Purpose: Renal allograft rejection, either acute or chronic, is prevalent among many recipients. This study aimed to identify multiple Doppler ultrasound parameters for predicting renal allograft rejection. Materials and Methods: Between November 2021 and April 2022, 61 renal allograft recipients were studied prospectively after excluding two patients with dual transplants and seven with hydronephrosis. The analysis excluded 11 cases (10 due to missing Doppler data or pathology reports and one due to a high interquartile range/median dispersion value), resulting in a final analysis of 50 patients. Clinical characteristics, color Doppler imaging, superb microvascular imaging, and shear-wave imaging parameters were assessed by three experienced genitourinary radiologists. The Banff classification of the biopsy tissue served as the reference standard. Univariable and multivariable logistic regression, contingency matrices, and multiple machine-learning models were employed to estimate the associations. Results: Fifty kidney transplant recipients (mean age, 53.26±8.86 years; 29 men) were evaluated. Elasticity (≤14.8 kPa) demonstrated significant associations for predicting the combination of (borderline) T cell-mediated rejection (TCMR) categories (Banff categories 3 and 4) (p=0.006) and yielded equal or higher area under the receiver operating characteristics curve (AUC) values compared to various classifiers. Dispersion (>15.0 m/s/kHz) was the only significant factor for predicting the combination of nonTCMR categories (Banff categories 2, 5, and 6) (p=0.026) and showed equal or higher AUC values than multiple machine learning classifiers. Conclusion Elasticity (≤14.8 kPa) showed a significant association with the combination of (borderline) TCMR categories, whereas dispersion (>15.0 m/s/kHz) was significantly associated with the combination of non-TCMR categories in renal allografts.

Purpose: Renal allograft rejection, either acute or chronic, is prevalent among many recipients. This study aimed to identify multiple Doppler ultrasound parameters for predicting renal allograft rejection. Materials and Methods: Between November 2021 and April 2022, 61 renal allograft recipients were studied prospectively after excluding two patients with dual transplants and seven with hydronephrosis. The analysis excluded 11 cases (10 due to missing Doppler data or pathology reports and one due to a high interquartile range/median dispersion value), resulting in a final analysis of 50 patients. Clinical characteristics, color Doppler imaging, superb microvascular imaging, and shear-wave imaging parameters were assessed by three experienced genitourinary radiologists. The Banff classification of the biopsy tissue served as the reference standard. Univariable and multivariable logistic regression, contingency matrices, and multiple machine-learning models were employed to estimate the associations. Results: Fifty kidney transplant recipients (mean age, 53.26±8.86 years; 29 men) were evaluated. Elasticity (≤14.8 kPa) demonstrated significant associations for predicting the combination of (borderline) T cell-mediated rejection (TCMR) categories (Banff categories 3 and 4) (p=0.006) and yielded equal or higher area under the receiver operating characteristics curve (AUC) values compared to various classifiers. Dispersion (>15.0 m/s/kHz) was the only significant factor for predicting the combination of nonTCMR categories (Banff categories 2, 5, and 6) (p=0.026) and showed equal or higher AUC values than multiple machine learning classifiers. Conclusion Elasticity (≤14.8 kPa) showed a significant association with the combination of (borderline) TCMR categories, whereas dispersion (>15.0 m/s/kHz) was significantly associated with the combination of non-TCMR categories in renal allografts.

Purpose: Renal allograft rejection, either acute or chronic, is prevalent among many recipients. This study aimed to identify multiple Doppler ultrasound parameters for predicting renal allograft rejection. Materials and Methods: Between November 2021 and April 2022, 61 renal allograft recipients were studied prospectively after excluding two patients with dual transplants and seven with hydronephrosis. The analysis excluded 11 cases (10 due to missing Doppler data or pathology reports and one due to a high interquartile range/median dispersion value), resulting in a final analysis of 50 patients. Clinical characteristics, color Doppler imaging, superb microvascular imaging, and shear-wave imaging parameters were assessed by three experienced genitourinary radiologists. The Banff classification of the biopsy tissue served as the reference standard. Univariable and multivariable logistic regression, contingency matrices, and multiple machine-learning models were employed to estimate the associations. Results: Fifty kidney transplant recipients (mean age, 53.26±8.86 years; 29 men) were evaluated. Elasticity (≤14.8 kPa) demonstrated significant associations for predicting the combination of (borderline) T cell-mediated rejection (TCMR) categories (Banff categories 3 and 4) (p=0.006) and yielded equal or higher area under the receiver operating characteristics curve (AUC) values compared to various classifiers. Dispersion (>15.0 m/s/kHz) was the only significant factor for predicting the combination of nonTCMR categories (Banff categories 2, 5, and 6) (p=0.026) and showed equal or higher AUC values than multiple machine learning classifiers. Conclusion Elasticity (≤14.8 kPa) showed a significant association with the combination of (borderline) TCMR categories, whereas dispersion (>15.0 m/s/kHz) was significantly associated with the combination of non-TCMR categories in renal allografts.

Purpose: Renal allograft rejection, either acute or chronic, is prevalent among many recipients. This study aimed to identify multiple Doppler ultrasound parameters for predicting renal allograft rejection. Materials and Methods: Between November 2021 and April 2022, 61 renal allograft recipients were studied prospectively after excluding two patients with dual transplants and seven with hydronephrosis. The analysis excluded 11 cases (10 due to missing Doppler data or pathology reports and one due to a high interquartile range/median dispersion value), resulting in a final analysis of 50 patients. Clinical characteristics, color Doppler imaging, superb microvascular imaging, and shear-wave imaging parameters were assessed by three experienced genitourinary radiologists. The Banff classification of the biopsy tissue served as the reference standard. Univariable and multivariable logistic regression, contingency matrices, and multiple machine-learning models were employed to estimate the associations. Results: Fifty kidney transplant recipients (mean age, 53.26±8.86 years; 29 men) were evaluated. Elasticity (≤14.8 kPa) demonstrated significant associations for predicting the combination of (borderline) T cell-mediated rejection (TCMR) categories (Banff categories 3 and 4) (p=0.006) and yielded equal or higher area under the receiver operating characteristics curve (AUC) values compared to various classifiers. Dispersion (>15.0 m/s/kHz) was the only significant factor for predicting the combination of nonTCMR categories (Banff categories 2, 5, and 6) (p=0.026) and showed equal or higher AUC values than multiple machine learning classifiers. Conclusion Elasticity (≤14.8 kPa) showed a significant association with the combination of (borderline) TCMR categories, whereas dispersion (>15.0 m/s/kHz) was significantly associated with the combination of non-TCMR categories in renal allografts.

Purpose: Renal allograft rejection, either acute or chronic, is prevalent among many recipients. This study aimed to identify multiple Doppler ultrasound parameters for predicting renal allograft rejection. Materials and Methods: Between November 2021 and April 2022, 61 renal allograft recipients were studied prospectively after excluding two patients with dual transplants and seven with hydronephrosis. The analysis excluded 11 cases (10 due to missing Doppler data or pathology reports and one due to a high interquartile range/median dispersion value), resulting in a final analysis of 50 patients. Clinical characteristics, color Doppler imaging, superb microvascular imaging, and shear-wave imaging parameters were assessed by three experienced genitourinary radiologists. The Banff classification of the biopsy tissue served as the reference standard. Univariable and multivariable logistic regression, contingency matrices, and multiple machine-learning models were employed to estimate the associations. Results: Fifty kidney transplant recipients (mean age, 53.26±8.86 years; 29 men) were evaluated. Elasticity (≤14.8 kPa) demonstrated significant associations for predicting the combination of (borderline) T cell-mediated rejection (TCMR) categories (Banff categories 3 and 4) (p=0.006) and yielded equal or higher area under the receiver operating characteristics curve (AUC) values compared to various classifiers. Dispersion (>15.0 m/s/kHz) was the only significant factor for predicting the combination of nonTCMR categories (Banff categories 2, 5, and 6) (p=0.026) and showed equal or higher AUC values than multiple machine learning classifiers. Conclusion Elasticity (≤14.8 kPa) showed a significant association with the combination of (borderline) TCMR categories, whereas dispersion (>15.0 m/s/kHz) was significantly associated with the combination of non-TCMR categories in renal allografts.

Moyamoya syndrome (MMS) associated with hyperthyroidism, such as Graves’ disease, is a rare condition that causes ischemic stroke with thyrotoxicity. A 43-year-old woman with symptoms of right hemiparesis was admitted. Brain magnetic resonance imaging revealed a small cerebral infarction in the left frontal lobe. Cerebral angiography revealed multi-vessel intracranial occlusive disease. Several days later, neurologic deterioration and aggravation of cerebral infarction developed due to a thyroid storm. A thyroid function test revealed the following: thyroid-stimulating hormone (TSH) ＜0.01 μunits/mL (reference range, 0.55–4.78 μunits/mL); triiodo-thyronine ＞8.0 ng/mL (reference range, 0.77–1.81 ng/mL); free thyroxine (T4) of 9.47 pmol/L (reference range, 11.4–22.6 pmol/L); and TSH receptor antibody of 37.10 U/L (reference range, 0–10 U/L). For thyroid storm management, we initiated treatment with methimazole, Gemstein’s solution, and hydrocortisone. Finally, the thyroid disease was controlled, and neurologic deficits improved. We describe a case of acute cerebral infarction combined with a thyroid storm in a patient with Moyamoya syndrome and Graves’ disease. Hyperthyroidism such as Graves’ disease should be considered in the differential diagnosis for patho-etiologic mechanisms associated with MMS. A cerebrovascular disease with a thyroid storm can lead to severe mortality and morbidity. Prompt diagnosis and strict treatment are important.

Objective: : This study aimed to assess the relationship between increased intracranial pressure (ICP) and mastoid effusions (ME). Methods: : Between January 2015 and October 2018, patients who underwent intracranial surgery and had ICP monitoring catheters placed were enrolled. ICP was recorded hourly for at least 3 days. ME was determined by the emergence of opacification in mastoid air cells on follow-up brain imaging. C-reactive protein (CRP) levels, presence of endotracheal tube (ETT) and nasogastric tube (NGT), duration of intensive care unit (ICU) stay, duration of mechanical ventilator application, diagnosis, surgical modalities, and presence of sinusitis were recorded. Each factor’s effect on the occurrence of ME was analyzed by binary logistic regression analyses. To analyze the independent effects of ICP as a predictor of ME a multivariable logistic regression analysis was performed. Results: : Total of 61 (53%) out of 115 patients had ME. Among the patients who had unilateral brain lesions, 94% of subject (43/50) revealed the ipsilateral development of ME. ME developed at a mean of 11.1±6.2 days. The variables including mean ICP, peak ICP, age, trauma, CRP, ICU stays, application of mechanical ventilators and presence of ETT and NGT showed statistically significant difference between ME groups and non-ME groups in univariate analysis. Sex and the occurrence of sinusitis did not differ between two groups. Adding the ICP variables significantly improved the prediction of ME in multivariable logistic regression analysis. Conclusion : While multiple factors affect ME, this study demonstrates that ICP and ME are probably related. Further studies are needed to determine the mechanistic relationship between ICP and middle ear pressure.

Objective: Consensus about the sequence of cranioplasty and ventriculoperitoneal shunt placement to reduce postoperative complications has not been established. This meta-analysis investigated and collated further evidence to determine whether staged cranioplasty with ventriculoperitoneal shunt placement would significantly reduce the risk of postoperative surgical-site infection (SSI) and symptomatic intracranial hemorrhage. Methods: Two independent reviewers identified articles and extracted the data of patients who underwent cranioplasty and ventriculoperitoneal shunt placement from PubMed, Embase, and Cochrane Central Register of Controlled Trials. A random effects model was used to compare the complication rates using odd ratios (ORs) and 95% confidence intervals (CIs). A meta-regression analysis for traumatic brain injury (TBI) was additionally performed. Results: Data from 7 studies with 391 patients were consecutively included. The metaanalysis revealed that staged surgery was significantly associated with lower rates of SSI after decompressive craniectomy (staged group vs. simultaneous group: 6.2% vs. 23.7%, OR:2.72, 95% CI: 1.46–5.06, I 2 =2.4%, p=0.407). Pooled analysis did not indicate a statistically significant difference between the 2 groups for symptomatic intracranial hemorrhage (staged group vs. simultaneous group: 10.4% vs. 23.0%, OR: 1.66, 95% CI: 0.74–3.73, I2 =0.0%, p=0.407). The meta-regression analysis did not indicate any modifying effect of TBI on postoperative SSI development (p=0.987). Conclusion This meta-analysis indicated that staged surgery is significantly associated with a lower rate of postoperative SSI as compared with simultaneous surgery, but there is no difference in symptomatic intracranial hemorrhage. Additionally, there is no modifying effect of TBI on SSI.

PURPOSE: Dexamethasone is a mainstay antiemetic regimen for the prevention of chemotherapy-induced nausea and vomiting. The aim of this pilot study was to assess the incidence of and factors associated with steroid-induced diabetes in cancer patients receiving chemotherapy with dexamethasone as an antiemetic. MATERIALS AND METHODS: Non-diabetic patients with newly diagnosed gastrointestinal cancer who received at least three cycles of highly or moderately emetogenic chemotherapy with dexamethasone as an antiemetic were enrolled. Fasting plasma glucose levels, 2-hour postprandial glucose levels, and hemoglobin A1C tests for the diagnosis of diabetes were performed before chemotherapy and at 3 and 6 months after the start of chemotherapy. The homeostasis model assessment of insulin resistance (HOMA-IR) was used as an index for measurement of insulin resistance, defined as a HOMA-IR ≥ 2.5. RESULTS: Between January 2012 and November 2013, 101 patients with no history of diabetes underwent laboratory tests for assessment of eligibility; 77 of these patients were included in the analysis. Forty-five patients (58.4%) were insulin resistant and 17 (22.1%) developed steroid-induced diabetes at 3 or 6 months after the first chemotherapy, which included dexamethasone as an antiemetic. Multivariate analysis showed significant association of the incidence of steroid-induced diabetes with the cumulative dose of dexamethasone (p=0.049). CONCLUSION: We suggest that development of steroid-induced diabetes after antiemetic dexamethasone therapy occurs in approximately 20% of non-diabetic cancer patients; this is particularly significant for patients receiving high doses of dexamethasone.
Antiemetics ; Blood Glucose ; Dexamethasone* ; Diabetes Mellitus ; Diagnosis ; Drug Therapy ; Fasting ; Gastrointestinal Neoplasms ; Glucose ; Homeostasis ; Humans ; Incidence ; Insulin ; Insulin Resistance ; Multivariate Analysis ; Nausea ; Pilot Projects* ; Vomiting

BACKGROUND/AIMS: Pleuropulmonary paragonimiasis produces no specific symptoms or radiologic findings, allowing for the possibility of misdiagnosis. We evaluated the specific clinical and pleural fluid features of pleuropulmonary paragonimiasis masquerading as pleural tuberculosis. METHODS: We retrospectively analyzed the clinical and radiologic characteristics of 20 patients diagnosed with pleuropulmonary paragonimiasis between 2001 and 2011. RESULTS: In total, 17 patients presented with respiratory symptoms, including dyspnea (30%), hemoptysis (20%), cough (20%), and pleuritic chest pain (15%). Chest radiographs revealed intrapulmonary parenchymal lesions, including air-space consolidation (30%), nodular opacities (20%), cystic lesions (15%), ground-glass opacities (10%), and pneumothorax (5%). A pleural f luid examination revealed eosinophilia, low glucose levels, and high lactate dehydrogenase (LDH) levels in 87%, 76%, and 88% of the patients, respectively. These traits helped to distinguish pleuropulmonary paragonimiasis from other pleural diseases such as parapneumonic effusion, malignancy, and pleural tuberculosis. CONCLUSIONS: Pleuropulmonary paragonimiasis is often initially misdiagnosed as other pleural diseases. Therefore, it is important to establish the correct diagnosis. In patients with unexplained pleural effusion living in paragonimiasis-endemic areas, pleural fluid obtained by thoracentesis should be examined to distinguish pleuropulmonary paragonimiasis. When marked eosinophilia, high LDH levels, and low glucose levels are identified in pleural fluid, physicians could consider a diagnosis of pleuropulmonary paragonimiasis.
Adolescent ; Adult ; Aged ; Animals ; Biological Markers/analysis ; Child ; Child, Preschool ; Diagnosis, Differential ; Enzyme-Linked Immunosorbent Assay ; Eosinophilia/diagnosis/parasitology ; Female ; Glucose/analysis ; Humans ; L-Lactate Dehydrogenase/analysis ; Lung Diseases, Parasitic/*diagnosis/metabolism/parasitology/radiography ; Male ; Middle Aged ; Paracentesis ; Paragonimiasis/*diagnosis/metabolism/parasitology/radiography ; Paragonimus westermani/*isolation & purification ; Pleural Effusion/*diagnosis/metabolism/parasitology/radiography ; Predictive Value of Tests ; Retrospective Studies ; Tomography, X-Ray Computed ; Tuberculosis, Pleural/*diagnosis ; Young Adult