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.

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 3,226 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.

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.

α2δ-1 switches the phenotype of synaptic AMPA receptors by physically disrupting heteromeric subunit assembly.

Many neurological disorders show an increased prevalence of GluA2-lacking, Ca2+-permeable AMPA receptors (CP-AMPARs), which dramatically alters synaptic function. However, the molecular mechanism underlying this distinct synaptic plasticity remains enigmatic. Here, we show that nerve injury potentiates postsynaptic, but not presynaptic, CP-AMPARs in the spinal dorsal horn via α2δ-1. Overexpressing α2δ-1, previously regarded as a Ca2+ channel subunit, augments CP-AMPAR levels at the cell surface and synapse. Mechanistically, α2δ-1 physically interacts with both GluA1 and GluA2 via its C terminus, inhibits the GluA1/GluA2 heteromeric assembly, and increases GluA2 retention in the endoplasmic reticulum. Consequently, α2δ-1 diminishes the availability and synaptic expression of GluA1/GluA2 heterotetramers in the spinal cord in neuropathic pain. Inhibiting α2δ-1 with gabapentin or disrupting the α2δ-1-AMPAR complex fully restores the intracellular assembly and synaptic dominance of heteromeric GluA1/GluA2 receptors. Thus, α2δ-1 is a pivotal AMPAR-interacting protein that controls the subunit composition and Ca2+ permeability of postsynaptic AMPARs.

Bioelectrical impedance analysis for predicting postoperative complications and survival after liver resection for hepatocellular carcinoma.

BACKGROUND: Bioelectrical impedance analysis provides information on body composition and nutritional status. However, it's unclear whether the preoperative edema index or phase angle predicts postoperative complication or mortality in patients with hepatocellular carcinoma (HCC). Thus, we investigated whether preoperative bioelectrical impedance analysis could predict postoperative complications and survival in patients with HCC. METHODS: Seventy-nine patients who underwent hepatectomy for hepatocellular carcinoma were prospectively enrolled and bioelectrical impedance analysis was performed before surgery. Postoperative ascites or acute kidney injury and patients' survival were monitored after surgery. RESULTS: Among 79 patients, 35 (44.3%) developed ascites or acute kidney injury after hepatectomy. In multivariate analysis, a high preoperative edema index (extracellular water/total body water) (>0.384) (odds ratio 3.96; 95% confidence interval: 1.03-15.17; P=0.045) and higher fluid infusion during surgery (odds ratio 1.36; 95% confidence interval: 1.04-1.79; P=0.026) were identified as significant risk factors for ascites or acute kidney injury after hepatectomy. Subgroup analyses showed that the edema index was a significant predictor of ascites or acute kidney injury in patients with cirrhosis. Tumor size was the only significant predictive factor for short-term survival after hepatectomy. CONCLUSIONS: The preoperative edema index using bioelectrical impedance analysis can be used as a predictor of post-hepatectomy complication, especially in patients with liver cirrhosis.

Repurposed agents in the Alzheimer's disease drug development pipeline.

BACKGROUND: Treatments are needed to address the growing prevalence of Alzheimer's disease (AD). Clinical trials have failed to produce any AD drugs for Food and Drug Administration (FDA) approval since 2003, and the pharmaceutical development process is both time-consuming and costly. Drug repurposing provides an opportunity to accelerate this process by investigating the AD-related effects of agents approved for other indications. These drugs have known safety profiles, pharmacokinetic characterization, formulations, doses, and manufacturing processes. METHODS: We assessed repurposed AD therapies represented in Phase I, Phase II, and Phase III of the current AD pipeline as registered on ClinicalTrials.gov as of February 27, 2020. RESULTS: We identified 53 clinical trials involving 58 FDA-approved agents. Seventy-eight percent of the agents in trials had putative disease-modifying mechanisms of action. Of the repurposed drugs in the pipeline 20% are hematologic-oncologic agents, 18% are drugs derived from cardiovascular indications, 14% are agents with psychiatric uses, 12% are drug used to treat diabetes, 10% are neurologic agents, and the remaining 26% of drugs fall under other conditions. Intellectual property strategies utilized in these programs included using the same drug but altering doses, routes of administration, or formulations. Most repurposing trials were supported by Academic Medical Centers and were not funded through the biopharmaceutical industry. We compared our results to a European trial registry and found results similar to those derived from ClinicalTrials.gov. CONCLUSIONS: Drug repurposing is a common approach to AD drug development and represents 39% of trials in the current AD pipeline. Therapies from many disease areas provide agents potentially useful in AD. Most of the repurposed agents are generic and a variety of intellectual property strategies have been adopted to enhance their economic value.

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.

Effects of genistein on anti-tumor activity of cisplatin in human cervical cancer cell lines.

OBJECTIVE: To investigate the effect of genistein on the anticancer effects of chemotherapeutic agents, we examined the effect of a genistein and cisplatin combination on CaSki human cervical cancer cells. METHODS: After the cervical cancer cells (HeLa cells, CaSki cells) had been cultured, cisplatin and genistein were added to the culture medium, and the cell activity was measured using MTT assay. The CaSki cells were cultured in a medium containing cisplatin and genistein, and then, the cells were collected in order to measure p53, Bcl2, ERK, and caspase 3 levels by western blotting. RESULTS: Both the HeLa and CaSki cells had decreased cell viabilities when the cisplatin concentration was 10 µM or higher. When combined with genistein, the cell viabilities of the HeLa and CaSki cells decreased at cisplatin concentrations of 8 µM and 6 µM, respectively. The administration of genistein increased the toxicity of cisplatin in the HeLa and CaSki cells. In the CaSki cells, the p-ERK1/2 level decreased by 37%, the p53 expression level increased by 304%, and the cleaved caspase 3 level increased by 115% in the cisplatin+genistein group compared to that in the cisplatin group. Bcl2 expression was reduced by 69% in the cisplatin+genistein group compared to that in the cisplatin group. CONCLUSION: Genistein enhances the anticancer effect of cisplatin in CaSki cells, and can be used as a chemotherapeutic adjuvant to increase the activity of a chemotherapeutic agent.

Incarvillateine produces antinociceptive and motor suppressive effects via adenosine receptor activation.

(-)-Incarvillateine (INCA) is a natural product that has garnered attention due to its purported analgesic effects and historical use as a pain reliever in China. α-Truxillic acid monoesters (TAMEs) constitute a class of inhibitors targeting fatty acid binding protein 5 (FABP5), whose inhibition produces analgesia in animal models. The structural similarity between INCA and TAMEs motivated us to assess whether INCA exerts its antinociceptive effects via FABP inhibition. We found that, in contrast to TAMEs, INCA did not exhibit meaningful binding affinities toward four human FABP isoforms (FABP3, FABP4, FABP5 and FABP7) in vitro. INCA-TAME, a putative monoester metabolite of INCA that closely resembles TAMEs also lacked affinity for FABPs. Administration of INCA to mice produced potent antinociceptive effects while INCA-TAME was without effect. Surprisingly, INCA also potently suppressed locomotor activity at the same dose that produces antinociception. The motor suppressive effects of INCA were reversed by the adenosine A2 receptor antagonist 3,7-dimethyl-1-propargylxanthine. Collectively, our results indicate that INCA and INCA-TAME do not inhibit FABPs and that INCA exerts potent antinociceptive and motor suppressive effects at equivalent doses. Therefore, the observed antinociceptive effects of INCA should be interpreted with caution.

Identifying subtype-specific associations between gene expression and DNA methylation profiles in breast cancer.

BACKGROUND: Breast cancer is a complex disease in which different genomic patterns exists depending on different subtypes. Recent researches present that multiple subtypes of breast cancer occur at different rates, and play a crucial role in planning treatment. To better understand underlying biological mechanisms on breast cancer subtypes, investigating the specific gene regulatory system via different subtypes is desirable. METHODS: Gene expression, as an intermediate phenotype, is estimated based on methylation profiles to identify the impact of epigenomic features on transcriptomic changes in breast cancer. We propose a kernel weighted l1-regularized regression model to incorporate tumor subtype information and further reveal gene regulations affected by different breast cancer subtypes. For the proper control of subtype-specific estimation, samples from different breast cancer subtype are learned at different rate based on target estimates. Kolmogorov Smirnov test is conducted to determine learning rate of each sample from different subtype. RESULTS: It is observed that genes that might be sensitive to breast cancer subtype show prediction improvement when estimated using our proposed method. Comparing to a standard method, overall performance is also enhanced by incorporating tumor subtypes. In addition, we identified subtype-specific network structures based on the associations between gene expression and DNA methylation. CONCLUSIONS: In this study, kernel weighted lasso model is proposed for identifying subtype-specific associations between gene expressions and DNA methylation profiles. Identification of subtype-specific gene expression associated with epigenomic changes might be helpful for better planning treatment and developing new therapies.

The Arabidopsis AtLEC gene encoding a lectin-like protein is up-regulated by multiple stimuli including developmental signal, wounding, jasmonate, ethylene, and chitin elicitor.

The Arabidopsis gene AtLEC (At3g15356) gene encodes a putative 30-kDa protein with a legume lectin-like domain. Likely to classic legume lectin family of genes, AtLEC is expressed in rosette leaves, primary inflorescences, and roots, as observed in Northern blot analysis. The accumulation of AtLEC transcript is induced very rapidly, within 30 min, by chitin, a fungal wall-derived oligosaccharide elictor of the plant defense response. Transgenic Arabidopsis carrying an AtLEC promoter-driven beta-glucuronidase (GUS) construct exhibited GUS activity in the leaf veins, secondary inflorescences, carpel heads, and silique receptacles, in which no expression could be seen in Northern blot analysis. This observation suggests that AtLEC expression is induced transiently and locally during developmental processes in the absence of an external signal such as chitin. In addition, mechanically wounded sites showed strong GUS activity, indicating that the AtLEC promoter responds to jasmonate. Indeed, methyl jasmonate and ethylene exposure induced AtLEC expression within 3-6 h. Thus, the gene appears to play a role in the jasmonate-/ethylene-responsive, in addition to the chitin-elicited, defense responses. However, chitin-induced AtLEC expression was also observed in jasmonate-insensitive (coi1) and ethylene-insensitive (etr1-1) Arabidopsis mutants. Thus, it appears that chitin promotes AtLEC expression via a jasmonate- and/or ethylene-independent pathway.