Overnight Sleep Staging Using Chest-Worn Accelerometry.

Overnight sleep staging is an important part of the diagnosis of various sleep disorders. Polysomnography is the gold standard for sleep staging, but less-obtrusive sensing modalities are of emerging interest. Here, we developed and validated an algorithm to perform "proxy" sleep staging using cardiac and respiratory signals derived from a chest-worn accelerometer. We collected data in two sleep centers, using a chest-worn accelerometer in combination with full PSG. A total of 323 participants were analyzed, aged 13-83 years, with BMI 18-47 kg/m2. We derived cardiac and respiratory features from the accelerometer and then applied a previously developed method for automatic cardio-respiratory sleep staging. We compared the estimated sleep stages against those derived from PSG and determined performance. Epoch-by-epoch agreement with four-class scoring (Wake, REM, N1+N2, N3) reached a Cohen's kappa coefficient of agreement of 0.68 and an accuracy of 80.8%. For Wake vs. Sleep classification, an accuracy of 93.3% was obtained, with a sensitivity of 78.7% and a specificity of 96.6%. We showed that cardiorespiratory signals obtained from a chest-worn accelerometer can be used to estimate sleep stages among a population that is diverse in age, BMI, and prevalence of sleep disorders. This opens up the path towards various clinical applications in sleep medicine.

Binary classification of copy number alteration profiles in liquid biopsy with potential clinical impact in advanced NSCLC.

Liquid biopsy has recently emerged as an important tool in clinical practice particularly for lung cancer patients. We retrospectively evaluated cell-free DNA analyses performed at our Institution by next generation sequencing methodology detecting the major classes of genetic alterations. Starting from the graphical representation of chromosomal alterations provided by the analysis software, we developed a support vector machine classifier to automatically classify chromosomal profiles as stable (SCP) or unstable (UCP). High concordance was found between our binary classification and tumor fraction evaluation performed using shallow whole genome sequencing. Among clinical features, UCP patients were more likely to have ≥ 3 metastatic sites and liver metastases. Longitudinal assessment of chromosomal profiles in 33 patients with lung cancer receiving immune checkpoint inhibitors (ICIs) showed that only patients that experienced early death or hyperprogressive disease retained or acquired an UCP within 3 weeks from the beginning of ICIs. UCP was not observed following ICIs among patients that experienced progressive disease or clinical benefit. In conclusion, our binary classification, applied to whole copy number alteration profiles, could be useful for clinical risk stratification during systemic treatment for non-small cell lung cancer patients.

Estimating the Severity of Obstructive Sleep Apnea Using ECG, Respiratory Effort and Neural Networks.

OBJECTIVE: wearable sensor technology has progressed significantly in the last decade, but its clinical usability for the assessment of obstructive sleep apnea (OSA) is limited by the lack of large and representative datasets simultaneously acquired with polysomnography (PSG). The objective of this study was to explore the use of cardiorespiratory signals common in standard PSGs which can be easily measured with wearable sensors, to estimate the severity of OSA. METHODS: an artificial neural network was developed for detecting sleep disordered breathing events using electrocardiogram (ECG) and respiratory effort. The network was combined with a previously developed cardiorespiratory sleep staging algorithm and evaluated in terms of sleep staging classification performance, apnea-hypopnea index (AHI) estimation, and OSA severity estimation against PSG on a cohort of 653 participants with a wide range of OSA severity. RESULTS: four-class sleep staging achieved a κ of 0.69 versus PSG, distinguishing wake, combined N1-N2, N3 and REM. AHI estimation achieved an intraclass correlation coefficient of 0.91, and high diagnostic performance for different OSA severity thresholds. CONCLUSIONS: this study highlights the potential of using cardiorespiratory signals to estimate OSA severity, even without the need for airflow or oxygen saturation (SpO2), traditionally used for assessing OSA. SIGNIFICANCE: while further research is required to translate these findings to practical and unobtrusive sensors, this study demonstrates how existing, large datasets can serve as a foundation for wearable systems for OSA monitoring. Ultimately, this approach could enable long-term assessment of sleep disordered breathing, facilitating new avenues for clinical research in this field.

Maximum a posteriori detection of heartbeats from a chest-worn accelerometer.

Objective. Unobtrusive long-term monitoring of cardiac parameters is important in a wide variety of clinical applications, such as the assesment of acute illness severity and unobtrusive sleep monitoring. Here we determined the accuracy and robustness of heartbeat detection by an accelerometer worn on the chest.Approach. We performed overnight recordings in 147 individuals (69 female, 78 male) referred to two sleep centers. Two methods for heartbeat detection in the acceleration signal were compared: one previously described approach, based on local periodicity, and a novel extended method incorporating maximumaposterioriestimation and a Markov decision process to approach an optimal solution.Main results. The maximumaposterioriestimation significantly improved performance, with a mean absolute error for the estimation of inter-beat intervals of only 3.5 ms, and 95% limits of agreement of -1.7 to +1.0 beats per minute for heartrate measurement. Performance held during posture changes and was only weakly affected by the presence of sleep disorders and demographic factors.Significance. The new method may enable the use of a chest-worn accelerometer in a variety of applications such as ambulatory sleep staging and in-patient monitoring.

Anti-VEGF therapy selects for clones resistant to glucose starvation in ovarian cancer xenografts.

BACKGROUND: Genetic and metabolic heterogeneity are well-known features of cancer and tumors can be viewed as an evolving mix of subclonal populations, subjected to selection driven by microenvironmental pressures or drug treatment. In previous studies, anti-VEGF therapy was found to elicit rewiring of tumor metabolism, causing marked alterations in glucose, lactate ad ATP levels in tumors. The aim of this study was to evaluate whether differences in the sensitivity to glucose starvation existed at the clonal level in ovarian cancer cells and to investigate the effects induced by anti-VEGF therapy on this phenotype by multi-omics analysis. METHODS: Clonal populations, obtained from both ovarian cancer cell lines (IGROV-1 and SKOV3) and tumor xenografts upon glucose deprivation, were defined as glucose deprivation resistant (GDR) or glucose deprivation sensitive (GDS) clones based on their in vitro behaviour. GDR and GDS clones were characterized using a multi-omics approach, including genetic, transcriptomic and metabolic analysis, and tested for their tumorigenic potential and reaction to anti-angiogenic therapy. RESULTS: Two clonal populations, GDR and GDS, with strikingly different viability following in vitro glucose starvation, were identified in ovarian cancer cell lines. GDR clones survived and overcame glucose starvation-induced stress by enhancing mitochondrial oxidative phosphorylation (OXPHOS) and both pyruvate and lipids uptake, whereas GDS clones were less able to adapt and died. Treatment of ovarian cancer xenografts with the anti-VEGF drug bevacizumab positively selected for GDR clones that disclosed increased tumorigenic properties in NOD/SCID mice. Remarkably, GDR clones were more sensitive than GDS clones to the mitochondrial respiratory chain complex I inhibitor metformin, thus suggesting a potential therapeutic strategy to target the OXPHOS-metabolic dependency of this subpopulation. CONCLUSION: A glucose-deprivation resistant population of ovarian cancer cells showing druggable OXPHOS-dependent metabolic traits is enriched in experimental tumors treated by anti-VEGF therapy.

Evidence-Based Lifestyle Guidelines and Self-Management Strategies Utilized by Women with Polycystic Ovary Syndrome.

Polycystic ovary syndrome (PCOS) is a complex endocrine disorder, affecting 13% of reproductive-aged women. While lifestyle management is the first-line treatment for improving complications, women experience challenges with implementation. This cross-sectional study aims to identify the types and sources of dietary and physical activity (PA) interventions implemented by women with PCOS and understand how they use self-management strategies to support lifestyle change. An online questionnaire was disseminated via a consumer-based PCOS website (May 2015-2016). Women (n = 1167) were aged 18-45 years and primarily born within the United States (70%). A quarter or less of women (diet 25%, PA 14%) sought lifestyle advice from health professionals (medical clinicians or dietitians) compared to over half (diet 59%, PA 67%) using alternative sources, namely from online platforms. While only 33% and 16% of women reported following formal dietary or PA guidelines, respectively, 57% had implemented a 'special diet' to manage their condition, many of which were inconsistent with evidence-based practice in PCOS. Participants also displayed a low level of engagement with important self-management behaviors, including goal setting and positive self-talk. These findings suggest that online information may promote inaccurate and ineffective lifestyle advice and emphasize the need to increase engagement with qualified health professionals.

Contactless Camera-Based Sleep Staging: The HealthBed Study.

Polysomnography (PSG) remains the gold standard for sleep monitoring but is obtrusive in nature. Advances in camera sensor technology and data analysis techniques enable contactless monitoring of heart rate variability (HRV). In turn, this may allow remote assessment of sleep stages, as different HRV metrics indirectly reflect the expression of sleep stages. We evaluated a camera-based remote photoplethysmography (PPG) setup to perform automated classification of sleep stages in near darkness. Based on the contactless measurement of pulse rate variability, we use a previously developed HRV-based algorithm for 3 and 4-class sleep stage classification. Performance was evaluated on data of 46 healthy participants obtained from simultaneous overnight recording of PSG and camera-based remote PPG. To validate the results and for benchmarking purposes, the same algorithm was used to classify sleep stages based on the corresponding ECG data. Compared to manually scored PSG, the remote PPG-based algorithm achieved moderate agreement on both 3 class (Wake-N1/N2/N3-REM) and 4 class (Wake-N1/N2-N3-REM) classification, with average κ of 0.58 and 0.49 and accuracy of 81% and 68%, respectively. This is in range with other performance metrics reported on sensing technologies for wearable sleep staging, showing the potential of video-based non-contact sleep staging.

Tγδ LGLL identifies a subset with more symptomatic disease: analysis of an international cohort of 137 patients.

Tγδ large granular lymphocyte leukemia (LGLL) is a rare variant of T-cell LGLL (T-LGLL) that has been less investigated as compared with the more frequent Tαß LGLL, particularly in terms of frequency of STAT3 and STAT5b mutations. In this study, we characterized the clinical and biological features of 137 patients affected by Tγδ LGLL; data were retrospectively collected from 1997 to 2020 at 8 referral centers. Neutropenia and anemia were the most relevant clinical features, being present in 54.2% and 49.6% of cases, respectively, including severe neutropenia and anemia in ∼20% of cases each. Among the various treatments, cyclosporine A was shown to provide the best response rates. DNA samples of 97 and 94 cases were available for STAT3 and STAT5b mutation analysis, with 38.1% and 4.2% of cases being mutated, respectively. Clinical and biological features of our series of Tγδ cases were also compared with a recently published Tαß cohort including 129 cases. Though no differences in STAT3 and STAT5b mutational frequency were found, Tγδ cases more frequently presented with neutropenia (P = .0161), anemia (P < .0001), severe anemia (P = .0065), and thrombocytopenia (P = .0187). Moreover, Vδ2- cases displayed higher frequency of symptomatic disease. Overall, Tγδ cases displayed reduced survival with respect to Tαß cases (P = .0017). Although there was no difference in STAT3 mutation frequency, our results showed that Tγδ LGLL represents a subset of T-LGLL characterized by more frequent symptoms and reduced survival as compared with Tαß LGLL.

ACE2 and TMPRSS2 SARS-CoV-2 infectivity genes: deep mutational scanning and characterization of missense variants.

The human angiotensin-converting enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2) proteins play key roles in the cellular internalization of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the coronavirus responsible for the coronavirus disease of 2019 (COVID-19) pandemic. We set out to functionally characterize the ACE2 and TMPRSS2 protein abundance for variant alleles encoding these proteins that contained non-synonymous single-nucleotide polymorphisms (nsSNPs) in their open reading frames (ORFs). Specifically, a high-throughput assay, deep mutational scanning (DMS), was employed to test the functional implications of nsSNPs, which are variants of uncertain significance in these two genes. Specifically, we used a 'landing pad' system designed to quantify the protein expression for 433 nsSNPs that have been observed in the ACE2 and TMPRSS2 ORFs and found that 8 of 127 ACE2, 19 of 157 TMPRSS2 isoform 1 and 13 of 149 TMPRSS2 isoform 2 variant proteins displayed less than ~25% of the wild-type protein expression, whereas 4 ACE2 variants displayed 25% or greater increases in protein expression. As a result, we concluded that nsSNPs in genes encoding ACE2 and TMPRSS2 might potentially influence SARS-CoV-2 infectivity. These results can now be applied to DNA sequence data for patients infected with SARS-CoV-2 to determine the possible impact of patient-based DNA sequence variation on the clinical course of SARS-CoV-2 infection.

Relationships between self-management strategies and physical activity and diet quality in women with polycystic ovary syndrome.

OBJECTIVE: To determine the relationships of self-management strategies and physical activity (PA) and diet quality in women with PCOS. METHODS: An online cross-sectional study involving women (n = 501), 18-45 years in the general Australian community with a self-reported PCOS diagnosis. The self-management and lifestyle behaviour questionnaires were completed between August 2017 and March 2018. RESULTS: Implementation of PA related self-management strategies increased the odds of meeting PA recommendations [Odds ratio (OR): 2.929 (95%CI: 2.172, 3.951), p < 0.001] but had no association with body mass index (BMI) [OR: 0.-0.984 (95%CI: -1.010, 0.959), p = 0.217] nor perception of self weight [OR: 1.382 (95% CI: 0.700, 2.725), p = 0.352]. Nutrition related self-management strategies were inversely associated with BMI [OR: - 0.115 (95%CI: -7.159, -0.980), p = 0.010] but had no association with diet quality [OR: 0.183 (95%CI: -2.328, 2.800), p = 0.855], energy intake [OR: - 0.092 (95%CI: -1204.443, 527.496) p = 0.438] or weight [OR: - 0.034 (95%CI: -4.020, 1.930), p = 0.491]. CONCLUSIONS: PA self-management strategies were associated with meeting PA recommendations. Nutrition strategies were associated with lower BMI but not diet quality, energy intake or weight in women with PCOS. PRACTICE IMPLICATIONS: Other behaviour change determinants (e.g. education, skills and self-efficacy) should be considered when designing a PCOS lifestyle programme in conjunction with self-management strategies.

Corrigendum to "Genetic perturbation of IFN-α transcriptional modulators in human endothelial cells uncovers pivotal regulators of angiogenesis" [Comput Struct Biotechnol J. 2020 Dec 2;18:3977-3986. doi: https://doi.org//10.1016/j.csbj.2020.11.048. eCollection 2020].

[This corrects the article DOI: 10.1016/j.csbj.2020.11.048.].

Estimation of respiratory rate and effort from a chest-worn accelerometer using constrained and recursive principal component analysis.

Objective. Measurement of respiratory rate and effort is useful in various applications, such as the diagnosis of sleep apnea and early detection of patient deterioration in medical conditions, such as infections. A chest-worn accelerometer may be an easy and non-intrusive method, provided it is accurate and robust. We investigate the use of a novel method that can perform under realistic sleeping conditions such as variable sensor positions and body posture.Approach. Twenty subjects (aged 46-65 years) wore an accelerometer on the chest and a respiratory impedance plethysmography band as a reference. The subjects underwent an experimental protocol lasting approximately 90 min, under various postures and with different sensor positions. We used a novel, constrained, and recursive form of principal component analysis (PCA) to estimate the respiratory effort signal robustly. To obtain an estimate for the respiratory rate, first, multiple estimates were aggregated into a single frequency. Subsequently, a quality index was determined, such that unreliable estimates could be identified, and a trade-off could be made between coverage (percentage of time that the quality index is above a threshold) and limits of agreement.Main results. Results were determined over all recorded data, including changes in sensor position and posture. For respiratory effort, it was found that recursive and constrained computation of PCA reduced the estimation error significantly. For respiratory rate, a relation between coverage and limits of agreement was determined. If a minimum coverage of 80% was required, the limits of agreement could be kept below 1.45 breaths per minute. If the limits of agreement were constrained to 0.2 breaths per minute, a mean coverage of 5% was still attainable.Significance. We have shown that chest-worn accelerometery can be a robust and accurate method for measurement of respiratory features under realistic conditions.

Genetic Perturbation of Pyruvate Dehydrogenase Kinase 1 Modulates Growth, Angiogenesis and Metabolic Pathways in Ovarian Cancer Xenografts.

Pyruvate dehydrogenase kinase 1 (PDK1) blockade triggers are well characterized in vitro metabolic alterations in cancer cells, including reduced glycolysis and increased glucose oxidation. Here, by gene expression profiling and digital pathology-mediated quantification of in situ markers in tumors, we investigated effects of PDK1 silencing on growth, angiogenesis and metabolic features of tumor xenografts formed by highly glycolytic OC316 and OVCAR3 ovarian cancer cells. Notably, at variance with the moderate antiproliferative effects observed in vitro, we found a dramatic negative impact of PDK1 silencing on tumor growth. These findings were associated with reduced angiogenesis and increased necrosis in the OC316 and OVCAR3 tumor models, respectively. Analysis of viable tumor areas uncovered increased proliferation as well as increased apoptosis in PDK1-silenced OVCAR3 tumors. Moreover, RNA profiling disclosed increased glucose catabolic pathways-comprising both oxidative phosphorylation and glycolysis-in PDK1-silenced OVCAR3 tumors, in line with the high mitotic activity detected in the viable rim of these tumors. Altogether, our findings add new evidence in support of a link between tumor metabolism and angiogenesis and remark on the importance of investigating net effects of modulations of metabolic pathways in the context of the tumor microenvironment.

Genetic perturbation of IFN-α transcriptional modulators in human endothelial cells uncovers pivotal regulators of angiogenesis.

Interferon-α (IFN-α) comprises a family of 13 cytokines involved in the modulation of antiviral, immune, and anticancer responses by orchestrating a complex transcriptional network. The activation of IFN-α signaling pathway in endothelial cells results in decreased proliferation and migration, ultimately leading to suppression of angiogenesis. In this study, we knocked-down the expression of seven established or candidate modulators of IFN-α response in endothelial cells to reconstruct a gene regulatory network and to investigate the antiangiogenic activity of IFN-α. This genetic perturbation approach, along with the analysis of interferon-induced gene expression dynamics, highlighted a complex and highly interconnected network, in which the angiostatic chemokine C-X-C Motif Chemokine Ligand 10 (CXCL10) was a central node targeted by multiple modulators. IFN-α-induced secretion of CXCL10 protein by endothelial cells was blunted by the silencing of Signal Transducer and Activator of Transcription 1 (STAT1) and of Interferon Regulatory Factor 1 (IRF1) and it was exacerbated by the silencing of Ubiquitin Specific Peptidase 18 (USP18). In vitro sprouting assay, which mimics in vivo angiogenesis, confirmed STAT1 as a positive modulator and USP18 as a negative modulator of IFN-α-mediated sprouting suppression. Our data reveal an unprecedented physiological regulation of angiogenesis in endothelial cells through a tonic IFN-α signaling, whose enhancement could represent a viable strategy to suppress tumor neoangiogenesis.

Prognostic Stratification of Bladder Cancer Patients with a MicroRNA-based Approach.

Robust non-invasive tests for prognostic stratification of bladder cancer (BCa) patients are in high demand. Following a comprehensive analysis of studies on BCa, we selected a panel of 29 microRNAs (miRNAs) and analyzed their levels in urine and plasma samples in a prospective cohort of 63 BCa patients (32 at high risk of recurrence and 31 low-risk cases) and 37 healthy controls using RT-qPCR. To design an assay suitable for large-scale testing, we applied a hierarchical pipeline to select the miRNAs that were not affected by confounding factors such as haematuria and urine specific gravity, and exceeded stringent cut-off criteria (fold change >2.5 and p-value < 0.005). Using a two-step decision tree based on the urine levels of miR-34a-5p, miR-200a-3p and miR-193a-5p, normalized against miR-125b-5p, patients could be classified as high- or low-risk with a sensitivity of 0.844, specificity of 0.806 and accuracy of 0.825. Furthermore, univariate Cox proportional hazards regression analyses indicated that increased urine levels of miR-29a-3p, miR-34a-5p, miR-193a-5p, miR-200c-3p, miR-205-5p and miR-532-5p were associated with a shorter event-free survival (hazard ratios > 3.1, p-value < 0.05). Taken together, our findings suggest that measuring the urine levels of these miRNAs could provide a novel cost-effective, noninvasive test for risk assessment of BCa patients.

miR-22-3p Negatively Affects Tumor Progression in T-Cell Acute Lymphoblastic Leukemia.

T-cell acute lymphoblastic leukemia (T-ALL) is a rare, aggressive disease arising from T-cell precursors. NOTCH1 plays an important role both in T-cell development and leukemia progression, and more than 60% of human T-ALLs harbor mutations in components of the NOTCH1 signaling pathway, leading to deregulated cell growth and contributing to cell transformation. Besides multiple NOTCH1 target genes, microRNAs have also been shown to regulate T-ALL initiation and progression. Using an established mouse model of T-ALL induced by NOTCH1 activation, we identified several microRNAs downstream of NOTCH1 activation. In particular, we found that NOTCH1 inhibition can induce miR-22-3p in NOTCH1-dependent tumors and that this regulation is also conserved in human samples. Importantly, miR-22-3p overexpression in T-ALL cells can inhibit colony formation in vitro and leukemia progression in vivo. In addition, miR-22-3p was found to be downregulated in T-ALL specimens, both T-ALL cell lines and primary samples, relative to immature T-cells. Our results suggest that miR-22-3p is a functionally relevant microRNA in T-ALL whose modulation can be exploited for therapeutic purposes to inhibit T-ALL progression.

Rewiring of Lipid Metabolism and Storage in Ovarian Cancer Cells after Anti-VEGF Therapy.

Anti-angiogenic therapy triggers metabolic alterations in experimental and human tumors, the best characterized being exacerbated glycolysis and lactate production. By using both Liquid Chromatography-Mass Spectrometry (LC-MS) and Nuclear Magnetic Resonance (NMR) analysis, we found that treatment of ovarian cancer xenografts with the anti-Vascular Endothelial Growth Factor (VEGF) neutralizing antibody bevacizumab caused marked alterations of the tumor lipidomic profile, including increased levels of triacylglycerols and reduced saturation of lipid chains. Moreover, transcriptome analysis uncovered up-regulation of pathways involved in lipid metabolism. These alterations were accompanied by increased accumulation of lipid droplets in tumors. This phenomenon was reproduced under hypoxic conditions in vitro, where it mainly depended from uptake of exogenous lipids and was counteracted by treatment with the Liver X Receptor (LXR)-agonist GW3965, which inhibited cancer cell viability selectively under reduced serum conditions. This multi-level analysis indicates alterations of lipid metabolism following anti-VEGF therapy in ovarian cancer xenografts and suggests that LXR-agonists might empower anti-tumor effects of bevacizumab.