CPR5-mediated nucleo-cytoplasmic localization of IAA12 and IAA19 controls lateral root development during abiotic stress.

Plasticity of the root system architecture (RSA) is essential in enabling plants to cope with various environmental stresses and is mainly controlled by the phytohormone auxin. Lateral root development is a major determinant of RSA. Abiotic stresses reduce auxin signaling output, inhibiting lateral root development; however, how abiotic stress translates into a lower auxin signaling output is not fully understood. Here, we show that the nucleo-cytoplasmic distribution of the negative regulators of auxin signaling AUXIN/INDOLE-3-ACETIC ACID INDUCIBLE 12 (AUX/IAA12 or IAA12) and IAA19 determines lateral root development under various abiotic stress conditions. The cytoplasmic localization of IAA12 and IAA19 in the root elongation zone enforces auxin signaling output, allowing lateral root development. Among components of the nuclear pore complex, we show that CONSTITUTIVE EXPRESSOR OF PATHOGENESIS-RELATED GENES 5 (CPR5) selectively mediates the cytoplasmic translocation of IAA12/19. Under abiotic stress conditions, CPR5 expression is strongly decreased, resulting in the accumulation of nucleus-localized IAA12/19 in the root elongation zone and the suppression of lateral root development, which is reiterated in the cpr5 mutant. This study reveals a regulatory mechanism for auxin signaling whereby the spatial distribution of AUX/IAA regulators is critical for lateral root development, especially in fluctuating environmental conditions.

Non-invasive prediction of massive transfusion during surgery using intraoperative hemodynamic monitoring data.

OBJECTIVE: Failure to receive prompt blood transfusion leads to severe complications if massive bleeding occurs during surgery. For the timely preparation of blood products, predicting the possibility of massive transfusion (MT) is essential to decrease morbidity and mortality. This study aimed to develop a model for predicting MT 10 min in advance using non-invasive bio-signal waveforms that change in real-time. METHODS: In this retrospective study, we developed a deep learning-based algorithm (DLA) to predict intraoperative MT within 10 min. MT was defined as the transfusion of 3 or more units of red blood cells within an hour. The datasets consisted of 18,135 patients who underwent surgery at Seoul National University Hospital (SNUH) for model development and internal validation and 621 patients who underwent surgery at the Boramae Medical Center (BMC) for external validation. We constructed the DLA by using features extracted from plethysmography (collected at 500 Hz) and hematocrit measured during surgery. RESULTS: Among 18,135 patients in SNUH and 621 patients in BMC, 265 patients (1.46%) and 14 patients (2.25%) received MT during surgery, respectively. The area under the receiver operating characteristic curve (AUROC) of DLA predicting intraoperative MT before 10 min was 0.962 (95% confidence interval [CI], 0.948-0.974) in internal validation and 0.922 (95% CI, 0.882-0.959) in external validation, respectively. CONCLUSION: The DLA can successfully predict intraoperative MT using non-invasive bio-signal waveforms.

Harnessing endophenotypes and network medicine for Alzheimer's drug repurposing.

Following two decades of more than 400 clinical trials centered on the "one drug, one target, one disease" paradigm, there is still no effective disease-modifying therapy for Alzheimer's disease (AD). The inherent complexity of AD may challenge this reductionist strategy. Recent observations and advances in network medicine further indicate that AD likely shares common underlying mechanisms and intermediate pathophenotypes, or endophenotypes, with other diseases. In this review, we consider AD pathobiology, disease comorbidity, pleiotropy, and therapeutic development, and construct relevant endophenotype networks to guide future therapeutic development. Specifically, we discuss six main endophenotype hypotheses in AD: amyloidosis, tauopathy, neuroinflammation, mitochondrial dysfunction, vascular dysfunction, and lysosomal dysfunction. We further consider how this endophenotype network framework can provide advances in computational and experimental strategies for drug-repurposing and identification of new candidate therapeutic strategies for patients suffering from or at risk for AD. We highlight new opportunities for endophenotype-informed, drug discovery in AD, by exploiting multi-omics data. Integration of genomics, transcriptomics, radiomics, pharmacogenomics, and interactomics (protein-protein interactions) are essential for successful drug discovery. We describe experimental technologies for AD drug discovery including human induced pluripotent stem cells, transgenic mouse/rat models, and population-based retrospective case-control studies that may be integrated with multi-omics in a network medicine methodology. In summary, endophenotype-based network medicine methodologies will promote AD therapeutic development that will optimize the usefulness of available data and support deep phenotyping of the patient heterogeneity for personalized medicine in AD.

Transforming growth factor ß1 alters the 3'-UTR of mRNA to promote lung fibrosis.

Idiopathic pulmonary fibrosis (IPF) is a chronic disease characterized by the pathological remodeling of air sacs as a result of excessive accumulation of extracellular matrix (ECM) proteins, but the mechanism governing the robust protein expression is poorly understood. Our recent findings demonstrate that alternative polyadenylation (APA) caused by NUDT21 reduction is important for the increased expression of fibrotic mediators and ECM proteins in lung fibroblasts by shortening the 3'-untranslated regions (3'-UTRs) of mRNAs and stabilizing their transcripts, therefore activating pathological signaling pathways. Despite the importance of NUDT21 reduction in the regulation of fibrosis, the underlying mechanisms for the depletion are unknown. We demonstrate here that NUDT21 is depleted by TGFß1. We found that miR203, which is increased in IPF, was induced by TGFß1 to target the NUDT21 3'-UTR, thus depleting NUDT21 in human and mouse lung fibroblasts. TGFß1-mediated NUDT21 reduction was attenuated by the miR203 inhibitor antagomiR203 in fibroblasts. TGFß1 transgenic mice revealed that TGFß1 down-regulates NUDT21 in fibroblasts in vivo Furthermore, TGFß1 promoted differential APA of fibrotic genes, including FGF14, RICTOR, TMOD2, and UCP5, in association with increased protein expression. This unique differential APA signature was also observed in IPF fibroblasts. Altogether, our results identified TGFß1 as an APA regulator through NUDT21 depletion amplifying pulmonary fibrosis.

Nonthermal Plasma-Stimulated C-N Coupling from CH4 and N2 Depends on the Presence of Surface CHx and Plasma-Phase CN Species.

Formation of C-N containing compounds from plasma-catalytic coupling of CH4 and N2 over various transition metals (Ni, Pd, Cu, Ag, and Au) is investigated using a multimodal spectroscopic approach, combining polarization-modulation infrared reflection-absorption spectroscopy (PM-IRAS) and optical emission spectroscopy (OES). Through sequential experiments utilizing CH4 and N2 nonthermal plasmas, we minimize plasma-phase reactions and identify key intermediates for C-N coupling on metal surfaces. Results show that simultaneous CH4 and N2 exposure with plasma stimulation produces surface C-N species. However, N2-CH4 sequential exposure does not lead to C-N species formation, while CH4-N2 sequential exposure reveals the presence of CHx surface species and CN radical species as key precursors to C-N species formation. From further analysis using X-ray photoelectron spectroscopy and liquid chromatography-mass spectrometry, the influence of exposure conditions on the degree of nitrogen incorporation and the nature of C-N species formed were revealed. The work highlights the importance of surface chemistry and exposure conditions in surface C-N coupling with plasma stimulation.

Alzheimer's disease drug development pipeline: 2024.

INTRODUCTION: New therapies to prevent or delay the onset of symptoms, slow progression, or improve cognitive and behavioral symptoms of Alzheimer's disease (AD) are needed. METHODS: We interrogated clinicaltrials.gov including all clinical trials assessing pharmaceutical therapies for AD active in on January 1, 2024. We used the Common Alzheimer's Disease Research Ontology (CADRO) to classify the targets of therapies in the pipeline. RESULTS: There are 164 trials assessing 127 drugs across the 2024 AD pipeline. There were 48 trials in Phase 3 testing 32 drugs, 90 trials in Phase 2 assessing 81 drugs, and 26 trials in Phase 1 testing 25 agents. Of the 164 trials, 34% (N = 56) assess disease-modifying biological agents, 41% (N = 68) test disease-modifying small molecule drugs, 10% (N = 17) evaluate cognitive enhancing agents, and 14% (N = 23) test drugs for the treatment of neuropsychiatric symptoms. DISCUSSION: Compared to the 2023 pipeline, there are fewer trials (164 vs. 187), fewer drugs (127 vs. 141), fewer new chemical entities (88 vs. 101), and a similar number of repurposed agents (39 vs. 40). Highlights: In the 2024 Alzheimer's disease drug development pipeline, there are 164 clinical trials assessing 127 drugs.The 2024 Alzheimer's disease drug development pipeline has contracted compared to the 2023 Alzheimer pipeline with fewer trials, fewer drugs, and fewer new chemical entities.Drugs in the Alzheimer's disease drug development pipeline target a wide array of targets; the most common processes targeted include neurotransmitter receptors, inflammation, amyloid, and synaptic plasticity.The total development time for a potential Alzheimer's disease therapy to progress from nonclinical studies to FDA review is approximately 13 years.

In Situ Formation of an Artificial Lithium Oxalate-Rich Solid Electrolyte Interphase on 3D Ni Host for Highly Stable Lithium Metal Batteries.

Li metal, with a high theoretical capacity, is considered the most promising anode for next-generation high-energy-density batteries. However, the commercialization of the Li metal anode is limited owing to its high reactivity, significant volume expansion, continuous solid electrolyte interphase (SEI) layer degradation caused by undesirable Li deposition, and uncontrollable dendrite growth. This study demonstrates the in situ construction of a Li2C2O4-enriched SEI layer from NiC2O4 nanowires on three-dimensional Ni foam. The lithiophilic Li2C2O4-enriched SEI layer provides a uniform distribution of the electrical field and sufficient nucleation and deposition sites for Li without dendrite formation. Consequently, the stable Li2C2O4-enriched SEI layer successfully inhibits the formation of lithium dendrites, resulting in reversible Li stripping/plating behavior, maintained over an extended period of 5000 h with a deposition capacity of 1 mAh cm-2 at 1 mA cm-2. Additionally, a high cycling stability is observed in the full cell test with ∼70% capacity retention after 1300 cycles at 3 C. This approach offers a large-scale and facile synthesis process via the in situ precipitation growth of NiC2O4 followed by lithiation to form Li2C2O4. Furthermore, the significant stability of the Li2C2O4-enriched SEI layer aids the design of in situ-constructed SEI layers for highly stable Li metal batteries.

Frequency Domain Deep Learning With Non-Invasive Features for Intraoperative Hypotension Prediction.

BACKGROUND: Intraoperative hypotension can lead to postoperative organ dysfunction. Previous studies primarily used invasive arterial pressure as the key biosignal for the detection of hypotension. However, these studies had limitations in incorporating different biosignal modalities and utilizing the periodic nature of biosignals. To address these limitations, we utilized frequency-domain information, which provides key insights that time-domain analysis cannot provide, as revealed by recent advances in deep learning. With the frequency-domain information, we propose a deep-learning approach that integrates multiple biosignal modalities. METHODS: We used the discrete Fourier transform technique, to extract frequency information from biosignal data, which we then combined with the original time-domain data as input for our deep learning model. To improve the interpretability of our results, we incorporated recent interpretable modules for deep-learning models into our analysis. RESULTS: We constructed 75 994 segments from the data of 3226 patients to predict hypotension during surgery. Our proposed frequency-domain deep-learning model outperformed conventional approaches that rely solely on time-domain information. Notably, our model achieved a greater increase in AUROC performance than the time-domain deep learning models when trained on non-invasive biosignal data only (AUROC 0.898 [95% CI: 0.885-0.91] vs. 0.853 [95% CI: 0.839-0.867]). Further analysis revealed that the 1.5-3.0 Hz frequency band played an important role in predicting hypotension events. CONCLUSION: Utilizing the frequency domain not only demonstrated high performance on invasive data but also showed significant performance improvement when applied to non-invasive data alone. Our proposed framework offers clinicians a novel perspective for predicting intraoperative hypotension.

Observation and Characterization of Vibrationally Active Surface Species Accessed with Nonthermal Nitrogen Plasmas.

Polycrystalline Ni, Pd, Cu, Ag, and Au foils exposed to nonthermal plasma (NTP)-activated N2 are found to exhibit a vibrational feature near 2200 cm-1 in polarization-modulation infrared reflection-absorption spectroscopy (PM-IRAS) observations that are not present in the same materials exposed to N2 under nonplasma conditions. The feature is similar to that reported elsewhere and is typically assigned to chemisorbed N2. We employ a combination of temperature-dependent experiments, sequential dosing, X-ray photoelectron spectroscopy, isotopic labeling, and density functional theory calculations to characterize the feature. Results are most consistent with a triatomic species, likely NCO, with the C and O likely originating from ppm-level impurities in the ultrahigh-purity (UHP) Ar and/or N2 gas cylinders. The work highlights the potential for nonthermal plasmas to access adsorbates inaccessible thermally as well as the potential contributions of ppm-level impurities to corrupt the interpretation of plasma catalytic chemistry.

Privacy-Preserving Prediction of Postoperative Mortality in Multi-Institutional Data: Development and Usability Study.

BACKGROUND: To circumvent regulatory barriers that limit medical data exchange due to personal information security concerns, we use homomorphic encryption (HE) technology, enabling computation on encrypted data and enhancing privacy. OBJECTIVE: This study explores whether using HE to integrate encrypted multi-institutional data enhances predictive power in research, focusing on the integration feasibility across institutions and determining the optimal size of hospital data sets for improved prediction models. METHODS: We used data from 341,007 individuals aged 18 years and older who underwent noncardiac surgeries across 3 medical institutions. The study focused on predicting in-hospital mortality within 30 days postoperatively, using secure logistic regression based on HE as the prediction model. We compared the predictive performance of this model using plaintext data from a single institution against a model using encrypted data from multiple institutions. RESULTS: The predictive model using encrypted data from all 3 institutions exhibited the best performance based on area under the receiver operating characteristic curve (0.941); the model combining Asan Medical Center (AMC) and Seoul National University Hospital (SNUH) data exhibited the best predictive performance based on area under the precision-recall curve (0.132). Both Ewha Womans University Medical Center and SNUH demonstrated improvement in predictive power for their own institutions upon their respective data's addition to the AMC data. CONCLUSIONS: Prediction models using multi-institutional data sets processed with HE outperformed those using single-institution data sets, especially when our model adaptation approach was applied, which was further validated on a smaller host hospital with a limited data set.

Alzheimer's disease drug development pipeline: 2023.

Introduction: Drugs that prevent the onset, slow progression, or improve cognitive and behavioral symptoms of Alzheimer's disease (AD) are needed. Methods: We searched ClinicalTrials.gov for all current Phase 1, 2 and 3 clinical trials for AD and mild cognitive impairment (MCI) attributed to AD. We created an automated computational database platform to search, archive, organize, and analyze the derived data. The Common Alzheimer's Disease Research Ontology (CADRO) was used to identify treatment targets and drug mechanisms. Results: On the index date of January 1, 2023, there were 187 trials assessing 141 unique treatments for AD. Phase 3 included 36 agents in 55 trials; 87 agents were in 99 Phase 2 trials; and Phase 1 had 31 agents in 33 trials. Disease-modifying therapies were the most common drugs comprising 79% of drugs in trials. Twenty-eight percent of candidate therapies are repurposed agents. Populating all current Phase 1, 2, and 3 trials will require 57,465 participants. Discussion: The AD drug development pipeline is advancing agents directed at a variety of target processes. HIGHLIGHTS: There are currently 187 trials assessing 141 drugs for the treatment of Alzheimer's disease (AD).Drugs in the AD pipeline address a variety of pathological processes.More than 57,000 participants will be required to populate all currently registered trials.

Protective Effects of Melatonin in High-Fat Diet-Induced Hepatic Steatosis via Decreased Intestinal Lipid Absorption and Hepatic Cholesterol Synthesis.

BACKGRUOUND: The preventative effect of melatonin on the development of obesity and the progression of fatty liver under a high-fat diet (HFD) has been well elucidated through previous studies. We investigated the mechanism behind this effect regarding cholesterol biosynthesis and regulation of cholesterol levels. METHODS: Mice were divided into three groups: normal chow diet (NCD); HFD; and HFD and melatonin administration group (HFD+M). We assessed the serum lipid profile, mRNA expression levels of proteins involved in cholesterol synthesis and reabsorption in the liver and nutrient transporters in the intestines, and cytokine levels. Additionally, an in vitro experiment using HepG2 cells was performed. RESULTS: Expression of hepatic sterol regulatory element-binding protein 2 (SREBP-2), 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR), and low-density lipoprotein receptor (LDLR) demonstrated that melatonin administration significantly reduces hepatic cholesterol synthesis in mice fed an HFD. Expression of intestinal sodium-glucose transporter 1 (SGLT1), glucose transporter 2 (GLUT2), GLUT5, and Niemann-pick C1-like 1 (NPC1L1) demonstrated that melatonin administration significantly reduces intestinal carbohydrate and lipid absorption in mice fed an HFD. There were no differences in local and circulatory inflammatory cytokine levels among the NCD, HFD, and HFD+M group. HepG2 cells stimulated with palmitate showed reduced levels of SREBP, LDLR, and HMGCR indicating these results are due to the direct mechanistic effect of melatonin on hepatocytes. CONCLUSION: Collectively, these data indicate the mechanism behind the protective effects of melatonin from weight gain and liver steatosis under HFD is through a reduction in intestinal caloric absorption and hepatic cholesterol synthesis highlighting its potential in the treatment of obesity and fatty liver disease.

Preparation of Remote Plasma Atomic Layer-Deposited HfO2 Thin Films with High Charge Trapping Densities and Their Application in Nonvolatile Memory Devices.

Optimization of equipment structure and process conditions is essential to obtain thin films with the required properties, such as film thickness, trapped charge density, leakage current, and memory characteristics, that ensure reliability of the corresponding device. In this study, we fabricated metal-insulator-semiconductor (MIS) structure capacitors using HfO2 thin films separately deposited by remote plasma (RP) atomic layer deposition (ALD) and direct-plasma (DP) ALD and determined the optimal process temperature by measuring the leakage current and breakdown strength as functions of process temperature. Additionally, we analyzed the effects of the plasma application method on the charge trapping properties of HfO2 thin films and properties of the interface between Si and HfO2. Subsequently, we synthesized charge-trapping memory (CTM) devices utilizing the deposited thin films as charge-trapping layers (CTLs) and evaluated their memory properties. The results indicated excellent memory window characteristics of the RP-HfO2 MIS capacitors compared to those of the DP-HfO2 MIS capacitors. Moreover, the memory characteristics of the RP-HfO2 CTM devices were outstanding as compared to those of the DP-HfO2 CTM devices. In conclusion, the methodology proposed herein can be useful for future implementations of multiple levels of charge-storage nonvolatile memories or synaptic devices that require many states.

An interpretable Alzheimer's disease oligogenic risk score informed by neuroimaging biomarkers improves risk prediction and stratification.

Introduction: Stratification of Alzheimer's disease (AD) patients into risk subgroups using Polygenic Risk Scores (PRS) presents novel opportunities for the development of clinical trials and disease-modifying therapies. However, the heterogeneous nature of AD continues to pose significant challenges for the clinical broadscale use of PRS. PRS remains unfit in demonstrating sufficient accuracy in risk prediction, particularly for individuals with mild cognitive impairment (MCI), and in allowing feasible interpretation of specific genes or SNPs contributing to disease risk. We propose adORS, a novel oligogenic risk score for AD, to better predict risk of disease by using an optimized list of relevant genetic risk factors. Methods: Using whole genome sequencing data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort (n = 1,545), we selected 20 genes that exhibited the strongest correlations with FDG-PET and AV45-PET, recognized neuroimaging biomarkers that detect functional brain changes in AD. This subset of genes was incorporated into adORS to assess, in comparison to PRS, the prediction accuracy of CN vs. AD classification and MCI conversion prediction, risk stratification of the ADNI cohort, and interpretability of the genetic information included in the scores. Results: adORS improved AUC scores over PRS in both CN vs. AD classification and MCI conversion prediction. The oligogenic model also refined risk-based stratification, even without the assistance of APOE, thus reflecting the true prevalence rate of the ADNI cohort compared to PRS. Interpretation analysis shows that genes included in adORS, such as ATF6, EFCAB11, ING5, SIK3, and CD46, have been observed in similar neurodegenerative disorders and/or are supported by AD-related literature. Discussion: Compared to conventional PRS, adORS may prove to be a more appropriate choice of differentiating patients into high or low genetic risk of AD in clinical studies or settings. Additionally, the ability to interpret specific genetic information allows the focus to be shifted from general relative risk based on a given population to the information that adORS can provide for a single individual, thus permitting the possibility of personalized treatments for AD.

Alzheimer's disease drug development pipeline: 2022.

Introduction: Alzheimer's disease (AD) represents a global health crisis. Treatments are needed to prevent, delay the onset, slow the progression, improve cognition, and reduce behavioral disturbances of AD. We review the current clinical trials and drugs in development for the treatment of AD. Methods: We searched the governmental website clinicaltrials.gov where are all clinical trials conducted in the United States must be registered. We used artificial intelligence (AI) and machine learning (ML) approaches to ensure comprehensive detection and characterization of trials and drugs in development. We use the Common Alzheimer's Disease Research Ontology (CADRO) to classify drug targets and mechanisms of action of drugs in the pipeline. Results: As of January 25, 2022 (index date for this study) there were 143 agents in 172 clinical trials for AD. The pipeline included 31 agents in 47 trials in Phase 3, 82 agents in 94 trials in Phase 2, and 30 agents in 31 trials in Phase 1. Disease-modifying therapies represent 83.2% of the total number of agents in trials; symptomatic cognitive enhancing treatments represent 9.8% of agents in trials; and drugs for the treatment of neuropsychiatric symptoms comprise 6.9%. There is a diverse array of drug targets represented by agents in trials including nearly all CADRO categories. Thirty-seven percent of the candidate agents in the pipeline are repurposed drugs approved for other indications. A total of 50,575 participants are needed to fulfill recruitment requirements for all currently active clinical trials. Discussion: The AD drug development pipeline has agents representing a substantial array of treatment mechanisms and targets. Advances in drug design, outcome measures, use of biomarkers, and trial conduct promise to accelerate the delivery of new and better treatments for patients with AD. Highlights: There are 143 drugs in the current Alzheimer's disease (AD) drug development pipeline.Disease-modifying therapies represent 83.2% of the candidate treatments.Current trials require 50,575 participants who will donate 3,878,843 participant-weeks to clinical trials.The biopharmaceutical industry sponsors 50% of all clinical trials including 68% of Phase 3 trials.Sixty-three percent of Phase 3 trials and 46% of Phase 2 trials include non-North American clinical trial site locations indicating the global ecosystem required for AD drug development.

Development and Validation of a Prediction Model for Need for Massive Transfusion During Surgery Using Intraoperative Hemodynamic Monitoring Data.

Importance: Massive transfusion is essential to prevent complications during uncontrolled intraoperative hemorrhage. As massive transfusion requires time for blood product preparation and additional medical personnel for a team-based approach, early prediction of massive transfusion is crucial for appropriate management. Objective: To evaluate a real-time prediction model for massive transfusion during surgery based on the incorporation of preoperative data and intraoperative hemodynamic monitoring data. Design, Setting, and Participants: This prognostic study used data sets from patients who underwent surgery with invasive blood pressure monitoring at Seoul National University Hospital (SNUH) from 2016 to 2019 and Boramae Medical Center (BMC) from 2020 to 2021. SNUH represented the development and internal validation data sets (n = 17 986 patients), and BMC represented the external validation data sets (n = 494 patients). Data were analyzed from November 2020 to December 2021. Exposures: A deep learning-based real-time prediction model for massive transfusion. Main Outcomes and Measures: Massive transfusion was defined as a transfusion of 3 or more units of red blood cells over an hour. A preoperative prediction model for massive transfusion was developed using preoperative variables. Subsequently, a real-time prediction model using preoperative and intraoperative parameters was constructed to predict massive transfusion 10 minutes in advance. A prediction model, the massive transfusion index, calculated the risk of massive transfusion in real time. Results: Among 17 986 patients at SNUH (mean [SD] age, 58.65 [14.81] years; 9036 [50.2%] female), 416 patients (2.3%) underwent massive transfusion during the operation (mean [SD] duration of operation, 170.99 [105.03] minutes). The real-time prediction model constructed with the use of preoperative and intraoperative parameters significantly outperformed the preoperative prediction model (area under the receiver characteristic curve [AUROC], 0.972; 95% CI, 0.968-0.976 vs AUROC, 0.824; 95% CI, 0.813-0.834 in the SNUH internal validation data set; P < .001). Patients with the highest massive transfusion index (ie, >90th percentile) had a 47.5-fold increased risk for a massive transfusion compared with those with a lower massive transfusion index (ie, <80th percentile). The real-time prediction model also showed excellent performance in the external validation data set (AUROC of 0.943 [95% CI, 0.919-0.961] in BMC). Conclusions and Relevance: The findings of this prognostic study suggest that the real-time prediction model for massive transfusion showed high accuracy of prediction performance, enabling early intervention for high-risk patients. It suggests strong confidence in artificial intelligence-assisted clinical decision support systems in the operating field.

Natural occurrence of grape poisoning in two dogs.

Clinical grape poisoning in two dogs (a 1.6-year-old male Shih Tzu and a 5-year-old female Yorkshire Terrier) was described in the present study. Clinical signs included decreased urine output in the Shih Tzu and ataxia in the Yorkshire Terrier after grape ingestion. The Shih Tzu died 5 days post-grape ingestion, while the Yorkshire Terrier died 3 days post-grape ingestion. Erythematous serosae and mucosae, multifocal red small intestinal foci, and blood and grape seeds were identified in the intestinal lumen. Brownish-yellow crystals were bilaterally identified in the renal pelvis. The primary histological findings were acute tubular necrosis of the proximal convoluted tubules, severe necrosis, and mineralization in the renal cortical tubules. Blood urea nitrogen, creatinine, and alanine aminotransferase were increased in the dogs. Many Korean veterinary clinicians have suspected clinical grape poisoning. However, to our knowledge, grape poisoning has not been identified by pathologic and clinicopathologic basis until this writing in Korea. Education and knowledge about the risks of grape poisoning is necessary for the prevention of accidental exposures.

Who funds Alzheimer's disease drug development?

INTRODUCTION: Despite the increase in Alzheimer's disease (AD) cases in the United States, no new treatments have been approved in the United States since 2003. The costs associated with drug development programs are high and serve as a significant deterrent to AD therapeutic investigations. In this study, we analyze the sponsorship data for AD clinical trials conducted since 2016 to assess the fiscal support for AD clinical trials. METHODS: We analyzed the funding sources of all AD trials over the past 5 years as reported on ClinicalTrials.gov. RESULTS: There were 136 trials being conducted for treatments in the US AD therapeutic pipeline on the index date of this study. Among non-prevention trials, disease-modifying therapies (DMT) in Phase 3 were almost entirely sponsored by the biopharmaceutical industry; Phase 2 DMT trials were split between the biopharmaceutical industry and funding from the National Institutes of Health (NIH) to academic medical centers (AMCs). The majority of prevention trials received sponsorship from public-private partnerships (PPP). Trials of symptomatic agents are equally likely to have biopharmaceutical or NIH/AMC sponsorship. Most trials with repurposed agents had NIH/AMC funding (89%). Since 2016, there has been consistent growth in the number of trials sponsored both in part and fully by NIH/AMC sources and in PPP, and there has been a reduction in biopharmaceutical company-sponsored trials. DISCUSSION: The number of trials supported by the biopharmaceutical industry has decreased over the past 5 years; trials supported from federal sources and PPP have increased. Repurposed compounds are mostly in Phase 2 trials and provide critical mechanistic information.

Alzheimer's disease drug development pipeline: 2021.

INTRODUCTION: The number of individuals worldwide with Alzheimer's disease (AD) is growing at a rapid rate. New treatments are urgently needed. We review the current pipeline of drugs in clinical trials for the treatment of AD. METHODS: We interrogated ClinicalTrials.gov, the federal registry of clinical trials to identify drugs in trials. RESULTS: There are 126 agents in 152 trials assessing new therapies for AD: 28 treatments in Phase 3 trials, 74 in Phase 2, and 24 in Phase 1. The majority of drugs in trials (82.5%) target the underlying biology of AD with the intent of disease modification; 10.3% are putative cognitive enhancing agents; and 7.1% are drugs being developed to reduce neuropsychiatric symptoms. DISCUSSION: This pipeline analysis shows that target biological processes are more diversified, biomarkers are more regularly used, and repurposed agents are being explored to determine their utility for the treatment of AD.

Direct Observation of Plasma-Stimulated Activation of Surface Species Using Multimodal In Situ/Operando Spectroscopy Combining Polarization-Modulation Infrared Reflection-Absorption Spectroscopy, Optical Emission Spectroscopy, and Mass Spectrometry.

Nonthermal plasmas (NTPs) produce reactive chemical environments, including electrons, ions, radicals, and vibrationally excited molecules, that can drive chemistry at temperatures at which such species are thermally inaccessible. There has been growing interest in the integration of conventional catalysis with reactive NTPs to promote novel chemical transformations. Unveiling the full potential of plasma-catalytic processes requires a comprehensive understanding of plasma-catalytic synergies, including characterization of plasma-catalytic surface interactions. In this work, we report on a newly designed multimodal spectroscopic instrument combining polarization-modulation infrared reflection-absorption spectroscopy (PM-IRAS), mass spectrometry, and optical emission spectroscopy (OES) for the investigation of plasma-surface interactions such as those found in plasma catalysis. In particular, this tool has been utilized to correlate plasma-phase chemistry with both surface chemistry and gas-phase products in situ (1) during the deposition of carbonaceous surface species via NTP-promoted nonoxidative coupling of methane and (2) during subsequent activation of surface deposits with an atmospheric pressure and temperature argon plasma jet on both nickel (Ni) and silicon dioxide (SiO2) surfaces. For the first time, the activation of carbonaceous surface species by a NTP on Ni and SiO2 surfaces to form hydrogen gas and C2 hydrocarbons was directly observed, where both PM-IRAS and OES measurements suggest that they may form through different pathways. This unique tool for studying plasma-surface interactions could enable more rational design of plasma-stimulated catalytic processes.