to deal with the issue of delayed diagnosis which is very common in EHR data analysis and evaluate how the length of longitudinal information after the index date affects disease prediction. We observed that modeling time-varying covariates improved the performance of the algorithms and transfer learning mitigated reduced performance caused by the lack of data. In addition, covariate imbalance, such as sex bias in data impaired performance. Deep learning models trained on one sex and evaluated in the other sex showed reduced performance, indicating the importance of assessing covariate imbalance while preparing data for model training. CONCLUSIONS: The strategies developed in this work can significantly improve the performance of hepatocellular carcinoma risk prediction among patients with nonalcoholic fatty liver disease. Furthermore, our novel strategies can be generalized to apply to other disease risk predictions using structured electronic health records, especially for disease risks on condition of another disease.

Advancing entity recognition in biomedicine via instruction tuning of large language models.

MOTIVATION: Large Language Models (LLMs) have the potential to revolutionize the field of Natural Language Processing, excelling not only in text generation and reasoning tasks but also in their ability for zero/few-shot learning, swiftly adapting to new tasks with minimal fine-tuning. LLMs have also demonstrated great promise in biomedical and healthcare applications. However, when it comes to Named Entity Recognition (NER), particularly within the biomedical domain, LLMs fall short of the effectiveness exhibited by fine-tuned domain-specific models. One key reason is that NER is typically conceptualized as a sequence labeling task, whereas LLMs are optimized for text generation and reasoning tasks. RESULTS: We developed an instruction-based learning paradigm that transforms biomedical NER from a sequence labeling task into a generation task. This paradigm is end-to-end and streamlines the training and evaluation process by automatically repurposing pre-existing biomedical NER datasets. We further developed BioNER-LLaMA using the proposed paradigm with LLaMA-7B as the foundational LLM. We conducted extensive testing on BioNER-LLaMA across three widely recognized biomedical NER datasets, consisting of entities related to diseases, chemicals, and genes. The results revealed that BioNER-LLaMA consistently achieved higher F1-scores ranging from 5% to 30% compared to the few-shot learning capabilities of GPT-4 on datasets with different biomedical entities. We show that a general-domain LLM can match the performance of rigorously fine-tuned PubMedBERT models and PMC-LLaMA, biomedical-specific language model. Our findings underscore the potential of our proposed paradigm in developing general-domain LLMs that can rival SOTA performances in multi-task, multi-domain scenarios in biomedical and health applications. AVAILABILITY AND IMPLEMENTATION: Datasets and other resources are available at https://github.com/BIDS-Xu-Lab/BioNER-LLaMA.