BACKGROUND: Some reports have shown that co-inheritance of alpha-thalassemia and sickle cell disease improves hematological parameters and results in a relatively mild clinical picture for patients; however, the exact molecular basis and clinical significance of the interaction between alpha-thalassemia and sickle cell disease in India has not yet been described. There is little agreement on the clinical effects of alpha-thalassemia on the phenotype of sickle cell disease. METHODS: Complete blood count and red cell indices were measured by an automated cell analyzer. Quantitative assessment of hemoglobin variants HbF, HbA, HbA2, and HbS was performed by high performance liquid chromatography (HPLC). DNA extraction was performed using the phenol-chloroform method, and molecular study for common alpha-deletions was done by gap-PCR. RESULTS: Out of 60 sickle cell anemia patients, the alpha-thalassemia genotype was found in 18 patients. Three patients had the triplicated alpha-genotype (Anti alpha-3.7 kb), and the remaining patients did not have alpha-deletions. This study indicates that patients with co-existing alpha-thalassemia and sickle cell disease had a mild phenotype, significantly improved hematological parameters, and fewer blood transfusions than the patients with sickle cell anemia without co-existing alpha-deletions. CONCLUSION: Co-existence of alpha-thalassemia and sickle cell anemia has significant effects on the phenotype of Indian sickle cell patients.
alpha-Thalassemia ; Anemia, Sickle Cell ; Blood Cell Count ; Blood Transfusion ; Chromatography, Liquid ; DNA ; Erythrocyte Indices ; Genotype ; Hemoglobinopathies ; Hemoglobins ; Humans ; India ; Phenotype

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.

BACKGROUNDS/AIMS: Mirizzi's syndrome (MS) poses great diagnostic and management challenge to the treating physician. We presented our experience of MS cases with respect to clinical presentation, diagnostic difficulties, surgical procedures and outcome. METHODS: Prospectively maintained data of all surgically treated MS patients were analyzed. RESULTS: A total of 169 MS patients were surgically managed between 1989 and 2011. Presenting symptoms were jaundice (84%), pain (75%) and cholangitis (56%). Median symptom duration s was 8 months (range, <1 to 240 months). Preoperative diagnosis was possible only in 32% (54/169) of patients based on imaging study. Csendes Type II was the most common diagnosis (57%). Fistulization to the surrounding organs (bilio-enteric fistulization) were found in 14% of patients (24/169) during surgery. Gall bladder histopathology revealed xanthogranulomatous cholecystitis in 33% of patients (55/169). No significant difference in perioperative morbidity was found between choledochoplasty (use of gallbladder patch) (15/89, 17%) and bilio-enteric anastomosis (4/28, 14%) (p=0.748). Bile leak was more common with choledochoplasty (5/89, 5.6%) than bilio-enteric anastomosis (1/28, 3.5%), without statistical significance (p=0.669). CONCLUSIONS: Preoperative diagnosis of MS was possible in only one-third of patients in our series. Significant number of patients had associated fistulae to the surrounding organs, making the surgical procedure more complicated. Awareness of this entity is important for intraoperative diagnosis and consequently, for optimal surgical strategy and good outcome.
Bile ; Bile Duct Diseases ; Cholangitis ; Cholecystitis ; Cholestasis ; Diagnosis ; Fistula ; Gallbladder ; Humans ; Jaundice ; Mirizzi Syndrome* ; Prospective Studies ; Urinary Bladder

BACKGROUND/AIMS: Splenic involvement of tuberculosis, which is rare, warrants better definition in the current era of resurgence of tuberculosis. METHODS: Out of 339 splenectomies performed between January 1989 and December 2008 for indications other than trauma, histopathologic analysis of the spleen revealed tuberculosis in 8 patients. RESULTS: All eight patients were referred for splenectomy due to fever of unknown origin (FUO). No patient was infected with HIV, and all had at least moderate splenomegaly and hepatomegaly. Three patients had hypersplenism with bleeding manifestations. Radiologic evaluations demonstrated that splenic lesions were present in five patients. Five patients had evidence of tuberculosis manifested as enlarged splenic hilar lymph nodes, cystic lymph nodes, or liver. Two patients exhibited tubercle bacilli in their sputum during the postoperative period. CONCLUSIONS: In areas where tuberculosis is prevalent, tuberculosis should be considered in the differential diagnosis of patients presenting with FUO and splenomegaly. Extrasplenic involvement is usually seen in splenic tuberculosis, although it may not be apparent at presentation. Splenic tuberculosis can present in isolation without extrasplenic involvement, and even in immunocompetent individuals.
Diagnosis, Differential ; Fever ; Fever of Unknown Origin ; Hemorrhage ; Hepatomegaly ; HIV ; Humans ; Hypersplenism ; Liver ; Lymph Nodes ; Spleen ; Splenectomy ; Splenomegaly ; Sputum ; Tuberculosis ; Tuberculosis, Splenic

BACKGROUND/AIMS: This study was aimed at determining the factors associated with the development of benign biliary stricture (BBS) in patients who had sustained a bile duct injury (BDI) at cholecystectomy and developed bile leaks. METHODS: A retrospective analysis of 214 patients with BDI who were referred to our center between January 1989 and December 2009 was done. RESULTS: One hundred fifty-three (71%) patients developed BBS (group I), and 61 (29%) were normal (group II). By univariate analysis, female gender (p=0.02), open cholecystectomy as the index operation (p=0.0001), delay in the referral from identification of injury (p=0.04), persistence of an external biliary fistula (EBF) beyond 4 weeks (p=0.0001), EBF output >400 mL (p=0.01), presence of jaundice (p=0.0001), raised serum total bilirubin level (p=0.0001), raised serum alkaline phosphatase level (p=0.0001), and complete BDI (p=0.0001) were associated with the development of BBS. Furthermore, open cholecystectomy as the index operation (p=0.04), delayed referral (p=0.02), persistent EBF (p=0.03), and complete BDI (p=0.001) were found to predict patient outcome in the multivariate analysis. CONCLUSIONS: For the majority of patients with BDI, the risk of developing BBS could have been predicted at the initial presentation.
Alkaline Phosphatase ; Bile ; Bile Ducts ; Biliary Fistula ; Bilirubin ; Cholecystectomy ; Constriction, Pathologic ; Female ; Humans ; Jaundice ; Referral and Consultation ; Retrospective Studies ; Risk Factors