Medicinal chemistry aspects of uracil containing dUTPase inhibitors targeting colorectal cancer.

Deoxyuridine-5'-triphosphate nucleotidohydrolase (dUTPase), a vital enzyme in pyrimidine metabolism, is a prime target for treating colorectal cancer. Uracil shares structural traits with DNA/RNA bases, prompting exploration by medicinal chemists for pharmacological modifications. Some existing drugs, including thymidylate synthase (TS) and dUTPase inhibitors, incorporate uracil moieties. These derivatives hinder crucial cell proliferation pathways encompassing TS, dUTPases, dihydropyrimidine dehydrogenase, and uracil-DNA glycosylase. This review compiles uracil derivatives that have served as dUTPase inhibitors across various organisms, forming a library for targeting human dUTPase. Insights into their structural requisites for human applications and comparative analyses of binding pockets are provided for analyzing the compounds against human dUTPase.

Identification of Aztreonam as a potential antibacterial agent against Pasteurella multocida sialic acid binding protein: A combined in silico and in-vitro analysis.

Pasteurella multocida, a Gram-negative zoonotic bacterial pathogen, interacts with the host environment, immune response, and infection through outer membrane proteins, adhesins, and sialic acid binding proteins. Sialic acids provide nutrition and mask bacterial identity, hindering the complement system, facilitates tissue access and biofilm formation. Sialic acid binding protein (SAB) enable adhesion to host cells, immune evasion, and nutrient acquisition, making them potential targets for preventing Pasteurella multocida infections. In this study, in silico molecular docking assessed 11 antibiotics targeting SAB (4MMP) comparing their docking scores to Amoxicillin. As SAB (4MMP) exhibits a highly conserved sequence in various Pasteurella multocida strains, including the specific strain PMR212 studied in this article, with a 96.09% similarity score. Aztreonam and Gentamicin displayed the highest docking scores (-6.025 and -5.718), followed by a 100ns molecular dynamics simulation. Aztreonam exhibited stable simulation with protein RMSD fluctuations of 1.8-2.2 Å. The ligand initially had an RMSD of 1.6 Å, stabilizing at 4.8 Å. Antibiotic sensitivity testing confirmed Aztreonam's efficacy with the largest inhibition zone of 42 mm, while Amoxicillin and Gentamicin had inhibition zones of 32 mm and 25 mm, respectively. According to CLSI guidelines, all three antibiotics were effective against Pasteurella multocida. Aztreonam's superior efficacy positions it as a promising candidate for further investigation in targeting Pasteurella multocida.

Preoperative Versus Perioperative Risk Factors for Delayed Pain and Opioid Cessation After Total Joint Arthroplasty: A Prospective Cohort Study.

INTRODUCTION: The evolution of pre- versus postoperative risk factors remains unknown in the development of persistent postoperative pain and opioid use. We identified preoperative versus comprehensive perioperative models of delayed pain and opioid cessation after total joint arthroplasty including time-varying postoperative changes in emotional distress. We hypothesized that time-varying longitudinal measures of postoperative psychological distress, as well as pre- and postoperative use of opioids would be the most significant risk factors for both outcomes. METHODS: A prospective cohort of 188 patients undergoing total hip or knee arthroplasty at Stanford Hospital completed baseline pain, opioid use, and emotional distress assessments. After surgery, a modified Brief Pain Inventory was assessed daily for 3 months, weekly thereafter up to 6 months, and monthly thereafter up to 1 year. Emotional distress and pain catastrophizing were assessed weekly to 6 months, then monthly thereafter. Stepwise multivariate time-varying Cox regression modeled preoperative variables alone, followed by all perioperative variables (before and after surgery) with time to postoperative opioid and pain cessation. RESULTS: The median time to opioid and pain cessation was 54 and 152 days, respectively. Preoperative total daily oral morphine equivalent use (hazard ratio-HR 0.97; 95% confidence interval-CI 0.96-0.98) was significantly associated with delayed postoperative opioid cessation in the perioperative model. In contrast, time-varying postoperative factors: elevated PROMIS (Patient-Reported Outcomes Measurement Information System) depression scores (HR 0.92; 95% CI 0.87-0.98), and higher Pain Catastrophizing Scale scores (HR 0.85; 95% CI 0.75-0.97) were independently associated with delayed postoperative pain resolution in the perioperative model. CONCLUSIONS: These findings highlight preoperative opioid use as a key determinant of delayed postoperative opioid cessation, while postoperative elevations in depressive symptoms and pain catastrophizing are associated with persistent pain after total joint arthroplasty providing the rationale for continued risk stratification before and after surgery to identify patients at highest risk for these distinct outcomes. Interventions targeting these perioperative risk factors may prevent prolonged postoperative pain and opioid use.

In Silico Studies of Indole Derivatives as Antibacterial Agents.

Objectives: Molecular docking and QSAR studies of indole derivatives as antibacterial agents. Methods: In this study, we used a multiple linear regressions (MLR) approach to construct a 2D quantitative structure activity relationship of 14 reported indole derivatives. It was performed on the reported antibacterial activity data of 14 compounds based on theoretical chemical descriptors to construct statistical models that link structural properties of indole derivatives to antibacterial activity. We have also performed molecular docking studies of same compounds by using Maestro module of Schrodinger. A set the molecular descriptors like hydrophobic, geometric, electronic and topological characters were calculated to represent the structural features of compounds. The conventional antibiotics sultamicillin and ampicillin were not used in the model development since their structures are different from those of the created compounds. Biological activity data was first translated into pMIC values (i.e. -log MIC) and used as a dependent variable in QSAR investigation. Results: Compounds with high electronic energy and dipole moment were effective antibacterial agents against S. aureus, indole derivatives with lower κ2 values were excellent antibacterial agents against MRSA standard strain, and compounds with lower R value and a high 2χv value were effective antibacterial agents against MRSA isolate. Conclusion: Compounds 12 and 2 showed better binding score against penicillin binding protein 2 and penicillin binding protein 2a respectively.

Elastic Net Regularization Paths for All Generalized Linear Models.

The lasso and elastic net are popular regularized regression models for supervised learning. Friedman, Hastie, and Tibshirani (2010) introduced a computationally efficient algorithm for computing the elastic net regularization path for ordinary least squares regression, logistic regression and multinomial logistic regression, while Simon, Friedman, Hastie, and Tibshirani (2011) extended this work to Cox models for right-censored data. We further extend the reach of the elastic net-regularized regression to all generalized linear model families, Cox models with (start, stop] data and strata, and a simplified version of the relaxed lasso. We also discuss convenient utility functions for measuring the performance of these fitted models.

Cooperative learning for multiview analysis.

We propose a method for supervised learning with multiple sets of features ("views"). The multiview problem is especially important in biology and medicine, where "-omics" data, such as genomics, proteomics, and radiomics, are measured on a common set of samples. "Cooperative learning" combines the usual squared-error loss of predictions with an "agreement" penalty to encourage the predictions from different data views to agree. By varying the weight of the agreement penalty, we get a continuum of solutions that include the well-known early and late fusion approaches. Cooperative learning chooses the degree of agreement (or fusion) in an adaptive manner, using a validation set or cross-validation to estimate test set prediction error. One version of our fitting procedure is modular, where one can choose different fitting mechanisms (e.g., lasso, random forests, boosting, or neural networks) appropriate for different data views. In the setting of cooperative regularized linear regression, the method combines the lasso penalty with the agreement penalty, yielding feature sparsity. The method can be especially powerful when the different data views share some underlying relationship in their signals that can be exploited to boost the signals. We show that cooperative learning achieves higher predictive accuracy on simulated data and real multiomics examples of labor-onset prediction. By leveraging aligned signals and allowing flexible fitting mechanisms for different modalities, cooperative learning offers a powerful approach to multiomics data fusion.

Accuracy of Electronic Medical Record Follow-Up Data for Estimating the Survival Time of Patients With Cancer.

PURPOSE: For real-world evidence, it is convenient to use routinely collected data from the electronic medical record (EMR) to measure survival outcomes. However, patients can become lost to follow-up, causing incomplete data and biased survival time estimates. We quantified this issue for patients with metastatic cancer seen in an academic health system by comparing survival estimates from EMR data only and from EMR data combined with high-quality cancer registry data. MATERIALS AND METHODS: Patients diagnosed with metastatic cancer from 2008 to 2014 were included in this retrospective study. Patients who were diagnosed with cancer or received their initial treatment within our system were included in the institutional cancer registry and this study. Overall survival was calculated using the Kaplan-Meier method. Survival curves were generated in two ways: using EMR follow-up data alone and using EMR data supplemented with data from the Stanford Cancer Registry/California Cancer Registry. RESULTS: Four thousand seventy-seven patients were included. The median follow-up using EMR + Cancer Registry data was 19.9 months, and the median follow-up in surviving patients was 67.6 months. There were 1,301 deaths recorded in the EMR and 3,140 deaths recorded in the Cancer Registry. The median overall survival from the date of cancer diagnosis using EMR data was 58.7 months (95% CI, 54.2 to 63.2); using EMR + Cancer Registry data, it was 20.8 months (95% CI, 19.6 to 22.3). A similar pattern was seen using the date of first systemic therapy or date of first hospital admission as the baseline date. CONCLUSION: Using EMR data alone, survival time was overestimated compared with EMR + Cancer Registry data.

The changing impact of cytomegalovirus among hematopoietic cell transplant recipients during the past decade: A single institutional cohort study.

BACKGROUND: With advancements in allogeneic hematopoietic cell transplantation (alloHCT), the need for cytomegalovirus (CMV) surveillance persists. METHODS: We present a retrospective analysis on the impact of CMV with preemptive therapy in 1065 alloHCT patients with donor and/or recipient CMV seropositivity from 2009 to 2019. RESULTS: Fifty-one percent developed clinically significant CMV infection (CMV-CSI); 6.5% had CMV disease. In multivariate analysis stratified by serostatus and preparative regimen, the use of anti-thymocyte globulin (hazard ratios 2.97, 95% confidence interval 2.00-4.42, p < .001) was associated with development of CMV-CSI. Median length of stay for index hospitalization was longer in patients with CMV-CSI (27 vs. 25 days, respectively; p = .002), as were rates (32.9% vs. 17.7%; p < .001) and duration (9 d vs. 6 d; p < .001) of rehospitalization, and median total inpatient days (28 d vs. 26 d; p < .001). Patients with CMV-CSI had higher rates of neutropenia (47% vs. 20%; p < .001) and transfusion support (packed red blood cell, median 5 vs. 3; p < .001; platelets, median 3 vs. 3; p < .001). CONCLUSION: Preemptive therapy does not negate the impact of CMV-CSI on peri-engraftment toxicity and healthcare utilization. This cohort represents a large single center study on the impact of CMV in the preletermovir era and serves as a real-world comparator for assessing the impact of future prophylaxis.

An open repository of real-time COVID-19 indicators.

The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: Operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID-19 activity, such as signals extracted from deidentified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data are available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making.

Penalized regression for left-truncated and right-censored survival data.

High-dimensional data are becoming increasingly common in the medical field as large volumes of patient information are collected and processed by high-throughput screening, electronic health records, and comprehensive genomic testing. Statistical models that attempt to study the effects of many predictors on survival typically implement feature selection or penalized methods to mitigate the undesirable consequences of overfitting. In some cases survival data are also left-truncated which can give rise to an immortal time bias, but penalized survival methods that adjust for left truncation are not commonly implemented. To address these challenges, we apply a penalized Cox proportional hazards model for left-truncated and right-censored survival data and assess implications of left truncation adjustment on bias and interpretation. We use simulation studies and a high-dimensional, real-world clinico-genomic database to highlight the pitfalls of failing to account for left truncation in survival modeling.

Fast numerical optimization for genome sequencing data in population biobanks.

MOTIVATION: Large-scale and high-dimensional genome sequencing data poses computational challenges. General-purpose optimization tools are usually not optimal in terms of computational and memory performance for genetic data. RESULTS: We develop two efficient solvers for optimization problems arising from large-scale regularized regressions on millions of genetic variants sequenced from hundreds of thousands of individuals. These genetic variants are encoded by the values in the set {0,1,2,NA}. We take advantage of this fact and use two bits to represent each entry in a genetic matrix, which reduces memory requirement by a factor of 32 compared to a double precision floating point representation. Using this representation, we implemented an iteratively reweighted least square algorithm to solve Lasso regressions on genetic matrices, which we name snpnet-2.0. When the dataset contains many rare variants, the predictors can be encoded in a sparse matrix. We utilize the sparsity in the predictor matrix to further reduce memory requirement and computational speed. Our sparse genetic matrix implementation uses both the compact two-bit representation and a simplified version of compressed sparse block format so that matrix-vector multiplications can be effectively parallelized on multiple CPU cores. To demonstrate the effectiveness of this representation, we implement an accelerated proximal gradient method to solve group Lasso on these sparse genetic matrices. This solver is named sparse-snpnet, and will also be included as part of snpnet R package. Our implementation is able to solve Lasso and group Lasso, linear, logistic and Cox regression problems on sparse genetic matrices that contain 1 000 000 variants and almost 100 000 individuals within 10 min and using less than 32GB of memory. AVAILABILITY AND IMPLEMENTATION: https://github.com/rivas-lab/snpnet/tree/compact.

Naphthoquinone derivatives exhibit apoptosis-like effect and anti-trypanosomal activity against Trypanosoma evansi.

Trypanosoma evansi is an extracellular flagellate blood protozoan parasite and an etiological agent of animal trypanosomosis. Presently, only a few drugs are registered and have been used for the treatment of animal trypanosomosis, but they show severe toxic effects and also face the problem of drug resistance. Naphthoquinones (NTQ) are considered as fortunate structures in the field of medicinal chemistry as they have been reported for their antitrypanosomal potential against other trypanosomes-T. brucei and T. cruzi. In the present study, six naphthoquinones (NTQ1-NTQ6) derivatives were evaluated for anti-trypanosomal activity by demonstrating their growth inhibitory effect against T. evansi. All NTQs significantly (p < 0.001) exhibited activity against parasite growth and multiplication with IC50 values of 11.48 µM, 373.6 µM, 12.97 µM, 21.97 µM, 18.19 µM and 5.758 µM but NTQ1, NTQ3 and NTQ6 were selected based on their IC50 value for further studies. The dose-and time-dependent morphological effect on parasite was evaluated including the measurement of reactive oxygen species (ROS) by spectrofluorometery and measurement of apoptosis by flow cytometry. The selected NTQs exhibited a significant production of ROS and displayed a significant AV+ and PI+ labelled cells in the axenic culture of T. evansi than quinapyramine methyl sulphate (QPS), as reference control. NTQs also showed more cytotoxic effect on horse peripheral blood mononuclear cells as compare to QPS. Therefore, we confirmed the antitrypanosomal activity and apoptotic-like mechanism of NTQs in an axenic culture of T. evansi.

Synthesis and biological evaluation of heterocyclic 1,2,4-triazole scaffolds as promising pharmacological agents.

BACKGROUND: Triazole is an important heterocyclic moiety that occupies a unique position in heterocyclic chemistry, due to its large number of biological activities. It exists in two isomeric forms i.e. 1,2,4-triazole and 1,2,3-triazole and is used as core molecule for the design and synthesis of many medicinal compounds. 1,2,4-Triazole possess broad spectrum of therapeutically interesting drug candidates such as analgesic, antiseptic, antimicrobial, antioxidant, anti-urease, anti-inflammatory, diuretics, anticancer, anticonvulsant, antidiabetic and antimigraine agents. METHODS: The structures of all synthesized compounds were characterized by physicochemical properties and spectral means (IR and NMR). The synthesized compounds were evaluated for their in vitro antimicrobial activity against Gram-positive (B. subtilis), Gram-negative (P. aeruginosa and E. coli) bacterial and fungal (C. albicans and A. niger) strains by tube dilution method using ciprofloxacin, amoxicillin and fluconazole as standards. In-vitro antioxidant and anti-urease screening was done by DPPH assay and indophenol method, respectively. The in-vitro anticancer evaluation was carried out against MCF-7 and HCT116 cancer cell lines using 5-FU as standards. RESULTS, DISCUSSION AND CONCLUSION: The biological screening results reveal that the compounds T5 (MICBS, EC = 24.7 µM, MICPA, CA = 12.3 µM) and T17 (MICAN = 27.1 µM) exhibited potent antimicrobial activity as comparable to standards ciprofloxacin, amoxicillin (MICCipro = 18.1 µM, MICAmo = 17.1 µM) and fluconazole (MICFlu = 20.4 µM), respectively. The antioxidant evaluation showed that compounds T2 (IC50 = 34.83 µg/ml) and T3 (IC50 = 34.38 µg/ml) showed significant antioxidant activity and comparable to ascorbic acid (IC50 = 35.44 µg/ml). Compounds T3 (IC50 = 54.01 µg/ml) was the most potent urease inhibitor amongst the synthesized compounds and compared to standard thiourea (IC50 = 54.25 µg/ml). The most potent anticancer activity was shown by compounds T2 (IC50 = 3.84 µM) and T7 (IC50 = 3.25 µM) against HCT116 cell lines as compared to standard 5-FU (IC50 = 25.36 µM).

Phytochemical and pharmacological review of Cinnamomum verum J. Presl-a versatile spice used in food and nutrition.

Cinnamomum verum is the widely used spice for its medicinal and culinary uses since ages. It is native to Sri Lanka and southern India but also distributed in many Asian, Caribbean, Australian and African countries. It is widely used in food preparations and industrial products like candies, chewing gums, mouthwash and toothpaste. It is also used to treat asthma, bronchitis, diarrhea, headache, inflammation and cardiac disorders. Cinnamaldehyde, eugenol, caryophyllene, cinnamyl acetate and cinnamic acid are the major compounds found in its essential oil. These compounds exhibit a wide range of pharmacological activities including antioxidant, antimicrobial, anti-inflammatory, anticancer, antidiabetic, wound healing, anti-HIV, anti-anxiety and antidepressant, etc. This review highlights its comprehensive and up-to-date information on taxonomy, ethnomedicinal uses, phytochemical composition, pharmacological and toxicity activities. Structure-activity relationship, mechanism of action and some research gaps has also been provided. Owing to its immense medicinal importance, more well-designed in-vivo and clinical studies are required.

Efficacy of motivational-interviewing and guided opioid tapering support for patients undergoing orthopedic surgery (MI-Opioid Taper): A prospective, assessor-blind, randomized controlled pilot trial.

BACKGROUND: Postoperative opioid use can lead to chronic use and misuse. Few studies have examined effective approaches to taper postoperative opioid use while maintaining adequate analgesia. METHODS: This randomized, assessor-blinded, pilot trial of postoperative motivational interviewing and guided opioid tapering support (MI-Opioid Taper) added to usual care (UC) enrolled patients undergoing total hip or knee arthroplasty at a single U.S. academic medical center. MI-Opioid Taper involved weekly (to seven weeks) and monthly (to one year) phone calls until patient-reported opioid cessation. Opioid tapering involved 25% weekly dose reductions. The primary feasibility outcome was study completion in the group to which participants were randomized. The primary efficacy outcome, time to baseline opioid use, was the first of five consecutive days of return to baseline preoperative dose. Intention-to-treat analysis with Cox proportional hazards regression was adjusted for operation. ClinicalTrials.gov registration: NCT02070003. FINDINGS: From November 26, 2014, to April 27, 2018, 209 patients were screened, and 104 patients were assigned to receive MI-Opioid Taper (49 patients) or UC only (55 patients). Study completion after randomization was similar between groups (96.4%, 53 patients receiving UC, 91.8%, 45 patients receiving MI-Opioid Taper). Patients receiving MI-Opioid Taper had a 62% increase in the rate of return to baseline opioid use after surgery (HR 1.62; 95%CI 1.06-2.46; p = 0•03). No trial-related adverse events occurred. INTERPRETATION: In patients undergoing total joint arthroplasty, MI-Opioid Taper is feasible and future research is needed to establish the efficacy of MI-Opioid Taper to promote postoperative opioid cessation. FUNDING: National Institute on Drug Abuse.

Defining the features and duration of antibody responses to SARS-CoV-2 infection associated with disease severity and outcome.

SARS-CoV-2-specific antibodies, particularly those preventing viral spike receptor binding domain (RBD) interaction with host angiotensin-converting enzyme 2 (ACE2) receptor, can neutralize the virus. It is, however, unknown which features of the serological response may affect clinical outcomes of COVID-19 patients. We analyzed 983 longitudinal plasma samples from 79 hospitalized COVID-19 patients and 175 SARS-CoV-2-infected outpatients and asymptomatic individuals. Within this cohort, 25 patients died of their illness. Higher ratios of IgG antibodies targeting S1 or RBD domains of spike compared to nucleocapsid antigen were seen in outpatients who had mild illness versus severely ill patients. Plasma antibody increases correlated with decreases in viral RNAemia, but antibody responses in acute illness were insufficient to predict inpatient outcomes. Pseudovirus neutralization assays and a scalable ELISA measuring antibodies blocking RBD-ACE2 interaction were well correlated with patient IgG titers to RBD. Outpatient and asymptomatic individuals' SARS-CoV-2 antibodies, including IgG, progressively decreased during observation up to five months post-infection.

Letermovir Prophylaxis Decreases Burden of Cytomegalovirus (CMV) in Patients at High Risk for CMV Disease Following Hematopoietic Cell Transplant.

Despite effective therapies, cytomegalovirus (CMV) continues to have a significant impact on morbidity and mortality in hematopoietic cell transplant recipients. At particular risk are recipients of alternative grafts such as umbilical cord blood (UCB), haploidentical transplants (haplo), or patients conditioned with T-cell depleting regimens such as anti-thymocyte globulin (ATG). With the approval of letermovir, its impact on high-risk patients is of particular interest. To evaluate the impact of letermovir prophylaxis at our center, we performed a retrospective analysis of 114 high-risk patients who received letermovir as prophylaxis (LET PPX) between January 2018 through December 2019, including 30 UCB and 22 haplo recipients, compared with 637 historical controls with comparable risk between January 2013 and December 2019. By post-transplant day 100 (D+100), letermovir prophylaxis significantly decreased the incidence of both CMV DNAemia compared with controls (45.37% versus 74.1%; P < .001) and clinically significant CMV infection (12.04% versus 48.82%; P < .001). The impact of LET PPX was even more profound on the incidence of clinically significant CMV infection (CSI), defined as the administration of antiviral therapy as preemptive therapy for CMV DNAemia or treatment for CMV disease. CSI was significantly lower in haplo recipients on LET PPX compared with controls (13.64% versus 73.33%; P= .02) and UCB recipients on LET PPX compared with controls (3.45% versus 37.5%; P < .001). No patients on LET primary PPX developed CMV disease in any treatment group by D+100 compared with controls (0% versus 5.34%, respectively; P = .006). Patients on LET PPX had fewer hospitalizations involving initiation of anti-CMV therapy compared with controls (0.93% versus 15.23%, respectively). Our analysis of the largest cohort of patients at high risk for CMV reactivation published to date demonstrates that letermovir prophylaxis significantly reduces the number of patients who receive CMV-active antiviral therapy for either DNAemia or disease due to CMV.

Derivation and validation of a prognostic score for neonatal mortality in Ethiopia: a case-control study.

BACKGROUND: Early warning scores for neonatal mortality have not been designed for low income countries. We developed and validated a score to predict mortality upon admission to a NICU in Ethiopia. METHODS: We conducted a retrospective case-control study at the University of Gondar Hospital, Gondar, Ethiopia. Neonates hospitalized in the NICU between January 1, 2016 to June 31, 2017. Cases were neonates who died and controls were neonates who survived. RESULTS: Univariate logistic regression identified variables associated with mortality. The final model was developed with stepwise logistic regression. We created the Neonatal Mortality Score, which ranged from 0 to 52, from the model's coefficients. Bootstrap analysis internally validated the model. The discrimination and calibration were calculated. In the derivation dataset, there were 207 cases and 605 controls. Variables associated with mortality were admission level of consciousness, admission respiratory distress, gestational age, and birthweight. The AUC for neonatal mortality using these variables in aggregate was 0.88 (95% CI 0.85-0.91). The model achieved excellent discrimination (bias-corrected AUC) under internal validation. Using a cut-off of 12, the sensitivity and specificity of the Neonatal Mortality Score was 81 and 80%, respectively. The AUC for the Neonatal Mortality Score was 0.88 (95% CI 0.85-0.91), with similar bias-corrected AUC. In the validation dataset, there were 124 cases and 122 controls, the final model and the Neonatal Mortality Score had similar discrimination and calibration. CONCLUSIONS: We developed, internally validated, and externally validated a score that predicts neonatal mortality upon NICU admission with excellent discrimination and calibration.

Synthesis, Characterization, Antimicrobial and Anticancer Studies of Metal Complexes of 2-methoxy-4-((3-methylpyridin-2-ylimino)methyl)phenol.

BACKGROUND: Being derived from primary amine and aromatic aldehyde, Schiff base and their complexes have an imperative role in the improvement of inorganic chemistry, which are broadly studied as coordination compounds and are gradually becoming more important in biochemical and analytical applications. METHODS: They have also been used for antibacterial, antifungal, anticancer, antitubercular activities. Novel synthesised Schiff's base 2-methoxy-4-((3-methylpyridin-2-ylimino)methyl)phenol (SB) and its metal complexes (Zn[II], Cu[II], Co[II] and Ni[II]) were characterised by UV, IR and NMR spectroscopy. Formation of the Schiff base and the metal (Zn[II], Cu[II], Co[II] and Ni[II]) chelates was supported by spectral and analytical data. The ligand and metal complexes have been screened for their antibacterial activity against Staphylococcus aureus, Salmonella typhi, Escherichia coli, Klebsiella pneumoniae and antifungal activity against the fungi Candida albicans and Aspergillus niger. Further, the synthesised compounds were also screened for antiproliferative activity against the human colorectal carcinoma (HCT116) cell line using the Sulforhodamine B assay. RESULT: Metal complexes formed were found to enhance the potency of the Schiff base due to coordination with a copper complex, showing better activity than others. CONCLUSION: Copper complex was observed to be more potent than other complexes against all the pathogenic microbes and cancer cell line (HCT116).

The automatic construction of bootstrap confidence intervals.

The standard intervals, e.g., θ ^ ± 1.96 σ ^ for nominal 95% two-sided coverage, are familiar and easy to use, but can be of dubious accuracy in regular practice. Bootstrap confidence intervals offer an order of magnitude improvement-from first order to second order accuracy. This paper introduces a new set of algorithms that automate the construction of bootstrap intervals, substituting computer power for the need to individually program particular applications. The algorithms are described in terms of the underlying theory that motivates them, along with examples of their application. They are implemented in the R package bcaboot.