The Variability of the Components of Fitness along the Menstrual Cycle.

This study aims to determine the variability of the components of fitness along the menstrual cycle (MC) of physically active eumenorrheic women. Fifteen subjects were monitored along two consecutive MCs through the calendar-based counting method in combination with a urine luteinizing hormone surge kit. Subjects were tested at the early follicular phase (EFP), pre-ovulatory or late follicular phase (LFP), and post-ovulatory or mid-luteal phase (MLP). In each session, the back squat one-repetition maximum (1-RM), maximum fat oxidation (MFO), maximum oxygen uptake (VO2max) and acute recovery capacity were determined. The results revealed a wide variability among components of fitness and a low to high variability among subjects (acute recovery: 3.6% [range 1.5 to 9.5%]; back squat 1-RM: 6.1% [range 2.2 to 11%]; VO2max: 6.6% [range 1.1 to 15%]; MFO: 23% [range 4.6 to 35%]). Despite the individual nature, considering the number and magnitude of the responses in each MC phase, VO2max and acute recovery capacity tended to be enhanced at the LFP, the MFO at the MLP, and the back squat 1-RM remained stable along the MC. Thus, practitioners are aware of which components are susceptible to change along the MC phase, but an individual monitoring is recommended.

Does the Menstrual Cycle Impact the Maximal Neuromuscular Capacities of Women? An Analysis Before and After a Graded Treadmill Test to Exhaustion.

ABSTRACT: Morenas-Aguilar, MD, Ruiz-Alias, SA, Blanco, AM, Lago-Fuentes, C, García-Pinillos, F, and Pérez-Castilla, A. Does the menstrual cycle impact the maximal neuromuscular capacities of women? An analysis before and after a graded treadmill test to exhaustion. J Strength Cond Res 37(11): 2185-2191, 2023. This study explored the effect of the menstrual cycle (MC) on the maximal neuromuscular capacities of the lower-body muscles obtained before and after a graded exercise test conducted on a treadmill to exhaustion. Sixteen physically active women were tested at -11 ± 3, -5 ± 3, and 5 ± 3 days from the luteinizing peak for the early follicular, late follicular, and midluteal phases. In each session, the individualized load-velocity (L-V) relationship variables (load-axis intercept [L0], velocity-axis intercept [v0], and area under the L-V relationship line [Aline]) were obtained before and after a graded exercise test conducted on a treadmill to exhaustion using the 2-point method (3 countermovement jumps with a 0.5-kg barbell and 2 back squats against a load linked to a mean velocity of 0.55 m·second-1). At the beginning of each session, no significant differences were reported for L0 (p = 0.726; ES ≤ 0.18), v0 (p = 0.202; ES ≤ 0.37), and Aline (p = 0.429; ES ≤ 0.30) between the phases. The MC phase × time interaction did not reach statistical significance for any L-V relationship variable (p ≥ 0.073). A significant main effect of "time" was observed for L0 (p < 0.001; ES = -0.77) and Aline (p = 0.002; ES = -0.59) but not for v0 (p = 0.487; ES = 0.12). These data suggest that the lower-body maximal neuromuscular capacities obtained before and after a graded treadmill test are not significantly affected by MC, although there is a high variability in the individual response.

Perceptions of intelligence & sentience shape children's interactions with robot reading companions.

The potential for robots to support education is being increasingly studied and rapidly realised. However, most research evaluating education robots has neglected to examine the fundamental features that make them more or less effective, given the needs and expectations of learners. This study explored how children's perceptions, expectations and experiences are shaped by aesthetic and functional features during interactions with different robot 'reading buddies'. We collected a range of quantitative and qualitative measures of subjective experience before and after children read a book with one of three different robots. An inductive thematic analysis revealed that robots have the potential offer children an engaging and non-judgemental social context to promote reading engagement. This was supported by children's perceptions of robots as being intelligent enough to read, listen and comprehend the story, particularly when they had the capacity to talk. A key challenge in the use of robots for this purpose was the unpredictable nature of robot behaviour, which remains difficult to perfectly control and time using either human operators or autonomous algorithms. Consequently, some children found the robots' responses distracting. We provide recommendations for future research seeking to position seemingly sentient and intelligent robots as an assistive tool within and beyond education settings.

Correction: Gene expression profiling identifies FLT3 mutation-like cases in wild-type FLT3 acute myeloid leukemia.

[This corrects the article DOI: 10.1371/journal.pone.0247093.].

Real-world study of children and young adults with myeloproliferative neoplasms: identifying risks and unmet needs.

Myeloproliferative neoplasms (MPNs) are uncommon in children/young adults. Here, we present data on unselected patients diagnosed before 25 years of age included from 38 centers in 15 countries. Sequential patients were included. We identified 444 patients, with median follow-up 9.7 years (0-47.8). Forty-nine (11.1%) had a history of thrombosis at diagnosis, 49 new thrombotic events were recorded (1.16% patient per year [pt/y]), perihepatic vein thromboses were most frequent (47.6% venous events), and logistic regression identified JAK2V617F mutation (P = .016) and hyperviscosity symptoms (visual disturbances, dizziness, vertigo, headache) as risk factors (P = .040). New hemorrhagic events occurred in 44 patients (9.9%, 1.04% pt/y). Disease transformation occurred in 48 patients (10.9%, 1.13% pt/y), usually to myelofibrosis (7.5%) with splenomegaly as a novel risk factor for transformation in essential thrombocythemia (ET) (P= .000) in logistical regression. Eight deaths (1.8%) were recorded, 3 after allogeneic stem cell transplantation. Concerning conventional risk scores: International Prognostic Score for Essential Thrombocythemia-Thrombosis and new International Prognostic Score for Essential Thrombocythemia-Thrombosis differentiated ET patients in terms of thrombotic risk. Both scores identified high-risk patients with the same median thrombosis-free survival of 28.5 years. No contemporary scores were able to predict survival for young ET or polycythemia vera patients. Our data represents the largest real-world study of MPN patients age < 25 years at diagnosis. Rates of thrombotic events and transformation were higher than expected compared with the previous literature. Our study provides new and reliable information as a basis for prospective studies, trials, and development of harmonized international guidelines for the specific management of young patients with MPN.

Intestinal Akkermansia muciniphila predicts clinical response to PD-1 blockade in patients with advanced non-small-cell lung cancer.

Aside from PD-L1 expression, biomarkers of response to immune checkpoint inhibitors (ICIs) in non-small-cell lung cancer (NSCLC) are needed. In a previous retrospective analysis, we documented that fecal Akkermansia muciniphila (Akk) was associated with clinical benefit of ICI in patients with NSCLC or kidney cancer. In the current study, we performed shotgun-metagenomics-based microbiome profiling in a large cohort of patients with advanced NSCLC (n = 338) treated with first- or second-line ICIs to prospectively validate the predictive value of fecal Akk. Baseline stool Akk was associated with increased objective response rates and overall survival in multivariate analyses, independent of PD-L1 expression, antibiotics, and performance status. Intestinal Akk was accompanied by a richer commensalism, including Eubacterium hallii and Bifidobacterium adolescentis, and a more inflamed tumor microenvironment in a subset of patients. However, antibiotic use (20% of cases) coincided with a relative dominance of Akk above 4.8% accompanied with the genus Clostridium, both associated with resistance to ICI. Our study shows significant differences in relative abundance of Akk that may represent potential biomarkers to refine patient stratification in future studies.

Reliability of the RunScribe™ system to determine kinematic variables of the pelvis during locomotion at different speeds.

PURPOSE: The aim of this study was to determine the reliability of the RunScribe™ system to measure kinematic variables of the pelvis during walking and running. METHODS: In this study, a treadmill protocol was implemented where the participants (n = 23) completed 3 sets of 1 minute at 5, 10 and 15 km/h. RESULTS: All the recorded measurements during walking reported a low reliability with coefficients of variation (CV) greater than 10% in all variables and small-moderate intraclass correlation coefficient (ICC) (<0.6) in seven out of ten variables. Similarly, the CVs reported in running were greater than 10%, except for the maximum angular rate in the obliquity of the pelvis and the vertical oscillation that together with the angular velocity variables showed almost perfect ICCs (>0.92). CONCLUSIONS: Therefore, the data obtained suggests that the RunScribe™ system with 3 IMUs does not provide reliable metrics about the kinematics of the pelvis during locomotion (i.e., walking and running).

Survival prediction and treatment optimization of multiple myeloma patients using machine-learning models based on clinical and gene expression data.

Multiple myeloma (MM) remains mostly an incurable disease with a heterogeneous clinical evolution. Despite the availability of several prognostic scores, substantial room for improvement still exists. Promising results have been obtained by integrating clinical and biochemical data with gene expression profiling (GEP). In this report, we applied machine learning algorithms to MM clinical and RNAseq data collected by the CoMMpass consortium. We created a 50-variable random forests model (IAC-50) that could predict overall survival with high concordance between both training and validation sets (c-indexes, 0.818 and 0.780). This model included the following covariates: patient age, ISS stage, serum B2-microglobulin, first-line treatment, and the expression of 46 genes. Survival predictions for each patient considering the first line of treatment evidenced that those individuals treated with the best-predicted drug combination were significantly less likely to die than patients treated with other schemes. This was particularly important among patients treated with a triplet combination including bortezomib, an immunomodulatory drug (ImiD), and dexamethasone. Finally, the model showed a trend to retain its predictive value in patients with high-risk cytogenetics. In conclusion, we report a predictive model for MM survival based on the integration of clinical, biochemical, and gene expression data with machine learning tools.

Detection of new drivers of frequent B-cell lymphoid neoplasms using an integrated analysis of whole genomes.

B-cell lymphoproliferative disorders exhibit a diverse spectrum of diagnostic entities with heterogeneous behaviour. Multiple efforts have focused on the determination of the genomic drivers of B-cell lymphoma subtypes. In the meantime, the aggregation of diverse tumors in pan-cancer genomic studies has become a useful tool to detect new driver genes, while enabling the comparison of mutational patterns across tumors. Here we present an integrated analysis of 354 B-cell lymphoid disorders. 112 recurrently mutated genes were discovered, of which KMT2D, CREBBP, IGLL5 and BCL2 were the most frequent, and 31 genes were putative new drivers. Mutations in CREBBP, TNFRSF14 and KMT2D predominated in follicular lymphoma, whereas those in BTG2, HTA-A and PIM1 were more frequent in diffuse large B-cell lymphoma. Additionally, we discovered 31 significantly mutated protein networks, reinforcing the role of genes such as CREBBP, EEF1A1, STAT6, GNA13 and TP53, but also pointing towards a myriad of infrequent players in lymphomagenesis. Finally, we report aberrant expression of oncogenes and tumor suppressors associated with novel noncoding mutations (DTX1 and S1PR2), and new recurrent copy number aberrations affecting immune check-point regulators (CD83, PVR) and B-cell specific genes (TNFRSF13C). Our analysis expands the number of mutational drivers of B-cell lymphoid neoplasms, and identifies several differential somatic events between disease subtypes.

Personalized Survival Prediction of Patients With Acute Myeloblastic Leukemia Using Gene Expression Profiling.

Acute Myeloid Leukemia (AML) is a heterogeneous neoplasm characterized by cytogenetic and molecular alterations that drive patient prognosis. Currently established risk stratification guidelines show a moderate predictive accuracy, and newer tools that integrate multiple molecular variables have proven to provide better results. In this report, we aimed to create a new machine learning model of AML survival using gene expression data. We used gene expression data from two publicly available cohorts in order to create and validate a random forest predictor of survival, which we named ST-123. The most important variables in the model were age and the expression of KDM5B and LAPTM4B, two genes previously associated with the biology and prognostication of myeloid neoplasms. This classifier achieved high concordance indexes in the training and validation sets (0.7228 and 0.6988, respectively), and predictions were particularly accurate in patients at the highest risk of death. Additionally, ST-123 provided significant prognostic improvements in patients with high-risk mutations. Our results indicate that survival of patients with AML can be predicted to a great extent by applying machine learning tools to transcriptomic data, and that such predictions are particularly precise among patients with high-risk mutations.

Detection of Rare Germline Variants in the Genomes of Patients with B-Cell Neoplasms.

There is growing evidence indicating the implication of germline variation in cancer predisposition and prognostication. Here, we describe an analysis of likely disruptive rare variants across the genomes of 726 patients with B-cell lymphoid neoplasms. We discovered a significant enrichment for two genes in rare dysfunctional variants, both of which participate in the regulation of oxidative stress pathways (CHMP6 and GSTA4). Additionally, we detected 1675 likely disrupting variants in genes associated with cancer, of which 44.75% were novel events and 7.88% were protein-truncating variants. Among these, the most frequently affected genes were ATM, BIRC6, CLTCL1A, and TSC2. Homozygous or germline double-hit variants were detected in 28 cases, and coexisting somatic events were observed in 17 patients, some of which affected key lymphoma drivers such as ATM, KMT2D, and MYC. Finally, we observed that variants in six different genes were independently associated with shorter survival in CLL. Our study results support an important role for rare germline variation in the pathogenesis and prognosis of B-cell lymphoid neoplasms.

Gene expression profiling identifies FLT3 mutation-like cases in wild-type FLT3 acute myeloid leukemia.

BACKGROUND: FLT3 mutation is present in 25-30% of all acute myeloid leukemias (AML), and it is associated with adverse outcome. FLT3 inhibitors have shown improved survival results in AML both as upfront treatment and in relapsed/refractory disease. Curiously, a variable proportion of wild-type FLT3 patients also responded to these drugs. METHODS: We analyzed 6 different transcriptomic datasets of AML cases. Differential expression between mutated and wild-type FLT3 AMLs was performed with the Wilcoxon-rank sum test. Hierarchical clustering was used to identify FLT3-mutation like AMLs. Finally, enrichment in recurrent mutations was performed with the Fisher's test. RESULTS: A FLT3 mutation-like gene expression pattern was identified among wild-type FLT3 AMLs. This pattern was highly enriched in NPM1 and DNMT3A mutants, and particularly in combined NPM1/DNMT3A mutants. CONCLUSIONS: We identified a FLT3 mutation-like gene expression pattern in AML which was highly enriched in NPM1 and DNMT3A mutations. Future analysis about the predictive role of this biomarker among wild-type FLT3 patients treated with FLT3 inhibitors is envisaged.

Personally Tailored Survival Prediction of Patients With Follicular Lymphoma Using Machine Learning Transcriptome-Based Models.

Follicular Lymphoma (FL) has a 10-year mortality rate of 20%, and this is mostly related to lymphoma progression and transformation to higher grades. In the era of personalized medicine it has become increasingly important to provide patients with an optimal prediction about their expected outcomes. The objective of this work was to apply machine learning (ML) tools on gene expression data in order to create individualized predictions about survival in patients with FL. Using data from two different studies, we were able to create a model which achieved good prediction accuracies in both cohorts (c-indexes of 0.793 and 0.662 in the training and test sets). Integration of this model with m7-FLIPI and age rendered high prediction accuracies in the test set (cox c-index 0.79), and a simplified approach identified 4 groups with remarkably different outcomes in terms of survival. Importantly, one of the groups comprised 27.35% of patients and had a median survival of 4.64 years. In summary, we have created a gene expression-based individualized predictor of overall survival in FL that can improve the predictions of the m7-FLIPI score.

The risk of thrombosis in essential thrombocythemia is associated with the type of CALR mutation: A multicentre collaborative study.

OBJECTIVES: In patients with essential thrombocythemia (ET), after the JAK2V617F driver mutation, mutations in CALR are common (classified as type 1, 52-bp deletion or type 2, 5-bp insertion). CALR mutations have generally been associated with a lower risk of thrombosis. This study aimed to confirm the impact of CALR mutation type on thrombotic risk. METHODS: We retrospectively investigated 983 ET patients diagnosed in Spanish and Polish hospitals. RESULTS: With 7.5 years of median follow-up from diagnosis, 155 patients (15.8%) had one or more thrombotic event. The 5-year thrombosis-free survival (TFS) rate was 83.8%, 91.6% and 93.9% for the JAK2V617F, CALR-type 1 and CALR-type 2 groups, respectively (P = .002). Comparing CALR-type 1 and CALR-type 2 groups, TFS for venous thrombosis was lower in CALR-type 1 (P = .046), with no difference in TFS for arterial thrombosis observed. The cumulative incidence of thrombosis was significantly different comparing JAK2V617F vs CALR-type 2 groups but not JAK2V617F vs CALR-type 1 groups. Moreover, CALR-type 2 mutation was a statistically significant protective factor for thrombosis with respect to JAK2V617F in multivariate logistic regression (OR: 0.45, P = .04) adjusted by age. CONCLUSIONS: Our results suggest that CALR mutation type has prognostic value for the stratification of thrombotic risk in ET patients.

Improved personalized survival prediction of patients with diffuse large B-cell Lymphoma using gene expression profiling.

BACKGROUND: Thirty to forty percent of patients with Diffuse Large B-cell Lymphoma (DLBCL) have an adverse clinical evolution. The increased understanding of DLBCL biology has shed light on the clinical evolution of this pathology, leading to the discovery of prognostic factors based on gene expression data, genomic rearrangements and mutational subgroups. Nevertheless, additional efforts are needed in order to enable survival predictions at the patient level. In this study we investigated new machine learning-based models of survival using transcriptomic and clinical data. METHODS: Gene expression profiling (GEP) of in 2 different publicly available retrospective DLBCL cohorts were analyzed. Cox regression and unsupervised clustering were performed in order to identify probes associated with overall survival on the largest cohort. Random forests were created to model survival using combinations of GEP data, COO classification and clinical information. Cross-validation was used to compare model results in the training set, and Harrel's concordance index (c-index) was used to assess model's predictability. Results were validated in an independent test set. RESULTS: Two hundred thirty-three and sixty-four patients were included in the training and test set, respectively. Initially we derived and validated a 4-gene expression clusterization that was independently associated with lower survival in 20% of patients. This pattern included the following genes: TNFRSF9, BIRC3, BCL2L1 and G3BP2. Thereafter, we applied machine-learning models to predict survival. A set of 102 genes was highly predictive of disease outcome, outperforming available clinical information and COO classification. The final best model integrated clinical information, COO classification, 4-gene-based clusterization and the expression levels of 50 individual genes (training set c-index, 0.8404, test set c-index, 0.7942). CONCLUSION: Our results indicate that DLBCL survival models based on the application of machine learning algorithms to gene expression and clinical data can largely outperform other important prognostic variables such as disease stage and COO. Head-to-head comparisons with other risk stratification models are needed to compare its usefulness.

General Distributed Neural Control and Sensory Adaptation for Self-Organized Locomotion and Fast Adaptation to Damage of Walking Robots.

Walking animals such as invertebrates can effectively perform self-organized and robust locomotion. They can also quickly adapt their gait to deal with injury or damage. Such a complex achievement is mainly performed via coordination between the legs, commonly known as interlimb coordination. Several components underlying the interlimb coordination process (like distributed neural control circuits, local sensory feedback, and body-environment interactions during movement) have been recently identified and applied to the control systems of walking robots. However, while the sensory pathways of biological systems are plastic and can be continuously readjusted (referred to as sensory adaptation), those implemented on robots are typically static. They first need to be manually adjusted or optimized offline to obtain stable locomotion. In this study, we introduce a fast learning mechanism for online sensory adaptation. It can continuously adjust the strength of sensory pathways, thereby introducing flexible plasticity into the connections between sensory feedback and neural control circuits. We combine the sensory adaptation mechanism with distributed neural control circuits to acquire the adaptive and robust interlimb coordination of walking robots. This novel approach is also general and flexible. It can automatically adapt to different walking robots and allow them to perform stable self-organized locomotion as well as quickly deal with damage within a few walking steps. The adaptation of plasticity after damage or injury is considered here as lesion-induced plasticity. We validated our adaptive interlimb coordination approach with continuous online sensory adaptation on simulated 4-, 6-, 8-, and 20-legged robots. This study not only proposes an adaptive neural control system for artificial walking systems but also offers a possibility of invertebrate nervous systems with flexible plasticity for locomotion and adaptation to injury.

Natural disasters and indicators of social cohesion.

Do adversarial environmental conditions create social cohesion? We provide new answers to this question by exploiting spatial and temporal variation in exposure to earthquakes across Chile. Using a variety of methods and controlling for a number of socio-economic variables, we find that exposure to earthquakes has a positive effect on several indicators of social cohesion. Social cohesion increases after a big earthquake and slowly erodes in periods where environmental conditions are less adverse. Our results contribute to the current debate on whether and how environmental conditions shape formal and informal institutions.