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Late detection of Breast cancer(BC) and progressing with advanced-stage diagnosis after early detection contribute differently to the challenges of managing BC in Africa. Understanding the difference may improve cancer education programs and their effectiveness.

Background The help-seeking interval and primary-care interval are points of delays in breast cancer presentation. To inform future intervention targeting early diagnosis of breast cancer, we described the contribution of each interval to the delay and the impact of delay on tumor progression. Method We conducted a multicentered survey from June 2017 to May 2018 hypothesizing that most patients visited the first healthcare provider within 60 days of tumor detection. Inferential statistics were by t-test, chi-square test, and Wilcoxon-Signed Rank test at p -value 0.05 or 95% confidence limits. Time-to-event was by survival method. Multivariate analysis was by logistic regression. Results Respondents were females between 24 and 95 years ( n  = 420). Most respondents visited FHP within 60 days of detecting symptoms (230 (60, 95% CI 53–63). Most had long primary-care (237 of 377 (64 95% CI 59–68) and detection-to-specialist (293 (73% (95% CI 68–77)) intervals. The primary care interval (median 106 days, IQR 13–337) was longer than the help-seeking interval (median 42 days, IQR 7–150) Wilcoxon signed-rank test p  = 0.001. There was a strong correlation between the length of primary care interval and the detection-to-specialist interval (r = 0.9, 95% CI 0.88–0.92). Patronizing the hospital, receiving the correct advice, and having a big tumor (> 5 cm) were associated with short intervals. Tumors were detected early, but most became advanced before arriving at the specialist clinic. The difference in tumor size between detection and arriving at a specialist clinic was 5.0 ± 4.9 cm (95% CI 4.0–5.0). The hazard of progressing from early to locally advanced disease was least in the first 30 days (3%). The hazard was 31% in 90 days. Conclusion Most respondents presented early to the first healthcare provider, but most arrived late at a specialist clinic. The primary care interval was longer than the help-seeking interval. Most tumors were early at detection but locally advanced before arriving in a specialist clinic. Interventions aiming to shorten the primary care interval will have the most impact on time to breast cancer presentation for specialist oncology care in Nigeria.

Background: Obstructive jaundice is a common clinical condition with a varying diverse spectrum of aetiology from region to region and its management is often associated with challenges. Aims: To highlight the spectrum of aetiology, challenges, and limitations in the management of obstructive jaundice in a resource-limited setting. Patients, Materials and Methods: Prospective descriptive study over a period of seven years in two teaching hospitals in Nigeria. Patients with a diagnosis of obstructive jaundice between April 2015 and March 2022 were consecutively enrolled into the study. Relevant data were obtained through a pretested paper pro forma. Results: Eighty-four patients were studied. The mean age of the patients was 54.8 ± 2.1 with male to female ratio of 1.1: 1. Malignant cases were responsible for 84.5% of cases. The mean duration of the disease was 119.6 ± 13.7. All patients had an abdominal ultrasound scan done. Only 42.4% of patients could afford computed axial tomography (CAT), and other minimally invasive investigations were not available. The mean bilirubin level shows a statistically significant difference between benign and malignant cases (301 ± 9.8 vs. 398 ± 11.1; P < 0.001). Sixty-five (80.2%) patients were operated upon. Fifty-five (84.6) patients' surgeries were delayed. None of the patients operated upon had intraoperative cholangiography due to lack of facility and no laparoscopic procedure was done. Conclusion: Obstructive Jaundice is a common surgical condition in our environment. The majority of cases were malignant in origin and delayed presentation and lack of modern diagnostic and therapeutic facilities were responsible for poor outcomes.

BackgroundTumour biology, physiologic features such as growth fraction and physical features such as size may influence response of breast cancer to neoadjuvant chemotherapy. Molecular biology is an established basis for predicting response and selecting neoadjuvant chemotherapy. Whether physical characteristics such as size should influence chemotherapy regimen is inconclusive and has not been adequately studied in developing countries. Aim To determine the relationship between breast tumour size and response to neoadjuvant chemotherapy and hence define the role of tumour size during selection of neoadjuvant chemotherapy regimen for locally advanced breast cancer. Method: Records of women managed at the University of Ilorin Teaching Hospital, Ilorin Nigeria, with neoadjuvant chemotherapy (NAC) for locally advanced breast cancer were reviewed between January 2013 and June 2015. Data was analyzed as 2 subgroups; primary tumour ≥100 mm as group 1 and primary tumour ≥100mm as group 2. Primary outcome was 50% reduction in tumour size. Comparison was by chi-square test of independence at p value 0.05. Results57 records were reviewed (group1=24, group2=33). Majority (37( 65%)) were premenopausal. Mean age was 47.9 ± 13.1 (range 28-85). NAC was either taxane or anthracycline based regimen. Median chemotherapy dose was 4 (range 2-6). Widest diameter of tumours was 30mm to 180mm (mean 96 ±3.8mm, median 100mm). Mean tumour diameter for groups 1 and 2 was 7.2 ±1.6mm and 12.2± 2.9mm respectively. 50% reduction in tumour size was 45.8% and 6.0% for groups 1 and 2 respectively (p=0.0001) Conclusion There was relationship between breast tumour size and response to neoadjuvant chemotherapy at a cut-off of 10mm. Well-designed prospective studies are required to confirm this relationship.

ObjectiveTo describe the risk factors for late detection and advanced-stage diagnosis among patients who detected their BC early.MethodUsing secondary data, we analyzed the impact of socio-demographic factors, premorbid experience, BC knowledge, and health-seeking pattern on the risk of late detection and advanced-stage diagnosis after early BC detection. Test of statistical significance in SPSS and EasyR was set at 5% using Sign-test, chi-square tests (of independence and goodness of fit), odds ratio, or risk ratio as appropriate.ResultMost socio-demographic factors did not affect detection size or risk of disease progression in the 405 records analyzed. High BC knowledge, p-value = 0.001, and practicing breast self-examination (BSE) increased early detection, p-value = 0.04, with a higher probability (OR 1.6 (95% CI 1.1-2.5) of detecting <2cm lesions. Visiting alternative care (RR 1.5(95% CI 1.2-1.9), low BC knowledge (RR 1.3(95% CI 1.1-1.9), and registering concerns for hospital care increased the risk of advanced-stage diagnosis after early detection (64% (95% CI 55-72)). Adhering to the monthly BSE schedule reduced the risk of advanced-stage diagnosis by -25% (95% CI -49, -1.1) in the presence of socioeconomic barriers.ConclusionStrategies to increase BC knowledge and BSE may help BC downstaging, especially among women with common barriers to early diagnosis.

Osteoporosis is a skeletal disorder, characterized by low bone mineral density (BMD) and bone quality deterioration, which may lead to bone fragility fractures. It is estimated that 200 million people have osteoporosis with 2% to 8% of men and 9% to 38% of women are affected. Various methodologies are currently used to predict bone fragility fractures. Among these modalities, DXA is the most accessible and affordable means that could be used to determine both BMD and microstructural features of bone. The goal of this study is to determine whether DXA image-based deep learning (DL) model could predict the elastic modulus of human trabecular bone. To achieve the goal, 591 trabecular bone cubes were digitally dissected out from six human cadaveric proximal femurs. Simulated DXA images were generated from each trabecular cube as input, whereas the elastic modulus that was estimated using FEM simulations as output for training the DL model. The results of this study verified the efficacy of the DL model in predicting the elastic modulus of trabecular bone (R=0.95) and showed that the prediction accuracy of the DL model was dependent on the sample size and the number of input DXA images. In addition, the DL model was compared to a multiple linear regression model where six histomorphometric parameters (i.e., BV/TV, BS, Tb.Th, DA, Conn.D, and SMI) were used as independent variables and the elastic modulus as dependent variable. A strong correlation (R=0.93) was observed between the two models in predicting the elastic modulus of trabecular bone, suggesting that the DXA image based DL model could capture the effect of microstructural features on the elastic modulus of trabecular bone as did the regression model. The outcome of this study indicates a potential of using DL techniques for predicting the mechanical properties of trabecular bone solely from DXA images.

BACKGROUND: The help-seeking interval and primary-care interval are points of delays in breast cancer presentation. To inform future intervention targeting early diagnosis of breast cancer, we described the contribution of each interval to the delay and the impact of delay on tumor progression. METHOD: We conducted a multicentered survey from June 2017 to May 2018 hypothesizing that most patients visited the first healthcare provider within 60 days of tumor detection. Inferential statistics were by t-test, chi-square test, and Wilcoxon-Signed Rank test at p-value 0.05 or 95% confidence limits. Time-to-event was by survival method. Multivariate analysis was by logistic regression. RESULTS: Respondents were females between 24 and 95 years (n=420). Most respondents visited FHP within 60 days of detecting symptoms (230 (60%, 95% CI 53-63). Most had long primary-care (237 of 377 (64% 95% CI 59-68) and detection-to-specialist (293 (73% (95% CI 68-77)) intervals. The primary care interval (median 106 days, IQR 13-337 ) was longer than the help-seeking interval ( median 42 days, IQR 7-150 ) Wilcoxon signed-rank test p= 0.001. There was a strong correlation between the length of primary care interval and the detection-to-specialist interval (r= 0.9, 95% CI 0.88- 0.92). Patronizing the hospital, receiving the correct advice, and having a big tumor (>5cm) were associated with short intervals. Tumors were detected early, but most became advanced before arriving at the specialist clinic. The difference in tumor size between detection and arriving at a specialist clinic was 5.0±4.9cm (95% CI 4.0-5.0). The hazard of progressing from early to locally advanced disease was least in the first 30 days (3%). The hazard was 31% in 90 days. CONCLUSION: Most respondents presented early to the first healthcare provider, but most arrived late at a specialist clinic. The primary care interval was longer than the help-seeking interval. Most tumors were early at detection but locally advanced before arriving in a specialist clinic. Interventions aiming to shorten the primary care interval will have the most impact on time to breast cancer presentation for specialist oncology care in Nigeria.

Batch experiments were performed to determine how Pb (II) ion adsorption was affected by contact time, adsorbent dosage, temperature, and pH. Adsorption increased with higher adsorbent doses and decreased with higher adsorbate doses. Time speeds up the adsorption process, with pH 6 and 650 °C showing the highest levels of adsorption. The Langmuir and Dubinin-Radushkevic isotherm models best fitted the sorption data, with R2 values of 0.995 and 0.9956 and standard errors of regression of 1.8209 and 0.0298, respectively. The pseudo-first order kinetic model best captured the kinetics. Analysis of Variance (ANOVA) and the Bonferroni-Holm Posthoc significance test showed that the adsorption was statistically and parametrically justified.

This study is aimed at determining the optimum adsorption of lead from waste water onto activated carbon prepared from bean husk. The optimization was designed using response surface methodology. Box-Behnken design was employed to generate a matrix and the factors considered were pH (2-10), temperature (25-65 °C) and contact time (20-120 minutes). It generated 12 experimental runs and the selected responses were adsorption capacity and removal efficiency. Run nine (9) gives the highest adsorption capacity (19.8 mg/g) and removal efficiency (99%) while run five (5) gives the lowest adsorption capacity (7 mg/g) and remover efficiency (33%). The result indicates that the Optimum condition for the adsorption Pb (II) from waste water were pH (10), temperature (65°C) and Contact time (120 minutes). This gave Adsorption Capacity of 19.940mg/g and 99.698% Removal Efficiency of Pb from waste water. There was good agreement between experimental value and predicted value. The study also showed that activated carbon from beans husk is an effective adsorbent for the removal of Pb (II) from waste water.