Analysis of the correlation between thyroid hormones and thyroid volume by gender: A volumetric computed tomography study

The aim of this study was to evaluate the correlation between triiodothyronine (T3), thyroxine (T4), and thyroid-stimulating hormone (TSH) hormones and thyroid gland volume with volumetric analysis performed by using computed tomography (CT) images. In this retrospective study, IV contrasted thoracic CT images taken for different indications between 2019 January and 2020 January were scanned from the archive system of the hospital. 67 (31F, 36M) individuals chosen randomly among patients whose CT results were reported as normal and who had taken thyroid hormone tests within the past week were included in the study. Images in Digital Imaging and Communications in Medicine format were transferred to the personal work station program (Horos Medical Image Viewer). By using the Region of Interest (ROI) console in the current program, a three dimensional model was obtained by drawing the border of the thyroid gland in sections varying between 15 and 25. Volume values of this three-dimensional model and TSH, T3, T4 values of the individuals were compared. While no correlation was found between thyroid gland volume and T3 and T4 hormones, a negative significant correlation was found with TSH. In terms of gender, thyroid gland volume, T3, T4 values were found to be statistically significantly higher in women when compared with men (p?0.05). TSH value was found to be higher in women when compared with men (p=0.005). No statistically significant difference was found in T4 value (p=0.057). Radio-anatomical volumetric data of the thyroid gland presented in this study and its correlation with thyroid functions will be beneficial to clinicians working in the field in both internal and surgical medicine branches and will also guide future studies

A study on sex estimation by using machine learning algorithms with parameters obtained from computerized tomography images of the cranium

The aim of this study is to test whether sex prediction can be made by using machine learning algorithms (ML) with parameters taken from computerized tomography (CT) images of cranium and mandible skeleton which are known to be dimorphic. CT images of the cranium skeletons of 150 men and 150 women were included in the study. 25 parameters determined were tested with different ML algorithms. Accuracy (Acc), Specificity (Spe), Sensitivity (Sen), F1 score (F1), Matthews correlation coefficient (Mcc) values were included as performance criteria and Minitab 17 package program was used in descriptive statistical analyses. p <= 0.05 value was considered as statistically significant. In ML algorithms, the highest prediction was found with 0.90 Acc, 0.80 Mcc, 0.90 Spe, 0.90 Sen, 0.90 F1 values as a result of LR algorithms. As a result of confusion matrix, it was found that 27 of 30 males and 27 of 30 females were predicted correctly. Acc ratios of other MLs were found to be between 0.81 and 0.88. It has been concluded that the LR algorithm to be applied to the parameters obtained from CT images of the cranium skeleton will predict sex with high accuracy

Comparison of respiratory tract pathogens and antibiotic susceptibility profiles of patients diagnosed with COVID-19 with pre-COVID-19

Objective: It is aimed to compare the respiratory tract agents and antibiotic resistance rates in patients with a diagnosis of COVID-19 with the non-COVID-19 period. Material and Method: Patients diagnosed with bacterial respiratory tract infection between March 2019 and March 2021 were included in the study. Bacteria identification and antibiotic susceptibility were evaluated according to automated system and EUCAST standards. Results: Between March 2019-March 2020 (before the pandemic), the most common bacterium was Pseudomonas aeruginosa (P. aeruginosa) 280 (15.5%) second Acinetobacter baumannii (A. baumannii) in a total of 1797 patients hospitalized in the service and intensive care units, and the resistance rates were the same. Between March 2020 and 2021, a total of 1357 COVID -19 patients were found in clinical and intensive care units, and the most common reproducing agent was A. baumannii 168 (12.3%), the second P. aeruginosa 164, and resistance rates were found to invrease. Conclusion: The increase in the resistance rates of bacteria causing respiratory tract infection was remarkable. It was determined that P. aeruginosa and A. baumannii, which were the most common isolates before the pandemic and showed high resistance rates against all antibiotic groups, were the most common bacteria during the pandemic period

Antibiotics resistance rates of gram negative esbl positive bacteria isolated from various clinical specimens

Amaç: Genişlemiş Spektrumlu Beta Laktamaz (GSBL) pozitif mikroorganizmaların neden olduğu enfeksiyonların tedavisindeki zorluklar günümüzde en önemli sağlık sorunlarından biridir. çeşitli bakteriyolojik kültürlerinden izole edilen Gram negatif GSBL pozitif bakterilerin örnek türüne göre ampirik ve semptomatik tedaviye etkili antibiyotiklerin belirlenmesi amaçlanmıştır. Gereç ve Yöntem: Bu çalışma 1 Ağustos 2019-1 Şubat 2020 tarihleri arasında çeşitli servis ve polikliniklerden rutin mikrobiyoloji laboratuvarına yollanan idrar, kan, yara, balgam, eklem sıvısı, plevral sıvı örneklerinden izole edilen bakteriyel etkenler ve antimikrobiyal duyarlılıkların retrospektif araştırılması ile yapılmıştır. Gram negatif GSBL pozitif bakterilerin tanımlanmaları ve antibiyotik duyarlılık testleri konvansiyonel yöntem ve MicroScan (Beckman Coultre, USA ) otomatize sistem ile yapılmıştır. Bulgular: Laboratuvara gönderilen 13,968 klinik örnekten izole edilen 1735 Gram negatif bakterinin 1041 (%60)’i GSBL pozitif idi. 1041 örneğin; 901 (%86,55)’i idrar, 47 (%4,51)’si Trakeal aspirat, 35 (%3,36)’i yara idi. GSBL pozitif bakterilerin en sık izole edilenleri 732(%70,3) ile Escherichia coli, 249(%23,9)’u Klebsiella pneumonia, 22(%2,11)’si Proteus mirabilis oluşturmaktaydı. Bu bakterilerin tamamı ampisilin, ampisilin-sulbaktam ve sefalosporin grubu antibiyotiklere dirençli iken bakterilerin en duyarlı olduğu grup ise karbapenem grubu antibiyotikler idi. Sonuç: Gram negatif bakterilerde, antibiyotiklere dirençli izole edilen suşların oranlarındaki artış, kullanılabilecek tedavi seçeneklerini oldukça kısıtlamaktadır. Özellikle çoklu antibiyotik dirençli, sıklıkla toplum ve hastane kökenli enfeksiyonlara neden olan Gram negatif GSBL pozitif bakterilerin antibiyotik direnç durumlarının belirlenmesi güncel direnç oranını ortaya çıkaracağı gibi tedavi başarısının artırılmasında yardımcı olacaktır. Her kurum kendi antibiyotik direncini saptayarak ülke verilerine katkı sunmalıdırAim: The difficulties in the treatment of infections caused by Extended Spectrum Beta-Lactamase (ESBL) positive microorganisms are one of the most important health problems today. In this study, it was aimed to determine the antibiotics effective in empirical and symptomatic treatment according to the sample type of Gram negative ESBL positive bacteria isolated from various bacteriological cultures. Materials and Methods: This study was carried out by retrospective investigation of bacterial agents and antimicrobial susceptibility various samples sent to routine microbiology laboratory from various services and outpatient clinics between 1 August 2019 and 1 February 2020. Identification of Gram-negative ESBL-positive bacteria and antibiotic susceptibility tests were performed using the conventional method and MicroScan (Beckman Coultre, USA) automated system. Results: Of the 1735 Gram-negative bacteria isolated from 13,968 clinical samples sent to the laboratory, 1041 (60%) were ESBL positive. 1041 for example; 901 (86.55%) were urine, 47 (4.51%) were aspirate, 35 (3.36%) were wounds. Escherichia coli with 732 (70.3%) of ESBL positive bacteria, Klebsiella pneumonia with 249 (23.9%) and Proteus mirabilis with 22 (2.11%) were the most frequently isolated bacteria. While all of these bacteria were resistant to ampicillin-sulbactam, cephalosporin group antibiotics, the group to which the bacteria were most susceptible was carbapenem group antibiotics. Conclusion: Determining the antibiotic resistance status of Gram-negative ESBL-positive bacteria, which are resistant to multiple antibiotics and frequently cause infections, will reveal the current resistance rate and will help increase the success of treatment. Each institution should contribute to country data by detecting its own antibiotic resistance