Chest X-ray Scan Research Articles

Post-Traumatic Left Diaphragmatic Rupture with Intrathoracic Migration of Abdominal Organs: A Case Report

Traumatic diaphragmatic rupture (TDR) is a rare but critical injury commonly resulting from high-velocity blunt or penetrating trauma. It often leads to abdominal organ herniation into the chest cavity, making timely diagnosis crucial for optimal management. Despite its potential severity, TDR remains frequently underdiagnosed, often due to the lack of specific early symptoms or delayed detection. This article presents a case of left diaphragmatic rupture in a 39-year-old male following blunt trauma from a motor vehicle accident, with associated abdominal organ herniation. We discuss the pathophysiology, diagnostic challenges, and treatment modalities for TDR, including surgical intervention, postoperative management, and potential complications. Early identification and surgical repair are essential to reducing morbidity and mortality associated with diaphragmatic ruptures, especially in polytrauma patients. The need for high clinical suspicion and prompt radiological investigations, including chest X-ray and CT scans, is emphasized. Despite successful initial repair and management, the patient ultimately succumbed to complications, highlighting the complex nature of multi-trauma cases.

CLARE-XR: explainable regression-based classification of chest radiographs with label embeddings

An automatic system for pathology classification in chest X-ray scans needs more than predictive performance, since providing explanations is deemed essential for fostering end-user trust, improving decision-making, and regulatory compliance. CLARE-XR is a novel methodology that, when presented with an X-ray image, identifies the associated pathologies and provides explanations based on the presentation of similar cases. The diagnosis is achieved using a regression model that maps an image into a 2D latent space containing the reference coordinates of all findings. The references are generated once through label embedding, before the regression step, by converting the original binary ground-truth annotations to 2D coordinates. The classification is inferred minding the distance from the coordinates of an inference image to the reference coordinates. Furthermore, as the regressor is trained on a known set of images, the distance from the coordinates of an inference image to the coordinates of the training set images also allows retrieving similar instances, mimicking the common clinical practice of comparing scans to confirm diagnoses. This inherently interpretable framework discloses specific classification rules and visual explanations through automatic image retrieval methods, outperforming the multi-label ResNet50 classification baseline across multiple evaluation settings on the NIH ChestX-ray14 dataset.

Pericardial effusion in an Indian context: clinical insights and dynamics from a tertiary care centre

BackgroundPericardial effusion (PE) indicates the build-up of fluid within the pericardial sac, which encases the heart. The present study was undertaken to assess the clinical profile, etiology of pericardial effusion and to determine the correlation of cardiac tamponade and constrictive pericarditis with etiology.MethodsA prospective observational hospital based longitudinal study was undertaken among the 88 newly diagnosed and known patients of pericardial effusion who are above 18 years. The clinical profile of pericardial effusion including history, examination, standard lab parameters routinely done including thyroid function tests, HIV Serology, ECG, Echocardiography and imaging if done (HRCT thorax), pericardial fluid analysis (if performed) were elicited.ResultsMajority of the patients were males (55.7%), with a mean age of 51.3 years. Among the 88 patients of pericardial effusion, 20 had cardiac tamponade, 13 individuals were diagnosed with constrictive pericarditis. Dyspnea was the most common presenting complaint (65.9%). Chronic kidney disease / uremia is the most common cause of pericardial effusion accounting for 25%, followed by neoplastic (20.5%) and tuberculosis (17%). While in cardiac tamponade patients neoplasm followed by tuberculosis were the most common etiology, patients with constrictive pericarditis had tuberculosis followed by chronic kidney disease as the most common etiology. Echocardiography features were not significantly different according to the etiology of the pericardial effusion (p > 0.05). Thickened pericardium found in the echocardiography showed maximum specificity (76.9%), while thickened fluid/exudates showed maximum sensitivity (65.2%) and negative predictive value (77.1%) for tuberculous pericardial effusion.ConclusionChronic kidney disease, closely followed by infections (mostly tuberculosis), are the frequent causes of PE in the present settings. Breathlessness is the most frequent clinical feature in the patients of PE. Fibrin strands, thickened pericardium, thickened fluid in Echocardiography assists in diagnosing tubercular pericardial effusion. Cardiomegaly in chest X-ray or CT scans should further prompt towards diagnosing pericardial effusion. It is essential to incorporate these findings into the clinical practice, by evaluating the patients presenting with breathlessness for PE. CKD needs to be placed on par with tuberculosis while suspecting the etiology of the PE in the present settings. ADA levels in pericardial fluid (> 40) can be considered as a specific marker for tubercular PE.

Outcome and toxicities of postmastectomy radiotherapy with integral cervicothoracic thermoplastic mask

BackgroundAppropriate immobilization setup for postmastectomy radiotherapy may help to improve tumor control and to reduce radiation-related toxicities. This study aims at retrospectively evaluate the outcome and toxicities of postmastectomy radiotherapy (PMRT) with a novel integral cervicothoracic thermoplastic mask strategy.MethodsBreast cancer patients were treated with modified radical mastectomy and PMRT. Patient immobilization setup was performed with the placement of a 1-cm thickened wax film on the ipsilateral chest wall and an integral cervicothoracic thermoplastic mask. PMRT was delivered according to the institutional protocol. Dose distribution, disease control, patient survival and radiation-induced toxicities were evaluated.ResultsFour-hundred nineteen eligible patients with complete follow-up information were included in the final analysis. The median follow-up was 40.2 (95%CI: 38.9–41.6) months. Two (0.5%) patients had local recurrence and 48 (11.4%) patients had distant metastasis. There were 22 (5.3%) deaths from all causes, of which 19 were caused by breast cancer. The 3-year overall survival (OS) rate was 94.8%. ER status, PR status, triple negative status, and T stages were significantly related to patient survival (p < 0.05). HER2 expression, N stage were not significantly related to patient survival. Most common radiation-induced toxicities included grade I (87.6%) and grade II (10.2%) dermatitis, and grade I pneumonitis (28.9%) found by chest X-ray or CT scans. No clinical detectable cardiovascular event related to radiotherapy was identified.ConclusionPostmastectomy radiotherapy with integral cervicothoracic thermoplastic mask leads to favorable outcome and moderate toxicities compared with results reported in literature and might be of clinical significance in breast cancer patient. However, this approach has not been compared directly with postmastectomy radiotherapy without immobilization, and its applicability in other regions with different treatment protocols requires further investigation.

Evaluating Explainable Artificial Intelligence (XAI) techniques in chest radiology imaging through a human-centered Lens.

The field of radiology imaging has experienced a remarkable increase in using of deep learning (DL) algorithms to support diagnostic and treatment decisions. This rise has led to the development of Explainable AI (XAI) system to improve the transparency and trust of complex DL methods. However, XAI systems face challenges in gaining acceptance within the healthcare sector, mainly due to technical hurdles in utilizing these systems in practice and the lack of human-centered evaluation/validation. In this study, we focus on visual XAI systems applied to DL-enabled diagnostic system in chest radiography. In particular, we conduct a user study to evaluate two prominent visual XAI techniques from the human perspective. To this end, we created two clinical scenarios for diagnosing pneumonia and COVID-19 using DL techniques applied to chest X-ray and CT scans. The achieved accuracy rates were 90% for pneumonia and 98% for COVID-19. Subsequently, we employed two well-known XAI methods, Grad-CAM (Gradient-weighted Class Activation Mapping) and LIME (Local Interpretable Model-agnostic Explanations), to generate visual explanations elucidating the AI decision-making process. The visual explainability results were shared through a user study, undergoing evaluation by medical professionals in terms of clinical relevance, coherency, and user trust. In general, participants expressed a positive perception of the use of XAI systems in chest radiography. However, there was a noticeable lack of awareness regarding their value and practical aspects. Regarding preferences, Grad-CAM showed superior performance over LIME in terms of coherency and trust, although concerns were raised about its clinical usability. Our findings highlight key user-driven explainability requirements, emphasizing the importance of multi-modal explainability and the necessity to increase awareness of XAI systems among medical practitioners. Inclusive design was also identified as a crucial need to ensure better alignment of these systems with user needs.

Deep Learning Methods for Detecting Chest X-Ray and CT Scan Abnormalities

Deep learning's quick development has created new opportunities to improve medical image analysis, especially in the identification of anomalies in chest CT and X-ray scans. This work investigates several deep learning techniques designed for this particular purpose to enhance the efficiency and accuracy of diagnosis in medical settings. We explore the use of 3D CNNs, transfer learning, and convolutional neural networks, or CNNs, for the analysis of volumetric CT scan information as well as 2D chest X-ray pictures. Comparative analyses show the benefits and drawbacks of various deep learning architectures for identifying a variety of anomalies, including tumors, tumors in the lungs, pneumonia, and other diseases. We also go over the significance of preprocessing methods, assessment metrics specifically designed for medical picture analysis, and dataset preparation. The results highlight how deep learning has the potential to revolutionize chest imaging diagnostics by facilitating the quicker and more accurate identification of anomalies, which will enhance patient outcomes and the effectiveness of healthcare delivery. To spur additional developments in the deep learning-powered analysis of medical images for chest problems, future research topics, and obstacles in this area are also covered.

Computed tomography–guided percutaneous transthoracic core needle biopsy for the diagnosis of mediastinal masses

Mediastinal masses encompass a wide spectrum of diseases; therefore, histopathological diagnosis is crucial for adequate surgical or medical treatments. Herein, we report six cases of mediastinal tumors that were definitively diagnosed by computed tomography (CT)-guided core needle biopsy. Case 1: A 35-year-old woman was found to have an anterior mediastinal mass on both a chest X-ray and a CT thorax. A CT-guided core needle biopsy of the mediastinal mass revealed type A thymoma. The patient underwent surgery and remained well 1 year of post-surgery. Case 2: A 47-year-old man was found to have a solid and cystic mass with areas of calcifications on a CT scan. A CT-guided core needle biopsy of the mediastinal mass revealed an immature teratoma. The patient is now under chemotherapy. Case 3: An 18-year-old boy was found to have a tumor in the anterior mediastinum on both a chest x-ray and CT scan. A CT-guided core needle biopsy of the mediastinal mass revealed a yolk sac tumor. The patient received only two cycles of chemotherapy and died. Case 4: A 26-year-old man was found to have a left anterior mediastinal mass with internal necrosis and calcification on a CT scan. A CT-guided core needle biopsy of the mediastinal mass revealed a malignant germ cell tumor. The patient is now under chemotherapy. Case 5: A 17-year-old boy was found to have an anterior mediastinal mass on both a chest X-ray and a CT scan. A CT-guided core needle biopsy of the mediastinal mass showed non-Hodgkin lymphoma. The patient received chemotherapy and remained well 6 months after the treatment. Case 6: An 8-year-old child was found to have an anterior mediastinal mass on a chest C-ray and a CT scan. A CT-guided core needle biopsy of the mediastinal mass revealed neuroblastoma. After six courses of chemotherapy the patient passed away. Percutaneous CT-guided core needle biopsy of mediastinal mass lesions is a safe, accurate, and cost-effective procedure.

Development of a clinical risk score for the prediction of Pneumocystis jirovecii pneumonia in hospitalised patients

BackgroundThe performance and availability of invasive and non-invasive investigations for the diagnosis of Pneumocystis jirovecii pneumonia (PCP) vary across clinical settings. Estimating the pre-test probability of PCP is essential to the optimal selection and interpretation of diagnostic tests, such as the 1,3-β-D-glucan assay (BDG), for the prioritization of bronchoscopy, and to guide empiric treatment decisions. We aimed to develop a multivariable risk score to estimate the pre-test probability of PCP.MethodsThe score was developed from a cohort of 626 individuals who underwent bronchoscopy for the purposes of identifying PCP in a Canadian tertiary-care centre, between 2015 and 2018. We conducted a nested case-control study of 57 cases and 228 unmatched controls. Demographic, clinical, laboratory, and radiological data were included in a multivariable logistic regression model to estimate adjusted odds ratios for PCP diagnosis. A clinical risk score was derived from the multivariable model and discrimination was assessed by estimating the score’s receiver operating characteristic curve.ResultsParticipants had a median age of 60 years (interquartile range [IQR] 49–68) and 115 (40%) were female; 40 (14%) had HIV and 49 (17%) had a solid organ transplant (SOT). The risk score included prior SOT or HIV with CD4 ≤ 200/µL (+ 2), serum lactate dehydrogenase ≥ 265.5 IU/mL (+ 2), radiological pattern typical of PCP on chest x-ray (+ 2) or CT scan (+ 2.5), and PCP prophylaxis with trimethoprim-sulfamethoxazole (-3) or other antimicrobials (-2). The median score was 4 points (IQR, 2-4.5) corresponding to a 28% probability of PCP. The risk prediction model had good discrimination with a c-statistic of 0.79 (0.71–0.84). Given the operating characteristics of the BDG assay, scores ≤ 3 in patients without HIV, and ≤ 5.5 in those with HIV, paired with a negative BDG, would be expected to rule out PCP with 95% certainty.ConclusionWe propose the PCP Score to estimate pre-test probability of PCP. Once validated, it should help clinicians determine which patients to refer for invasive investigations, when to rely on serological testing, and in whom to consider pre-emptive treatment.

Diagnostic approach to bone metastasis of unknown origin: a systematic review

BACKGROUND Among all MBD cases, 3% have an unidentified origin and are commonly misdiagnosed. On average, nearly 75% of MBD cases with an unknown origin take four months to diagnose. This difficulty in identifying the primary cancer limits the treatment of skeletal metastases to palliative measures, negatively impacting a patient’s prognosis. The purpose of this systematic review is to evaluate the current diagnostic work-up approaches available for MBD. METHODS This systematic review was carried out according to the guidelines of the (PRISMA) 2020 Statement, including studies with a population of interest of MBD with unknown primary origin. We excluded studies with non-English language and MBD cases with known origin. The intervention of the diagnostic approach was the primary outcome. The authors searched three electronic medical databases. Individual reviewers performed quality and risk of bias assessments. RESULTS All ten articles were cohort studies, with the median age of the subjects ranging from 55.6 to 65.2 years. The most frequently recommended supporting examination was biopsy, with a detection rate of 38.2%, followed by chest X-ray and CT scan, both with a detection rate of 26.7%. Overall, the detection rate of the primary lesion from individual examinations ranged from 2.8% to 38.2%, highlighting the difficulty of diagnosis when relying solely on a single examination. CONCLUSIONS Biopsy is crucial in identifying the primary lesion of metastatic bone disease with an unknown origin, with a detection rate of 38.2%, making it the most frequently recommended examination. However, this review has several limitations. A few assumptions were made in the synthesis of numerical data.

Pulmonary Shunt in Critical Care: A Comprehensive Review of Pathophysiology, Diagnosis, and Management Strategies.

Pulmonary shunt, an abnormal passage of blood through the pulmonary circulation without adequate gas exchange, poses significant challenges in critical care. This comprehensive review explores the pathophysiology, diagnostic approaches, and management strategies associated with pulmonary shunt. Pulmonary shunts are classified into anatomical and physiological types, each with distinct mechanisms and implications for gas exchange. Anatomical shunts result from structural heart defects, while physiological shunts arise from ventilation-perfusion (V/Q) mismatches. Both conditions can significantly impair oxygenation and contribute to multi-organ dysfunction. This review delves into various diagnostic modalities, including clinical assessment, imaging techniques such as chest X-ray and CT scans, and advanced diagnostic methods such as V/Q scanning and echocardiography. Challenges in diagnosing pulmonary shunt are discussed, emphasizing the limitations of current tools and the need for accurate differentiation of shunt types. Management strategies are examined, covering pharmacological interventions, non-pharmacological treatments such as mechanical ventilation and prone positioning, and surgical options. Emerging therapies and innovations in treatment are also highlighted. Special considerations are given to different patient populations, including pediatric and elderly patients and those with multiple comorbidities. This review concludes with an analysis of the prognosis and outcomes associated with pulmonary shunt, focusing on short-term and long-term impacts on survival and quality of life. This review aims to enhance understanding and guide effective management practices for pulmonary shunt in critical care settings by synthesizing current knowledge and identifying areas for further research.

Latent Class Analysis Reveals, in patient profiles, COVID-19–related better prognosis by calcifediol treatment than glucocorticoids

Calcifediol and glucocorticoids have been repositioned for the treatment of COVID-19 and may reduce severity, the need for intensive care unit admission and death. Objectiveto identify class or profiles of patients hospitalized and treated with COVID-19 pneumonia using latent class clustering methods to assess the clinical and prognostic relevance of the resulting patients’ profiles. Poor prognosis was defined as death or need for ICU admission, good prognosis, the opposite. With special interest in differential responses to calcifediol. SettingReina Sofia University Hospital, Córdoba Spain. PatientsRetrospective observational cohort study of patients admitted for COVID-19. ClinicalTrials.gov public database (NCT05819918). Inclusion criteria: (i) Age ≥ 18 and ≤ 90 years, (ii) Pneumonia characterized by the presence of infiltrates on chest X-ray or CT scan, (iii) SARS-CoV-2 infection, confirmed, and (iv) CURB Scale 65 >1. DesignLatent class analysis, for obtaining homogeneous clusters, without specifying a priori the belonging group, and selecting the optimal number of clusters by minimizing information criteria. Evaluating the differences between groups for each variable by means of chi-square, Fisher's exact test and Kruskal-Wallis test. Results707 patients hospitalized from 10 March 2020 until 4 March 2022 were included. For the treatment variable, differences were found between class 3 (60 % treated with calcifediol only) and classes 1 (less than 1 % calcifediol only vs. 82 % treated with both), 2 (less than 1 % calcifediol only vs. 82 % treated with both) and 4 (1 % calcifediol only vs. 84 % treated with both). Class 3, (60 % with calcifediol), had a significantly better prognosis compared to patients treated with glucocorticoids alone (OR: 15.2, 95 % CI: [3.73–142], p<0.001) or no treatment (OR: 7.38, 95 % CI: [2.63–30.2], p<0.001). Conclusionsour real-life study shows that calcifediol treatment significantly reduces the need for ICU admission and improved prognosis in patients hospitalized for COVID-19 pneumonia, especially in the profile of patients receiving it without glucocorticoids.

Low Diagnostic Accuracy of Transthoracic Ultrasound for the Assessment of Spontaneous Pneumothorax in the Emergency Setting: A Multicentric Study.

Background: Pneumothorax (PNX) represents a common clinical condition in emergency departments (EDs), requiring prompt recognition and treatment. The role of transthoracic ultrasounds (TUSs) in the diagnosis of PNX is still debated. We aimed to prospectively evaluate the accuracy of TUSs in the detection of spontaneous PNX in EDs. Methods: A total of 637 consecutive adult patients who presented to the EDs of four Italian hospitals complaining of acutely onset chest pain and dyspnoea were included in the study. Exclusion criteria were previous traumatic events, cardiogenic causes of pain/dyspnoea and suspected tension PNX. The absence of "lung sliding" (B-mode) and the "bar-code" sign (M-mode) were considered indicative of PNX in a TUS. Accuracy, sensitivity, specificity, and positive and negative predictive values (PPVs, NPVs) were calculated using a chest CT scan as reference. Results: Spontaneous PNX occurred in 93 patients: of those, 83 (89.2%) were correctly identified by TUSs. However, 306 patients with suspected PNX at TUS were not confirmed by chest CTs. The diagnostic accuracy of both the absence of "lung sliding" and "bar-code" sign during TUS was 50.4% (95% CI: 46.4-54.3), sensitivity was 89.2% (95% CI: 81.1-94.7), specificity was 43.8% (95% CI: 39.5-48.0), the PPV was 21.3% (95% CI: 19.7-23.1) and the NPV was 96.0% (95% CI: 92.9-97.7). Conclusions: TUS showed high sensitivity but low specificity in the identification of PNX in EDs. Relying exclusively on TUSs results for patients' management in ED settings is neither suitable nor recommendable. TUS examination can be useful to strengthen the clinical suspicion of PNX, but its results should be confirmed by a chest X-ray or CT scan.

IQAEvolNet: a novel unsupervised evolutionary image enhancement algorithm on chest X-ray scans

IQAEvolNet: a novel unsupervised evolutionary image enhancement algorithm on chest X-ray scans

Images of Chest Computer Tomography (CT) and Radiation (X-ray) Demonstrating Clinical Manifestations of COVID-19: Review Article

Apart from reverse-transcription polymerase chain reaction (RT-PCR) testing, chest radiographs (CXR) and computed tomography (CT) scans were employed as crucial diagnostic methods for detecting the 2019 new coronavirus disease (COVID-19). Our objective is to examine three notable COVID-19 instances from patients across the globe, along with their CXR and CT data. The evaluation of the imaging characteristics of the reported instances was the primary objective of a methodical examination of the literature. We located more than several articles that had been published between 2020 and 2023. After the papers were examined, three major cases were chosen, including a COVID-19 assessment of imaging features (chest X-ray and CT scan). Corona viral diseases (COVID-19) pose a significant risk to healthcare facilities, especially when the patient has additional medical issues. It is challenging to understand the various chest radiography results because of the use of specialized and ambiguous terminology such as “airspace disease”, “pneumonia”, “infiltrates”, “patchy opacities”, and “hazy opacities”. The current investigation considered peer-reviewed case reports with Images features. Study designs, including reporting cases, were considered for imaging feature analysis.

Multi-scale Lesion Feature Fusion and Location-Aware for Chest Multi-disease Detection.

Accurately identifying and locating lesions in chest X-rays has the potential to significantly enhance diagnostic efficiency, quality, and interpretability. However, current methods primarily focus on detecting of specific diseases in chest X-rays, disregarding the presence of multiple diseases in a single chest X-ray scan. Moreover, the diversity in lesion locations and attributes introduces complexity in accurately discerning specific traits for each lesion, leading to diminished accuracy when detecting multiple diseases. To address these issues, we propose a novel detection framework that enhances multi-scale lesion feature extraction and fusion, improving lesion position perception and subsequently boosting chest multi-disease detection performance. Initially, we construct a multi-scale lesion feature extraction network to tackle the uniqueness of various lesion features and locations, strengthening the global semantic correlation between lesion features and their positions. Following this, we introduce an instance-aware semantic enhancement network that dynamically amalgamates instance-specific features with high-level semantic representations across various scales. This adaptive integration effectively mitigates the loss of detailed information within lesion regions. Additionally, we perform lesion region feature mapping using candidate boxes to preserve crucial positional information, enhancing the accuracy of chest disease detection across multiple scales. Experimental results on the VinDr-CXR dataset reveal a 6% increment in mean average precision (mAP) and an 8.4% improvement in mean recall (mR) when compared to state-of-the-art baselines. This demonstrates the effectiveness of the model in accurately detecting multiple chest diseases by capturing specific features and location information.

Socio-economic Challenges in COVID Detection using Transfer Learning-Based Methods

Healthcare systems are at risk of collapsing unless significant structural and transformative measures are taken. Despite the global economy generating an additional 40 million jobs in the health sector by 2030, the World Health Organization projects a shortage of 9.9 million physicians, nurses, and midwives during the same period (WHO, 2016). The core of innovation in the healthcare industry lies in automation systems, particularly in the realm of image detection. As the ratio of healthcare workers to patients decreases, the integration of robotics and artificial intelligence plays a crucial role in bridging the gap. These technologies not only compensate for the declining workforce but also bring a level of accuracy and precision that eliminates the potential for human error in image detection processes. In this paper we focus on the COVID-19 pandemic that presents significant socio-economic challenges, impacting various aspects of daily life, including health, the economy, and social development. The need for chest X-ray (CXR) scans is rising due to pneumonia being a critical and common complication of COVID-19. Early detection and diagnosis are pivotal in curbing the spread of the virus, prompting the utilization of the reverse transcription polymerase chain reaction (RT-PCR) as the predominant screening technology. Nevertheless, the task's complexity, time-consuming nature, and reported insensitivity in this research emphasize the need for alternative approaches. CXR is a widely employed screening tool for lung-related diseases due to its straightforward and cost-effective application. In this paper, we have deployed different transfer learning methods to detect COVID-19 using chest X-ray images such as VGG19, ResNet-50, and InceptionResnetV2. The findings of our results indicate that the fine-tuned model utilizing the transfer learning and data augmentation techniques enhances the efficiency of COVID-19 detection. We performed a comparison of pre-trained networks and identified the InceptionResNetV2 model as having the highest classification performance with an accuracy of 97.33%.

Multi-center study on mortality in children, and adults with sickle cell anemia-risk factors and causes of death

Sickle cell disease (SCD) is a major public health burden worldwide with increasing morbidity and mortality. The study evaluates the risk factors associated with mortality in SCD patients, between the years 2006 and 2020 at three hospitals in Oman. The analysis includes clinical manifestations, haematological, biochemical, and radiological parameters, use of antibiotics, and blood and exchange transfusions. Our cohort included 123 patients (82 males, 41 females), with a median age of 27 (Interquartile Range 21–35 years). SCD related complications included acute chest syndrome (ACS) in 52.8%, splenic sequestration in 21.1%, right upper quadrant syndrome in 19.5%, more than > 6 VOC/year in 17.9%, and stroke in 13.8%. At the terminal admission, patients had cough, reduced O2 saturation, crepitation and fever in 24.4%, 49.6%, 53.6% and 68.3% respectively. Abnormal chest X-ray and chest CT scan were seen in 57.7%, and 76.4% respectively. Laboratory parameters showed a significant drop in hemoglobin (Hb) and platelet counts from baseline, with a significant rise in WBC, LDH and CRP from baseline (p < 0.05, Wilcoxon Signed Ranks test). All patients received antibiotics, whereas, 95.9% and 93.5% received simple blood transfusions, and exchange transfusions respectively, and 66.6% required non-invasive ventilation. Among the causes of death, ACS is seen in 32 (26%), sepsis in 49 (40%), and miscellaneous in 42 (34%). Sudden death was seen in 32 (26%) of patients. Male gender, with low HbF, rapid drop in Hb and platelet, and increased in WBC, LDH, ferritin, and CRP, correlated significantly with mortality in this cohort.

Hodgkin’s Lymphoma Transformation into Diffuse Large B Cell Lymphoma Presented as a Right Atrial Mass: A Case Report

Cardiac involvement can occur in lymphoma, with the right heart being predominantlyaffected. The prognosis for patients with cardiac involvement is poor. A 28-year-old man,with a 4-year history of Hodgkin’s lymphoma, presented with progressive shortnessof breath and ascites. Chest X-ray (CXR) and CT scan revealed multiple masses in themediastinum. To exclude a pulmonary embolism, the patient underwent CT angiography,which incidentally revealed a filling defect in the right atrium. Trans-esophagealechocardiography confirmed the presence of a mass in the right atrium. Surgery wasperformed to obtain samples of the mediastinal masses and the right atrium mass.Pathological results indicated that the nature of the right atrium mass was diffuse largeB-cell lymphoma (DLBCL), suggesting that the Hodgkin’s lymphoma had transformedinto DLBCL. The transformation of Hodgkin’s lymphoma to DLBCL can occur, and cardiacinvolvement is not common. Given that the transformation of Hodgkin’s lymphoma intoDLBCL can occur many years after the initial diagnosis, the authors recommend followup of these patients, as the prognosis may be worse than for non-transformed subtypes.

Serum Level of Vitamin K as Predicts Mortality in Iraqi COVID-19 Patients

Objectives: The aim of the presented study is to evaluate the discriminative ability of vitamin K markers for predicting, monitoring, and a prognosis of severity patients of Covid -19 .&#x0D; Methods: In this case-control study, a maximum of sixty patients (30–50 years old) were included. The patients' diagnoses were made using quantitative RT-PCR and a chest X-ray or CT scan performed seven to thirteen days after the onset of symptoms. Murray ratings were utilized to categorize COVID-19 patients based on the severity of their illness. (60) individuals of the same age and gender who appeared to be in good health were included in this study as a control group in order to compare the outcomes. The measurement of serum vitamin K was applied to all patients and healthy individuals.&#x0D; Results: The group of COVID-19 patients had a lower serum vitamin K level (612.32 ± 106.76 vs. 1198.95 ± 151.59 ng/ml, p = 0.0001) than the healthy control group. Furthermore, we discovered a negative correlation between serum vitamin K levels and COVID-19.&#x0D; Conclusion: The current findings showed that, when compared to healthy controls, patients with COVID-19 had decreased vitamin K levels, particularly in severe cases. These low levels suggest that COVID-19 patients may be more likely to die. These results imply that vitamin K may be involved in the COVID-19 disease processes.

ASPERGILLOMA PADA TUBERKULOSIS PARU: LITERATURE REVIEW

Indonesia ranking among the top six countries with the highest TB burden. In the aftermath of pulmonary TB, fungal infections, particularly those caused by Aspergillus species, have emerged as a notable complication. The predominant species, Aspergillus fumigatus, is responsible for a spectrum of conditions ranging from invasive to noninvasive aspergillosis. This literature review aims to provide an exhaustive analysis of post-TB pulmonary aspergilloma, a clinical condition characterized by the growth of Aspergillus fungal balls within pre-existing lung cavities. Utilizing databases such as PubMed, Science Direct, EBSCO, Emerald, Scopus, and Google Scholar, and employing keywords such as "tuberculosis," "aspergillus," "aspergilloma," and "pulmonary," we meticulously applied the PICO framework and the CRAAP (Currency, Relevance, Authority, Accuracy, and Purpose) criteria to select 20 pertinent articles. The findings indicate that pulmonary aspergilloma manifests in approximately 11% to 17% of individuals post-TB treatment, presenting a clinical spectrum from asymptomatic to severe, life-threatening hemoptysis. Classification into simple and complex aspergilloma is based on specific clinical and radiological criteria. Diagnosis is contingent upon the identification of clinical symptoms, assessment of risk factors, and the corroboration of Aspergillus infection through radiological (chest X-ray and CT scan) and serological or microbiological evidence. Management of pulmonary aspergilloma entails non-operative and operative strategies, with surgical intervention being the definitive treatment, especially in cases of recurrent or massive hemoptysis. The review underscores the persistent challenge of TB in Indonesia, exacerbated by secondary fungal infections such as aspergillosis among pulmonary TB survivors. Aspergillus fumigatus is identified as the primary fungal pathogen, leading to pulmonary aspergilloma.