Predictive Models for Maximum Recommended Therapeutic Dose of Antiretroviral Drugs

A novel method for predicting maximum recommended therapeutic dose (MRTD) is presented using quantitative structure property relationships (QSPRs) and artificial neural networks (ANNs). MRTD data of 31 structurally diverse Antiretroviral drugs (ARVs) were collected from FDA MRTD Database or package inserts. Molecular property descriptors of each compound, that is, molecular mass, aqueous solubility, lipophilicity, biotransformation half life, oxidation half life, and biodegradation probability were calculated from their SMILES codes. A training set (n = 23) was used to construct multiple linear regression and back propagation neural network models. The models were validated using an external test set (n = 8) which demonstrated that MRTD values may be predicted with reasonable accuracy. Model predictability was described by root mean squared errors (RMSEs), Kendall's correlation coefficients (tau), P-values, and Bland Altman plots for method comparisons. MRTD was predicted by a 6-3-1 neural network model (RMSE = 13.67, tau = 0.643, P = 0.035) more accurately than by the multiple linear regression (RMSE = 27.27, tau = 0.714, P = 0.019) model. Both models illustrated a moderate correlation between aqueous solubility of antiretroviral drugs and maximum therapeutic dose. MRTD prediction may assist in the design of safer, more effective treatments for HIV infection

A New Frontier: AI and ancient language pedagogy

In November 2022, ChatGPT 3.5 was released on a public research preview, gaining notoriety for its ability to pull from a vast body of information to create coherent and digestible bodies of text that accurately respond to queries (OpenAI, 2022). It is able to recognise the grammar and vocabulary of ancient languages, translate passages, and compose texts at an alarmingly accurate and rapid rate. For teachers, this AI has had mixed reviews. Some fear its ability to produce well-written work effortlessly, while others are excited by its abilities to push the boundaries of current teaching practices. This paper explores how well ChatGPT explains grammatical concepts, parses inflected forms, and translates Classical Latin, Ancient Greek, and Classical Sanskrit. Overall, ChatGPT is rather good at working with Classical Latin and Sanskrit, but its abilities with Ancient Greek are deeply problematic. Although it is quite flawed at this time, ChatGPT, when used properly, could become a useful a tool for ancient language study. With proper guiding phrases, students could use this AI to practise vocabulary, check their translations, and rephrase grammatical concepts

Towards a sounder fire ecology

This forum brings together fire ecologists from outside the current wildfire controversy in the US to give their views on three central topics related to ecosystems in which wildfires are an important process. First, how do fire behavior and ecological effects vary between ecosystems? Second, why does this variation require an understanding that goes beyond simple correlations between various fire and ecosystem variables to more careful causal models? Third, how can human values and goals be reconciled with fire disturbance processes in an ecologically sound manner

Characterization of the bias between oxygen saturation measured by pulse oximetry and calculated by an arterial blood gas analyzer in critically ill neonates

Continuous monitoring of oxygenation with pulse oximetry is the standard of care for critically ill neonates. A better understanding of its measurement bias compared to arterial oxygen saturation could be helpful both for the clinician and researcher. Towards that end, we examined the electronic database from a large neonatal ICU. From a 24-month period we identified 23,032 paired SpO2-SaO2 measurements from 1,007 infants who were receiving supplemental oxygen during mechanical ventilation. We found that SpO2 was consistently higher than SaO2. The size of the bias was fairly constant when SpO2 was between 75-93%, above which it dropped steadily. The median size of this bias was 1% SpO2 during hyperoxemia (SpO2 97-100%) with a median variation of 1.3% above and below. During periods of hypoxemia (SpO2 75-85%) and normoxemia (SpO2 89-93%) the bias was approximately 5% SpO2, with a median variation of 5% above and below

Thrombin A-Chain: Activation Remnant or Allosteric Effector?

Although prothrombin is one of the most widely studied enzymes in biology, the role of the thrombin A-chain has been neglected in comparison to the other domains. This paper summarizes the current data on the prothrombin catalytic domain A-chain region and the subsequent thrombin A-chain. Attention is given to biochemical characterization of naturally occurring prothrombin A-chain mutations and alanine scanning mutants in this region. While originally considered to be simply an activation remnant with little physiologic function, the thrombin A-chain is now thought to play a role as an allosteric effector in enzymatic reactions and may also be a structural scaffold to stabilize the protease domain

Impact of the SPOP Mutant Subtype on the Interpretation of Clinical Parameters in Prostate Cancer.

Purpose: Molecular characterization of prostate cancer, including The Cancer Genome Atlas, has revealed distinct subtypes with underlying genomic alterations. One of these core subtypes, SPOP (speckle-type POZ protein) mutant prostate cancer, has previously only been identifiable via DNA sequencing, which has made the impact on prognosis and routinely used risk stratification parameters unclear. Methods: We have developed a novel gene expression signature, classifier (Subclass Predictor Based on Transcriptional Data), and decision tree to predict the SPOP mutant subclass from RNA gene expression data and classify common prostate cancer molecular subtypes. We then validated and further interrogated the association of prostate cancer molecular subtypes with pathologic and clinical outcomes in retrospective and prospective cohorts of 8,158 patients. Results: The subclass predictor based on transcriptional data model showed high sensitivity and specificity in multiple cohorts across both RNA sequencing and microarray gene expression platforms. We predicted approximately 8% to 9% of cases to be SPOP mutant from both retrospective and prospective cohorts. We found that the SPOP mutant subclass was associated with lower frequency of positive margins, extraprostatic extension, and seminal vesicle invasion at prostatectomy; however, SPOP mutant cancers were associated with higher pretreatment serum prostate-specific antigen (PSA). The association between SPOP mutant status and higher PSA level was validated in three independent cohorts. Despite high pretreatment PSA, the SPOP mutant subtype was associated with a favorable prognosis with improved metastasis-free survival, particularly in patients with high-risk preoperative PSA levels. Conclusion: Using a novel gene expression model and a decision tree algorithm to define prostate cancer molecular subclasses, we found that the SPOP mutant subclass is associated with higher preoperative PSA, less adverse pathologic features, and favorable prognosis. These findings suggest a paradigm in which the interpretation of common risk stratification parameters, particularly PSA, may be influenced by the underlying molecular subtype of prostate cancer

Should active recruitment of health workers from sub-Saharan Africa be viewed as a crime?

[No abstract available]Articl