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.

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.

Ethnomedicinal Uses, Phytochemistry, Pharmacology, and Toxicology of Ruellia tuberosa L.: A Review.

Natural Products continue to be the purest source of physiologically active molecules employed in the identification of possible lead compounds in the drug discovery process. Acanthaceae is a big plant family with around 2500 species, found primarily in subtropical and tropical regions, as well as the Mediterranean, Australia, and the United States. Several species of the Acanthaceae family have been used traditionally to treat a variety of diseases, including gastrointestinal and cardiovascular ailments, etc. Ruellia tuberosa commonly known as "Mexican Bluebell" is a perennial herb that originated in Central America and has spread to some countries in the Southern tropics and Southeast Asia. It has been utilized as a traditional Rasayana plant from ancient times. R. tuberosa extracts and phytochemicals showed potent bioactivities, such as anticancer, anti-inflammatory, wound healing, antifungal, antimicrobial, anti-diabetic, hypoglycemic, hypolipidemic, gastro-protective, and anthelminthic activities. Chemical analyses have unveiled a range of bioactive constituents within the plant, including alkaloids, flavonoids, saponins, and phenolic compounds, suggestive of its therapeutic potential. Collectively, this review provides an overview of R. tuberosa, encompassing its traditional uses, ethnomedicinal importance, phytochemistry, pharmacological properties, and toxicity.

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.

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.

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.

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.

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.

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.

Big data modeling to predict platelet usage and minimize wastage in a tertiary care system.

Maintaining a robust blood product supply is an essential requirement to guarantee optimal patient care in modern health care systems. However, daily blood product use is difficult to anticipate. Platelet products are the most variable in daily usage, have short shelf lives, and are also the most expensive to produce, test, and store. Due to the combination of absolute need, uncertain daily demand, and short shelf life, platelet products are frequently wasted due to expiration. Our aim is to build and validate a statistical model to forecast future platelet demand and thereby reduce wastage. We have investigated platelet usage patterns at our institution, and specifically interrogated the relationship between platelet usage and aggregated hospital-wide patient data over a recent consecutive 29-mo period. Using a convex statistical formulation, we have found that platelet usage is highly dependent on weekday/weekend pattern, number of patients with various abnormal complete blood count measurements, and location-specific hospital census data. We incorporated these relationships in a mathematical model to guide collection and ordering strategy. This model minimizes waste due to expiration while avoiding shortages; the number of remaining platelet units at the end of any day stays above 10 in our model during the same period. Compared with historical expiration rates during the same period, our model reduces the expiration rate from 10.5 to 3.2%. Extrapolating our results to the ∼2 million units of platelets transfused annually within the United States, if implemented successfully, our model can potentially save ∼80 million dollars in health care costs.

Synthesis, biological evaluation and docking study of N-(2-(3,4,5-trimethoxybenzyl)benzoxazole-5-yl) benzamide derivatives as selective COX-2 inhibitor and anti-inflammatory agents.

A series of N-(2-(3,4,5-trimethoxybenzyl)-benzoxazole-5-yl)benzamide derivatives (3a-3n) was synthesized and evaluated for its in vitro inhibitory activity against COX-1 and COX-2. The compounds with considerable in vitro activity (IC50 < 1 µM), were evaluated in vivo for their anti-inflammatory and ulcerogenic potential. Out of the fourteen newly synthesized compounds; 3b, 3d, 3e, 3h, 3l and 3m were found to be most potent COX-2 inhibitors in in vitro enzymatic assay with IC50 in the range of 0.14-0.69 µM. In vivo anti-inflammatory activity of these six compounds (3b, 3d, 3e, 3h, 3l and 3m) was assessed by carrageenan induced rat paw edema method. The compound 3b (79.54%), 3l (75.00%), 3m (72.72%) and 3d (68.18%) exhibited significant anti-inflammatory activity than standard drug ibuprofen (65.90%). Ulcerogenic activity with histopathological studies was performed, and the screened compounds demonstrated significant gastric tolerance than ibuprofen. Molecular Docking study was also performed with resolved crystal structure of COX-2 to understand the interacting mechanisms of newly synthesized inhibitors with the active site of COX-2 enzyme and the results were found to be in line with the biological evaluation studies of the compounds.

Computer-aided design of negative allosteric modulators of metabotropic glutamate receptor 5 (mGluR5): Comparative molecular field analysis of aryl ether derivatives.

The metabotropic glutamate receptors (mGlu receptors) have emerged as attractive targets for number of neurological and psychiatric disorders. Recently, mGluR5 negative allosteric modulators (NAMs) have gained considerable attention in pharmacological research. Comparative molecular field analysis (CoMFA) was performed on 73 analogs of aryl ether which were reported as mGluR5 NAMs. The study produced a statistically significant model with high correlation coefficient and good predictive abilities.

2-AZETIDINONE DERIVATIVES: SYNTHESIS, ANTIMICROBIAL, ANTICANCER EVALUATION AND QSAR STUDIES.

A series of 2-azetidinone derivatives was synthesized from hippuric acid and evaluated for its in vitro antimicrobial and anticancer activities. Antimicrobial properties of the title compounds were investigated against Gram positive and Gram negative bacterial as well as fungal strains. Anticancer activity was performed against breast cancer (MCF7) cell lines. Antimicrobial activity results revealed that N-{2-[3-chloro-2-(2- chlorophenyl)-4-oxoazetidin-1-ylamino]-2-oxoethyl}benzamide (4) was found to be the most potent antimicrobial agent. Results of anticancer study indicated that the synthesized compounds exhibited average anticancer potential and N-[2-(3-chloro-2-oxo-4-styrylazetidin-1-ylamino)-2-oxoethyl]benzamide (17) was found to be most potent anticancer agent against breast cancer (MCF7) cell lines. QSAR models indicated that the antibacterial, antifungal and the overall antimicrobial activities of the synthesized compounds were governed by topological parameters, Balaban index (J) and valence zero and first order molecular connectivity indices (°χv and ¹χv).

SYNTHESIS, ANTIMICROBIAL, ANTICANCER EVALUATION AND QSAR STUDIES OF THIAZOLIDIN-4-ONE DERIVATIVES.

In this study, a novel series of 4-thiazolidinone derivatives (1-17) was synthesized and evaluated for its in vitro antimicrobial and anticancer potentials. N-(2-(5-(4-nitrobenzylidene)-2-(4-chlorophenyl)-4-oxothia- zolidin-3-ylamino)-2-oxoethyl) benzamide (7, pMICam = 1.86 µM/mL) was found to be the most active antimi- crobial agent. The anticancer study results demonstrated that N-(2-(5-(4-hydroxybenzylidene)-2-(4- methoxyphenyl)-4-oxothiazolidin-3-ylamino)-2-oxoethyl) benzamide (10, IC50 = 18.59 µM) was the most active anticancer agent. QSAR studies indicated the importance of topological parameter, Kier's α third order shape index (κα3) as well as electronic parameters, cosmic total energy (cos E) and energy of highest occupied molecular orbital (HOMO) in describing the antimicrobial activity of synthesized compounds.

Variability in CRP, regulatory T cells and effector T cells over time in gynaecological cancer patients: a study of potential oscillatory behaviour and correlations.

BACKGROUND: The inflammatory marker, C reactive protein has been proposed to also be a biomarker for adaptive immune responses in cancer patients with a possible application in time based chemotherapy. Fluxes in serum CRP levels were suggested to be indicative of a cyclical process in which, immune activation is followed by auto-regulating immune suppression. The applicability of CRP as a biomarker for regulatory or effector T cells was therefore investigated in a cohort of patients with gynaecological malignancies. METHODS: Peripheral blood samples were obtained from a cohort of patients at 7 time points over a period of 12 days. Serum and mononuclear cells were isolated and CRP levels in serum were detected using ELISA while regulatory and effector T cell frequencies were assessed using flow cytometry. To test periodicity, periodogram analysis of data was employed while Pearson correlation and the Wilcoxon signed rank test were used to determine correlations. RESULTS: The statistical analysis used showed no evidence of periodic oscillation in either serum CRP concentrations or Teff and Treg frequencies. Furthermore, there was no apparent correlation between serum CRP concentrations and the corresponding frequencies of Tregs or Teffs. Relative to healthy individuals, the disease state in the patients neither significantly affected the mean frequency of Tregs nor the mean coefficient of variation within the Treg population over time. However, both Teff mean frequency and mean coefficient of variation were significantly reduced in patients. CONCLUSION: Using our methods we were unable to detect CRP oscillations that could be used as a consistent serial biomarker for time based chemotherapy.

Prophylactic rituximab after allogeneic transplantation decreases B-cell alloimmunity with low chronic GVHD incidence.

B cells are involved in the pathogenesis of chronic GVHD (cGVHD). We hypothesized that prophylactic anti-B-cell therapy delivered 2 months after transplantation would decrease allogeneic donor B-cell immunity and possibly the incidence of cGVHD. Therefore, in the present study, patients with high-risk chronic lymphocytic leukemia (n = 22) and mantle-cell lymphoma (n = 13) received a total lymphoid irradiation of 80 cGy for 10 days and antithymocyte globulin 1.5 mg/kg/d for 5 days. Rituximab (375 mg/m(2)) was infused weekly on days 56, 63, 70, and 77 after transplantation. The incidence of acute GVHD was 6%. The cumulative incidence of cGVHD was 20%. Nonrelapse mortality was 3%. Rituximab treatment after allogeneic transplantation significantly reduced B-cell allogeneic immunity, with complete prevention of alloreactive H-Y Ab development in male patients with female donors (P = .01). Overall survival and freedom from progression at 4 years for chronic lymphocytic leukemia patients were 73% and 47%, respectively; for mantle-cell lymphoma patients, they were 69% and 53%, respectively.

A new approach to designing phase I-II cancer trials for cytotoxic chemotherapies.

Recently, there has been much work on early phase cancer designs that incorporate both toxicity and efficacy data, called phase I-II designs because they combine elements of both phases. However, they do not explicitly address the phase II hypothesis test of H0 : p ≤ p0 , where p is the probability of efficacy at the estimated maximum tolerated dose η from phase I and p0 is the baseline efficacy rate. Standard practice for phase II remains to treat p as a fixed, unknown parameter and to use Simon's two-stage design with all patients dosed at η. We propose a phase I-II design that addresses the uncertainty in the estimate p=p(η) in H0 by using sequential generalized likelihood theory. Combining this with a phase I design that incorporates efficacy data, the phase I-II design provides a common framework that can be used all the way from the first dose of phase I through the final accept/reject decision about H0 at the end of phase II, utilizing both toxicity and efficacy data throughout. Efficient group sequential testing is used in phase II that allows for early stopping to show treatment effect or futility. The proposed phase I-II design thus removes the artificial barrier between phase I and phase II and fulfills the objectives of searching for the maximum tolerated dose and testing if the treatment has an acceptable response rate to enter into a phase III trial.

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.

Comprehensive review on ethnomedicinal, phytochemistry and pharmacological profile of.

Ficus religiosa L. (F. religiosa) or sacred fig is a large perennial tree belonging to the family Moraceae or mulberry family. Though the tree has pan-tropical distribution but originally it is indigenous to the Indian subcontinent and Indochina region. Popularly the tree is named "Pepal or bodhi tree". Traditionally, it is practiced for the treatment of asthma, nose bleeding, heart disorders, diabetes, wound healing, ear problems, constipation, hyperlipidemia, gonorrhea, ulcers and infectious disorders. Chemical analysis demonstrated the presence of numerous bioactives including tannins, phenols, saponins, sugars, alkaloids, methionine, terpenoids, flavonoids, glycosides, proteins, separated amino acids, essential and volatile oils and steroids etc., which are probably responsible for its diverse pharmacological actions. The present work is an attempt to compile up-to-date comprehensive information on F. religiosa that covers its taxonomy, ethnomedicinal importance, phytochemistry, pharmacological attributes and clinical trials. Keeping in mind the various health attributes of F. religiosa, future research can be aimed at in-depth elucidation of the structure-function relationship and multifactorial signalings pathways.