Life Time Attributable Cancer Risk Estimated Using Scanner Reported Dose Length Product during Chest Computed Tomography Imaging in Young Children

This study aims to estimate the lifetime attributable cancer risk (LAR) for pediatric chest computed tomography (CT) examinations in five age groups using recently published age and region-specific conversion coefficients multiplying the widely available scanner registered dose length products (DLP) displayed on the CT console and hence calculating the Effective Dose (ED). The ED is then multiplied by the International Commission on Radiological Protection (ICRP) published risk factor for LAR. The obtained LAR values are compared with the international literature. Factors that may affect the LAR value are reported and discussed. The study included one hundred twenty five chest CT examinations for both males and females aged from less than one year to fifteen years. The patients reported data are from one single medical institution and using two CT scanners from June 2022 to December 2023. The results of this study may serve as benchmark for institutional radiation dose reference levels and risk estimation.

Comparison of efficacy of lung ultrasound and chest X-ray in diagnosing pulmonary edema and pleural effusion in ICU patients: A single centre, prospective, observational study

Background and Aims While chest X-ray (CXR) has been a conventional tool in intensive care units (ICUs) to identify lung pathologies, computed tomography (CT) scan remains the gold standard. Use of lung ultrasound (LUS) in resource-rich ICUs is still under investigation. The present study compares the utility of LUS to that of CXR in identifying pulmonary edema and pleural effusion in ICU patients. In addition, consolidation and pneumothorax were analyzed as secondary outcome measures. Material and Methods This is a prospective, single centric, observational study. Patients admitted in ICU were examined for lung pathologies, using LUS by a trained intensivist;and CXR done within 4 hours of each other. The final diagnosis was ascertained by an independent senior radiologist, based on the complete medical chart including clinical findings and the results of thoracic CT, if available. The results were compared and analyzed. Results Sensitivity, specificity and diagnostic accuracy of LUS was 95%, 94.4%, 94.67% for pleural effusion;and 98.33%, 97.78%, 98.00% for pulmonary edema respectively. Corresponding values with CXR were 48.33%, 76.67%, 65.33% for pleural effusion;and 36.67%, 82.22% and 64.00% for pulmonary edema respectively. Sensitivity, specificity and diagnostic accuracy of LUS was 91.30%, 96.85%, 96.00% for consolidation;and 100.00%, 79.02%, 80.00% for pneumothorax respectively. Corresponding values with CXR were 60.87%, 81.10%, 78.00% for consolidation;and 71.3%, 97.20%, 96.00% for pneumothorax respectively. Conclusion LUS has better diagnostic accuracy in diagnosis of pleural effusion and pulmonary edema when compared with CXR and is thus recommended as an effective alternative for diagnosis of these conditions in acute care settings. Our study recommends that a thoracic CT scan can be avoided in most of such cases.

Pulmonary Edema and Pleural Effusion Detection Using Efficient Net-V1-B4 Architecture and AdamW Optimizer from Chest X-Rays Images

This paper presents a novelmulticlass systemdesigned to detect pleural effusion and pulmonary edema on chest Xray images,addressing the critical need for early detection in healthcare.A new comprehensive dataset was formed by combining 28,309 samples from the ChestX-ray14,PadChest,and CheXpert databases,with 10,287,6022,and 12,000 samples representing Pleural Effusion,Pulmonary Edema,and Normal cases,respectively.Consequently,the preprocessing step involves applying the Contrast Limited Adaptive Histogram Equalization(CLAHE)method to boost the local contrast of the X-ray samples,then resizing the images to 380×380 dimensions,followed by using the data augmentation technique.The classification task employs a deep learning model based on the EfficientNet-V1-B4 architecture and is trained using the AdamW optimizer.The proposed multiclass system achieved an accuracy(ACC)of 98.3%,recall of 98.3%,precision of 98.7%,and F1-score of 98.7%.Moreover,the robustness of the model was revealed by the Receiver Operating Characteristic(ROC)analysis,which demonstrated an Area Under the Curve(AUC)of 1.00 for edema and normal cases and 0.99 for effusion.The experimental results demonstrate the superiority of the proposedmulti-class system,which has the potential to assist clinicians in timely and accurate diagnosis,leading to improved patient outcomes.Notably,ablation-CAM visualization at the last convolutional layer portrayed further enhanced diagnostic capabilities with heat maps on X-ray images,which will aid clinicians in interpreting and localizing abnormalities more effectively.

Relationship between neonatal respiratory distress syndrome pulmonary ultrasonography and respiratory distress score,oxygenation index,and chest radiography grading

BACKGROUND Accurate condition assessment is critical for improving the prognosis of neonatal respiratory distress syndrome(RDS),but current assessment methods for RDS pose a cumulative risk of harm to neonates.Thus,a less harmful method for assessing the health of neonates with RDS is needed.AIM To analyze the relationships between pulmonary ultrasonography and respiratory distress scores,oxygenation index,and chest X-ray grade of neonatal RDS to identify predictors of neonatal RDS severity.METHODS This retrospective study analyzed the medical information of 73 neonates with RDS admitted to the neonatal intensive care unit of Liupanshui Maternal and Child Care Service Center between April and December 2022.The pulmonary ultrasonography score,respiratory distress score,oxygenation index,and chest Xray grade of each newborn before and after treatment were collected.Spearman correlation analysis was performed to determine the relationships among these values and neonatal RDS severity.RESULTS The pulmonary ultrasonography score,respiratory distress score,oxygenation index,and chest X-ray RDS grade of the neonates were significantly lower after treatment than before treatment(P<0.05).Spearman correlation analysis showed that before and after treatment,the pulmonary ultrasonography score of neonates with RDS was positively correlated with the respiratory distress score,oxygenation index,and chest X-ray grade(ρ=0.429–0.859,P<0.05).Receiver operating characteristic curve analysis indicated that pulmonary ultrasonography screening effectively predicted the severity of neonatal RDS(area under the curve=0.805–1.000,P<0.05).CONCLUSION The pulmonary ultrasonography score was significantly associated with the neonatal RDS score,oxygenation index,and chest X-ray grade.The pulmonary ultrasonography score was an effective predictor of neonatal RDS severity.

Chest Radiography: General Practitioners’ Compliance with Recommendations

Introduction: Chest radiography is the most frequently prescribed imaging test in general practice in France. We aimed to assess the extent to which general practitioners follow the recommendations of the French National Authority for Health in prescribing chest radiography. Methodology: We conducted a retrospective analysis study, in two radiology centers belonging to the same group in Saint-Omer and Aire-sur-la-Lys, of requests for chest radiography sent by general practitioners over the winter period between December 22, 2013, and March 21, 2014, for patients aged over 18 years. Results: One hundred and seventy-seven requests for chest X-rays were analyzed, 71.75% of which complied with recommendations. The most frequent reason was the search for bronchopulmonary infection, accounting for 70.08% of prescriptions, followed by 11.2% for requests to rule out pulmonary neoplasia, whereas the latter reason did not comply with recommendations. Chest X-rays contributed to a positive diagnosis in 28.81% of cases. The positive diagnosis was given by 36.22% of the recommended chest X-rays, versus 10% for those not recommended. Conclusion: In most cases, general practitioners follow HAS recommendations for prescribing chest X-rays. Non-recommended chest X-rays do not appear to make a major contribution to diagnosis or patient management, confirming the value of following the recommendations of the French National Authority for Health.

Rib Osteosynthesis for Sub-Acute Management of a Flail Chest in a Tertiary Centre in a Low-Middle Income Country of Sub-Saharan Africa: Case Report at Douala Laquintinie Hospital

Background: Costal fracture surgical is still a debate, therefore we shall select between early and delay surgical management. Case Report: We are reporting two cases of post road traffic clash delay ribs fractures osteosynthesis involving a 63-year-old man with multistage fractures on the left and pulmonary pinning of one of the costal arches, complicated by a homolateral haemothorax and a 41-year-old man with a bilateral flail chest. Conclusion: The simple postoperative course and the immediate postoperative improvement in the patient’s clinical respiratory condition enabled us to discuss the time frame for management, in this case the indication for early or later surgery.

Impact of Optimizing Emergency Nursing Processes on Resuscitation Success in Patients with Acute Chest Pain

Objective:To analyze the effect of optimizing the emergency nursing process in the resuscitation of patients with acute chest pain and the impact on the resuscitation success rate.Methods:66 patients with acute chest pain received by the emergency department of our hospital from January 2022 to December 2023 were selected as the study subjects and divided into two groups according to the differences in the emergency nursing process,i.e.,33 patients receiving routine emergency care were included in the control group,and 33 patients receiving the optimization of emergency nursing process intervention were included in the observation group.Patients’resuscitation effect and satisfaction with nursing care in the two groups were compared.Results:The observation group’s consultation assessment time,reception time,admission to the start of resuscitation time,and resuscitation time were shorter than that of the control group,the resuscitation success rate was higher than that of the control group,and the incidence of adverse events was lower than that of the control group,with statistically significant differences(P<0.05);and the observation group’s satisfaction with nursing care was higher than that of the control group,with statistically significant differences(P<0.05).Conclusion:Optimization of emergency nursing process intervention in the resuscitation of acute chest pain patients can greatly shorten the rescue time and improve the success rate of resuscitation,with higher patient satisfaction.

Application Value of NLR,PLR,LMR,HEART score,and POCT in Early Warning and Accurate Graded Diagnosis of High-Risk Chest Pain in Emergency Medicine

Objective: To evaluate the application value of neutrophils/lymphocytes (NLR), platelets/lymphocytes (PLR), lymphocytes/monocytes (LMR), HEART (history, electrocardiogram, age, risk factors, and troponin) score, and point-of- care testing (POCT) in the early warning and precise diagnosis of high-risk chest pain in emergency medicine. Methods: A total of 157 patients with acute chest pain who were admitted to the emergency department and chest pain treatment unit of our hospital between August 2022 and September 2023 were selected. Rapid testing of bedside myocardial markers (ultrasensitive troponin (hs-cTnI), myoglobin (MYO), creatine kinase isoenzyme (CK-MB), D-dimer (D-Dimer), and N-terminal B-type natriuretic peptide precursor (NT-proBNP)) was performed on the patients using a POCT device (ThermoKing BioMQ60proB). A HEART score was used to classify the patients into low (n = 53), intermediate (n = 59), and high-risk (n = 45) groups, and the NLR, PLR, and LMR were calculated. The NLR, PLR, and LMR values were compared among the three groups of patients, and the optimal cutoff values as well as sensitivity and specificity were determined based on receiver operating characteristic (ROC) analysis. Results: The HEART scores of patients in the low-risk, intermediate-risk, and high-risk groups were (2.72 ± 0.24), (4.75 ± 0.56), and (5.32 ± 0.73) respectively, and the differences were statistically significant (P < 0.05). Compared with the low-risk group, the intermediate-risk group and high-risk group had a significantly higher NLR and PLR, and a significantly lower LMR;the high-risk group had higher NLR and PLR and lower values of LMR as compared to the other two groups, and the difference was statistically significant (P < 0.05). The ROC curves suggested that the area under the curve, sensitivity, and specificity of the combined diagnosis of NLR, PLR, LMR, HEART score, and POCT were greater than those of LR, PLR, and LMR with HEART score and POCT alone. Conclusion: The combined application of NLR, PLR, LMR, HEART score, and POCT has significant application value in the early warning and precise diagnosis of emergency high-risk chest pain. It provides a more simple, easy-to-access, and efficient assessment index for the clinical prediction and treatment of emergency high- risk chest pain.

Residual Feature Attentional Fusion Network for Lightweight Chest CT Image Super-Resolution

The diagnosis of COVID-19 requires chest computed tomography(CT).High-resolution CT images can provide more diagnostic information to help doctors better diagnose the disease,so it is of clinical importance to study super-resolution(SR)algorithms applied to CT images to improve the reso-lution of CT images.However,most of the existing SR algorithms are studied based on natural images,which are not suitable for medical images;and most of these algorithms improve the reconstruction quality by increasing the network depth,which is not suitable for machines with limited resources.To alleviate these issues,we propose a residual feature attentional fusion network for lightweight chest CT image super-resolution(RFAFN).Specifically,we design a contextual feature extraction block(CFEB)that can extract CT image features more efficiently and accurately than ordinary residual blocks.In addition,we propose a feature-weighted cascading strategy(FWCS)based on attentional feature fusion blocks(AFFB)to utilize the high-frequency detail information extracted by CFEB as much as possible via selectively fusing adjacent level feature information.Finally,we suggest a global hierarchical feature fusion strategy(GHFFS),which can utilize the hierarchical features more effectively than dense concatenation by progressively aggregating the feature information at various levels.Numerous experiments show that our method performs better than most of the state-of-the-art(SOTA)methods on the COVID-19 chest CT dataset.In detail,the peak signal-to-noise ratio(PSNR)is 0.11 dB and 0.47 dB higher on CTtest1 and CTtest2 at×3 SR compared to the suboptimal method,but the number of parameters and multi-adds are reduced by 22K and 0.43G,respectively.Our method can better recover chest CT image quality with fewer computational resources and effectively assist in COVID-19.

Exploring the Feasibility of Machine Learning to Predict Risk Stratification Within 3 Months in Chest Pain Patients with Suspected NSTE-ACS

Objective We aimed to assess the feasibility and superiority of machine learning(ML)methods to predict the risk of Major Adverse Cardiovascular Events(MACEs)in chest pain patients with NSTE-ACS.Methods Enrolled chest pain patients were from two centers,Beijing Anzhen Emergency Chest Pain Center Beijing Bo’ai Hospital,China Rehabilitation Research Center.Five classifiers were used to develop ML models.Accuracy,Precision,Recall,F-Measure and AUC were used to assess the model performance and prediction effect compared with HEART risk scoring system.Ultimately,ML model constructed by Naïve Bayes was employed to predict the occurrence of MACEs.Results According to learning metrics,ML models constructed by different classifiers were superior over HEART(History,ECG,Age,Risk factors,&Troponin)scoring system when predicting acute myocardial infarction(AMI)and all-cause death.However,according to ROC curves and AUC,ML model constructed by different classifiers performed better than HEART scoring system only in prediction for AMI.Among the five ML algorithms,Linear support vector machine(SVC),Naïve Bayes and Logistic regression classifiers stood out with all Accuracy,Precision,Recall and F-Measure from 0.8 to 1.0 for predicting any event,AMI,revascularization and all-cause death(vs.HEART≤0.78),with AUC from 0.88 to 0.98 for predicting any event,AMI and revascularization(vs.HEART≤0.85).ML model developed by Naïve Bayes predicted that suspected acute coronary syndrome(ACS),abnormal electrocardiogram(ECG),elevated hs-cTn I,sex and smoking were risk factors of MACEs.Conclusion Compared with HEART risk scoring system,the superiority of ML method was demonstrated when employing Linear SVC classifier,Naïve Bayes and Logistic.ML method could be a promising method to predict MACEs in chest pain patients with NSTE-ACS.

Chest wall osteochondroma resection with biologic acellular bovine dermal mesh reconstruction in pediatric hereditary multiple exostoses:A case report and review of literature

BACKGROUND Hereditary multiple exostoses is a rare genetic disorder characterized by the growth of multiple osteochondromas affecting primarily long bones.Chest wall lesions may represent a challenge,particularly in pediatric patients.Pain is a common manifestation.However,life-threatening complications can result from direct involvement of adjacent structures.Surgical resection with appropriate reconstruction is often required.CASE SUMMARY A 5-year-old male who was diagnosed with hereditary multiple exostoses presented with significant pain from a large growing chest wall exostosis lesion.After appropriate preoperative investigations,he underwent surgical resection with reconstruction of his chest wall using a biologic bovine dermal matrix mesh.CONCLUSION Resection of chest wall lesions in children represents a challenge.Preoperative planning to determine the appropriate reconstruction strategy is essential.

Optimal Synergic Deep Learning for COVID-19 Classification Using Chest X-Ray Images

A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the lungs.Chest X-ray(CXR)gained much interest after the COVID-19 outbreak thanks to its rapid imaging time,widespread availability,low cost,and portability.In radiological investigations,computer-aided diagnostic tools are implemented to reduce intra-and inter-observer variability.Using lately industrialized Artificial Intelligence(AI)algorithms and radiological techniques to diagnose and classify disease is advantageous.The current study develops an automatic identification and classification model for CXR pictures using Gaussian Fil-tering based Optimized Synergic Deep Learning using Remora Optimization Algorithm(GF-OSDL-ROA).This method is inclusive of preprocessing and classification based on optimization.The data is preprocessed using Gaussian filtering(GF)to remove any extraneous noise from the image’s edges.Then,the OSDL model is applied to classify the CXRs under different severity levels based on CXR data.The learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the work.OSDL model,applied in this study,was validated using the COVID-19 dataset.The experiments were conducted upon the proposed OSDL model,which achieved a classification accuracy of 99.83%,while the current Convolutional Neural Network achieved less classification accuracy,i.e.,98.14%.

COVID-19 Detection from Chest X-Ray Images Using Convolutional Neural Network Approach

COVID-19 is a respiratory illness caused by the SARS-CoV-2 virus, first identified in 2019. The primary mode of transmission is through respiratory droplets when an infected person coughs or sneezes. Symptoms can range from mild to severe, and timely diagnosis is crucial for effective treatment. Chest X-Ray imaging is one diagnostic tool used for COVID-19, and a Convolutional Neural Network (CNN) is a popular technique for image classification. In this study, we proposed a CNN-based approach for detecting COVID-19 in chest X-Ray images. The model was trained on a dataset containing both COVID-19 positive and negative cases and evaluated on a separate test dataset to measure its accuracy. Our results indicated that the CNN approach could accurately detect COVID-19 in chest X-Ray images, with an overall accuracy of 97%. This approach could potentially serve as an early diagnostic tool to reduce the spread of the virus.

Radiation Dose Survey of Pediatric Chest Computed Tomography Examinations: A Local Diagnostic Reference Levels Approach to Patient Safety

Objectives: The aim of this work was to initially establish both age and weight driven pediatric diagnostic reference levels (DRLs) for chest computed tomography (CT) examinations performed at tertiary care medical institution. Another aim was to compare the presented data with internationally published ones. This initial data shall serve as basis for establishing a national DRLs values for pediatric diagnostic CT examinations. Methods: Dosimetric indexes were collected for the chest examination for 93 patients during the past 2 years in a tertiary care medical city. Results: The results are within and below the international reported levels for chest CT in several countries. Conclusion: Continuous monitoring of the radiation doses received by the patients in computed tomography is continuous and ongoing process in order to ensure compliance and to optimize clinical imaging protocols. More extensive data acquisition and analysis are required to allow better understanding of the contributing factors leading to less patient radiation dose while preserving the clinical image quality. .

Chest Radiographs Based Pneumothorax Detection Using Federated Learning

Pneumothorax is a thoracic condition that occurs when a person’s lungs collapse,causing air to enter the pleural cavity,the area close to the lungs and chest wall.The most persistent disease,as well as one that necessitates particular patient care and the privacy of their health records.The radiologists find it challenging to diagnose pneumothorax due to the variations in images.Deep learning-based techniques are commonly employed to solve image categorization and segmentation problems.However,it is challenging to employ it in the medical field due to privacy issues and a lack of data.To address this issue,a federated learning framework based on an Xception neural network model is proposed in this research.The pneumothorax medical image dataset is obtained from the Kaggle repository.Data preprocessing is performed on the used dataset to convert unstructured data into structured information to improve the model’s performance.Min-max normalization technique is used to normalize the data,and the features are extracted from chest Xray images.Then dataset converts into two windows to make two clients for local model training.Xception neural network model is trained on the dataset individually and aggregates model updates from two clients on the server side.To decrease the over-fitting effect,every client analyses the results three times.Client 1 performed better in round 2 with a 79.0%accuracy,and client 2 performed better in round 2 with a 77.0%accuracy.The experimental result shows the effectiveness of the federated learning-based technique on a deep neural network,reaching a 79.28%accuracy while also providing privacy to the patient’s data.

Surgical Outcomes Following Partial Breast Reconstruction with Chest Wall Perforator Flaps

Introduction: In the last two decades, chest wall perforator flaps (CWPF) have become a versatile tissue replacement technique for partial breast reconstruction following breast-conserving surgery (BCS) in well-selected cases. We present the surgical outcome of 81 patients with chest wall perforator flaps used for breast-conserving surgery. Methods: We recorded the outcomes of three oncoplastic breast surgeons who performed partial breast reconstruction with chest wall perforator flaps from 1st January 2018 to 30th June 2022 at Sherwood Forest Hospitals NHS Foundation Trust. Data were collected on patient demographics, including age, BMI, smoking status, bra size, previous treatments, type of CWPF procedure, tumor size (measured clinically, via imaging and histologically), biopsy results, specimen weight, margins involvement, re-operation rate, surgical site infection (SSI), flap loss, flap shrinkage, hematoma, and seroma rates. Results: A total of 81 patients were included in this study, with an average age of 55.7 years and a body mass index (BMI) of 26.7 kg/m2. The bra size varied between A to FF with A (7.4%), B (28.3%), C (38.2%), D (13.6%), DD (11.1%), and FF (1.2%). 14.8% of the patients had neoadjuvant chemotherapy (NACT). For 45 patients, LICAP (lateral intercostal artery perforator), 16 AICAP (anterior intercostal artery perforator), 13 MICAP (medial intercostal artery perforator), and for seven patients, LTAP (lateral thoracic artery perforator) flaps were used. The average tumor was measured at 15.75 mm clinically, 19.1 mm via imaging, and 19.6 mm histologically. Biopsy showed that 16% of the tumors were ductal carcinoma in situ (DCIS), and 84% were invasive. 16% of patients had involved margins, and re-excision was required in 10 patients, and completion mastectomy was performed in 2 patients. A thirty-day SSI rate was 6.2%, with flap-related complications, including flap loss and shrinkage, at 3.7% and 4.9%, respectively. In addition, 3.7% had a hematoma, and 17.3% had other complications. Conclusion: Partial breast reconstruction with perforator flaps is an excellent volume replacement technique in breast-conserving surgery with acceptable complications in well-selected cases.

New scoring system for acute chest pain risk stratification: Is it worth SVEAT-ing it?

The emergency room is a very potent environment in the hospital.With the growing demands of the population,improved accessibility to health resources,and the onslaught of the triple pandemic,it is extremely crucial to triage patients at presentation.In the spectrum of complaints,chest pain is the commonest.Despite it being a daily ailment,chest pain brings concern to every physician at first.Chest pain could span from acute coronary syndrome,pulmonary embolism,and aortic dissection(all potentially fatal)to reflux,zoster,or musculoskeletal causes that do not need rapid interventions.We often employ scoring systems such as GRACE/PURSUIT/TIMI to assist in clinical decision-making.Over the years,the HEART score became a popular and effective tool for predicting the risk of 30-d major adverse cardiovascular events.Recently,a new scoring system called SVEAT was developed and compared to the HEART score.We have attempted to summarize how these scoring systems differ and their generalizability.With an increasing number of scoring systems being introduced,one must also prevent anchorage bias;i.e.,tools such as these are only diagnosis-specific and not organ-specific,and other emergent differential diagnoses must also be kept in mind before discharging the patient home without additional workup.

Histogram Matched Chest X-Rays Based Tuberculosis Detection Using CNN

Tuberculosis(TB)is a severe infection that mostly affects the lungs and kills millions of people’s lives every year.Tuberculosis can be diagnosed using chest X-rays(CXR)and data-driven deep learning(DL)approaches.Because of its better automated feature extraction capability,convolutional neural net-works(CNNs)trained on natural images are particularly effective in image cate-gorization.A combination of 3001 normal and 3001 TB CXR images was gathered for this study from different accessible public datasets.Ten different deep CNNs(Resnet50,Resnet101,Resnet152,InceptionV3,VGG16,VGG19,DenseNet121,DenseNet169,DenseNet201,MobileNet)are trained and tested for identifying TB and normal cases.This study presents a deep CNN approach based on histogram matched CXR images that does not require object segmenta-tion of interest,and this coupled methodology of histogram matching with the CXRs improves the accuracy and detection performance of CNN models for TB detection.Furthermore,this research contains two separate experiments that used CXR images with and without histogram matching to classify TB and non-TB CXRs using deep CNNs.It was able to accurately detect TB from CXR images using pre-processing,data augmentation,and deep CNN models.Without histogram matching the best accuracy,sensitivity,specificity,precision and F1-score in the detection of TB using CXR images among ten models are 99.25%,99.48%,99.52%,99.48%and 99.22%respectively.With histogram matching the best accuracy,sensitivity,specificity,precision and F1-score are 99.58%,99.82%,99.67%,99.65%and 99.56%respectively.The proposed meth-odology,which has cutting-edge performance,will be useful in computer-assisted TB diagnosis and aids in minimizing irregularities in TB detection in developing countries.

Individual with concurrent chest wall tuberculosis and triplenegative essential thrombocythemia:A case report

BACKGROUND Chest wall tuberculosis(TB)and triple-negative essential thrombocythemia(TNET)are rare medical conditions,and their combination is extremely rare globally.Only one case of TB peritonitis with thrombocytosis has been reported,which was identified in 1974.CASE SUMMARY Herein,we report the case of a 23-year-old man with concurrent chest wall mass and TN-ET.The patient presented to a local hospital due to having a headache and low-grade fever for 2 d,with their bodily temperature fluctuating at around 36.8°C.Hematological analysis showed a high platelet count of 1503×109/L.Subsequently,the patient visited our hospital for further investigation.Computed tomography of the chest suggested a submural soft tissue density shadow in the left lower chest wall.After surgical resection,the pathological findings of the swelling were reported as TB with massive caseous necrosis.According to the World Health Organization diagnostic criteria,the patient was diagnosed with TN-ET,as they met the requirement of four main criteria or the first three main criteria and one secondary criterion.The patient was eventually diagnosed with chest wall TB with TN-ET,which is extremely rare.CONCLUSION Chest wall TB is rare.TN-ET diagnosis requires secondary factor exclusion and satisfaction of primary diagnostic criteria.miRNA,combined with the methylation process,could explain suppressor of cytokine signaling(SOCS)1 and SOCS3 downregulation in ET-JAK2V617F-negative patients.The miRNA could participate in JAK2 pathway activation.SOCS3 may be a novel MPN biomarker.

Value of ultrasound guided biopsy combined with Xpert Mycobacterium tuberculosis/resistance to rifampin assay in the diagnosis of chest wall tuberculosis

BACKGROUND The thoracic wall lesions,particularly chest wall tuberculosis,and chest wall tumors and other pyogenic wall and actinomycetes infections,almost always present as a diagnostic challenge.AIM To explore the value of ultrasound-guided biopsy combined with the Xpert Mycobacterium tuberculosis/resistance to rifampin(MTB/RIF)assay to diagnose chest wall tuberculosis.METHODS We performed a retrospective study of patients with chest wall lesions from March 2018 to March 2021.All patients received the ultrasound-guided biopsy for pathology examination,acid-fast Bacillus staining,mycobacterial culture,and Xpert MTB/RIF analysis.The sensitivity,specificity,and area under the curve(AUC)were calculated for these diagnostic tests,either individually or combined.Rifampicin resistance results were compared between the mycobacterial culture and the Xpert MTB/RIF assay.RESULTS In 31 patients with the chest wall lesion biopsy,22 patients were diagnosed with chest wall tuberculosis.Of them,3,6,and 21 patients tested positive for mycobacterial culture,acid-fast stain,and Xpert MTB/RIF assay,respectively.The rifampicin resistance results of the 3 culture-positive patients were consistent with their Xpert MTB/RIF assay results.When considering the sensitivity,specificity,and AUC value,the Xpert MTB/RIF assay(95.5%,88.9%,and 0.92,respectively)was a better choice than the acid-fast Bacillus stain(27.3%,100.0%,and 0.64,respectively)and mycobacterial culture(13.6%,100.0%,0.57,respectively).No complications were reported during the procedure.CONCLUSION Ultrasound guided biopsy combined with Xpert MTB/RIF has high value in the diagnosis of chest wall tuberculosis,and can also detect rifampicin resistance.