This study aimed to identify virulent and antimicrobial resistant genes in fecal E. coli in Mbouda, Cameroon.

Epithelioid inflammatory myofibroblastic sarcoma (EIMS) is an aggressive subtype of inflammatory myofibroblastic tumor (IMT) harboring anaplastic lymphoma kinase (ALK) gene fusions and is associated with high risk of local recurrence and poor prognosis. Herein, we present a young, non-smoking male who presented with complaints of cough and dyspnoea and was found to harbor a large right lower lobe lung mass. Biopsy showed a high-grade epithelioid to rhabdoid tumor with ALK and desmin protein expression. The patient initially received 5 cycles of crizotinib and remained stable for 1 year; however, he then developed multiple bony metastases, for which complete surgical resection was performed. Histopathology confirmed the diagnosis of EIMS, with ALK gene rearrangement demonstrated by fluorescence in situ hybridization. Postoperatively, the patient is asymptomatic with stable metastatic disease on crizotinib and has been started on palliative radiotherapy. EIMS is a very rare subtype of IMT that needs to be included in the differential diagnosis of ALKexpressing lung malignancies in young adults.

Objective: : Glioblastoma multiforme (GBM), particularly the isocitrate dehydrogenase (IDH)-wildtype type, represents a significant clinical challenge due to its aggressive nature and poor prognosis. Despite advancements in medical imaging and its modalities, survival rates have not improved significantly, demanding innovative treatment planning and outcome prediction approaches. Methods: : This study utilizes a support vector machine (SVM) classifier using radiomics features to predict the overall survival (OS) of GBM, IDH-wildtype patients to short (<12 months) and long (≥12 months) survivors. A dataset comprising multi-parametric magnetic resonance imaging scans from 574 patients was analyzed. Radiomic features were extracted from T1, T2, fluid-attenuated inversion recovery, and T1 with gadolinium (T1GD) sequences. Low variance features were removed, and recursive feature elimination was used to select the most informative features. The SVM model was trained using a k-fold cross-validation approach. Furthermore, clinical parameters such as age, gender, and MGMT (O6-methylguanine-DNA methyltransferase) promoter methylation status were integrated to enhance prediction accuracy. Results: : The model showed reasonable results in terms of cross-validated area under the curve of 0.84 (95% confidence interval, 0.80–0.90) with (p<0.001) effectively categorizing patients into short and long survivors. Log-rank test (chi-square statistics) analysis for the developed model was 0.00029 along with the 1.20 Cohen’s d effect size. Most importantly, clinical data integration further refined the survival estimates, providing a more fitted prediction that considers individual patient characteristics by Kaplan-Meier curve with p-value <0.0001. Conclusion : The proposed method significantly enhances the predictive accuracy of OS outcomes in GBM, IDH-wildtype patients. By integrating detailed imaging features with key clinical indicators, this model offers a robust tool for personalized treatment planning, potentially improving OS.

Objective: : Glioblastoma multiforme (GBM), particularly the isocitrate dehydrogenase (IDH)-wildtype type, represents a significant clinical challenge due to its aggressive nature and poor prognosis. Despite advancements in medical imaging and its modalities, survival rates have not improved significantly, demanding innovative treatment planning and outcome prediction approaches. Methods: : This study utilizes a support vector machine (SVM) classifier using radiomics features to predict the overall survival (OS) of GBM, IDH-wildtype patients to short (<12 months) and long (≥12 months) survivors. A dataset comprising multi-parametric magnetic resonance imaging scans from 574 patients was analyzed. Radiomic features were extracted from T1, T2, fluid-attenuated inversion recovery, and T1 with gadolinium (T1GD) sequences. Low variance features were removed, and recursive feature elimination was used to select the most informative features. The SVM model was trained using a k-fold cross-validation approach. Furthermore, clinical parameters such as age, gender, and MGMT (O6-methylguanine-DNA methyltransferase) promoter methylation status were integrated to enhance prediction accuracy. Results: : The model showed reasonable results in terms of cross-validated area under the curve of 0.84 (95% confidence interval, 0.80–0.90) with (p<0.001) effectively categorizing patients into short and long survivors. Log-rank test (chi-square statistics) analysis for the developed model was 0.00029 along with the 1.20 Cohen’s d effect size. Most importantly, clinical data integration further refined the survival estimates, providing a more fitted prediction that considers individual patient characteristics by Kaplan-Meier curve with p-value <0.0001. Conclusion : The proposed method significantly enhances the predictive accuracy of OS outcomes in GBM, IDH-wildtype patients. By integrating detailed imaging features with key clinical indicators, this model offers a robust tool for personalized treatment planning, potentially improving OS.

Objective: : Glioblastoma multiforme (GBM), particularly the isocitrate dehydrogenase (IDH)-wildtype type, represents a significant clinical challenge due to its aggressive nature and poor prognosis. Despite advancements in medical imaging and its modalities, survival rates have not improved significantly, demanding innovative treatment planning and outcome prediction approaches. Methods: : This study utilizes a support vector machine (SVM) classifier using radiomics features to predict the overall survival (OS) of GBM, IDH-wildtype patients to short (<12 months) and long (≥12 months) survivors. A dataset comprising multi-parametric magnetic resonance imaging scans from 574 patients was analyzed. Radiomic features were extracted from T1, T2, fluid-attenuated inversion recovery, and T1 with gadolinium (T1GD) sequences. Low variance features were removed, and recursive feature elimination was used to select the most informative features. The SVM model was trained using a k-fold cross-validation approach. Furthermore, clinical parameters such as age, gender, and MGMT (O6-methylguanine-DNA methyltransferase) promoter methylation status were integrated to enhance prediction accuracy. Results: : The model showed reasonable results in terms of cross-validated area under the curve of 0.84 (95% confidence interval, 0.80–0.90) with (p<0.001) effectively categorizing patients into short and long survivors. Log-rank test (chi-square statistics) analysis for the developed model was 0.00029 along with the 1.20 Cohen’s d effect size. Most importantly, clinical data integration further refined the survival estimates, providing a more fitted prediction that considers individual patient characteristics by Kaplan-Meier curve with p-value <0.0001. Conclusion : The proposed method significantly enhances the predictive accuracy of OS outcomes in GBM, IDH-wildtype patients. By integrating detailed imaging features with key clinical indicators, this model offers a robust tool for personalized treatment planning, potentially improving OS.

Objective: : Glioblastoma multiforme (GBM), particularly the isocitrate dehydrogenase (IDH)-wildtype type, represents a significant clinical challenge due to its aggressive nature and poor prognosis. Despite advancements in medical imaging and its modalities, survival rates have not improved significantly, demanding innovative treatment planning and outcome prediction approaches. Methods: : This study utilizes a support vector machine (SVM) classifier using radiomics features to predict the overall survival (OS) of GBM, IDH-wildtype patients to short (<12 months) and long (≥12 months) survivors. A dataset comprising multi-parametric magnetic resonance imaging scans from 574 patients was analyzed. Radiomic features were extracted from T1, T2, fluid-attenuated inversion recovery, and T1 with gadolinium (T1GD) sequences. Low variance features were removed, and recursive feature elimination was used to select the most informative features. The SVM model was trained using a k-fold cross-validation approach. Furthermore, clinical parameters such as age, gender, and MGMT (O6-methylguanine-DNA methyltransferase) promoter methylation status were integrated to enhance prediction accuracy. Results: : The model showed reasonable results in terms of cross-validated area under the curve of 0.84 (95% confidence interval, 0.80–0.90) with (p<0.001) effectively categorizing patients into short and long survivors. Log-rank test (chi-square statistics) analysis for the developed model was 0.00029 along with the 1.20 Cohen’s d effect size. Most importantly, clinical data integration further refined the survival estimates, providing a more fitted prediction that considers individual patient characteristics by Kaplan-Meier curve with p-value <0.0001. Conclusion : The proposed method significantly enhances the predictive accuracy of OS outcomes in GBM, IDH-wildtype patients. By integrating detailed imaging features with key clinical indicators, this model offers a robust tool for personalized treatment planning, potentially improving OS.

Background/Aims: Liver cirrhosis is an important cause of morbidity and mortality globally. Every episode of decompensation and hospitalization reduces survival. We studied the clinical profile and long-term outcomes comparing alcohol-related cirrhosis (ALC) and non-ALC.
Methods: Cirrhosis patients at index hospitalisation (from January 2010 to June 2017), with ≥1 year follow-up were included.
Results: Five thousand and one hundred thirty-eight cirrhosis patients (age, 49.8±14.6 years; male, 79.5%; alcohol, 39.5%; Child-A:B:C, 11.7%:41.6%:46.8%) from their index hospitalization were analysed. The median time from diagnosis of cirrhosis to index hospitalization was 2 years (0.2–10). One thousand and seven hundred seven patients (33.2%) died within a year; 1,248 (24.3%) during index hospitalization. 59.5% (2,316/3,890) of the survivors, required at least one readmission, with additional mortality of 19.8% (459/2,316). ALC compared to non-ALC were more often (P<0.001) male (97.7% vs. 67.7%), younger (40–50 group, 36.2% vs. 20.2%; P<0.001) with higher liver related complications at baseline, (P<0.001 for each), sepsis: 20.3% vs. 14.9%; ascites: 82.2% vs. 65.9%; spontaneous bacterial peritonitis: 21.8% vs. 15.7%; hepatic encephalopathy: 41.0% vs. 25.0%; acute variceal bleeding: 32.0% vs. 23.7%; and acute kidney injury 30.5% vs. 19.6%. ALC patients had higher Child-Pugh (10.6±2.0 vs. 9.0±2.3), model for end-stage liver-disease scores (21.49±8.47 vs. 16.85±7.79), and higher mortality (42.3% vs. 27.3%, P<0.001) compared to non-ALC.
Conclusions One-third of cirrhosis patients die in index hospitalization. 60% of the survivors require at least one rehospitalization within a year. ALC patients present with higher morbidity and mortality and at a younger age.