Enhancing Early Detection of Cognitive Decline in the Elderly through Ensemble of NLP Techniques: A Comparative Study Utilizing Large Language Models in Clinical Notes.

Background: Early detection of cognitive decline in elderly individuals facilitates clinical trial enrollment and timely medical interventions. This study aims to apply, evaluate, and compare advanced natural language processing techniques for identifying signs of cognitive decline in clinical notes. Methods: This study, conducted at Mass General Brigham (MGB), Boston, MA, included clinical notes from the 4 years prior to initial mild cognitive impairment (MCI) diagnosis in 2019 for patients ≥ 50 years. Note sections regarding cognitive decline were labeled manually. A random sample of 4,949 note sections filtered with cognitive functions-related keywords were used for traditional AI model development, and 200 random subset were used for LLM and prompt development; another random sample of 1996 note sections without keyword filtering were used for testing. Prompt templates for large language models (LLM), Llama 2 on Amazon Web Service and GPT-4 on Microsoft Azure, were developed with multiple prompting approaches to select the optimal LLM-based method. Baseline comparisons were made with XGBoost and a hierarchical attention-based deep neural network model. An ensemble of the three models was then constructed using majority vote. Results: GPT-4 demonstrated superior accuracy and efficiency to Llama 2. The ensemble model outperformed individual models, achieving a precision of 90.3%, recall of 94.2%, and F1-score of 92.2%. Notably, the ensemble model demonstrated a marked improvement in precision (from a 70%-79% range to above 90%) compared to the best performing single model. Error analysis revealed 63 samples were wrongly predicted by at least one model; however, only 2 cases (3.2%) were mutual errors across all models, indicating diverse error profiles among them. Conclusion: Our findings indicate that LLMs and traditional models exhibit diverse error profiles. The ensemble of LLMs and locally trained machine learning models on EHR data was found to be complementary, enhancing performance and improving diagnostic accuracy.

Asparagine endopeptidase deficiency mitigates radiation-induced brain injury by suppressing microglia-mediated neuronal senescence.

Mounting evidence supports the role of neuroinflammation in radiation-induced brain injury (RIBI), a chronic disease characterized by delayed and progressive neurological impairment. Asparagine endopeptidase (AEP), also known as legumain (LGMN), participates in multiple malignancies and neurodegenerative diseases and may potentially be involved in RIBI. Here, we found AEP expression was substantially elevated in the cortex and hippocampus of wild-type (Lgmn+/+) mice following whole-brain irradiation. Lgmn knockout (Lgmn-/-) alleviated neurological impairment caused by whole-brain irradiation by suppressing neuronal senescence. Bulk RNA and metabolomic sequencing revealed AEP's involvement in the antigen processing and presentation pathway and neuroinflammation. This was further confirmed by co-culturing Lgmn+/+ primary neurons with the conditioned media derived from irradiated Lgmn+/+ or Lgmn-/- primary microglia. Furthermore, esomeprazole inhibited the enzymatic activity of AEP and RIBI. These findings identified AEP as a critical factor of neuroinflammation in RIBI, highlighting the prospect of targeting AEP as a therapeutic approach.

Explore the effect of apparent temperature and air pollutants on the admission rate of acute myocardial infarction in Chongqing, China: a time-series study.

OBJECTIVE: Limited research has been conducted on the correlation between apparent temperature and acute myocardial infarction (AMI), as well as the potential impact of air pollutants in modifying this relationship. The objective of this study is to investigate the lagged effect of apparent temperature on AMI and assess the effect modification of environmental pollutants on this association. DESIGN: A time-series study. SETTING AND PARTICIPANTS: The data for this study were obtained from the Academy of Medical Data Science at Chongqing Medical University, covering daily hospitalisations for AMI between 1 January 2015 and 31 December 2016. Meteorological and air pollutant data were provided by China's National Meteorological Information Centre. OUTCOME MEASURES: We used a combined approach of quasi-Poisson generalised linear model and distributed lag non-linear model to thoroughly analyse the relationships. Additionally, we employed a generalised additive model to investigate the interaction between air pollutants and apparent temperature on the effect of AMI. RESULT: A total of 872 patients admitted to hospital with AMI were studied based on the median apparent temperature (20.43°C) in Chongqing. Low apparent temperature (10th, 7.19℃) has obvious lagged effect on acute myocardial infarction, first appearing on the 8th day (risk ratio (RR) 1.081, 95% CI 1.010 to 1.158) and the greatest risk on the 11th day (RR 1.094, 95% CI 1.037 to 1.153). No lagged effect was observed at high apparent temperature. In subgroup analysis, women and individuals aged 75 and above were at high risk. The interaction analysis indicates that there exist significant interactions between PM2.5 and high apparent temperature, as well as nitrogen dioxide (NO2) and low apparent temperature. CONCLUSION: The occurrence of decreased apparent temperature levels was discovered to be linked with a heightened relative risk of hospitalisations for AMI. PM2.5 and NO2 have an effect modification on the association between apparent temperature and admission rate of AMI.

Achieving Ultra-Broad Microwave Absorption Bandwidth Around Millimeter-Wave Atmospheric Window Through an Intentional Manipulation on Multi-Magnetic Resonance Behavior.

The utilization of electromagnetic waves is rapidly advancing into the millimeter-wave frequency range, posing increasingly severe challenges in terms of electromagnetic pollution prevention and radar stealth. However, existing millimeter-wave absorbers are still inadequate in addressing these issues due to their monotonous magnetic resonance pattern. In this work, rare-earth La3+ and non-magnetic Zr4+ ions are simultaneously incorporated into M-type barium ferrite (BaM) to intentionally manipulate the multi-magnetic resonance behavior. By leveraging the contrary impact of La3+ and Zr4+ ions on magnetocrystalline anisotropy field, the restrictive relationship between intensity and frequency of the multi-magnetic resonance is successfully eliminated. The magnetic resonance peak-differentiating and imitating results confirm that significant multi-magnetic resonance phenomenon emerges around 35 GHz due to the reinforced exchange coupling effect between Fe3+ and Fe2+ ions. Additionally, Mössbauer spectra analysis, first-principle calculations, and least square fitting collectively identify that additional La3+ doping leads to a profound rearrangement of Zr4+ occupation and thus makes the portion of polarization/conduction loss increase gradually. As a consequence, the La3+-Zr4+ co-doped BaM achieves an ultra-broad bandwidth of 12.5 + GHz covering from 27.5 to 40 + GHz, which holds remarkable potential for millimeter-wave absorbers around the atmospheric window of 35 GHz.

Proton-triggered topological transformation in superbase-mediated selective polymerization enables access to ultrahigh-molar-mass cyclic polymers.

The selective synthesis of ultrahigh-molar-mass (UHMM, >2 million Da) cyclic polymers is challenging as an exceptional degree of spatiotemporal control is required to overcome the possible undesired reactions that can compete with the desired intramolecular cyclization. Here we present a counterintuitive synthetic methodology for cyclic polymers, represented here by polythioesters, which proceeds via superbase-mediated ring-opening polymerization of gem-dimethylated thiopropiolactone, followed by macromolecular cyclization triggered by protic quenching. This proton-triggered linear-to-cyclic topological transformation enables selective, linear polymer-like access to desired cyclic polythioesters, including those with UHMM surpassing 2 MDa. In addition, this method eliminates the need for stringent conditions such as high dilution to prevent or suppress linear polymer contaminants and presents the opposite scenario in which protic-free conditions are required to prevent cyclic polymer formation, which is capitalized to produce cyclic polymers on demand. Furthermore, such UHMM cyclic polythioester exhibits not only much enhanced thermostability and mechanical toughness, but it can also be quantitatively recycled back to monomer under mild conditions due to its gem-disubstitution.

High keratin 15 expression reflects favorable prognosis in early cervical cancer patients.

OBJECTIVE: Keratin 15 (KRT15) exhibits inconsistent prognostic roles in different cancers, and its prognostic value in early cervical cancer patients who receive tumor resection remains unknown. This study aimed to assess the relationship of KRT15 expression with prognosis in these patients. METHODS: Totally, 147 early cervical cancer patients who received tumor resection were reviewed in this retrospective study. KRT15 was detected in formalin-fixed paraffin-embedded tumor tissue by immunohistochemistry (IHC). KRT15 IHC scores were computed by multiplying the percentage of positively stained cells (scored as 0-4) and corresponding staining intensity (scored as 0-3), ranging from 0 to 12. RESULTS: Elevated KRT15 IHC score was linked with moderate to well differentiation (P = 0.005), tumor size ≤ 4 cm (P = 0.017), and International Federation of Gynecology and Obstetrics (FIGO) stage Ia/Ib (P < 0.001). KRT15 IHC score was inversely associated with adjuvant radiotherapy (P = 0.025) and adjuvant chemotherapy (P = 0.016). KRT15 IHC score ≥ 1 was linked with increased disease-free survival (DFS) (P = 0.003) and overall survival (OS) (P = 0.049). Meanwhile, KRT15 IHC score ≥ 1 independently predicted increased DFS (hazard ratio = 0.213, P = 0.017), but not OS (P > 0.05). KRT15 IHC score ≥ 3 and KRT15 IHC score ≥ 6 could not predict DFS or OS (all P > 0.05). By subgroup analyses, KRT15 IHC score ≥ 1 forecasted favorable DFS in patients with age > 45 years, human papillomavirus-positive, squamous carcinoma, and tumor size ≤ 4 cm (all P < 0.05). KRT15 IHC score ≥ 1 and KRT15 IHC score ≥ 3 predicted ascended DFS in patients without adjuvant radiotherapy or adjuvant chemotherapy (all P < 0.05). CONCLUSION: High KRT15 expression reflects favorable tumor features and longer survival in early cervical cancer patients who receive tumor resection.

[Mechanism of Shenling Baizhu Powder on treatment of alcoholic liver disease by regulating TLR4/NLRP3 pathway].

This study aims to investigate the regulatory effects of Shenling Baizhu Powder(SBP) on cellular autophagy in alcoholic liver disease(ALD) and its intervention effect through the TLR4/NLRP3 pathway. A rat model of chronic ALD was established by gavage of spirits. An ALD cell model was established by stimulating BRL3A cells with alcohol. High-performance liquid chromatography(HPLC) was utilized for the compositional analysis of SBP. Liver tissue from ALD rats underwent hematoxylin-eosin(HE) and oil red O staining for pathological evaluation. Enzyme-linked immunosorbent assay(ELISA) was applied to quantify lipopolysaccharides(LPS), tumor necrosis factor-alpha(TNF-α), interleukin-1 beta(IL-1ß), and interleukin-18(IL-18) levels. Quantitative reverse transcription polymerase chain reaction(qRT-PCR) was conducted to evaluate the mRNA expression of myeloid differentiation factor 88(MyD88) and Toll-like receptor 4(TLR4). The effect of different drugs on BRL3A cell proliferation activity was assessed through CCK-8 analysis. Western blot analysis was performed to examine the protein expression of NOD-like receptor pyrin domain-containing 3(NLRP3), nuclear factor-kappa B P65(NF-κB P65), phosphorylated nuclear factor-kappa B P65(p-P65), caspase-1, P62, Beclin1, and microtubule-associated protein 1 light chain 3(LC3Ⅱ). The results showed that SBP effectively ameliorated hepatic lipid accumulation, reduced liver function, mitigated hepatic tissue inflammation, and reduced levels of LPS, TNF-α, IL-1ß, and IL-18. Moreover, SBP exhibited the capacity to modulate hepatic autophagy induced by prolonged alcohol intake through the TLR4/NLRP3 signaling pathway. This modulation resulted in decreased expression of LC3Ⅱ and Beclin1, an elevation in P62 expression, and the promotion of autolysosome formation. These research findings imply that SBP can substantially enhance liver function and mitigate lipid irregularities in the context of chronic ALD. It achieves this by regulating excessive autophagic responses caused by prolonged spirit consumption, primarily through the inhibition of the TLR4/NLRP3 pathway.

Low-voltage stimulated denitrification performance of high-salinity wastewater using halotolerant microorganisms.

Nitrate is a common contaminant in high-salinity wastewater, which has adverse effects on both the environment and human health. However, conventional biological treatment exhibits poor denitrification performance due to the high-salinity shock. In this study, an innovative approach using an electrostimulating microbial reactor (EMR) was explored to address this challenge. With a low-voltage input of 1.2 V, the EMR reached nitrate removal kinetic parameter (kNO3-N) of 0.0166-0.0808 h-1 under high-salinities (1.5 %-6.5 %), which was higher than that of the microbial reactor (MR) (0.0125-0.0478 h-1). The mechanisms analysis revealed that low-voltage significantly enhanced microbial salt-in strategy and promoted the secretion of extracellular polymeric substances. Halotolerant denitrification microorganisms (Pseudomonas and Nitratireductor) were also enriched in EMR. Moreover, the EMR achieved a NO3-N removal efficiency of 73.64 % in treating high-salinity wastewater (salinity 4.69 %) over 18-cycles, whereas the MR only reached 54.67 %. In summary, this study offers an innovative solution for denitrification of high-salinity wastewater.

nanoBERT: a deep learning model for gene agnostic navigation of the nanobody mutational space.

Motivation: Nanobodies are a subclass of immunoglobulins, whose binding site consists of only one peptide chain, bestowing favorable biophysical properties. Recently, the first nanobody therapy was approved, paving the way for further clinical applications of this antibody format. Further development of nanobody-based therapeutics could be streamlined by computational methods. One of such methods is infilling-positional prediction of biologically feasible mutations in nanobodies. Being able to identify possible positional substitutions based on sequence context, facilitates functional design of such molecules. Results: Here we present nanoBERT, a nanobody-specific transformer to predict amino acids in a given position in a query sequence. We demonstrate the need to develop such machine-learning based protocol as opposed to gene-specific positional statistics since appropriate genetic reference is not available. We benchmark nanoBERT with respect to human-based language models and ESM-2, demonstrating the benefit for domain-specific language models. We also demonstrate the benefit of employing nanobody-specific predictions for fine-tuning on experimentally measured thermostability dataset. We hope that nanoBERT will help engineers in a range of predictive tasks for designing therapeutic nanobodies. Availability and implementation: https://huggingface.co/NaturalAntibody/.

XELOX (capecitabine plus oxaliplatin) plus bevacizumab (anti-VEGF-A antibody) with or without adoptive cell immunotherapy in the treatment of patients with previously untreated metastatic colorectal cancer: a multicenter, open-label, randomized, controlled, phase 3 trial.

Fluoropyrimidine-based combination chemotherapy plus targeted therapy is the standard initial treatment for unresectable metastatic colorectal cancer (mCRC), but the prognosis remains poor. This phase 3 trial (ClinicalTrials.gov: NCT03950154) assessed the efficacy and adverse events (AEs) of the combination of PD-1 blockade-activated DC-CIK (PD1-T) cells with XELOX plus bevacizumab as a first-line therapy in patients with mCRC. A total of 202 participants were enrolled and randomly assigned in a 1:1 ratio to receive either first-line XELOX plus bevacizumab (the control group, n = 102) or the same regimen plus autologous PD1-T cell immunotherapy (the immunotherapy group, n = 100) every 21 days for up to 6 cycles, followed by maintenance treatment with capecitabine and bevacizumab. The main endpoint of the trial was progression-free survival (PFS). The median follow-up was 19.5 months. Median PFS was 14.8 months (95% CI, 11.6-18.0) for the immunotherapy group compared with 9.9 months (8.0-11.8) for the control group (hazard ratio [HR], 0.60 [95% CI, 0.40-0.88]; p = 0.009). Median overall survival (OS) was not reached for the immunotherapy group and 25.6 months (95% CI, 18.3-32.8) for the control group (HR, 0.57 [95% CI, 0.33-0.98]; p = 0.043). Grade 3 or higher AEs occurred in 20.0% of patients in the immunotherapy group and 23.5% in the control groups, with no toxicity-associated deaths reported. The addition of PD1-T cells to first-line XELOX plus bevacizumab demonstrates significant clinical improvement of PFS and OS with well tolerability in patients with previously untreated mCRC.

A long-term cohort study: the immune evasion and decreasing neutralization dominated the SARS-CoV-2 breakthrough infection.

Most of vaccinees and COVID-19 convalescents can build effective anti-SARS-CoV-2 humoral immunity, which helps preventing infection and alleviating symptoms. However, breakthrough viral infections caused by emerging SARS-CoV-2 variants, especially Omicron subvariants, still pose a serious threat to global health. By monitoring the viral infections and the sera neutralization ability of a long-tracked cohort, we found out that the immune evasion of emerging Omicron subvariants and the decreasing neutralization led to the mini-wave of SARS-CoV-2 breakthrough infections. Meanwhile, no significant difference had been found in the infectivity of tested SARS-CoV-2 variants, even though the affinity between human angiotensin-converting enzyme 2 (hACE2) and receptor-binding domain (RBDs) of tested variants showed an increasing trend. Notably, the immune imprinting of inactivated COVID-19 vaccine can be relieved by infections of BA.5.2 and XBB.1.5 variants sequentially. Our data reveal the rising reinfection risk of immune evasion variants like Omicron JN.1 in China, suggesting the importance of booster with updated vaccines.

Effectiveness of WeChat-assisted preoperative education to reduce perioperative anxiety in breast cancer patients: a prospective randomized controlled study protocol.

BACKGROUND: Breast cancer is the most prevalent cancer among women globally, and surgical procedures continue to be the primary treatment. However, over 50% of patients experience preoperative anxiety due to the unknown and fear associated with surgery. Although drug therapy is commonly used to address this anxiety, its side effects have led to a heated debate regarding its effectiveness. Consequently, non-pharmacological therapies, such as preoperative education, have emerged as an alternative approach to alleviate anxiety. WeChat, a widely popular social media platform, offers a public platform that can potentially be utilized for effective preoperative education. This study aims to evaluate the use of WeChat public platform as a tool for preoperative education in patients undergoing breast surgery. METHODS: This is a prospective, randomized, and controlled trial will involve 392 adult women scheduled for breast cancer resection. Participants will be randomly assigned to either the WeChat education group or the regular group. In addition to regular preoperative visits, the WeChat education group will also watch science videos through the WeChat public platform. The regular group will only receive education from ward nurses during preoperative visits. The primary outcome measure will be the incidence of preoperative anxiety, defined by scores of the State Anxiety Inventory (SAI) exceeding 40 points. Secondary outcome measures include the incidence of severe anxiety (SAI > 44) on the day before surgery, incidence of anxiety 72 h after surgery, incidence of severe anxiety 72 h after surgery, NRS scores for pain at rest and during activity 24, 48, and 72 h after surgery, incidence of nausea and vomiting within 24 h after surgery, subjective sleep score at 1 week postoperatively, quality of life QoR-15 scores at 1 and 3 months postoperatively, incidence of chronic pain at 3 months postoperatively, bowel function recovery, length of hospital stay, and hospitalization expenses. DISCUSSION: This is the first clinical trial to investigate the use of WeChat public platform for delivering preoperative education on perioperative anxiety in breast cancer patients. By utilizing the renowned WeChat public platform, our study aims to improve patient outcomes by providing video education that explains the disease, surgery, and anesthesia in a more accessible manner, thereby reducing the incidence of perioperative anxiety. If our hypothesis is confirmed, this non-pharmacological approach can be universally acknowledged as a cost-effective and practical method in clinical care. Its application can also be extended to other medical fields beyond breast cancer. TRIAL REGISTRATION: ClinicalTrials.gov, NCT05291494. Registered on 29 December 2021.

Innexin hemichannel activation by Microplitis bicoloratus ecSOD monopolymer reduces ROS.

The extracellular superoxide dismutases (ecSODs) secreted by Microplitis bicoloratus reduce the reactive oxygen species (ROS) stimulated by the Microplitis bicoloratus bracovirus. Here, we demonstrate that the bacterial transferase hexapeptide (hexapep) motif and bacterial-immunoglobulin-like (BIg-like) domain of ecSODs bind to the cell membrane and transiently open hemichannels, facilitating ROS reductions. RNAi-mediated ecSOD silencing in vivo elevated ROS in host hemocytes, impairing parasitoid larva development. In vitro, the ecSOD-monopolymer needed to be membrane bound to open hemichannels. Furthermore, the hexapep motif in the beta-sandwich of ecSOD49 and ecSOD58, and BIg-like domain in the signal peptides of ecSOD67 were required for cell membrane binding. Hexapep motif and BIg-like domain deletions induced ecSODs loss of adhesion and ROS reduction failure. The hexapep motif and BIg-like domain mediated ecSOD binding via upregulating innexins and stabilizing the opened hemichannels. Our findings reveal a mechanism through which ecSOD reduces ROS, which may aid in developing anti-redox therapy.

Optoelectronic tuning of plasmon resonances via optically modulated hot electrons.

Fast optical modulation of nanoplasmonics is fundamental for on-chip integration of all-optical devices. Although various strategies have been proposed for dynamic modulation of surface plasmons, critical issues of device compatibility and extremely low efficiency in the visible spectrum hamper the application of optoplasmonic nanochips. Here we establish an optoplasmonic system based on Au@Cu2-xS hybrid core-shell nanoparticles. The optical excitation of hot electrons and their charge transfer to the semiconductor coating (Cu2-xS) lead to lowered electron density of Au, which results in the red shift of the localized surface plasmon resonance. The hot electrons can also transport through the Cu2-xS layer to the metal substrate, which increases the conductance of the nanogap. As such, the coupled gap plasmon blue-shifts with a magnitude of up to ∼15 nm, depending on the excitation power and the thickness of the coatings, which agrees with numerical simulations. All of this optoelectronic tuning process is highly reversible, controllable and fast with a modulated laser beam, which is highly compatible and sufficiently useful for on-chip integration of nanophotonic devices.

Identifying Psychosis Episodes in Psychiatric Admission Notes via Rule-based Methods, Machine Learning, and Pre-Trained Language Models.

Early and accurate diagnosis is crucial for effective treatment and improved outcomes, yet identifying psychotic episodes presents significant challenges due to its complex nature and the varied presentation of symptoms among individuals. One of the primary difficulties lies in the underreporting and underdiagnosis of psychosis, compounded by the stigma surrounding mental health and the individuals' often diminished insight into their condition. Existing efforts leveraging Electronic Health Records (EHRs) to retrospectively identify psychosis typically rely on structured data, such as medical codes and patient demographics, which frequently lack essential information. Addressing these challenges, our study leverages Natural Language Processing (NLP) algorithms to analyze psychiatric admission notes for the diagnosis of psychosis, providing a detailed evaluation of rule-based algorithms, machine learning models, and pre-trained language models. Additionally, the study investigates the effectiveness of employing keywords to streamline extensive note data before training and evaluating the models. Analyzing 4,617 initial psychiatric admission notes (1,196 cases of psychosis versus 3,433 controls) from 2005 to 2019, we discovered that the XGBoost classifier employing Term Frequency-Inverse Document Frequency (TF-IDF) features derived from notes pre-selected by expert-curated keywords, attained the highest performance with an F1 score of 0.8881 (AUROC [95% CI]: 0.9725 [0.9717, 0.9733]). BlueBERT demonstrated comparable efficacy an F1 score of 0.8841 (AUROC [95% CI]: 0.97 [0.9580, 0.9820]) on the same set of notes. Both models markedly outperformed traditional International Classification of Diseases (ICD) code-based detection methods from discharge summaries, which had an F1 score of 0.7608, thus improving the margin by 0.12. Furthermore, our findings indicate that keyword pre-selection markedly enhances the performance of both machine learning and pre-trained language models. This study illustrates the potential of NLP techniques to improve psychosis detection within admission notes and aims to serve as a foundational reference for future research on applying NLP for psychosis identification in EHR notes.

Tolvaptan therapy for edema in patients with chronic kidney disease.

OBJECTIVE: Tolvaptan is a vasopressin V2 receptor antagonist that is commonly prescribed to alleviate edema associated with renal diseases. However, the clinical benefits of tolvaptan in chronic kidney disease (CKD) remain unclear. This study aimed to evaluate the effectiveness of tolvaptan in managing edema caused by CKD. MATERIALS AND METHODS: The efficacy and treatment regimen of tolvaptan were assessed in a cohort of 96 patients with renal edema and CKD. During the treatment, the patients' creatinine (CR), uric acid (UA), and estimated glomerular filtration rate (eGFR) were monitored as important indicators of kidney function. Coagulation-associated molecules including fibrinogen, D-dimer, and fibrin degradation products (FDPs) were measured. Electrolyte disorders and acute kidney injury were closely monitored. Tolvaptan was administered at a daily dose of 7.5 mg, and 30 mg of edoxaban was administered to manage deep vein thrombosis. RESULTS: During the course of tolvaptan therapy, the eGFR of the patients was not declined. Edema was eliminated in 82.18% of patients. Proteinuria was reduced in the patients (p < 0.05). There were no significant changes in serum sodium levels throughout treatment, and no significant difference was observed in blood volume between the end of treatment and baseline levels. Importantly, acute kidney injury did not occur, and renal edema and deep vein thrombosis were successfully treated. CONCLUSION: As long as a rational treatment regimen is followed, tolvaptan is a safe and effective diuretic for treating edema in CKD, even in the late stages of CKD without reducing residual renal function in the patients.

Continuous Flow Electrochemical Synthesis of Olivine-Structured NaFePO4 Cathode Material for Sodium-Ion Batteries from Recycle LiFePO4.

To mitigate the environmental impact of the improper disposal of spent LiFePO4 batteries and reduce resource waste, the development of LiFePO4 recycling technologies is of paramount importance. Meanwhile, olivine-structured NaFePO4 in sodium-ion batteries has received great attention, due to its high theoretical specific capacity of 154 mAh g-1 and excellent stability. However, olivine NaFePO4 only can be synthesized from olivine LiFePO4. Accordingly, in this proposal, developing the continuous flow electrochemical solid-liquid reactor-based metal ion insertion technology is to utilize the olivine FePO4, recycled from LiFePO4, and to synthesize NaFePO4. Additionally, by employing I- as the reducing agent, NaFePO4 is successfully synthesized with a discharge-specific capacity of 134 mAh g-1 at 0.1C and a remarkable capacity retention rate of 86.5% after 100 cycles at 0.2C. And the reasons for sodium deficiency in the synthesized NFP are elucidated through first-principles calculations. Furthermore, the kinetics of the solid-solution reaction 2 (Na2/3+ßPO4→ Na1-αFePO4) mechanism improve with cycling and are sensitive to temperature. Utilizing a minimal amount of reducing agent in the electrochemical reactor, NaFePO4 synthesis is successfully achieved. This innovative approach offers a new, cost-effective, and environmentally friendly strategy for preparing NaFePO4 from recycling LiFePO4.

Identifying Psychosis Episodes in Psychiatric Admission Notes via Rule-based Methods, Machine Learning, and Pre-Trained Language Models.

Early and accurate diagnosis is crucial for effective treatment and improved outcomes, yet identifying psychotic episodes presents significant challenges due to its complex nature and the varied presentation of symptoms among individuals. One of the primary difficulties lies in the underreporting and underdiagnosis of psychosis, compounded by the stigma surrounding mental health and the individuals' often diminished insight into their condition. Existing efforts leveraging Electronic Health Records (EHRs) to retrospectively identify psychosis typically rely on structured data, such as medical codes and patient demographics, which frequently lack essential information. Addressing these challenges, our study leverages Natural Language Processing (NLP) algorithms to analyze psychiatric admission notes for the diagnosis of psychosis, providing a detailed evaluation of rule-based algorithms, machine learning models, and pre-trained language models. Additionally, the study investigates the effectiveness of employing keywords to streamline extensive note data before training and evaluating the models. Analyzing 4,617 initial psychiatric admission notes (1,196 cases of psychosis versus 3,433 controls) from 2005 to 2019, we discovered that the XGBoost classifier employing Term Frequency-Inverse Document Frequency (TF-IDF) features derived from notes pre-selected by expert-curated keywords, attained the highest performance with an F1 score of 0.8881 (AUROC [95% CI]: 0.9725 [0.9717, 0.9733]). BlueBERT demonstrated comparable efficacy an F1 score of 0.8841 (AUROC [95% CI]: 0.97 [0.9580,0.9820]) on the same set of notes. Both models markedly outperformed traditional International Classification of Diseases (ICD) code-based detection methods from discharge summaries, which had an F1 score of 0.7608, thus improving the margin by 0.12. Furthermore, our findings indicate that keyword pre-selection markedly enhances the performance of both machine learning and pre-trained language models. This study illustrates the potential of NLP techniques to improve psychosis detection within admission notes and aims to serve as a foundational reference for future research on applying NLP for psychosis identification in EHR notes.

Phenological characteristics of net ecosystem carbon exchange of Stipa krylovii steppe in Inner Mongolia, China and its remote sensing monitoring.

To accurately monitor the phenology of net ecosystem carbon exchange (NEE) in grasslands with remote sensing, we analyzed the variations in NEE and its phenology in the Stipa krylovii steppe and discussed the remote sensing vegetation index thresholds for NEE phenology, with the observational data from the Inner Mongolia Xilinhot National Climate Observatory's eddy covariance system and meteorological gradient observation system during 2018-2021, as well as Sentinel-2 satellite data from January 1, 2018 to December 31, 2021. Results showed that, from 2018 to 2021, NEE exhibited seasonal variations, with carbon sequestration occurring from April to October and carbon emission in other months, resulting in an overall carbon sink. The average Julian days for the start date (SCUP) and the end date (ECUP) of carbon uptake period were the 95th and 259th days, respectively, with an average carbon uptake period lasting 165 days. Photosynthetically active radiation showed a negative correlation with daily NEE, contributing to carbon absorption of grasslands. The optimal threshold for capturing SCUP was a 10% threshold of the red-edge chlorophyll index, while the normalized difference vegetation index effectively reflected ECUP with a threshold of 75%. These findings would provide a basis for remote sensing monitoring of grassland carbon source-sink dynamics.

DROSHA regulates mesenchymal gene expression in Wilms tumor.

Wilms tumor, the most common pediatric kidney cancer, resembles embryonic renal progenitors. Currently, there are no ways to therapeutically target Wilms tumor driver mutations, such as in the microRNA processing gene DROSHA. Here we used a "multi-omics" approach to define the effects of DROSHA mutation in Wilms tumor. We categorized Wilms tumor mutations into four mutational subclasses with unique transcriptional effects: microRNA processing, MYCN activation, chromatin remodeling, and kidney developmental factors. In particular, we find that DROSHA mutations are correlated with de-repressing microRNA target genes that regulate differentiation and proliferation and a self-renewing, mesenchymal state. We model these findings by inhibiting DROSHA expression in a Wilms tumor cell line, which led to upregulation of the cell cycle regulator cyclin D2 (CCND2). Furthermore, we observed that DROSHA mutations in Wilms tumor and DROSHA silencing in vitro were associated with a mesenchymal state with aberrations in redox metabolism. Accordingly, we demonstrate that Wilms tumor cells lacking microRNAs are sensitized to ferroptotic cell death through inhibition of glutathione peroxidase 4 (GPX4), the enzyme that detoxifies lipid peroxides. Implications: This study reveals genotype-transcriptome relationships in Wilms tumor and points to ferroptosis as a potentially therapeutic vulnerability in one subset of Wilms tumor.