Are the GLIM Criteria Guiding in the Course of Hematological Malignancies?

BACKGROUND: Different degrees of malnutrition are seen in patients with hematological malignancies. None of the approaches used to determine malnutrition risk have general acceptance. The use of the GLIM criteria developed by the Global Leadership Initiative on Malnutrition has promising results. MATERIALS AND METHODS: A total of 67 patients with leukemia, lymphoma, and multiple myeloma were included in the study. NRS-2002 (Nutritional Risk Screening-2002) was used to screen the nutritional status of the patients, and malnutrition was diagnosed and graded using the GLIM criteria in patients who were found to be at risk of malnutrition in this test. The parameters followed in the groups with and without malnutrition were compared. The Kolmogorov-Smirnov, Mann-Whitney U, and Chi-square test were used for statistical analysis. RESULTS: Patients were analyzed by dividing them into two groups as those with and without malnutrition. The presence of infection, duration of fever, antibiotic, and antifungal use were significantly higher in malnourished than in nonmalnourished patients. Platelet counts and sodium levels were significantly lower in the malnourished arm. CONCLUSION: Early nutritional support can increase the immunological status of patients with malignant disorders as well as their tolerability to treatment. Minimizing the risk of malnutrition and providing timely calorie and vitamin support are factors that may directly affect febrile neutropenia, duration of fever, and antifungal use, which will consequently lead to a decrease in the length of hospitalization.

The prognostic impact of comorbidity, nutritional and performance status on patients with diffuse large B cell lymphoma.

Background: The aim of the study was to investigate the impact of nutritional status, comorbidity, and performance status on patients with diffuse large B-cell lymphoma (DLBCL). Methods: A retrospective study was conducted on 112 DLBCL patients who were diagnosed at our center between 2009 and 2018. Demographic and disease characteristics and laboratory test results were recorded. Assessments were made using the age-adjusted Charlson comorbidity index (CCI-A) for comorbidity, albumin level for nutritional status, and Eastern Cooperative Oncology Group (ECOG) score for performance status. Results: The mean age of the patients was found to be 62.63 ± 15.16 years. The ECOG score of 65 patients (69.1%) was in the range of 0-1. The mean follow-up time of the patients was determined to be 25.24 ± 25.11 months, and at the end of the follow-up period, 64 patients (57.1%) were survivors. The progression-free survival (PFS), overall survival (OS), and 5-year OS rates of those with CCI-A > 4 were found to be significantly lower than those with CCI-A score ≤4 (P < 0.05). As a result of the Cox-Regression (Backward: LR method) analysis, ECOG and albumin levels were found to be independent risk factors for both OS and PFS (P < 0.05). Conclusion: This study demonstrated that CCI-A, ECOG, and nutritional status are independent prognostic markers for DLBCL patients. Initial evaluation of these patients should include all these parameters, which are easily available at the time of diagnosis.

The utility of CONUT score in indolent lymphoma patients.

Background and Aim: Similar to the uncertainties in the treatment criteria for indolent non-Hodgkin lymphoma (iNHL), the prognostic criteria have not been fully clarified. The Controlled Nutritional Status (CONUT) score is not only used as a predictor of malnutrition but also indicates prognosis in many chronic or malignant diseases. The aim of this study is to investigate the predictive and prognostic significance of the CONUT score in patients with iNHL. Patients and Methods: A retrospective evaluation was made of 109 patients with iNHL. The CONUT scores of the patients were compared between those with an indication for treatment and those followed without treatment. The same analysis was performed between patients who developed relapse after treatment. Survival analysis was performed on all patients, and associations between survival and the CONUT score were examined. Results: The median CONUT score was found to be higher in those who had treatment indications compared to those who did not (2 vs 1; P = 0.014). In the regression model, a CONUT absolute value above 5 was found as an independent risk factor predicting relapse. In the whole study population, a CONUT absolute value >2 predicted the risk of mortality with 53.9% sensitivity and 68.7% specificity (AUC ± SE = 0.639 ± 0.07; +PV = 35%; -PV = 82.6%; P = 0.034). Conclusion: CONUT score is a predictive and prognostic factor for patients with iNHL. The development of simple, low-budget prognostic and predictive biomarkers is critical not only for determining the course of the disease but also for follow-up and treatment management.

The prognostic significance of blood counts in patients with hodgkin lymphoma.

Background and Aim: The aim of the study was to examine the demographic, clinical features, treatment responses, and outcomes of Hodgkin lymphoma (HL) patients and to investigate the factors affecting their survival. Patients and Methods: A retrospective analysis was made of patients diagnosed with HL in our department between 2009 and 2019. Treatment regimen, treatment response, and follow-up times were recorded for all patients. Using these data, complete response (CR) rates, overall survival (OS), and progression-free survival (PFS) were calculated. The effects of parameters on survival were investigated with Cox regression analysis. Results: Evaluation was made of 60 patients with a median age of 33.5 years [18.0-80.0] and mean follow-up duration of 29.34 ± 23.64 months. Median OS and PFS could not be reached with a mean OS of 85.6 months, and PFS of 71.7 months at the final visit. Only initial leukocyte and neurophil count were determined to have a statistically significant impact on survival (OR = 1.004; P = 0.031 vs OR = 0.996; P = 0.036). Conclusion: In HL patients, in addition to the many prognostic scoring systems, leukocyte and neutrophil count can be used as an independent prognostic parameter. Patients with higher leukocyte and lower neutrophil counts at the time of diagnosis should be managed more carefully.

El valor de la gammagrafía paratiroidea en la detección de lesiones en pacientes con hiperparatiroidismo primario normocalcémico / The value of parathyroid scintigraphy on lesion detection in patients with normocalcemic primary hyperparathyroidism

Objetivo: El hiperparatiroidismo primario (HPP) es una de las enfermedades endocrinas más frecuentes. La mayoría de los pacientes con HPP son asintomáticos, y solo el 20% de ellos se vuelven sintomáticos con niveles crecientes de calcio (Ca). Se ha informado que el hiperparatiroidismo primario normocalcémico puede ser el período incipiente de HPP en el que los niveles de Ca están en un rango normal, y puede avanzar a HPP abierto. El diagnóstico temprano de la HPP es importante para prevenir sus complicaciones. En este estudio retrospectivo, nos propusimos evaluar el papel de la gammagrafía paratiroidea 99mTc-MIBI en la detección de lesiones en pacientes con HPP normocalcémico.Material y métodos: La base de datos de gammagrafía paratiroidea fue revisada retrospectivamente en pacientes con HPP. 117 pacientes que se sometieron a la gammagrafía 99mTc-MIBI fueron reclutados para el estudio. El nivel de Ca sérico superior a 10,5mg/dl se consideró como hipercalcemia.Resultados: Los niveles medios de PTH sérica (media±DE) de un total de 117 pacientes (mujeres/mujeres: 98/19) fueron de 149±97pg/ml en el grupo normocalcémico (Ca: 9,6±0,6mg/dl, n=38) y de 189±135pg/ml en el grupo hipercalcémico (Ca: 11,4±0,6mg/dl, n=79) (p=0,072). El sexo y la edad no fueron diferentes entre los grupos de gammagrafía positiva y negativa, pero las tasas de detección de lesiones con gammagrafía paratiroidea fueron del 42% en el grupo normocalcémico y del 81% en el grupo hipercalcémico (p<0,0001).Conclusiones: Varios factores, entre los que se incluyen el Ca sérico, el protocolo de imágenes, la existencia de enfermedad multiglandular, el tamaño y la biocinética MIBI del adenoma pueden influir en la detectabilidad de la lesión en la gammagrafía paratiroidea. Aunque un alto nivel de Ca en suero es un parámetro importante para predecir su éxito, la gammagrafía paratiroidea sigue siendo un método de diagnóstico valioso incluso en pacientes con HPP normocalcémico (AU)

Objective: Primary hyperparathyroidism (PHPT) is one of the most frequent endocrine diseases. Most of the patients with PHPT are asymptomatic, and only 20% of them become symptomatic with increasing levels of calcium. It has been reported that normocalcemic primary hyperparathyroidism (NPHPT) may be the incipient period of PHPT where calcium (Ca) levels are in normal range, and it may advance to overt PHPT. Early diagnosis of PHPT is important in order to prevent its complications. In this retrospective study, we aimed to evaluate the role of 99mTc-MIBI parathyroid scintigraphy on lesion detection in patients with NPHPT.Material and methods: The parathyroid scintigraphy database was reviewed retrospectively in patients with PHPT. 117 patients who underwent 99mTc-MIBI scintigraphy were recruited to the study. Serum calcium level above 10.5mg/dl was considered as hypercalcemia.Results: A total of 117 patients’ (female/male:98/19) mean serum PTH levels (mean±SD) were 149±97 pg/ml in normocalcemic group (Ca:9.6±0.6mg/dL, n:38) and 189±135 pg/ml in hypercalcemic group (Ca:11.4±0.6mg/dL, n:79) (p:0.072). The sex and ages were not different between the scintigraphy positive and negative groups, but the lesion detection rates with parathyroid scintigraphy were 42% in normocalcemic group and 81% in hypercalcemic group (p<0.0001).Conclusions: Several factors including serum Ca, the imaging protocol, existence of multiglandular disease, the size and MIBI biokinetics of the adenoma may influence lesion detectability in parathyroid scintigraphy. Although high serum Ca level is an important parameter in predicting its success, parathyroid scintigraphy remains a valuable diagnostic method even in patients with NPHPT (AU)

COV-ECGNET: COVID-19 detection using ECG trace images with deep convolutional neural network.

The reliable and rapid identification of the COVID-19 has become crucial to prevent the rapid spread of the disease, ease lockdown restrictions and reduce pressure on public health infrastructures. Recently, several methods and techniques have been proposed to detect the SARS-CoV-2 virus using different images and data. However, this is the first study that will explore the possibility of using deep convolutional neural network (CNN) models to detect COVID-19 from electrocardiogram (ECG) trace images. In this work, COVID-19 and other cardiovascular diseases (CVDs) were detected using deep-learning techniques. A public dataset of ECG images consisting of 1937 images from five distinct categories, such as normal, COVID-19, myocardial infarction (MI), abnormal heartbeat (AHB), and recovered myocardial infarction (RMI) were used in this study. Six different deep CNN models (ResNet18, ResNet50, ResNet101, InceptionV3, DenseNet201, and MobileNetv2) were used to investigate three different classification schemes: (i) two-class classification (normal vs COVID-19); (ii) three-class classification (normal, COVID-19, and other CVDs), and finally, (iii) five-class classification (normal, COVID-19, MI, AHB, and RMI). For two-class and three-class classification, Densenet201 outperforms other networks with an accuracy of 99.1%, and 97.36%, respectively; while for the five-class classification, InceptionV3 outperforms others with an accuracy of 97.83%. ScoreCAM visualization confirms that the networks are learning from the relevant area of the trace images. Since the proposed method uses ECG trace images which can be captured by smartphones and are readily available facilities in low-resources countries, this study will help in faster computer-aided diagnosis of COVID-19 and other cardiac abnormalities.

Contemporary Sterilization Protocols of Healing Abutments for Reusability: A Systematic Review.

INTRODUCTION: Although healing abutments are designated for single use by most implant manufacturers, it is common practice for clinicians to reuse healing abutments. However, there is a lack of adequate references that describe detailed sterilization protocols for reuse of healing abutments. OBJECTIVES: The purpose of this systematic review was to compile, organize, and describe the most common techniques for the sterilization of healing abutments and their efficiency in eliminating traces of microorganisms. METHODS: An electronic search in 5 different databases was performed, including the National Library of Medicine (MEDLINE via PubMed), Embase, Cochrane Central Register of Controlled Trials, Web of Science, and Google Scholar from January 2000 to December 2020. Search variables included were dental implant, healing abutment, contaminate, contamination, reuse, and sterilization. Studies reporting with a minimum sample size of 10 healing abutments (5 per group) published in the English language were evaluated. Risk of bias assessment was elaborated for included investigations. RESULTS: In total, 812 articles were identified, of which 8 were included in the analysis. Steam autoclave was the most widely used form of resterilization. Not a single protocol, however, was able to achieve 100% virgin surface of the healing abutments. CONCLUSION: Although reuse of dental implant healing abutments is a cost-effective measure in dental practice, thorough surface decontamination followed by resterilization is highly recommended before reuse. KNOWLEDGE TRANSFER STATEMENT: With consideration of cost and patient preference, results of this review would be useful in knowing various sterilization protocols for reusing healing abutments that could lead to more appropriate therapeutic decisions.

The value of parathyroid scintigraphy on lesion detection in patients with normocalcemic primary hyperparathyroidism. / El valor de la gammagrafía paratiroidea en la detección de lesiones en pacientes con hiperparatiroidismo primario normocalcémico.

OBJECTIVE: Primary hyperparathyroidism (PHPT) is one of the most frequent endocrine diseases. Most of the patients with PHPT are asymptomatic, and only 20% of them become symptomatic with increasing levels of calcium. It has been reported that normocalcemic primary hyperparathyroidism (NPHPT) may be the incipient period of PHPT where calcium (Ca) levels are in normal range, and it may advance to overt PHPT. Early diagnosis of PHPT is important in order to prevent its complications. In this retrospective study, we aimed to evaluate the role of 99mTc-MIBI parathyroid scintigraphy on lesion detection in patients with NPHPT. MATERIAL AND METHODS: The parathyroid scintigraphy database was reviewed retrospectively in patients with PHPT. 117 patients who underwent 99mTc-MIBI scintigraphy were recruited to the study. Serum calcium level above 10.5mg/dl was considered as hypercalcemia. RESULTS: A total of 117 patients' (female/male:98/19) mean serum PTH levels (mean±SD) were 149±97 pg/ml in normocalcemic group (Ca:9.6±0.6mg/dL, n:38) and 189±135 pg/ml in hypercalcemic group (Ca:11.4±0.6mg/dL, n:79) (p:0.072). The sex and ages were not different between the scintigraphy positive and negative groups, but the lesion detection rates with parathyroid scintigraphy were 42% in normocalcemic group and 81% in hypercalcemic group (p<0.0001). CONCLUSIONS: Several factors including serum Ca, the imaging protocol, existence of multiglandular disease, the size and MIBI biokinetics of the adenoma may influence lesion detectability in parathyroid scintigraphy. Although high serum Ca level is an important parameter in predicting its success, parathyroid scintigraphy remains a valuable diagnostic method even in patients with NPHPT.

Deep learning approaches for COVID-19 detection based on chest X-ray images.

COVID-19 is a novel virus that causes infection in both the upper respiratory tract and the lungs. The numbers of cases and deaths have increased on a daily basis on the scale of a global pandemic. Chest X-ray images have proven useful for monitoring various lung diseases and have recently been used to monitor the COVID-19 disease. In this paper, deep-learning-based approaches, namely deep feature extraction, fine-tuning of pretrained convolutional neural networks (CNN), and end-to-end training of a developed CNN model, have been used in order to classify COVID-19 and normal (healthy) chest X-ray images. For deep feature extraction, pretrained deep CNN models (ResNet18, ResNet50, ResNet101, VGG16, and VGG19) were used. For classification of the deep features, the Support Vector Machines (SVM) classifier was used with various kernel functions, namely Linear, Quadratic, Cubic, and Gaussian. The aforementioned pretrained deep CNN models were also used for the fine-tuning procedure. A new CNN model is proposed in this study with end-to-end training. A dataset containing 180 COVID-19 and 200 normal (healthy) chest X-ray images was used in the study's experimentation. Classification accuracy was used as the performance measurement of the study. The experimental works reveal that deep learning shows potential in the detection of COVID-19 based on chest X-ray images. The deep features extracted from the ResNet50 model and SVM classifier with the Linear kernel function produced a 94.7% accuracy score, which was the highest among all the obtained results. The achievement of the fine-tuned ResNet50 model was found to be 92.6%, whilst end-to-end training of the developed CNN model produced a 91.6% result. Various local texture descriptors and SVM classifications were also used for performance comparison with alternative deep approaches; the results of which showed the deep approaches to be quite efficient when compared to the local texture descriptors in the detection of COVID-19 based on chest X-ray images.

The investigation of multiresolution approaches for chest X-ray image based COVID-19 detection.

COVID-19 is a novel virus, which has a fast spreading rate, and now it is seen all around the world. The case and death numbers are increasing day by day. Some tests have been used to determine the COVID-19. Chest X-ray and chest computerized tomography (CT) are two important imaging tools for determination and monitoring of COVID-19. And new methods have been searching for determination of the COVID-19. In this paper, the investigation of various multiresolution approaches in detection of COVID-19 is carried out. Chest X-ray images are used as input to the proposed approach. As recent trend in machine learning shifts toward the deep learning, we would like to show that the traditional methods such as multiresolution approaches are still effective. To this end, the well-known multiresolution approaches namely Wavelet, Shearlet and Contourlet transforms are used to decompose the chest X-ray images and the entropy and the normalized energy approaches are employed for feature extraction from the decomposed chest X-ray images. Entropy and energy features are generally accompanied with the multiresolution approaches in texture recognition applications. The extreme learning machines (ELM) classifier is considered in the classification stage of the proposed study. A dataset containing 361 different COVID-19 chest X-ray images and 200 normal (healthy) chest X-ray images are used in the experimental works. The performance evaluation is carried out by employing various metric namely accuracy, sensitivity, specificity and precision. As deep learning is mentioned, a comparison between proposed multiresolution approaches and deep learning approaches is also carried out. To this end, deep feature extraction and fine-tuning of pretrained convolutional neural networks (CNNs) are considered. For deep feature extraction, pretrained, ResNet50 model is employed. For classification of the deep features, the Support Vector Machines (SVM) classifier is used. The ResNet50 model is also used in the fine-tuning. The experimental works show that multiresolution approaches produced better performance than the deep learning approaches. Especially, Shearlet transform outperformed at all. 99.29% accuracy score is obtained by using Shearlet transform.

Two-stepped majority voting for efficient EEG-based emotion classification.

In this paper, a novel approach that is based on two-stepped majority voting is proposed for efficient EEG-based emotion classification. Emotion recognition is important for human-machine interactions. Facial features- and body gestures-based approaches have been generally proposed for emotion recognition. Recently, EEG-based approaches become more popular in emotion recognition. In the proposed approach, the raw EEG signals are initially low-pass filtered for noise removal and band-pass filters are used for rhythms extraction. For each rhythm, the best performed EEG channels are determined based on wavelet-based entropy features and fractal dimension-based features. The k-nearest neighbor (KNN) classifier is used in classification. The best five EEG channels are used in majority voting for getting the final predictions for each EEG rhythm. In the second majority voting step, the predictions from all rhythms are used to get a final prediction. The DEAP dataset is used in experiments and classification accuracy, sensitivity and specificity are used for performance evaluation metrics. The experiments are carried out to classify the emotions into two binary classes such as high valence (HV) vs low valence (LV) and high arousal (HA) vs low arousal (LA). The experiments show that 86.3% HV vs LV discrimination accuracy and 85.0% HA vs LA discrimination accuracy is obtained. The obtained results are also compared with some of the existing methods. The comparisons show that the proposed method has potential in the use of EEG-based emotion classification.

Study of kissing molars in Turkish population sample.

BACKGROUND: "Kissing" or "rosetting" of molars are extremely rare phenomenon with limited cases in the dental literature. It was first described in 1973, refers to contacting occlusal surfaces of the impacted mandibular second and third molars. The aim of the present study was to report the incidence of kissing molars (KMs), classification, incorporated pathologies, and its management in a group of Turkish population. MATERIALS AND METHODS: The panoramic radiographs of the patients who referred to Gaziantep University Faculty of Dentistry between January 2012 and November 2014 for surgical treatment retrospectively were evaluated. The cases of KM were determined and evaluated with respect to its type, combined pathology, and treatment. RESULTS: Of the 6570 radiographs included in the study, 4 were seen to present as KM illustrating 0.060% of the sample. Three cases were Class II (0.045%), and only one case was encountered as Class III (0.015%). The mean age at the time that the KM teeth were identified was 34 years with a range from 29 to 40 years. Three of the patients were male, one of the patients was female, and all cases were seen unilaterally. One of the KMs was associated with dentigerous cyst formation. CONCLUSION: KM is a very rare clinical condition and few treatment options described. Early detection is essential to preclude complications and to provide more successful treatment. In this study, we evaluated the cases of KM and review of the literature also presented.

A potential pitfall in the use of 68Ga-PSMA PET/CT: anthracosis / Un problema potencial en el uso del 68Ga-PSMA PET/TC: antracosis

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Is Ultrasonography a Useful Method to Evaluate the Effectiveness of Complex Decongestive Therapy in Breast Cancer-related Lymphedema?

In recent years the use of ultrasonography has become widespread in the field of lymphedema especially as an aid for diagnosis. The aim of this study was to evaluate whether ultrasonography is a useful method to assess the efficacy of complex decongestive therapy (CDT). Circumferences and ultrasonographic evaluations (cutis and subcutis thickness) were performed at 10 cm proximal and distal to the elbow and limb volume (upper and forearm) was calculated from circumferences at six anatomic landmarks by using truncated cone formula. Measurements were recorded before and after CDT on both sides. A total of twenty-six women (mean age 51.3 ± 10.8) with the diagnosis of breast cancer-related lymphedema (BCRL) were enrolled in the study. Significant reduction in the subcutis thickness was observed on the affected side after the treatment period, and the percentage change in subcutis thickness was correlated with the percentage change in edema. This study also demonstrated that the soft tissue thickness was higher in the affected arm and ultrasonographic findings were consistent with the other measurement methods (circumferences and limb volumes). Considering that ultrasound imaging is patient-friendly, non-invasive, and cost-effective, we recommend its more widespread use for evaluating treatment efficacy in BCRL.

The risk factors and prevalence of upper extremity impairments and an analysis of effects of lymphoedema and other impairments on the quality of life of breast cancer patients.

This study aimed to determine the prevalence and identify the risk factors associated with upper extremity impairments (UEIs) in breast cancer patients and to investigate the degree to which these impairments and other characteristics influence quality of life (QoL). A total of 201 women over the age of 18 who underwent breast cancer treatment at least 6 months were included in this cross-sectional study. All of the patients were evaluated for the presence of lymphoedema and any UEIs. UEIs divided into five subgroups: pain, restriction of shoulder range of motion (ROM), numbness and heaviness, loss of strength, and sensory deficit. QoL of the patients was evaluated by SF-36. The prevalence of the upper extremity impairments was as follows: pain 31.8%, restriction of shoulder ROM 23.9%, numbness and heaviness 35.3%, loss of strength 8.5%, and sensory deficit 18.4%. Furthermore, lymphoedema was seen in 41.3% of patients. The multivariate model showed that lymphoedema is the only statistically significant risk factor that affects the development of UEIs (P = 0.001). However, it also revealed that lymphoedema (P = 0.001) and increased age negatively affect QoL, whereas prolongation of the follow-up period has a favourable impact (P = 0.016). Therefore, lymphoedema diminishes QoL via an increased number of UEIs.

Infected concha bullosa mimicking a malignant lesion on 18F-FDG PET/CT / Concha bullosa infectada imitando una lesión maligna en 18F-FDG PET/TC

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Disseminated nocardia infection mimicking malignancy on FDG PET/CT / Infección diseminada por nocardia imitando malignidad en FDG PET/TC

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Effects of low-level laser therapy on osteoblastic bone formation and relapse in an experimental rapid maxillary expansion model.

AIMS AND OBJECTIVES: The aim of this study was to investigate the effects of low-level laser therapy (LLLT) on osteoblastic bone formation and relapse during expansion of rat palatal sutures. MATERIALS AND METHODS: Thirty-two Wistar rats were randomly allocated into two groups of 16 rats each. In the first group, LLLT was applied 4 days after expansion commenced. Seven days after expansion, retainers were applied for 10 days. The second group was similarly treated, with the exception of laser therapy. All rats were sacrificed on day 7 (n = 1) (the end of the expansion period; laser group (LG) 1 [LLLT 1] and control group (CG) 1 [control 1]) and day 17 (n = 8) (the end of the retention period; LG 2 [LLLT 2] and CG 2 [control 2]) for histological assessment. RESULTS: The LLLT 1 group had significantly higher numbers of osteoclasts than did the control 1 group (P = 0.036). No significant between-group difference in osteoblast cell or capillary numbers was evident when day 7 and 17 data were compared. CONCLUSION: Histologically, LLLT stimulated bone formation, as revealed by analysis after the retention period. LLLT during expansion may accelerate bone healing.