Risk and predictors of penile cancer in US Veterans with HIV.

OBJECTIVES: People living with HIV (PWH) may have an increased burden of penile cancer. We aimed to evaluate the risk of penile cancer in PWH compared to that of the general population. DESIGN: We conducted a nationwide retrospective matched cohort study of penile cancer incidence among veterans living with HIV (VWH) compared to veterans without HIV. METHODS: We compared penile cancer incidence rates in 44,173 VWH to those of veterans without Human Immunodeficiency virus (HIV) (N = 159,443; 4:1 matched in age. We used Cox regression models to estimate Hazard ratios (HRs) and 95% confidence intervals (CIs) for associations with HIV infection and for penile cancer risk factors. RESULTS: HIV positivity was associated with an increased risk of penile cancer, with adjusted hazard ratios (HR) of 2.63 (95% CI: 1.64-4.23) when adjusting for age, race/ethnicity, baseline BMI, smoking and alcohol use, economic means test, and history of condyloma. The risk increased to HR = 4.25 (95% CI: 2.75-6.57) when adjusting for all factors except history of condyloma. Risk factors for penile cancer in VWH included lower nadir CD4 count, <50% of follow-up time with undetectable HIV viral load, and history of condyloma. CONCLUSIONS: VWH--particularly those with low CD4 counts, detectable HIV viral loads, or history of condyloma--are at increased risk of penile cancer, suggesting the penile cancer prevention activities are needed in this population.

Efficient Online Stream Clustering Based on Fast Peeling of Boundary Micro-Cluster.

A growing number of applications generate streaming data, making data stream mining a popular research topic. Classification-based streaming algorithms require pre-training on labeled data. Manually labeling a large number of samples in the data stream is impractical and cost-prohibitive. Stream clustering algorithms rely on unsupervised learning. They have been widely studied for their ability to effectively analyze high-speed data streams without prior knowledge. Stream clustering plays a key role in data stream mining. Currently, most data stream clustering algorithms adopt the online-offline framework. In the online stage, micro-clusters are maintained, and in the offline stage, they are clustered using an algorithm similar to density-based spatial clustering of applications with noise (DBSCAN). When data streams have clusters with varying densities and ambiguous boundaries, traditional data stream clustering algorithms may be less effective. To overcome the above limitations, this article proposes a fully online stream clustering algorithm called fast boundary peeling stream clustering (FBPStream). First, FBPStream defines a decay-based kernel density estimation (KDE). It can discover clusters with varying densities and identify the evolving trend of streams well. Then, FBPStream implements an efficient boundary micro-cluster peeling technique to identify the potential core micro-clusters. Finally, FBPStream employs a parallel clustering strategy to effectively cluster core and boundary micro-clusters. The proposed algorithm is compared with ten popular algorithms on 15 data streams. Experimental results show that FBPStream is competitive with the other ten popular algorithms.

Corticosteroid in non-COVID-19 induced community-acquired pneumonia, a meta-analysis.

BACKGROUND: Corticosteroid treatment in non-COVID-19 induced Community-acquired pneumonia (CAP) remains inconclusive. OBJECTIVES: We aimed to assess the role of corticosteroid treatment in CAP. METHODS: We conducted a comprehensive search of online databases, including PubMed, Embase, and Cochrane, to identify articles published from January 1, 2000, to May 5, 2023. Double-blind RCTs were selected. Two authors screened studies and extracted data. The evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation approach. RESULTS: We analyzed data from 12 RCTs, involving 2446 patients. Corticosteroids therapy may reduce short-term mortality in patients with severe CAP (sCAP) and shorten the hospital length of stay in patients with CAP. Furthermore, corticosteroids treatment can decrease the risk of requiring mechanical ventilation, developing septic shock and multiple organ dysfunction syndrome (MODS). There were no significant differences between the corticosteroid and control groups concerning gastrointestinal bleeding and nosocomial infection. The use of corticosteroids could increase the risk of hyperglycemia. CONCLUSION: Corticosteroid treatment for sCAP has the potential to provide benefits in reducing short-term mortality, but this conclusion necessitates more evidence. Besides, we found no evidence that strongly prevents us from using corticosteroids in patients with sCAP or those at risk of progressing to sCAP.

Analysis of the gut microbiome associated to PVC biodegradation in yellow mealworms.

The potential of invertebrates in the biodegradation of plastic polymers such as polyvinyl chloride (PVC) is receiving increasing attention. The present study is aimed to identify the gut microbiome involved in this degradation in yellow mealworms, i.e., the larvae of Tenebrio molitor Linnaeus. The egested PVC polymer experienced a dramatic reduction in both number average molecular weight (Mn) and weight average molecular weight (Mw) of 99.3% and 99.6%, respectively, whereas FTIR analysis revealed chemical alterations. Mass spectrometry analysis identified two potential degradation products: phthalic acid, di(2-propylpentyl) ester and 2-Propenoic acid, tridecyl ester. Further, we used metagenomic sequencing to elucidate the response of the gut microbiome when transitioning from bran to PVC as a food source, identifying four microorganisms actively involved in PVC degradation. Additionally, metagenomic functional analysis of the gut microbiome identified 111 key gene modules that were significantly enriched. In summary, our findings suggest that yellow mealworms adapt to PVC degradation by modifying their gut microbiome both structurally and functionally.

Case Sampling for Evaluating Hospital Postoperative Morbidity in US Surgical Quality Improvement Programs.

Importance: US surgical quality improvement (QI) programs use data from a systematic sample of surgical cases, rather than universal review of all cases, to assess and compare risk-adjusted hospital postoperative complication rates. Given decreasing postoperative complication rates over time and the types of cases eligible for abstraction, it is unclear whether case sampling is robust for identifying hospitals with higher than expected complications. Objective: To compare the assessment of hospital 30-day complication rates derived from sampling strategy used by some US surgical QI programs relative to universal review of all cases. Design, Setting, and Participants: This US hospital-level analysis took place from January 1, 2016, through September 30, 2020. Data analysis was performed from July 1, 2022, through December 21, 2022. Quarterly, risk-adjusted, 30-day complication observed to expected (O-E) ratios were calculated for each hospital using the sample (n = 502 730) and universal review (n = 1 725 364). Outlier hospitals (ie, those with higher than expected mortality) were identified using an O-E ratio significantly greater than 1.0. Patients 18 years and older who underwent a noncardiac operation at US Department of Veterans Affairs (VA) hospitals with a record in the VA Surgical Quality Improvement Program (systematic sample) and the VA Corporate Data Warehouse surgical domain (100% of surgical cases) were included. Main Outcome Measure: Thirty-day complications. Results: Most patients in both the representative sample and the universal sample were men (90.2% vs 91.2%) and White (74.7% vs 74.5%). Overall, 30-day complication rates were 7.6% and 5.3% for the sample and universal review cohorts, respectively (P < .001). Over 2145 hospital quarters of data, hospitals were identified as an outlier in 15.0% of quarters using the sample and 18.2% with universal review. Average hospital quarterly complication rates were 4.7%, 7.2%, and 7.4% for outliers identified using the sample only, universal review only, and concurrent identification in both data sources, respectively. For nonsampled cases, average hospital quarterly complication rates were 7.0% at outliers and 4.4% at nonoutliers. Among outlier hospital quarters in the sample, 54.2% were concurrently identified with universal review. For those identified with universal review, 44.6% were concurrently identified using the sample. Conclusion: In this observational study, case sampling identified less than half of hospitals with excess risk-adjusted postoperative complication rates. Future work is needed to ascertain how to best use currently collected data and whether alternative data collection strategies may be needed to better inform local QI efforts.

Clinical Timing-Sequence Warning Models for Serious Bacterial Infections in Adults Based on Machine Learning: Retrospective Study.

BACKGROUND: Serious bacterial infections (SBIs) are linked to unplanned hospital admissions and a high mortality rate. The early identification of SBIs is crucial in clinical practice. OBJECTIVE: This study aims to establish and validate clinically applicable models designed to identify SBIs in patients with infective fever. METHODS: Clinical data from 945 patients with infective fever, encompassing demographic and laboratory indicators, were retrospectively collected from a 2200-bed teaching hospital between January 2013 and December 2020. The data were randomly divided into training and test sets at a ratio of 7:3. Various machine learning (ML) algorithms, including Boruta, Lasso (least absolute shrinkage and selection operator), and recursive feature elimination, were utilized for feature filtering. The selected features were subsequently used to construct models predicting SBIs using logistic regression (LR), random forest (RF), and extreme gradient boosting (XGBoost) with 5-fold cross-validation. Performance metrics, including the receiver operating characteristic (ROC) curve and area under the ROC curve (AUC), accuracy, sensitivity, and other relevant parameters, were used to assess model performance. Considering both model performance and clinical needs, 2 clinical timing-sequence warning models were ultimately confirmed using LR analysis. The corresponding predictive nomograms were then plotted for clinical use. Moreover, a physician, blinded to the study, collected additional data from the same center involving 164 patients during 2021. The nomograms developed in the study were then applied in clinical practice to further validate their clinical utility. RESULTS: In total, 69.9% (661/945) of the patients developed SBIs. Age, hemoglobin, neutrophil-to-lymphocyte ratio, fibrinogen, and C-reactive protein levels were identified as important features by at least two ML algorithms. Considering the collection sequence of these indicators and clinical demands, 2 timing-sequence models predicting the SBI risk were constructed accordingly: the early admission model (model 1) and the model within 24 hours of admission (model 2). LR demonstrated better stability than RF and XGBoost in both models and performed the best in model 2, with an AUC, accuracy, and sensitivity of 0.780 (95% CI 0.720-841), 0.754 (95% CI 0.698-804), and 0.776 (95% CI 0.711-832), respectively. XGBoost had an advantage over LR in AUC (0.708, 95% CI 0.641-775 vs 0.686, 95% CI 0.617-754), while RF achieved better accuracy (0.729, 95% CI 0.673-780) and sensitivity (0.790, 95% CI 0.728-844) than the other 2 approaches in model 1. Two SBI-risk prediction nomograms were developed for clinical use based on LR, and they exhibited good performance with an accuracy of 0.707 and 0.750 and a sensitivity of 0.729 and 0.927 in clinical application. CONCLUSIONS: The clinical timing-sequence warning models demonstrated efficacy in predicting SBIs in patients suspected of having infective fever and in clinical application, suggesting good potential in clinical decision-making. Nevertheless, additional prospective and multicenter studies are necessary to further confirm their clinical utility.

Long Short-Term Memory-Based Twin Support Vector Regression for Probabilistic Load Forecasting.

A probabilistic load forecast that is accurate and reliable is crucial to not only the efficient operation of power systems but also to the efficient use of energy resources. In order to estimate the uncertainties in forecasting models and nonstationary electric load data, this study proposes a probabilistic load forecasting model, namely BFEEMD-LSTM-TWSVRSOA. This model consists of a data filtering method named fast ensemble empirical model decomposition (FEEMD) method, a twin support vector regression (TWSVR) whose features are extracted by deep learning-based long short-term memory (LSTM) networks, and parameters optimized by seeker optimization algorithms (SOAs). We compared the probabilistic forecasting performance of the BFEEMD-LSTM-TWSVRSOA and its point forecasting version with different machine learning and deep learning algorithms on Global Energy Forecasting Competition 2014 (GEFCom2014). The most representative month data of each season, totally four monthly data, collected from the one-year data in GEFCom2014, forming four datasets. Several bootstrap methods are compared in order to determine the best prediction intervals (PIs) for the proposed model. Various forecasting step sizes are also taken into consideration in order to obtain the best satisfactory point forecasting results. Experimental results on these four datasets indicate that the wild bootstrap method and 24-h step size are the best bootstrap method and forecasting step size for the proposed model. The proposed model achieves averaged 46%, 11%, 36%, and 44% better than suboptimal model on these four datasets with respect to point forecasting, and achieves averaged 53%, 48%, 46%, and 51% better than suboptimal model on these four datasets with respect to probabilistic forecasting.

Case Sampling vs Universal Review for Evaluating Hospital Postoperative Mortality in US Surgical Quality Improvement Programs.

Importance: Representative surgical case sampling, rather than universal review, is used by US Department of Veterans Affairs (VA) and private-sector national surgical quality improvement (QI) programs to assess program performance and to inform local QI and performance improvement efforts. However, it is unclear whether case sampling is robust for identifying hospitals with safety or quality concerns. Objective: To evaluate whether the sampling strategy used by several national surgical QI programs provides hospitals with data that are representative of their overall quality and safety, as measured by 30-day mortality. Design, Setting, and Participants: This comparative effectiveness study was a national, hospital-level analysis of data from adult patients (aged ≥18 years) who underwent noncardiac surgery at a VA hospital between January 1, 2016, and September 30, 2020. Data were obtained from the VA Surgical Quality Improvement Program (representative sample) and the VA Corporate Data Warehouse surgical domain (100% of surgical cases). Data analysis was performed from July 1 to December 21, 2022. Main Outcomes and Measures: The primary outcome was postoperative 30-day mortality. Quarterly, risk-adjusted, 30-day mortality observed-to-expected (O-E) ratios were calculated separately for each hospital using the sample and universal review cohorts. Outlier hospitals (ie, those with higher-than-expected mortality) were identified using an O-E ratio significantly greater than 1.0. Results: In this study of data from 113 US Department of Veterans Affairs hospitals, the sample cohort comprised 502 953 surgical cases and the universal review cohort comprised 1 703 140. The majority of patients in both the representative sample and the universal sample were men (90.2% vs 91.1%) and were White (74.7% vs 74.5%). Overall, 30-day mortality was 0.8% and 0.6% for the sample and universal review cohorts, respectively (P < .001). Over 2145 quarters of data, hospitals were identified as an outlier in 11.7% of quarters with sampling and in 13.2% with universal review. Average hospital quarterly 30-day mortality rates were 0.4%, 0.8%, and 0.9% for outlier hospitals identified using the sample only, universal review only, and concurrent identification in both data sources, respectively. For nonsampled cases, average hospital quarterly 30-day mortality rates were 1.0% at outlier hospitals and 0.5% at nonoutliers. Among outlier hospital quarters in the sample, 47.4% were concurrently identified with universal review. For those identified with universal review, 42.1% were concurrently identified using the sample. Conclusions and Relevance: In this national, hospital-level study, sampling strategies employed by national surgical QI programs identified less than half of hospitals with higher-than-expected perioperative mortality. These findings suggest that sampling may not adequately represent overall surgical program performance or provide stakeholders with the data necessary to inform QI efforts.

Risk-Adjusted Cumulative Sum for Early Detection of Hospitals With Excess Perioperative Mortality.

Importance: National surgical quality improvement programs lack tools for early detection of quality or safety concerns, which risks patient safety because of delayed recognition of poor performance. Objective: To compare the risk-adjusted cumulative sum (CUSUM) with episodic evaluation for early detection of hospitals with excess perioperative mortality. Design, Setting, and Participants: National, observational, hospital-level, comparative effectiveness study of 697 566 patients. Identification of hospitals with excess, risk-adjusted, quarterly 30-day mortality using observed to expected ratios (ie, current criterion standard in the Veterans Affairs Surgical Quality Improvement Program) was compared with the risk-adjusted CUSUM. Patients included in the study underwent a noncardiac operation at a Veterans Affairs hospital, had a record in the Veterans Affairs Surgical Quality Improvement Program (January 1, 2011, through December 31, 2016), and were aged 18 years or older. Main Outcome and Measure: Number of hospitals identified as having excess risk-adjusted 30-day mortality. Results: The cohort included 697 566 patients treated at 104 hospitals across 24 quarters. The mean (SD) age was 60.9 (13.2) years, 91.4% were male, and 8.6% were female. For each hospital, the median number of quarters detected with observed to expected ratios, at least 1 CUSUM signal, and more than 1 CUSUM signal was 2 quarters (IQR, 1-4 quarters), 8 quarters (IQR, 4-11 quarters), and 3 quarters (IQR, 1-4 quarters), respectively. During 2496 total quarters of data, outlier hospitals were identified 33.3% of the time (830 quarters) with at least 1 CUSUM signal within a quarter, 12.5% (311 quarters) with more than 1 CUSUM signal, and 11.0% (274 quarters) with observed to expected ratios at the end of the quarter. The CUSUM detection occurred a median of 49 days (IQR, 25-63 days) before observed to expected ratio reporting (1 signal, 35 days [IQR, 17-54 days]; 2 signals, 49 days [IQR, 26-61 days]; 3 signals, 58 days [IQR, 44-69 days]; ≥4 signals, 49 days [IQR, 42-69 days]; trend test, P < .001). Of 274 hospital quarters detected with observed to expected ratios, 72.6% (199) were concurrently detected by at least 1 CUSUM signal vs 42.7% (117) by more than 1 CUSUM signal. There was a dose-response relationship between the number of CUSUM signals in a quarter and the median observed to expected ratio (0 signals, 0.63; 1 signal, 1.28; 2 signals, 1.58; 3 signals, 2.08; ≥4 signals, 2.49; trend test, P < .001). Conclusions: This study found that with CUSUM, hospitals with excess perioperative mortality can be identified well in advance of standard end-of-quarter reporting, which suggests episodic evaluation strategies fail to detect out-of-control processes and place patients at risk. Continuous performance evaluation tools should be adopted in national quality improvement programs to prevent avoidable patient harm.

Automated fundus ultrasound image classification based on siamese convolutional neural networks with multi-attention.

Fundus ultrasound image classification is a critical issue in the medical field. Vitreous opacity (VO) and posterior vitreous detachment (PVD) are two common eye diseases, Now, the diagnosis of these two diseases mainly relies on manual identification by doctors. This method has the disadvantages of time-consuming and manual investment, so it is very meaningful to use computer technology to assist doctors in diagnosis. This paper is the first to apply the deep learning model to VO and PVD classification tasks. Convolutional neural network (CNN) is widely used in image classification. Traditional CNN requires a large amount of training data to prevent overfitting, and it is difficult to learn the differences between two kinds of images well. In this paper, we propose an end-to-end siamese convolutional neural network with multi-attention (SVK_MA) for automatic classification of VO and PVD fundus ultrasound images. SVK_MA is a siamese-structure network in which each branch is mainly composed of pretrained VGG16 embedded with multiple attention models. Each image first is normalized, then is sent to SVK_MA to extract features from the normalized images, and finally gets the classification result. Our approach has been validated on the dataset provided by the cooperative hospital. The experimental results show that our approach achieves the accuracy of 0.940, precision of 0.941, recall of 0.940, F1 of 0.939 which are respectively increased by 2.5%, 1.9%, 3.4% and 2.5% compared with the second highest model.

Survival outcomes in veterans with hepatocellular carcinoma, with and without HIV infection.

BACKGROUND: HIV infection has been associated with survival disparities among persons with hepatocellular carcinoma (HCC). However, most studies examining survival do not control for provider (e.g. type of HCC treatment given) or individual-level factors (e.g. homelessness, substance use) that could impact survival. In this study, we evaluate the effect of HIV status on survival among persons with HCC, in a comprehensive model that accounts for key individual, provider, and systems-level factors. METHODS: We conducted a retrospective cohort study of persons with HIV (PWH) matched 1 : 1 to HIV-negative controls based on age and year of HCC diagnosis in the national Veterans Administration (VA) health system. The primary outcome was survival. We used Cox regression models to evaluate the effect of HIV status on risk of death. RESULTS: This cohort included 200 matched pairs diagnosed with HCC between 2009 and 2016. A total of 114 PWH (57.0%) and 115 HIV-negative patients (57.5%) received guideline-concordant therapy ( P  = 0.92). Median survival was 13.4 months [95% confidence interval (CI) 8.7-18.1] among PWH and 19.1 months (95% CI 14.6-24.9) for HIV-negative patients. In adjusted models, older age, homelessness, advanced Barcelona Clinic Liver Cancer (BCLC) stage, and not receiving any HCC treatment predicted risk of death. HIV status was not associated with risk of death [adjusted hazard ratio (aHR) 0.95; 95% CI 0.75-1.20; P  = 0.65]. CONCLUSION: HIV status was not associated with worse survival among HCC patients, in a single-payer, equal access healthcare system. These results suggest that HIV infection alone should not exclude PWH from receiving standard therapy.

Area deprivation index and segregation on the risk of HIV: a U.S. Veteran case-control study.

Background: Preventing HIV infection remains a critically important tool in the continuing fight against HIV/AIDS. The primary aim is to evaluate the effect and interactions between a composite area-level social determinants of health measure and an area-level measure of residential segregation on the risk of HIV/AIDS in U.S. Veterans. Methods: Using the individual-level patient data from the U.S. Department of Veterans Affairs, we constructed a case-control study of veterans living with HIV/AIDS (VLWH) and age-, sex assigned at birth- and index date-matched controls. We geocoded patient's residential address to ascertain their neighborhood and linked their information to two measures of neighborhood-level disadvantage: area deprivation index (ADI) and isolation index (ISOL). We used logistic regression to estimate the odds ratio (OR) and 95% confidence interval (CI) for comparing VLWH with matched controls. We performed analyses for the entire U.S. and separately for each U.S. Census division. Findings: Overall, living in minority-segregated neighborhoods was associated with a higher risk of HIV (OR: 1.88 (95% CI: 1.79-1.97) while living in higher ADI neighborhoods was associated with a lower risk of HIV (OR: 0.88; 95% CI: 0.84-0.92). The association between living in a higher ADI neighborhood and HIV was inconsistent across divisions, while living in minority-segregated neighborhoods was consistently associated with increased risk across all divisions. In the interaction model, individuals from low ADI and high ISOL neighborhoods had a higher risk of HIV in three divisions: East South Central; West South Central, and Pacific. Interpretation: Our results suggest that residential segregation may prevent people in disadvantaged neighborhoods from protecting themselves from HIV independent from access to health care. There is the need to advance knowledge about the neighborhood-level social-structural factors that influence HIV vulnerability toward developing interventions needed to achieve the goal of ending the HIV epidemic. Funding: US National Cancer Institute.

Obstructive sleep apnea is related to alterations in fecal microbiome and impaired intestinal barrier function.

Obstructive Sleep Apnea (OSA) is related to repeated upper airway collapse, intermittent hypoxia, and intestinal barrier dysfunction. The resulting damage to the intestinal barrier may affect or be affected by the intestinal microbiota. A prospective case-control was used, including 48 subjects from Sleep Medicine Center of Nanfang Hospital. Sleep apnea was diagnosed by overnight polysomnography. Fecal samples and blood samples were collected from subjects to detect fecal microbiome composition (by 16S rDNA gene amplification and sequencing) and intestinal barrier biomarkers-intestinal fatty acid-binding protein (I-FABP) and D-lactic acid (D-LA) (by ELISA and colorimetry, respectively). Plasma D-LA and I-FABP were significantly elevated in patients with OSA. The severity of OSA was related to differences in the structure and composition of the fecal microbiome. Enriched Fusobacterium, Megamonas, Lachnospiraceae_UCG_006, and reduced Anaerostipes was found in patients with severe OSA. Enriched Ruminococcus_2, Lachnoclostridium, Lachnospiraceae_UCG_006, and Alloprevotella was found in patients with high intestinal barrier biomarkers. Lachnoclostridium and Lachnospiraceae_UCG_006 were the common dominant bacteria of OSA and intestinal barrier damage. Fusobacterium and Peptoclostridium was independently associated with apnea-hypopnea index (AHI). The dominant genera of severe OSA were also related to glucose, lipid, neutrophils, monocytes and BMI. Network analysis identified links between the fecal microbiome, intestinal barrier biomarkers, and AHI. The study confirms that changes in the intestinal microbiota are associated with intestinal barrier biomarkers among patients in OSA. These changes may play a pathophysiological role in the systemic inflammation and metabolic comorbidities associated with OSA, leading to multi-organ morbidity of OSA.

Effect of HIV and antiretroviral therapy use on body weight changes in a cohort of U.S. veterans living with and without HIV.

OBJECTIVE: People living with HIV have high rates of obesity and obesity-related comorbidities. Our study sought to evaluate weight trajectory in a retrospective cohort of people living with HIV and matched HIV-negative veterans (controls) and to evaluate risk factors for weight gain. METHODS: This was a retrospective database analysis of data extracted from the VA Corporate Data Warehouse that included people living with HIV (n = 22 421) and age-matched HIV-negative controls (n = 63 072). The main outcomes were baseline body weight and weight change from baseline at 1, 2, and 5 years after diagnosis (baseline visit for controls). RESULTS: Body weight at baseline was lower in people living with HIV than in controls. People living with HIV on antiretroviral therapy (ART) gained more weight than did controls. In a sub-analysis of ART-exposed people living with HIV, age >50 years, African American race, body mass index (BMI) <25, CD4 ≤200, and HIV diagnosis year after 2000 were associated with more weight gain at year 1. Nucleoside reverse transcriptase inhibitors (NRTI) plus non-NRTIs (NNRTIs) were associated with less weight gain than NRTIs plus protease inhibitors, NRTIs plus integrase inhibitors, or NRTIs plus other agents at year 1. CONCLUSIONS: Among US veterans, those living with HIV had lower rates of obesity than age-matched HIV-negative controls; however, primarily in the first 2 years after starting ART, people living with HIV gained more weight than did controls.

Diagnostic accuracy of metagenomic next-generation sequencing in diagnosing infectious diseases: a meta-analysis.

Many common pathogens are difficult or impossible to detect using conventional microbiological tests. However, the rapid and untargeted nature of metagenomic next-generation sequencing (mNGS) appears to be a promising alternative. To perform a systematic review and meta-analysis of evidence regarding the diagnostic accuracy of mNGS in patients with infectious diseases. An electronic literature search of Embase, PubMed and Scopus databases was performed. Quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Summary receiver operating characteristics (sROC) and the area under the curve (AUC) were calculated; A random-effects model was used in cases of heterogeneity. A total of 20 papers were eligible for inclusion and synthesis. The sensitivity and specificity of diagnostic mNGS were 75% and 68%, respectively. The AUC from the SROC was 85%, corresponding to excellent performance. mNGS demonstrated satisfactory diagnostic performance for infections and yielded an overall detection rate superior to conventional methods.

M3W: Multistep Three-Way Clustering.

Three-way clustering has been an active research topic in the field of cluster analysis in recent years. Some efforts are focused on the technique due to its feasibility and rationality. We observe, however, that the existing three-way clustering algorithms struggle to obtain more information and limit the fault tolerance excessively. Moreover, although the one-step three-way allocation based on a pair of fixed, global thresholds is the most straightforward way to generate the three-way cluster representations, the clusters derived from a pair of global thresholds cannot exactly reveal the inherent clustering structure of the dataset, and the threshold values are often difficult to determine beforehand. Inspired by sequential three-way decisions, we propose an algorithm, called multistep three-way clustering (M3W), to address these issues. Specifically, we first use a progressive erosion strategy to construct a multilevel structure of data, so that lower levels (or external layers) can gather more available information from higher levels (or internal layers). Then, we further propose a multistep three-way allocation strategy, which sufficiently considers the neighborhood information of every eroded instance. We use the allocation strategy in combination with the multilevel structure to ensure that more information is gradually obtained to increase the probability of being assigned correctly, capturing adaptively the inherent clustering structure of the dataset. The proposed algorithm is compared with eight competitors using 18 benchmark datasets. Experimental results show that M3W achieves superior performance, verifying its advantages and effectiveness.

Successful treatment of pulmonary injury due to nitrogen oxide exposure with extracorporeal membrane oxygenation: a report of two cases and literature review.

Background: Severe nitrogen oxide poisoning can lead to life-threatening pulmonary injury.Methods: we report two cases of severe nitrogen-oxide-induced hypoxia treated with veno-venous extracorporeal membrane oxygenation (ECMO). After exposure, the conditions of both patients continued to deteriorate despite maximal mechanical ventilation with a fraction of inspired oxygen of 100%; therefore, we started veno-venous ECMO. The times from presentation to the initiation of ECMO in the two patients were 1 and 2 days. The hypoxemia and respiratory failure improved quickly after ECMO support.Results: The patients were discharged without complications. The durations of ECMO for the two patients were 5 and 6 days. Conclusion: This report describes how early ECMO support was used to treat potentially fatal pulmonary injury after exposure to nitrogen oxide. The duration of the ECMO run is a critical determinant of patient survival.

The Change of Soluble Programmed Death Ligand 1 (sPD-L1) in Plasma of Small Cell Lung Cancer and Its Clinical Significance.

Background soluble programmed death-ligand 1 (sPD-L1) expression in lung squamous cell carcinoma and lung adenocarcinoma is associated with disease progression, and sPD-L1 expression in small cell lung cancer (SCLC) may have similar manifestations and become a potential marker for treatment. The purpose of this study was to observe the changes of plasma sPD-L1 expression in SCLC patients. Methods. 90 patients diagnosed with SCLC from January 2019 to November 2020 were selected as the test group, including 72 males and 18 women, 58.7 ± 6.6 years; 30 healthy subjects were selected from the physical examination center, including 18 males, 12 females, and 60.3 ± 7.0 years. There were no statistical difference in sex and age factors between the trial and control groups (p > 0.05). Selected SCLC used chemotherapy regimen: cisplatin + etoposide (EP), carboplatin + etoposide (CE), and SCLC group were divided into three subgroups of disease progression group, partial remission group, and disease stability group according to the treatment effect. Comparison of the differences in sPD-L1 expression content between the experimental and control populations. Plasma sPD-L1 levels were dynamically monitored pre- and posttreatment in 90 patients with small-cell lung cancer and were associated with efficacy among subgroups. Meanwhile, the risk factors for patient sPD-L1 expression content were analyzed by logistic regression. Results. Plasma sPD-L1 levels were higher in the SCLC group than in the healthy people group (t = 7.40, p < 0.01). In the disease progression group of the SCLC group, sPD-L1 levels were decreased in the SCLC group, sPD-L1 in some remission group was increased after treatment, and sPD-L1 levels in the disease-stable group (p > 0.05). Multivariate logistic regression analysis showed that factors promoting increased sPD-L1 expression in SCLC patients included increased smoking, brain metastasis, and ProGRP expression (both p values < 0.05). Conclusion. (1) Higher peripheral sPD-L1 expression in SCLC patients than in healthy patients, and the expression levels were closely related to efficacy. (2) Dynamic changes in s PD-L1 were correlated with clinical efficacy. (3) The progression of sPD-L1 and ProGRP in SCLC patients showed the same extent during remission and stabilization, suggesting the effect of s PD-L1 in the evaluation of SCLC tumors and the reflection of the tumor marker ProGRP.

High miR-3648 expression and low APC2 expression are associated with shorter survival and tumor progression in NSCLC.

BACKGROUND: Emerging studies have demonstrated that microRNAs (miRNAs) play crucial roles in the carcinogenesis of many developing human tumors. However, the clinical significance and biological function of microRNA-3648 (miR-3648) in non-small cell lung cancer (NSCLC) have been largely undefined. METHODS: The expression of miR-3648 and the mRNA of adenomatous polyposis coli 2 (APC2) in NSCLC tissues and cell lines were analyzed using quantitative real-time RT-PCR. The prognostic value of miR-3648 and APC2 was examined using the Kaplan-Meier method and Cox regression analyses. Experiments using NSCLC cells were conducted to explore the influences of miR-3648 on tumor cell proliferation, migration and invasion. RESULT: Increased expression of miR-3648 was observed in NSCLC tissues and cell lines compared with the corresponding controls (all P<0.05). miR-3648 expression was associated with the differentiation, lymph node metastasis and TNM stage (all P<0.05) of NSCLC patients, and high expression of miR-3648 was associated with poor overall survival rate. NSCLC cell proliferation, migration and invasion were significantly enhanced by miR-3648 overexpression. The further luciferase reporter assay and expression results showed that the decreased APC2 might also be a prognostic biomarker, and served as a target of miR-3648 in NSCLC. CONCLUSION: The findings from the present study indicate that the overexpression of miR-3648 serves as a useful biomarker for the prediction of prognosis in NSCLC, and promotes tumor cell proliferation, migration and invasion. APC2, as another prognosis-related molecule, may be a target of miR-3648 in NSCLC.

The association between protease inhibitors and anal cancer outcomes in veterans living with HIV treated with definitive chemoradiation: a retrospective study.

BACKGROUND: The incidence of anal squamous cell carcinoma has been increasing, particularly in people living with HIV (PLWH). There is concern that radiosensitizing drugs, such as protease inhibitors, commonly used in the management of HIV, may increase toxicities in patients undergoing chemoradiation. This study examines treatment outcomes and toxicities in PLWH managed with and without protease inhibitors who are receiving chemoradiation for anal cancer. METHODS: Patient demographic, HIV management, and cancer treatment information were extracted from multiple Veterans Affairs databases. Patients were also manually chart reviewed. Among PLWH undergoing chemoradiation for anal carcinoma, therapy outcomes and toxicities were compared between those treated with and without protease inhibitors at time of cancer treatment. Statistical analysis was performed using chi-square, Cox regression analysis, and logistic regression. RESULTS: A total of 219 PLWH taking anti-retroviral therapy undergoing chemoradiation for anal cancer were identified and included in the final analysis. The use of protease inhibitors was not associated with any survival outcome including colostomy-free survival, progression-free survival, or overall survival (all adjusted hazard ratio p-values> 0.05). Regarding toxicity, protease inhibitor use was not associated with an increased odds of hospitalizations or non-hematologic toxicities; however, protease inhibitor use was associated with increased hospitalizations for hematologic toxicities, including febrile neutropenia (p < 0.01). CONCLUSION: The use of protease inhibitors during chemoradiation for anal carcinoma was not associated with any clinical outcome or increase in non-hematologic toxicity. Their use was associated with increased hospitalizations for hematologic toxicities. Further prospective research is needed to evaluate the safety and efficacy of protease inhibitors for patients undergoing chemoradiation.