Objective: To identify and evaluate the main functional assessment tools applied in the postoperative monitoring of patients undergoing total knee arthroplasty (TKA), and to synthesize the functional outcomes reported through these instruments in the current scientific literature. Methodology: A structured review was conducted following PRISMA 2020 guidelines. Thirty-one peer-reviewed studies were selected through a targeted manual search based on predefined eligibility criteria. Included studies evaluated functional recovery following TKA using validated outcome measures such as the WOMAC, KSS, KOOS, IKDC, SF-36, and SANE. Data extraction focused on the instruments used, patient population characteristics, and reported outcomes. A descriptive synthesis was compiled in Table 1. Additionally, 15 studies with quantitative data were analyzed using a forest plot to illustrate risk ratios (RR) and 95% confidence intervals (CI) for functional improvement. Risk of bias was assessed qualitatively based on methodological rigor, clarity of reporting, and validation of the outcome tools. Results: All included studies reported improvements in functional status following TKA. Most risk ratios ranged from 0.66 to 0.85, indicating a consistent reduction in the risk of postoperative functional limitation. High-quality studies demonstrated more precise effect estimates and greater internal validity. The SANE scale emerged as a valid and practical tool with high responsiveness, including in its culturally adapted Brazilian version. Despite heterogeneity in study design, the direction of effect remained consistent across all included studies. Conclusion: Validated functional assessment tools are essential for monitoring recovery after total knee arthroplasty. Instruments such as WOMAC and SANE demonstrate strong clinical utility and psychometric validity. Their systematic use enhances outcome comparability, supports individualized rehabilitation planning, and improves decision-making in orthopedic care.

The Mambaka watershed is extends between latitudes 1 3°45'E and 14°15'E and longitudes 7°16'N and 6°45'N. The geology, various tectonic and structural events that have affected the Adamawa Plateau in Cameroon make it rich in multi-substance mining. The objective of this study is to map rare earth (REE) geochemical anomalies in the sediments of the watershed streams west of Mambaka, and to trace their origins and geochemical processes. Predictive maps from inverse distance interpolations (IDW), factor analysis (F1) or principal component analysis (PCA) and hierarchical bottom-up classification maps provided a better understanding of the central tendency, distribution and dispersion of REE in the samples and in the study area, based on standard deviation and variance values that generated two factors F1 (Ho-Tm-Er-Yb-Lu-Dy-Tb-Gd-Eu-Sm) and F2 (Pr-Nd-Ce-La-Sm) representing 92.44% of the total cumulative variance. The ratios Ce/Ce* > 0.78 and Eu/Eu* > 1 demonstrate positive anomalies in Ce and Eu, and clear differentiation. The normalized concentrations used to calculate fractionation ratios show that the values for LaN/YbN (0.58 to 1.34), LaN/SmN (0.61 to 0.88) and LaN/LuN (0.62 to 1.43) suggest higher fractionation in SS09 and lower fractionation in SS01. Similarly, the ratios La/Lu (61.71 to 143.46), La/Yb (9.00 to 20.72), La/Sm (4.02 to 5.83) and La/ Lu (61.71 to 143.46) confirm these higher ratios in SS09 and lower in SS01. The REE in the study area comes from hydrothermal processes based on high lineament densities at sampling points in igneous rocks with a mean ∑REE value of between 174-219 ppm.

Background: Peripartum cardiomyopathy (PPCM) is a life-threatening heart muscle disease of unknown aetiology that affects women during the peripartum period, particularly in sub-Saharan Africa. While many studies have observed normal blood pressure (BP) in PPCM patients, none have explored whether their BP is appropriate for their body size. This study investigated the correlation between body anthropometric parameters and BP in PPCM patients, comparing the findings with those of age-matched normal peripartum controls. Methods: A cohort of 105 women, each from PPCM and matched normal peripartum control groups, were recruited from three healthcare facilities in Sokoto. Blood pressure (BP) parameters were assessed in relation to their anthropometric measurements, and the findings were compared between the two groups. Results: The PPCM patients were significantly smaller in body weight (57.0 ±11.6 Kg vs 66.8 ±13.8 Kg, P <.0001), body mass index (BMI) (21.9 ±4.1 Kg/m2 vs 25.4 ±5.4 Kg/m2, P <.0001, body surface area (BSA) (1.3 ±0.7 m2 vs 1.7 ±0.2 m2, P <.0001), Lean body mass (LBM) (45.3 ±7.0 Kg vs 49.4 ±4.1 Kg, P <.0001) and Percentage body fat (BF) (23.5 ±10.9 % vs 31.2 ±6.9 %, P <.0001). Similarly, PPCM patients had significantly higher systolic BP (SBP), Pulse pressure (PP) and Mean arterial blood pressure (MABP) compared to the normal peripartum PPCM control. Further, linear regression analysis showed that there was higher slope of the relationship between anthropometric indices and SBP and PP in the PPCM cohort, compared to the normal peripartum control group. A similar trend of the slope was seen in the Pearson’s coefficient of the relationship of the anthropometries and BP parameters. Conclusions: This study found that women with peripartum cardiomyopathy (PPCM) exhibited disproportionately higher systolic blood pressure (SBP) and pulse pressure (PP) for each unit increase in anthropometric measurements compared to normal peripartum controls. Notably, PPCM patients had significantly lower anthropometric measures, potentially attributable to poverty and chronic undernutrition. Additionally, the effects of poor antenatal care, lack of immunization and recurrent infection should be considered. These findings suggest an abnormal relationship between anthropometry and blood pressure in PPCM patients, which may have detrimental effects on their cardiovascular health. This abnormal relationship may contribute to the development of heart failure (HF) in PPCM patients and potentially increase the risk in women susceptible to PPCM. Even-though our assumption, yet to be proven. To address this concerning trend in vulnerable populations, improvements in nutritional status, socioeconomic determinants health, adequate antenatal care (ANC), immunization, and infection prevention should be considered.

Artificial Immune Systems (AIS) are bio-inspired computational frameworks that emulate the adaptive mechanisms of the human immune system, such as self/non-self discrimination, clonal selection, and immune memory. These systems have demonstrated significant potential in addressing complex challenges across optimization, anomaly detection, and adaptive system control. This paper provides a comprehensive exploration of AIS applications in domains such as cybersecurity, resource allocation, and autonomous systems, highlighting the growing importance of hybrid AIS models. Recent advancements, including integrations with machine learning, quantum computing, and bioinformatics, are discussed as solutions to scalability, high-dimensional data processing, and efficiency challenges. Core algorithms, such as the Negative Selection Algorithm (NSA) and Clonal Selection Algorithm (CSA), are examined, along with limitations in interpretability and compatibility with emerging AI paradigms. The paper concludes by proposing future research directions, emphasizing scalable hybrid frameworks, quantum-inspired approaches, and real-time adaptive systems, underscoring AIS's transformative potential across diverse computational fields.