54 research outputs found
Molecular subtype analysis determines the association of advanced breast cancer in Egypt with favorable biology
<p>Abstract</p> <p>Background</p> <p>Prognostic markers and molecular breast cancer subtypes reflect underlying biological tumor behavior and are important for patient management. Compared to Western countries, women in North Africa are less likely to be prognosticated and treated based on well-characterized markers such as the estrogen receptor (ER), progesterone receptor (PR) and Her2. We conducted this study to determine the prevalence of breast cancer molecular subtypes in the North African country of Egypt as a measure of underlying biological characteristics driving tumor manifestations.</p> <p>Methods</p> <p>To determine molecular subtypes we characterized over 200 tumor specimens obtained from Egypt by performing ER, PR, Her2, CK5/6, EGFR and Ki67 immunohistochemistry.</p> <p>Results</p> <p>Our study demonstrated that the Luminal A subtype, associated with favorable prognosis, was found in nearly 45% of cases examined. However, the basal-like subtype, associated with poor prognosis, was found in 11% of cases. These findings are in sharp contrast to other parts of Africa in which the basal-like subtype is over-represented.</p> <p>Conclusions</p> <p>Egyptians appear to have favorable underlying biology, albeit having advanced disease at diagnosis. These data suggest that Egyptians would largely profit from early detection of their disease. Intervention at the public health level, including education on the benefits of early detection is necessary and would likely have tremendous impact on breast cancer outcome in Egypt.</p
VARIABILIDADE DO DESEMPENHO NO LINE-DRILL TEST EM ADOLESCENTES JOGADORES DE BASQUETEBOL
Species composition of sand flies and bionomics of Phlebotomus papatasi and P. sergenti (Diptera: Psychodidae) in cutaneous leishmaniasis endemic foci, Morocco
The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance
INTRODUCTION
Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic.
RATIONALE
We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs).
RESULTS
Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants.
CONCLUSION
Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century
Physiological responses to ball-drills in regional level male basketball players.
Abstract The main aim of this study was to assess the physiological responses of male basketball players during usual basketball ball-drills. Fourteen male basketball players (age 18.9\ua0\ub1\ua02.3 years) performed the following full-court (28\ua0
7\ua015 m) basketball ball-drills (3\ua0
7\ua04-min with 3-min passive rest): five-a-side (5v5), three-a-side (3v3) and two-a-side (2v2). A main effect (P\ua0<\ua00.0001) for ball-drills modes was evident for all variables (5v5\ua0<\ua03v3\ua0<\ua02v2). Mean [Vdot]O(2) during the 5v5, 3v3 and 2v2 were 39.0\ua0\ub1\ua07.2, 42.0\ua0\ub1\ua07.5 and 45.0\ua0\ub1\ua06.5 ml\ua0\ub7\ua0kg(-1)\ua0\ub7\ua0min(-1) (69\ua0\ub1\ua011, 74\ua0\ub1\ua012 and 79\ua0\ub1\ua011\% of [Vdot]O(2peak)) respectively (5v5\ua0=\ua03v3\ua0<\ua02v2, P\ua0<\ua00.001). Mean blood-lactate concentrations for 5v5, 3v3 and 2v2 were 4.2\ua0\ub1\ua01.8, 6.2\ua0\ub1\ua02.3 and 7.8\ua0\ub1\ua01.2 mmol (l(-1) respectively (5v5\ua0<\ua03v3\ua0<\ua02v2, P\ua0<\ua00.01). During the 5v5, 3v3 and 2v2 mean heart-rate (HR) was 84.0\ua0\ub1\ua09.2, 88.0\ua0\ub1\ua08.4 and 92.0\ua0\ub1\ua05.6\% of the individual peak respectively (5v5\ua0<\ua03v3\ua0<\ua02v2; P\ua0<\ua00.001). No significant differences were found between the regression-line slope (P\ua0=\ua00.86) and intercept (P\ua0=\ua00.45) of the HR-[Vdot]O(2) relationships of the multistage maximal fitness test (r(2) from 0.80 to 0.96, P\ua0<\ua00.001) and ball-drills (r(2) from 0.70 to 0.95, P\ua0<\ua00.001) conditions. Reducing the number of players over the same playing court resulted in increments in physiological demands. The 2v2 condition provided responses in the range of those reported to improve aerobic and anaerobic fitness. The aerobic demands of ball-drills can be accurately assessed using heart-rate monitoring in basketball
Exploring simultaneous perturbation profiles in milk yield and body weight reveals a diversity of animal responses and new opportunities to identify resilience proxies
International audienceLivestock husbandry aims to manage the environment in which animals are reared to enable them to express their production potential. However, animals are often confronted with perturbations that affect their performance. Evaluating effects of these perturbations on animal performance could provide metrics to quantify and understand how animals cope with their environment, and therefore to better manage them. Body weight (BW) and milk yield (MY) dynamics over lactation may be used for this purpose. The goal of this study was to estimate an unperturbed performance trajectory using a differential smoothing approach on both MY and BW time series, and then to identify the perturbations and extract their phenotypic features. Daily MY and BW records from 490 primiparous Holstein cows from 33 commercial French herds were used. From the fitting procedure, estimated unperturbed performance trajectories of BW and MY were clustered into 3 groups. After the fitting procedure, 1,754 deviations were detected in the MY time series and 964 were detected in the BW time series across all cows. Overall, 425 of these deviations were detected during the same period (±10 d) in both MY and BW time series, 76 of which started at the same time. Results suggest that combining various individual dynamic measures and revealing the relationship that exists between them could be of great value in obtaining reliable estimates of resilience components in large populations
Fitness field tests{'} correlation with game performance in U-19-category basketball referees
Training Mode’s Influence on the Relationships between Training-Load Models During Basketball Conditioning
Purpose: To compare perceptual and physiological training load responses during various basketball training modes. Methods: Eight semi-professional male basketball players (age: 26.3 ± 6.7 years; height: 188.1 ± 6.2 cm; body mass: 92.0 ± 13.8 kg) were monitored across a 10-week period in the preparatory phase of the training plan. Player session ratings of perceived exertion (sRPE) and heart rate (HR) responses were gathered across base, specific, and tactical/game-play training modes. Pearson correlations were used to determine the relationships between the sRPE model and two HR-based models, the training impulse (TRIMP) and summated-heart-rate-zones (SHRZ). One-way ANOVAs were used to compare training loads between training modes for each model. Results: Stronger relationships between perceptual and physiological models were evident during base (sRPE-TRIMP: r = 0.53, P < 0.05; sRPE-SHRZ: r = 0.75, P < 0.05) and tactical/game-play conditioning (sRPE-TRIMP: r = 0.60, P < 0.05; sRPE-SHRZ: r = 0.63; P < 0.05) than during specific conditioning (sRPE-TRIMP: r = 0.38, P < 0.05; sRPE-SHRZ: r = 0.52; P < 0.05). Further, the sRPE model detected greater increases (126-429 AU) in training load than the TRIMP (15-65 AU) and SHRZ models (27-170 AU) transitioning between training modes. Conclusions: While the training load models were significantly correlated during each training mode, weaker relationships were observed during specific conditioning. Comparisons suggest the HR-based models were less effective in detecting periodized increases in training load, particularly during court-based, intermittent, multidirectional drills. The practical benefits and sensitivity of the sRPE model support its use across different basketball training modes
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