BACKGROUNDBoth early repolarization and altered heart rate profile are associated with sudden death. In this study, we aimed to demonstrate an association between early repolarization and heart rate profile during exercise.

METHODSA total of 84 subjects were included in the study. Comparable 44 subjects with early repolarization and 40 subjects with normal electrocardiogram underwent exercise stress testing. Resting heart rate, maximum heart rate, heart rate increment and decrement were analyzed.

RESULTSBoth groups were comparable for baseline characteristics including resting heart rate. Maximum heart rate, heart rate increment and heart rate decrement of the subjects in early repolarization group had significantly decreased maximum heart rate, heart rate increment and heart rate decrement compared to control group (all P < 0.05). The lower heart rate increment (< 106 beats/min) and heart rate decrement (< 95 beats/min) were significantly associated with the presence of early repolarization. After adjustment for age and sex, the multiple-adjusted OR of the risk of presence of early repolarization was 2.98 (95%CI 1.21-7.34) (P = 0.018) and 7.73 (95%CI 2.84-21.03) (P < 0.001) for the lower heart rate increment and heart rate decrement compared to higher levels, respectively.

CONCLUSIONSSubjects with early repolarization have altered heart rate profile during exercise compared to control subjects. This can be related to sudden death.

Adult ; Autonomic Nervous System ; physiology ; Case-Control Studies ; Electrocardiography ; Exercise ; physiology ; Exercise Test ; Female ; Heart Conduction System ; physiopathology ; Heart Rate ; Humans ; Male

BACKGROUNDBrucellosis can mimic various multisytem diseases, showing wide clinical polymorphism that frequently leads to misdiagnosis and treatment delay, further increasing the complication rates. In this study, we aimed to examine bone marrow biopsy findings in brucellosis cases presenting with hematologic abnormalities.

METHODSForty-eight brucellosis cases were prospectively investigated. Complaints and physical examination findings of patients were recorded. Patients' complete blood count, routine biochemical tests, erythrocyte sedimentation rate, C-reactive protein and serological screenings were performed. Bone marrow biopsy and aspiration was performed in patients with cytopenia, for bone marrow examination and brucella culture, in accordance with the standard procedures from spina iliaca posterior superior region of pelvic bone.

RESULTSOf the 48 patients, 35 (73%) were female and 13 (27%) were male. Mean age was (34.8 ± 15.4) years (age range: 15 - 70 years). Anemia, leukopenia, thrombocytopenia and pancytopenia were found in 39 (81%), 28 (58%), 22 (46%) and 10 patients (21%), respectively. In the examination of bone marrow, hypercellularity was found in 35 (73%) patients. Increased megacariocytic, erythroid and granulocytic series were found in 28 (58%), 15 (31%) and 5 (10%) patients, respectively. In addition, hemophagocytosis was observed in 15 (31%) patients, granuloma observed in 12 (25%) and increased eosinophil and plasma cells observed in 9 (19%) patients.

CONCLUSIONAccording to the results of our series, hemophagocytosis, microgranuloma formation and hypersplenism may be responsible for hematologic complications of brucellosis.

Adolescent ; Adult ; Aged ; Biopsy ; methods ; Bone Marrow ; metabolism ; pathology ; Brucellosis ; complications ; metabolism ; physiopathology ; C-Reactive Protein ; metabolism ; Female ; Granuloma ; etiology ; metabolism ; physiopathology ; Humans ; Hypersplenism ; etiology ; metabolism ; physiopathology ; Male ; Middle Aged ; Prospective Studies ; Young Adult

Anaphylaxis have been documented as adverse effects of ciprofloxacin, ofloxacin, norfloxacin, levofloxacin, and moxifloxacin. However resistant and biphasic anaphlylactic reactions to gemifloxacin have not been reported to date. Management of severe anaphylaxis in the elderly can be complicated by concurrent medications such as beta (β) adrenergic, alpha (α) adrenergic blockers and angiotensin-converting enzyme (ACE) inhibitors. We report here in the case of a 60-year-old male who was taking on ACE inhibitor, α and β blockers and experienced a severe, resistant and biphasic anaphlylactic reaction to gemifloxacin mesylate.
Adrenergic Antagonists ; Aged ; Anaphylaxis ; Ciprofloxacin ; Humans ; Levofloxacin ; Male ; Mesylates ; Middle Aged ; Norfloxacin ; Ofloxacin

Background Brucellosis can mimic various multisytem diseases,showing wide clinical polymorphism that frequently leads to misdiagnosis and treatment delay,further increasing the complication rates.In this study,we aimed to examine bone marrow biopsy findings in brucellosis cases presenting with hematologic abnormalities.Methods Forty-eight brucellosis cases were prospectively investigated.Complaints and physical examination findings of patients were recorded.Patients' complete blood count,routine biochemical tests,erythrocyte sedimentation rate,C-reactive protein and serological screenings were performed.Bone marrow biopsy and aspiration was performed in patients with cytopenia,for bone marrow examination and brucella culture,in accordance with the standard procedures from spina iliaca posterior superior region of pelvic bone.Results Of the 48 patients,35 (73％) were female and 13 (27％) were male.Mean age was (34.8±15.4) years (age range:15-70 years).Anemia,leukopenia,thrombocytopenia and pancytopenia were found in 39 (81％),28 (58％),22 (46％) and 10 patients (21％),respectively.In the examination of bone marrow,hypercellularity was found In 35 (73％) patients.Increased megacariocytic,erythroid and granulocytic series were found in 28 (58％),15 (31％) and 5 (10％) patients,respectively.In addition,hemophagocytosis was observed in 15 (31％) patients,granuloma observed in 12 (25％) and increased eosinophil and plasma cells observed in 9 (19％) patients.Conclusion According to the results of our series,hemophagocytosis,microgranuloma formation and hypersplenism may be responsible for hematologic complications of brucellosis.

Purpose: To assess the predictive capability of hematological inflammatory markers for urethral stricture recurrence after primary urethroplasty and to compare traditional statistical methods with a machine-learning-based artificial intelligence algorithm. Materials and Methods: Two hundred eighty-seven patients who underwent primary urethroplasty were scanned. Ages, smoking status, comorbidities, hematological inflammatory parameters (neutrophil-lymphocyte ratios, platelet-lymphocyte ratios [PLR], systemic immune-inflammation indexes [SII], and pan-immune-inflammation values [PIV]), stricture characteristics, history of previous direct-visual internal urethrotomy, urethroplasty techniques, and grafts/flaps placements were collected. Patients were followed up for one year for recurrence and grouped accordingly. Univariate and multivariate logistic regression analyses were conducted to create a predictive model. Additionally, a machine-learning-based logistic regression analysis was implemented to compare predictive performances. p<0.05 was considered statistically significant. Results: Comparative analysis between the groups revealed statistically significant differences in stricture length (p=0.003), localization (p=0.027), lymphocyte counts (p=0.008), PLR (p=0.003), SII (p=0.003), and PIV (p=0.001). In multivariate analysis, stricture length (odds ratio [OR] 1.230, 95% confidence interval [CI] 1.142–1.539, p<0.0001) and PIV (OR 1.002, 95% CI 1.000–1.003, p=0.039) were identified as significant predictors of recurrence. Classical logistic regression model exhibited a sensitivity of 0.76, specificity of 0.43 with an area under curve (AUC) of 0.65. However, the machine-learning algorithm outperformed traditional methods achieving a sensitivity of 0.80, specificity of 0.76 with a higher AUC of 0.82. Conclusions PIV and machine-learning algorithms shows promise on predicting urethroplasty outcomes, potentially leading to develop possible nomograms. Evolving machine-learning algorithms will contribute to more personalized and accurate approaches in managing urethral stricture.

Purpose: To assess the predictive capability of hematological inflammatory markers for urethral stricture recurrence after primary urethroplasty and to compare traditional statistical methods with a machine-learning-based artificial intelligence algorithm. Materials and Methods: Two hundred eighty-seven patients who underwent primary urethroplasty were scanned. Ages, smoking status, comorbidities, hematological inflammatory parameters (neutrophil-lymphocyte ratios, platelet-lymphocyte ratios [PLR], systemic immune-inflammation indexes [SII], and pan-immune-inflammation values [PIV]), stricture characteristics, history of previous direct-visual internal urethrotomy, urethroplasty techniques, and grafts/flaps placements were collected. Patients were followed up for one year for recurrence and grouped accordingly. Univariate and multivariate logistic regression analyses were conducted to create a predictive model. Additionally, a machine-learning-based logistic regression analysis was implemented to compare predictive performances. p<0.05 was considered statistically significant. Results: Comparative analysis between the groups revealed statistically significant differences in stricture length (p=0.003), localization (p=0.027), lymphocyte counts (p=0.008), PLR (p=0.003), SII (p=0.003), and PIV (p=0.001). In multivariate analysis, stricture length (odds ratio [OR] 1.230, 95% confidence interval [CI] 1.142–1.539, p<0.0001) and PIV (OR 1.002, 95% CI 1.000–1.003, p=0.039) were identified as significant predictors of recurrence. Classical logistic regression model exhibited a sensitivity of 0.76, specificity of 0.43 with an area under curve (AUC) of 0.65. However, the machine-learning algorithm outperformed traditional methods achieving a sensitivity of 0.80, specificity of 0.76 with a higher AUC of 0.82. Conclusions PIV and machine-learning algorithms shows promise on predicting urethroplasty outcomes, potentially leading to develop possible nomograms. Evolving machine-learning algorithms will contribute to more personalized and accurate approaches in managing urethral stricture.

Purpose: To assess the predictive capability of hematological inflammatory markers for urethral stricture recurrence after primary urethroplasty and to compare traditional statistical methods with a machine-learning-based artificial intelligence algorithm. Materials and Methods: Two hundred eighty-seven patients who underwent primary urethroplasty were scanned. Ages, smoking status, comorbidities, hematological inflammatory parameters (neutrophil-lymphocyte ratios, platelet-lymphocyte ratios [PLR], systemic immune-inflammation indexes [SII], and pan-immune-inflammation values [PIV]), stricture characteristics, history of previous direct-visual internal urethrotomy, urethroplasty techniques, and grafts/flaps placements were collected. Patients were followed up for one year for recurrence and grouped accordingly. Univariate and multivariate logistic regression analyses were conducted to create a predictive model. Additionally, a machine-learning-based logistic regression analysis was implemented to compare predictive performances. p<0.05 was considered statistically significant. Results: Comparative analysis between the groups revealed statistically significant differences in stricture length (p=0.003), localization (p=0.027), lymphocyte counts (p=0.008), PLR (p=0.003), SII (p=0.003), and PIV (p=0.001). In multivariate analysis, stricture length (odds ratio [OR] 1.230, 95% confidence interval [CI] 1.142–1.539, p<0.0001) and PIV (OR 1.002, 95% CI 1.000–1.003, p=0.039) were identified as significant predictors of recurrence. Classical logistic regression model exhibited a sensitivity of 0.76, specificity of 0.43 with an area under curve (AUC) of 0.65. However, the machine-learning algorithm outperformed traditional methods achieving a sensitivity of 0.80, specificity of 0.76 with a higher AUC of 0.82. Conclusions PIV and machine-learning algorithms shows promise on predicting urethroplasty outcomes, potentially leading to develop possible nomograms. Evolving machine-learning algorithms will contribute to more personalized and accurate approaches in managing urethral stricture.

Purpose: To assess the predictive capability of hematological inflammatory markers for urethral stricture recurrence after primary urethroplasty and to compare traditional statistical methods with a machine-learning-based artificial intelligence algorithm. Materials and Methods: Two hundred eighty-seven patients who underwent primary urethroplasty were scanned. Ages, smoking status, comorbidities, hematological inflammatory parameters (neutrophil-lymphocyte ratios, platelet-lymphocyte ratios [PLR], systemic immune-inflammation indexes [SII], and pan-immune-inflammation values [PIV]), stricture characteristics, history of previous direct-visual internal urethrotomy, urethroplasty techniques, and grafts/flaps placements were collected. Patients were followed up for one year for recurrence and grouped accordingly. Univariate and multivariate logistic regression analyses were conducted to create a predictive model. Additionally, a machine-learning-based logistic regression analysis was implemented to compare predictive performances. p<0.05 was considered statistically significant. Results: Comparative analysis between the groups revealed statistically significant differences in stricture length (p=0.003), localization (p=0.027), lymphocyte counts (p=0.008), PLR (p=0.003), SII (p=0.003), and PIV (p=0.001). In multivariate analysis, stricture length (odds ratio [OR] 1.230, 95% confidence interval [CI] 1.142–1.539, p<0.0001) and PIV (OR 1.002, 95% CI 1.000–1.003, p=0.039) were identified as significant predictors of recurrence. Classical logistic regression model exhibited a sensitivity of 0.76, specificity of 0.43 with an area under curve (AUC) of 0.65. However, the machine-learning algorithm outperformed traditional methods achieving a sensitivity of 0.80, specificity of 0.76 with a higher AUC of 0.82. Conclusions PIV and machine-learning algorithms shows promise on predicting urethroplasty outcomes, potentially leading to develop possible nomograms. Evolving machine-learning algorithms will contribute to more personalized and accurate approaches in managing urethral stricture.