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Preterm birth (PTB) is the leading cause of infant mortality globally. Research has focused on developing predictive models for PTB without prioritizing cost-effective interventions. Physical activity and sleep present unique opportunities for interventions in low- and middle-income populations (LMICs). However, objective measurement of physical activity and sleep remains challenging and self-reported metrics suffer from low-resolution and accuracy. In this study, we use physical activity data collected using a wearable device comprising over 181,944 h of data across N = 1083 patients. Using a new state-of-the art deep learning time-series classification architecture, we develop a 'clock' of healthy dynamics during pregnancy by using gestational age (GA) as a surrogate for progression of pregnancy. We also develop novel interpretability algorithms that integrate unsupervised clustering, model error analysis, feature attribution, and automated actigraphy analysis, allowing for model interpretation with respect to sleep, activity, and clinical variables. Our model performs significantly better than 7 other machine learning and AI methods for modeling the progression of pregnancy. We found that deviations from a normal 'clock' of physical activity and sleep changes during pregnancy are strongly associated with pregnancy outcomes. When our model underestimates GA, there are 0.52 fewer preterm births than expected (P = 1.01e − 67, permutation test) and when our model overestimates GA, there are 1.44 times (P = 2.82e − 39, permutation test) more preterm births than expected. Model error is negatively correlated with interdaily stability (P = 0.043, Spearman's), indicating that our model assigns a more advanced GA when an individual's daily rhythms are less precise. Supporting this, our model attributes higher importance to sleep periods in predicting higher-than-actual GA, relative to lower-than-actual GA (P = 1.01e − 21, Mann-Whitney U). Combining prediction and interpretability allows us to signal when activity behaviors alter the likelihood of preterm birth and advocates for the development of clinical decision support through passive monitoring and exercise habit and sleep recommendations, which can be easily implemented in LMICs. [ABSTRACT FROM AUTHOR]

Comparing brain structure across species and regions enables key functional insights. Leveraging publicly available data from a novel mass cytometry-based method, synaptometry by time of flight (SynTOF), we applied an unsupervised machine learning approach to conduct a comparative study of presynapse molecular abundance across three species and three brain regions. We used neural networks and their attractive properties to model complex relationships among high dimensional data to develop a unified, unsupervised framework for comparing the profile of more than 4.5 million single presynapses among normal human, macaque, and mouse samples. An extensive validation showed the feasibility of performing cross-species comparison using SynTOF profiling. Integrative analysis of the abundance of 20 presynaptic proteins revealed near-complete separation between primates and mice involving synaptic pruning, cellular energy, lipid metabolism, and neurotransmission. In addition, our analysis revealed a strong overlap between the presynaptic composition of human and macaque in the cerebral cortex and neostriatum. Our unique approach illuminates species- and region-specific variation in presynapse molecular composition. [ABSTRACT FROM AUTHOR]

Aging and increased cardiovascular risk are major drivers for HIV-associated neurocognitive disorders (HAND), for which accurate screenings are lacking. Mini-Addenbrooke's Cognitive Examination (MACE) reliably detects vascular and neurodegenerative cognitive decline among HIV-negative patients. We evaluated MACE diagnostic accuracy in detecting HAND in people living with HIV (PLWH) and we compared it with the International HIV Dementia Scale (IHDS). A single-centre double-blind study of diagnostic accuracy on adult outpatient PLWH without neurocognitive confounding was performed. MACE and IHDS were administered in 5 and 10 min by clinicians, followed by the reference standard battery (14 tests) by neuropsychologists. HAND diagnosis was based on the modified version of Frascati's criteria by Gisslén to reduce false positives. Exploratory cut-offs were evaluated for MACE. Diagnostic accuracy and clinical utility parameters were assessed. 231 patients were enrolled. 75.7% men with a median age, education, and length of infection of 54 (48–59), 10 (8–13) and 16 (5–25) years. HAND prevalence was 48.5% (38.9% asymptomatic impairment). Compared to IHDS, MACE sensitivity (89.3% vs 70.5%), specificity (94.1% vs 63.0%), correct classification rate (86.5% vs 66.7%), J index (0.83 vs 0.34), AUROC (0.97 vs 0.79), agreement with the gold standard (k 0.84 vs 0.33) and effect size in distinguishing HAND vs non-HAND (d 2.11 vs 1.15) were higher. Among PLWH aged 65 years and above (n = 37) MACE performance was consistently better than IHDS. The quick and easy-to-perform MACE could possess an accurate and useful screening performance for HAND in otherwise neurocognitively healthy cohorts of PLWH. [ABSTRACT FROM AUTHOR]

Cerebrospinal fluid (CSF) neurofilament light protein (NfL) is a marker of central nervous system neuro-axonal injury. A novel, ultra-sensitive assay can determine plasma NfL. In untreated people-with-HIV (PWH), CSF and plasma NfL are strongly correlated. We aimed to assess this correlation in PWH on suppressive antiretroviral treatment (ART) and lifestyle-similar HIV-negative individuals enrolled into the COmorBidity in Relation to AIDS (COBRA) study. Differences in paired CSF (sandwich ELISA, UmanDiagnostics) and plasma (Simoa digital immunoassay, Quanterix™) NfL between PWH and HIV-negative participants were tested using Wilcoxon's test; associations were assessed using Pearson's correlation. CSF and plasma NfL, standardised to Z-scores, were included as dependent variables in linear regression models to identify factors independently associated with values in PWH and HIV-negative participants. Overall, 132 PWH (all with plasma HIV RNA < 50 copies/mL) and 79 HIV-negative participants were included. Neither CSF (median 570 vs 568 pg/mL, p = 0.37) nor plasma (median 10.7 vs 9.9 pg/mL, p = 0.15) NfL differed significantly between PWH and HIV-negative participants, respectively. CSF and plasma NfL correlated moderately, with no significant difference by HIV status (PWH: rho = 0.52; HIV-negative participants: rho = 0.47, p (interaction) = 0.63). In multivariable regression analysis, higher CSF NfL Z-score was statistically significantly associated with older age and higher CSF protein, and higher plasma NfL Z-score with older age, higher serum creatinine and lower bodyweight. In conclusion, in PWH on ART, the correlation between CSF and plasma NfL is moderate and similar to that observed in lifestyle-similar HIV-negative individuals. Consideration of renal function and bodyweight may be required when utilising plasma NfL. [ABSTRACT FROM AUTHOR]

Background: Platinum-based doublets are the standard chemotherapy for lung cancer. The identification of markers associated with drug toxicity may improve the success of the treatment. Single nucleotide polymorphisms (SNPs) mapping into the genes involved in platinum transport or detoxification may explain the occurrence of toxicities. In this study, we evaluated the role of three SNPs in predicting the onset of adverse events for lung cancer patients receiving cisplatin or carboplatin in adjuvant, neo-adjuvant and metastatic settings.Methods: Eighty-two patients affected by non-small-cell and small-cell lung cancer treated with cisplatin- or carboplatin-based chemotherapy (stage II-IV) were enrolled. Before genetic analysis, patients signed a written informed consent. DNA was extracted from peripheral blood samples and genotypes were determined by real-time PCR. We selected and analyzed three SNPs: ABCB1 c.3435C>T/rs1045642, ABCC2 -24C>T/rs717620 and GSTP1 c.313A>G/rs1695. Patient characteristics and genotypes were correlated with hematological, gastrointestinal and renal toxicity as recorded by Common Terminology Criteria for Adverse Event (CTCAE) v4.03. No neurological toxicity was observed in our patients.Results: Variant alleles were present in 53% of patients for ABCB1 c.3435C >T, 18.3% for ABCC2 -24C> T, and 34.8% for GSTP1 c.313A>G. Heterozygous CT at ABCB1 c.3435 was associated to a lower risk of hematological toxicity compared to homozygous CC (OR = 0.20; 95% CI 0.05, 0.69; p = 0.01). Similar results were observed by genetic dominant model (CT + TT vs CC) and hematological toxicity (OR = 0.26; 95% CI 0.09, 0.79; p = 0.02). No other significant associations were found between toxicity and SNPs. Multivariate analysis confirmed an independent value for the ABCB1 c.3435 C >T polymorphism.Conclusions: The present study reveals that ABCB1 c.3435C>T polymorphism influences platinum toxicity. The T allele seems to exert a protective effect on the development of toxicities. Further studies, such as epigenetic regulation ones, are needed to validate and shed more light on this association. [ABSTRACT FROM AUTHOR]

Advanced measurement and data storage technologies have enabled high-dimensional profiling of complex biological systems. For this, modern multiomics studies regularly produce datasets with hundreds of thousands of measurements per sample, enabling a new era of precision medicine. Correlation analysis is an important first step to gain deeper insights into the coordination and underlying processes of such complex systems. However, the construction of large correlation networks in modern high-dimensional datasets remains a major computational challenge owing to rapidly growing runtime and memory requirements. Here we address this challenge by introducing CorALS (Correlation Analysis of Large-scale (biological) Systems), an open-source framework for the construction and analysis of large-scale parametric as well as non-parametric correlation networks for high-dimensional biological data. It features off-the-shelf algorithms suitable for both personal and high-performance computers, enabling workflows and downstream analysis approaches. We illustrate the broad scope and potential of CorALS by exploring perspectives on complex biological processes in large-scale multiomics and single-cell studies., Competing Interests: Competing interests The authors declare no competing interests.