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With the huge development of the usage of computer over network and advancement in applications running on various platforms captures the attention towards network security. The Intrusion Detection System (IDS) plays a vital role in detecting anomalies and attacks in the network. Earlier approaches of IDS relied on Machine Learning (M L) techniques. Due to some limitations, a better approach is needed. A combination of Machine Learning techniques and data preprocessing is an effective approach for IDS. In this work, a new dimensionality reduction technique combined with the Multi-class SVM (Support Vector Machine) is proposed for intrusion detection. In the proposed model, Multi-Linear Dimensionality Reduction (ML-DR) is proposed as a feature extraction technique to reduce the dimension in order to shorten the training time. A Multi-class SVM (M-SVM) is used to detect whether the action is an attack or not. Here the Multi-class SVM is adopted to perform multi-attack classification in a layered fashion. Radial Basis Function Kernel is used as a SVM kernel. NSL-KDD data set is used for the performance evaluation of the proposed approach. The performance metrics such as classification accuracy, false alarm rate and the correlation coefficient are evaluated to measure its efficiency. In comparison with other detection approaches, the experimental results show that the proposed model outperforms the higher classification accuracy. [ABSTRACT FROM AUTHOR]

Semantic Textual Similarity (STS) measures the degree of semantic equivalence between two segments of text, even though the similar context is expressed using different words. The textual segments are word phrases, sentences, paragraphs or documents. The similarity can be measured using lexical, syntactic and semantic information embedded in the sentences. The STS task in SemEval workshop is viewed as a regression problem, where real-valued output is clipped to the range 0-5 on a sentence pair. In this paper, empirical evaluations are carried using lexical, syntactic and semantic features on STS 2016 dataset. A new syntactic feature, Phrase Entity Alignment (PEA) is proposed. A phrase entity is a conceptual unit in a sentence with a subject or an object and its describing words. PEA aligns phrase entities present in the sentences based on their similarity scores. STS score is measured by combing the similarity scores of all aligned phrase entities. The impact of PEA on semantic textual equivalence is depicted using Pearson correlation between system generated scores and the human annotations. The proposed system attains a mean score of 0.7454 using random forest regression model. The results indicate that the system using the lexical, syntactic and semantic features together with PEA feature perform comparably better than existing systems. [ABSTRACT FROM AUTHOR]

Restoration of a partially edentulous geriatric patient's mouth with complete esthetic and functional reconstruction may present numerous problems to the dental professional. The major concerns include centric relation, esthetics, phonetics and the patient's occlusal vertical dimension. Taking this into consideration this article tries to explain in detail the step-by-step procedure in treatment planning, surgical and prosthetic aspects taken to completely rehabilitate a 65-year-old partially edentulous patient using a combination of implants, fixed and removable prosthesis. The completed treatment was successful both subjectively and objectively, with the patient reporting good results even after 2-years post-treatment. [ABSTRACT FROM AUTHOR]

CRISPR guide RNA sequences deriving exactly from natural sequences may not perform optimally in every application. Here we implement and evaluate algorithms for designing maximally fit, artificial CRISPR-Cas13a guides with multiple mismatches to natural sequences that are tailored for diagnostic applications. These guides offer more sensitive detection of diverse pathogens and discrimination of pathogen variants compared with guides derived directly from natural sequences and illuminate design principles that broaden Cas13a targeting., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Objective: Examine pathogen distribution, antibiotic resistance patterns, and hospital outcomes of infants with bacterial meningitis in neonatal intensive care units (NICUs) in the US from 2013-2018., Study Design: Infants were divided into 2 groups based on age at the time of meningitis: early-onset (0-3 days) and late-onset (>3 days). We compared demographics, clinical characteristics, epidemiology, hospital outcomes, distribution of organisms and resistance, and blood culture timing relative to cerebrospinal fluid culture., Results: From 345 NICUs, 659 infants were diagnosed with bacterial meningitis. The cumulative incidence was 1.1-1.3 cases/1000 NICU discharges. Median gestational age was 33 weeks, median birth weight was 1910 grams, 12% failed hearing screening, and 9% died prior to discharge. Of 141 cases of E. coli meningitis, 53% were resistant to ampicillin., Conclusions: Significant morbidities occur in infants with culture-proven meningitis in NICUs. Culture and subsequent discernment of sensitivity are crucial to guide definitive therapy., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Multiple sclerosis (MS) is a chronic disease with an underlying pathology characterized by inflammation-driven neuronal loss, axonal injury, and demyelination. Bruton's tyrosine kinase (BTK), a nonreceptor tyrosine kinase and member of the TEC family of kinases, is involved in the regulation, migration, and functional activation of B cells and myeloid cells in the periphery and the central nervous system (CNS), cell types which are deemed central to the pathology contributing to disease progression in MS patients. Herein, we describe the discovery of BIIB129 ( 25 ), a structurally distinct and brain-penetrant targeted covalent inhibitor (TCI) of BTK with an unprecedented binding mode responsible for its high kinome selectivity. BIIB129 ( 25 ) demonstrated efficacy in disease-relevant preclinical in vivo models of B cell proliferation in the CNS, exhibits a favorable safety profile suitable for clinical development as an immunomodulating therapy for MS, and has a low projected total human daily dose.

Primary open-angle glaucoma (POAG), characterized by retinal ganglion cell death, is a leading cause of irreversible blindness worldwide. However, its molecular and cellular causes are not well understood. Elevated intraocular pressure (IOP) is a major risk factor, but many patients have normal IOP. Colocalization and Mendelian randomization analysis of >240 POAG and IOP genome-wide association study (GWAS) loci and overlapping expression and splicing quantitative trait loci (e/sQTLs) in 49 GTEx tissues and retina prioritizes causal genes for 60% of loci. These genes are enriched in pathways implicated in extracellular matrix organization, cell adhesion, and vascular development. Analysis of single-nucleus RNA-seq of glaucoma-relevant eye tissues reveals that the POAG and IOP colocalizing genes and genome-wide associations are enriched in specific cell types in the aqueous outflow pathways, retina, optic nerve head, peripapillary sclera, and choroid. This study nominates IOP-dependent and independent regulatory mechanisms, genes, and cell types that may contribute to POAG pathogenesis., (© 2024. The Author(s).)

Water quality surveillance is tough, and a specific timely management is necessary for the inland aquaculture ponds and ecology as well. Real time quality monitoring involves the study of numerous parameters includes physical (turbidity, temperature, and specific conductivity), chemical (pH, calcium, manganese, chlorides, iron, biochemical oxygen demand), and biological (bacteria and algae). It is also crucial to recognize the inter-dependence among the parameters. Alternatively, these relationships can be predicted with statistical and numerical modelling. Organic strength parameter 5-day biochemical oxygen demand (BOD) is a significant parameter to evaluate since its impact is very high on the quality of water, aquatic life, and other biological concerns. This study focuses on the prediction of BOD using six traditional and four boosting algorithms considering ten input physicochemical attributes. The attributes were fine-tuned for highly precise predictions by removing extreme values from the data set using data outlier treatment. The prediction results are compared using performance metrics such as coefficient of determination (R 2 ), root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE). The findings revealed that boosting algorithms outperform the results of traditional models with the highest prediction accuracy. Among the boosting algorithms, eXtreme Gradient Boosting algorithm (XGBM) is found highly appropriate for the inland aquaculture waters with R 2  = 0.95, RMSE = 0.31, MSE = 0.09, MAE = 0.1. Finally, this study provides a systematic evaluation of the BOD in the aquaculture waters and has a significant contribution to water management and eco-balance., Competing Interests: Declaration of competing interest The authors have no relevant financial or non-financial interests to disclose., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Soil salinization is a widespread phenomenon leading to land degradation, particularly in regions with brackish inland aquaculture ponds. However, because of the high geographical and temporal fluctuation, monitoring vast areas provides substantial challenges. This study uses remote sensing data and machine learning techniques to predict soil salinity. Four linear models, namely linear regression, least absolute shrinkage and selection operator (LASSO), ridge, and elastic net regression, and three boosting algorithms, namely XGB regressor, LightGBM, and CatBoost regressor, were used to predict soil salinity. Cross-validation was performed by splitting the data into 30% for model testing and 70% for model training. Multiple metrics such as determination coefficient (R 2 ), root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) were used to compare the performances of these algorithms. By comparison, the CatBoost regressor model performed better than the other models in both testing (MAE = 0.42, MSE = 0.28, RMSE = 0.53, R 2 = 0.92) and training (MAE = 0.49, MSE = 0.36, RMSE = 0.60, R 2 = 0.90) phases. Hence, the CatBoost regressor model was recommended for monitoring soil salinity in India's massive inland aquaculture zone., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Background Mortality prediction in critically ill patients with cardiogenic shock can guide triage and selection of potentially high-risk treatment options. Methods and Results We developed and externally validated a checklist risk score to predict in-hospital mortality among adults admitted to the cardiac intensive care unit with Society for Cardiovascular Angiography & Interventions Shock Stage C or greater cardiogenic shock using 2 real-world data sets and Risk-Calibrated Super-sparse Linear Integer Modeling (RiskSLIM). We compared this model to those developed using conventional penalized logistic regression and published cardiogenic shock and intensive care unit mortality prediction models. There were 8815 patients in our training cohort (in-hospital mortality 13.4%) and 2237 patients in our validation cohort (in-hospital mortality 22.8%), and there were 39 candidate predictor variables. The final risk score (termed BOS,MA 2 ) included maximum blood urea nitrogen ≥25 mg/dL, minimum oxygen saturation <88%, minimum systolic blood pressure <80 mm Hg, use of mechanical ventilation, age ≥60 years, and maximum anion gap ≥14 mmol/L, based on values recorded during the first 24 hours of intensive care unit stay. Predicted in-hospital mortality ranged from 0.5% for a score of 0 to 70.2% for a score of 6. The area under the receiver operating curve was 0.83 (0.82-0.84) in training and 0.76 (0.73-0.78) in validation, and the expected calibration error was 0.9% in training and 2.6% in validation. Conclusions Developed using a novel machine learning method and the largest cardiogenic shock cohorts among published models, BOS,MA 2 is a simple, clinically interpretable risk score that has improved performance compared with existing cardiogenic-shock risk scores and better calibration than general intensive care unit risk scores.

Hypoglycemia is a common occurrence in critically ill patients and is associated with significant mortality and morbidity. We developed a machine learning model to predict hypoglycemia by using a multicenter intensive care unit (ICU) electronic health record dataset. Machine learning algorithms were trained and tested on patient data from the publicly available eICU Collaborative Research Database. Forty-four features including patient demographics, laboratory test results, medications, and vitals sign recordings were considered. The outcome of interest was the occurrence of a hypoglycemic event (blood glucose < 72 mg/dL) during a patient's ICU stay. Machine learning models used data prior to the second hour of the ICU stay to predict hypoglycemic outcome. Data from 61,575 patients who underwent 82,479 admissions at 199 hospitals were considered in the study. The best-performing predictive model was the eXtreme gradient boosting model (XGBoost), which achieved an area under the received operating curve (AUROC) of 0.85, a sensitivity of 0.76, and a specificity of 0.76. The machine learning model developed has strong discrimination and calibration for the prediction of hypoglycemia in ICU patients. Prospective trials of these models are required to evaluate their clinical utility in averting hypoglycemia within critically ill patient populations., (© 2021. The Author(s), under exclusive licence to Springer Nature B.V.)

The widespread transmission and evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) call for rapid nucleic acid diagnostics that are easy to use outside of centralized clinical laboratories. Here we report the development and performance benchmarking of Cas13-based nucleic acid assays leveraging lyophilised reagents and fast sample inactivation at ambient temperature. The assays, which we named SHINEv.2 (for 'streamlined highlighting of infections to navigate epidemics, version 2'), simplify the previously reported RNA-extraction-free SHINEv.1 technology by eliminating heating steps and the need for cold storage of the reagents. SHINEv.2 detected SARS-CoV-2 in nasopharyngeal samples with 90.5% sensitivity and 100% specificity (benchmarked against the reverse transcription quantitative polymerase chain reaction) in less than 90 min, using lateral-flow technology and incubation in a heat block at 37 °C. SHINEv.2 also allows for the visual discrimination of the Alpha, Beta, Gamma, Delta and Omicron SARS-CoV-2 variants, and can be run without performance losses by using body heat. Accurate, easy-to-use and equipment-free nucleic acid assays could facilitate wider testing for SARS-CoV-2 and other pathogens in point-of-care and at-home settings., (© 2022. The Author(s), under exclusive licence to Springer Nature Limited.)

Objective: To evaluate the safety and effectiveness of a ketamine-based anesthesia package to support emergency cesarean section when no anesthetist is available., Methods: A prospective case-series was conducted between December 11, 2013 and September 30, 2021 across nine sub-county hospitals in Kenya. Non-anesthetist healthcare providers undertook an evidence-based five-day training course. A structured instrument was used to collect preoperative, intraoperative, and postoperative data, and patients were contacted 6 months following the surgery to collect outcomes. The primary outcome measures were maternal and newborn survival and the ability of the ketamine package (ESM-Ketamine) to safely support cesarean deliveries., Results: A total of 401 emergency cesarean sections were performed using ketamine, administered by 54 non-anesthetist providers. All mothers survived to discharge. Brief oxygen desaturations were recorded among 33 (8.2%) mothers, and agitation and hallucinations occurred among 13 (3.2%). There were no maternal serious adverse events. At 6-month follow-up, 94.2% of mothers who could be reached reported no complaints. Additionally, 402 (92.4%) of the 435 operative births survived to discharge., Conclusion: The ESM-Ketamine package can be used by trained non-anesthetist providers to support emergency cesarean sections when no anesthetist is available. Ketamine has significant potential to increase access to emergency cesarean deliveries in resource-limited settings., (© 2021 The Authors. International Journal of Gynecology & Obstetrics published by John Wiley & Sons Ltd on behalf of International Federation of Gynecology and Obstetrics.)

Design of nucleic acid-based viral diagnostics typically follows heuristic rules and, to contend with viral variation, focuses on a genome's conserved regions. A design process could, instead, directly optimize diagnostic effectiveness using a learned model of sensitivity for targets and their variants. Toward that goal, we screen 19,209 diagnostic-target pairs, concentrated on CRISPR-based diagnostics, and train a deep neural network to accurately predict diagnostic readout. We join this model with combinatorial optimization to maximize sensitivity over the full spectrum of a virus's genomic variation. We introduce Activity-informed Design with All-inclusive Patrolling of Targets (ADAPT), a system for automated design, and use it to design diagnostics for 1,933 vertebrate-infecting viral species within 2 hours for most species and within 24 hours for all but three. We experimentally show that ADAPT's designs are sensitive and specific to the lineage level and permit lower limits of detection, across a virus's variation, than the outputs of standard design techniques. Our strategy could facilitate a proactive resource of assays for detecting pathogens., (© 2022. The Author(s).)

Objectives: Federated learning (FL) allows multiple institutions to collaboratively develop a machine learning algorithm without sharing their data. Organizations instead share model parameters only, allowing them to benefit from a model built with a larger dataset while maintaining the privacy of their own data. We conducted a systematic review to evaluate the current state of FL in healthcare and discuss the limitations and promise of this technology., Methods: We conducted a literature search using PRISMA guidelines. At least two reviewers assessed each study for eligibility and extracted a predetermined set of data. The quality of each study was determined using the TRIPOD guideline and PROBAST tool., Results: 13 studies were included in the full systematic review. Most were in the field of oncology (6 of 13; 46.1%), followed by radiology (5 of 13; 38.5%). The majority evaluated imaging results, performed a binary classification prediction task via offline learning (n = 12; 92.3%), and used a centralized topology, aggregation server workflow (n = 10; 76.9%). Most studies were compliant with the major reporting requirements of the TRIPOD guidelines. In all, 6 of 13 (46.2%) of studies were judged at high risk of bias using the PROBAST tool and only 5 studies used publicly available data., Conclusion: Federated learning is a growing field in machine learning with many promising uses in healthcare. Few studies have been published to date. Our evaluation found that investigators can do more to address the risk of bias and increase transparency by adding steps for data homogeneity or sharing required metadata and code., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: Leo Anthony Celi is the Editor-in Chief of PLOS Digital Health., (Copyright: © 2022 Crowson et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The coronavirus disease 2019 (COVID-19) pandemic has demonstrated a clear need for high-throughput, multiplexed and sensitive assays for detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and other respiratory viruses and their emerging variants. Here, we present a cost-effective virus and variant detection platform, called microfluidic Combinatorial Arrayed Reactions for Multiplexed Evaluation of Nucleic acids (mCARMEN), which combines CRISPR-based diagnostics and microfluidics with a streamlined workflow for clinical use. We developed the mCARMEN respiratory virus panel to test for up to 21 viruses, including SARS-CoV-2, other coronaviruses and both influenza strains, and demonstrated its diagnostic-grade performance on 525 patient specimens in an academic setting and 166 specimens in a clinical setting. We further developed an mCARMEN panel to enable the identification of 6 SARS-CoV-2 variant lineages, including Delta and Omicron, and evaluated it on 2,088 patient specimens with near-perfect concordance to sequencing-based variant classification. Lastly, we implemented a combined Cas13 and Cas12 approach that enables quantitative measurement of SARS-CoV-2 and influenza A viral copies in samples. The mCARMEN platform enables high-throughput surveillance of multiple viruses and variants simultaneously, enabling rapid detection of SARS-CoV-2 variants., (© 2022. The Author(s).)

Conventional means of Parkinson's Disease (PD) screening rely on qualitative tests typically administered by trained neurologists. Tablet technologies that enable data collection during handwriting and drawing tasks may provide low-cost, portable, and instantaneous quantitative methods for high-throughput PD screening. However, past efforts to use data from tablet-based drawing processes to distinguish between PD and control populations have demonstrated only moderate classification ability. Focusing on digitized drawings of Archimedean spirals, the present study utilized data from the open-access ParkinsonHW dataset to improve existing PD drawing diagnostic pipelines. Random forest classifiers were constructed using previously documented features and highly-predictive, newly-proposed features that leverage the many unique mathematical characteristics of the Archimedean spiral. This approach yielded an AUC of 0.999 on the particular dataset we tested on, and more importantly identified interpretable features with good promise for generalization across diverse patient cohorts. It demonstrated the potency of mathematical relationships inherent to the drawing shape and the usefulness of sparse feature sets and simple models, which further enhance interpretability, in the face of limited sample size. The results of this study also inform suggestions for future drawing task design and data analytics (feature extraction, shape selection, task diversity, drawing templates, and data sharing).

Competing Interests: Competing interests: None declared.

Purpose: Specular and confocal microscopes are important tools to monitor the health of the corneal endothelium (CE), but their high costs significantly limit accessibility in low-resource environments. We developed and validated a low-cost, fully automated method to quantitatively evaluate the CE using smartphone-based specular microscopy., Methods: A OnePlus 7 Pro smartphone attached to a Topcon SL-D701 slit-lamp was used to image the central corneal endothelium of 30 eyes using the specular reflection technique. A novel on-device image processing algorithm automatically computed endothelial cell density (ECD), percentage of hexagonal cells (HEX), and coefficient of variation (CV) values. These values were compared with the ECD, HEX, and CV generated by a Tomey EM-4000 specular microscope used to image the same set of eyes., Results: No significant differences were found in ECD (2799 ± 156 cells/mm2 vs. 2779 ± 166 cells/mm2; P = 0.28) and HEX (52 ± 6% vs. 53 ± 6%; P = 0.50) computed by smartphone-based specular imaging and specular microscope, respectively. A statistically significant difference in CV (34 ± 3% vs. 30 ± 3%; P < 0.01) was found between the two methods. The concordance achieved between the smartphone-based method and the Tomey specular microscope is very similar to the concordance between two specular microscopes reported in the literature., Conclusions: Smartphone-based specular imaging and automated analysis is a low-cost method to quantitatively evaluate the CE with accuracy comparable to the clinical standard., Translational Relevance: This tool can be used to screen the CE in low-resource regions and prompt investigation of suspected corneal endotheliopathies.

COVID-19 is devastating health systems globally and causing severe ventilator shortages. Since the beginning of the outbreak, the provision and use of ventilators has been a key focus of public discourse. Scientists and engineers from leading universities and companies have rushed to develop low-cost ventilators in hopes of supporting critically ill patients in developing countries. Philanthropists have invested millions in shipping ventilators to low-resource settings, and agencies such as the World Health Organization and the World Bank are prioritizing the purchase of ventilators. While we recognize the humanitarian nature of these efforts, merely shipping ventilators to low-resource environments may not improve outcomes of patients and could potentially cause harm. An ecosystem of considerable technological and human resources is required to support the usage of ventilators within intensive care settings. Medical-grade oxygen supplies, reliable electricity, bioengineering support, and consumables are all needed for ventilators to save lives. However, most ICUs in resource-poor settings do not have access to these resources. Patients on ventilators require continuous monitoring from physicians, nurses, and respiratory therapists skilled in critical care. Health care workers in many low-resource settings are already exceedingly overburdened, and pulling these essential human resources away from other critical patient needs could reduce the overall quality of patient care. When deploying medical devices, it is vital to align the technological intervention with the clinical reality. Low-income settings often will not benefit from resource-intensive equipment, but rather from contextually appropriate devices that meet the unique needs of their health systems., Competing Interests: The authors have disclosed no conflicts of interests., (Copyright © 2020 by Daedalus Enterprises.)

Objective: To evaluate how frequently surfactant is used off-label in preterm infants., Study Design: We conducted a retrospective cohort analysis of prospectively collected administrative data for 2005-2015 from 348 neonatal intensive care units in the US. We quantified off-label administration of poractant alfa, calfactant, or beractant in inborn infants born at <37 weeks of gestational age (GA). Off-label surfactant administration was defined according to the Food and Drug Administration (FDA) label., Results: Of a total of 110 822 preterm infants who received surfactant, 68 226 (62%) received the surfactant off-label. The majority of infants who received surfactant off-label had a higher birth weight than those who received surfactant on-label (40 716 [37%]), had an older GA than those who received surfactant on-label (35 191 [32%]), or were treated with intubation and surfactant administration followed by immediate extubation (INSURE) (32 310 [29%]). Poractant alfa was administered via INSURE more frequently than beractant or calfactant (16 688 [38%], 7137 [20%], and 8485 [27%], respectively). An increasing number of infants received surfactant via INSURE from 2005 to 2015 (from 1697 [19%] to 3368 [36%])., Conclusions: The majority of surfactant given to preterm infants is administered off-label. The uptrend in administration via INSURE coincides with increased supporting evidence. The gap between FDA labeling and current clinic practice exemplifies an opportunity for label expansion, which may require additional prospective or retrospective safety and/or effectiveness data for infants of older GA and higher birth weight., (Copyright © 2018 Elsevier Inc. All rights reserved.)

The radioprotective effect of 5-aminosalicylic acid (5ASA) was investigated in mouse bone marrow. The present study was aimed at investigating the radioprotective effect of pre-irradiation treatment with 5ASA against a range of whole-body lethal (8-11 Gy) and sublethal (1-4 Gy) doses of gamma-radiation (RT) in adult Swiss albino mice. Protection against lethal irradiation was evaluated from 30-day mouse survival and against sublethal doses was assessed from chromosomal aberrations in the bone marrow 24 h after irradiation. An intraperitoneal injection of 5ASA at a dose of 25mg/kg body weight (b. wt.) 30 min before lethal RT increased survival, giving a dose modification factor (DMF) of 1.08. Injection of 5ASA (25 mg/kg b. wt.) 60 or 30 min before or within 15 min after 3 Gy whole body RT resulted in a significant decrease in the radiation-induced aberrant metaphases, at 24 h post-irradiation. Maximum effect was seen when the drug was administered 30 min before irradiation. 5ASA (25 mg/kg b. wt.) significantly reduced the number of aberrant metaphases and the different types of aberrations at all the radiation doses (1-4 Gy) tested, giving a DMFs of 1.43 for number of aberrant metaphases. 5ASA pretreatment also significantly enhanced the endogenous spleen colonies in mouse exposed to 11 Gy RT. Pretreatment with 5ASA, protected plasmid DNA (pGEM-7Zf) against breakage induced by RT and Fenton reactants. Using nanosecond pulse radiolysis technique, the bimolecular rate constant of the reaction of 5ASA with hydroxyl radical was found to be 6.7x10(9)M(-1)s(-1). The p53 and p21 protein levels of bone marrow and spleen were evaluated to identify the specific molecular mechanisms. Both p53 and p21 increased 24h after 6 Gy irradiation, while treatment with 5ASA inhibited this RT-induced increase. Therefore, the present data suggest that 5ASA pretreatment decreases death caused by RT-induced gastrointestinal and hemopoeitic syndromes. The proposed mechanism of radioprotection by 5ASA is through the inhibition of damage to DNA, lipids, and proteins; and prevention of RT-induced increased expression of p53 and p21.

Conjugates of curcumin to two differently sized poly(ethylene glycol) molecules were synthesized in an attempt to overcome the low aqueous solubility of this natural product with cytotoxic activity against some human cancer cell lines. The soluble conjugates exhibited enhanced cytotoxicity as compared to that of the parent drug. Synthesis, analyses of the rate of drug release, and cytotoxicity studies are herein reported. The water-soluble conjugates may provide information useful for the development of injectable curcumin conjugates.

A standardized terminology for medical indications is essential for building e-prescription applications with decision support. The FDA has adopted the Veteran Administration and Kaiser Permanente (VA/KP) Problem List Subset of SNOMED as the terminology to represent indications in electronic labels. In this paper, we evaluate the ability of this subset to represent the text phrases extracted from a medication decision support system and the indications section of existing drug labels. We compiled a test set of 1265 distinct indication phrases and mapped them to (1) UMLS, (2) Entire SNOMED, (3) All Precoordinated concepts from the "Clinical Finding" hierarchy of SNOMED, and (4) VA/KP Subset. 95% of the phrases mapped to concepts in UMLS, 90.3% to SNOMED, 79.5% to SNOMED Precordinated and 71.1% mapped completely or partially to concepts in the VA/KP subset. Our study suggests that the VA/KP Subset has significant limitations for coding drug indications; however, when focusing on indications as medical conditions only, the coverage seems more adequate.