Abstract Heart failure (HF) and cirrhosis are frequently associated with pleural effusions (PEs). Despite their apparently benign nature, both HF-related effusions and hepatic hydrothorax (HH) have poor prognosis because they represent an advanced stage of the disease. Optimization of medical therapy in these two entities involve not only the use of diuretics, but also other pharmacological therapies. For instance, all HF patients with reduced or mildly reduced left ventricular ejection fraction can benefit from angiotensin receptor–neprilysin inhibitors, beta blockers, mineralocorticoid receptor antagonists, and sodium-glucose cotransporter 2 inhibitors. Conversely, it is better for HH patients to avoid nonselective beta blockers. Refractory cardiac- and cirrhosis-related PEs are commonly managed by iterative therapeutic thoracentesis. When repeated aspirations are needed, thereby diminishing quality of life, the insertion of an indwelling pleural catheter (IPC) may be warranted. However, in selected HH patients who are diuretic-resistant or diuretic-intractable, placement of transjugular intrahepatic portosystemic shunts should be considered as a bridge to liver transplantation, whereas in transplant candidates the role of IPC is debatable. Another benign condition, pleural tuberculosis (TB) is a serious health problem in developing countries. Diagnostic certainty is still a concern due to the paucibacillary nature of the infection, although the use of more sensitive nucleic acid amplification tests is becoming more widespread. Its treatment is the same as that of pulmonary TB, but the potential drug interactions between antiretroviral and anti-TB drugs in HIV-coinfected patients as well as the current recommended guidelines for the different types of anti-TB drugs resistance should be followed.

Expert Review on Contemporary Management of Common Benign Pleural Effusions

PURPOSE OF REVIEW
Tuberculous pleuritis (TBP) is one of the most common types of extrapulmonary tuberculosis. We highlight the latest epidemiology of TBP, the heterogeneity of its presentation and the performance of different diagnostic strategies.


RECENT FINDINGS
There are differential trends in the incidences of TBP worldwide. Its incidence increased in China but decreased in the United States in the past decade. The presentation of TBP is heterogeneous regarding clinical symptoms, radiological findings and pleural fluid analysis results. Conventional microbiological tests have low sensitivities to diagnose TBP. Recent research focused on various diagnostic tools with better yield. The sensitivity of nucleic acid amplification tests (NAAT) in pleural fluid, including the latest generation of PCR and sequencing-based techniques for detecting tuberculosis, remains suboptimal. Various pleural fluid biomarkers have been explored, but there is a lack of consensus on their clinical utility and cutoff levels.


SUMMARY
The heterogeneity of clinical presentation poses obstacles to diagnosing TBP. Further development of diagnostic tools, including more robust NAAT and biomarkers with additional validation, is needed before incorporation into routine clinical practice.

Tuberculous pleuritis: clinical presentations and diagnostic challenges.

Diagnostic nomogram to differentiate between epididymal tuberculosis and bacterial epididymitis

Aim: It was aimed to examine the patients diagnosed with extrapulmonary tuberculosis (EPTB) in terms of diagnostic methods and demographic characteristics.
Materials and Methods: The files of patients with EPTB who were followed up in the infectious disease’s outpatient clinic between 2012 and 2022 in our study were retrospectively reviewed.
Results: Of the patients diagnosed with EPTB, 70.8% (102) were female and 29.2% (42) were male. The ages of the patients ranged from 20 to 88, the mean age of women was 54.2, and the mean age of men was 55. The most common site of involvement in these patients was lymph node involvement. This was followed by bone-joint, peritoneal, central nervous system and genitourinary system involvement, respectively. Histopathological methods were used most frequently in 81 (56.5%) of the patients.
Conclusion: The signs and symptoms of EPTB differ according to the organs and tissues involved in the body. We believe that EPTB should be considered in the differential diagnosis in endemic regions.

Enfeksiyon hastalıkları polikliniğinde ekstrapulmoner tüberküloz hastalarının epidemiyolojik ve klinik tutulumlarının yıllara göre dağılımı

In this study, we explore the application of machine learning (ML) models for predicting human immunodeficiency virus (HIV) progression and patient outcomes, aiming to enhance patient-trial matching in clinical trials. We evaluate several ML algorithms, including Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), Naïve Bayes (NB), Gradient Boosting Methods (XGBoost), and Logistic Regression, to determine their suitability for the task. Our rigorous analysis reveals that Random Forest and XGBoost outperform other models, achieving accuracy rates of 87.85% and 89.02%, respectively, in predicting HIV progression.Additionally, we delve into precision, recall, and F1-score metrics, providing a comprehensive assessment of each model’s performance. The results show the potential of ML in accurately classifying individuals living with HIV into distinct outcome categories. This research marks a significant stride toward optimizing patient-trial matching processes, ultimately improving the efficiency of clinical trials, and enhancing patient care. Future work will focus on fine-tuning these ML models to improve their predictive capabilities and advance personalized healthcare in the context of HIV clinical trials.

HIV Progression and Outcome Prediction to Enhance Patient Matching for Clinical Trials

Epidemiology of extrapulmonary tuberculosis in central Guangxi from 2016 to 2021

Abstract Objective This study aimed to construct a coronary heart disease (CHD) risk-prediction model in people living with human immunodeficiency virus (PLHIV) with the help of machine learning (ML) per electronic medical records (EMRs). Methods Sixty-one medical characteristics (including demography information, laboratory measurements, and complicating disease) readily available from EMRs were retained for clinical analysis. These characteristics further aided the development of prediction models by using seven ML algorithms [light gradient-boosting machine (LightGBM), support vector machine (SVM), eXtreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), decision tree, multilayer perceptron (MLP), and logistic regression]. The performance of this model was assessed using the area under the receiver operating characteristic curve (AUC). Shapley additive explanation (SHAP) was further applied to interpret the findings of the best-performing model. Results The LightGBM model exhibited the highest AUC (0.849; 95% CI, 0.814–0.883). Additionally, the SHAP plot per the LightGBM depicted that age, heart failure, hypertension, glucose, serum creatinine, indirect bilirubin, serum uric acid, and amylase can help identify PLHIV who were at a high or low risk of developing CHD. Conclusion This study developed a CHD risk prediction model for PLHIV utilizing ML techniques and EMR data. The LightGBM model exhibited improved comprehensive performance and thus had higher reliability in assessing the risk predictors of CHD. Hence, it can potentially facilitate the development of clinical management techniques for PLHIV care in the era of EMRs. 

Predictive model and risk analysis for coronary heart disease in people living with HIV using machine learning

Cytopenia is a frequent complication among HIV-infected patients who require hospitalization. It can have a negative impact on the treatment outcomes for these patients. However, by leveraging machine learning techniques and electronic medical records, a predictive model can be developed to evaluate the risk of cytopenia during hospitalization in HIV patients. Such a model is crucial for designing a more individualized and evidence-based treatment strategy for HIV patients.The present study was conducted on HIV patients who were admitted to Guangxi Chest Hospital between June 2016 and October 2021. We extracted a total of 66 clinical features from the electronic medical records and employed them to train five machine learning prediction models (artificial neural network [ANN], adaptive boosting [AdaBoost], k-nearest neighbour [KNN] and support vector machine [SVM], decision tree [DT]). The models were tested using 20% of the data. The performance of the models was evaluated using indicators such as the area under the receiver operating characteristic curve (AUC). The best predictive models were interpreted using the shapley additive explanation (SHAP).The ANN models have better predictive power. According to the SHAP interpretation of the ANN model, hypoproteinemia and cancer were the most important predictive features of cytopenia in HIV hospitalized patients. Meanwhile, the lower hemoglobin-to-RDW ratio (HGB/RDW), low-density lipoprotein cholesterol (LDL-C) levels, CD4+ T cell counts, and creatinine clearance (Ccr) levels increase the risk of cytopenia in HIV hospitalized patients.The present study constructed a risk prediction model for cytopenia in HIV patients during hospitalization with machine learning and electronic medical record information. The prediction model is important for the rational management of HIV hospitalized patients and the personalized treatment plan setting. 

Prediction of the risk of cytopenia in hospitalized HIV/AIDS patients using machine learning methods based on electronic medical records

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Epidemiology of extrapulmonary tuberculosis in central Guangxi from 2016 to 2021

Predictive model and risk analysis for coronary heart disease in people living with HIV using machine learning

Prediction of the risk of cytopenia in hospitalized HIV/AIDS patients using machine learning methods based on electronic medical records