Diagnostic nomogram based on ultrasound and clinical data of predicting malignant lymph nodes in HIV patients with lymphadenopathy

Background and aimsAcquired Immune Deficiency Syndrome (AIDS), caused by Human Immunodeficiency Virus (HIV), leads to severe immunodeficiency, making patients susceptible to opportunistic infections and malignancies. Lymphadenopathy is a common symptom in AIDS patients, reflecting immune system responses but also indicating potential disease progression. Distinguishing between benign and malignant lymphadenopathy is crucial for appropriate treatment. This study aimed to develop a diagnostic method for differentiating benign and malignant lymph nodes in HIV-infected patients using clinical and ultrasound data.MethodsThe study was conducted at Hangzhou Xixi Hospital from March 2016 to March 2024, including 149 HIV patients with confirmed lymphadenopathy. Ultrasound examinations were performed to assess lymph node characteristics, and biopsies were conducted for pathological confirmation. Statistical analysis involved the least absolute shrinkage and selection operator (LASSO) regression to identify significant predictors and construct a nomogram for predicting lymph node malignancy.ResultsThe malignant lymph nodes had larger short and long diameters, and differences in shape, echogenicity, and hilum compared to benign lymph nodes. Lymphocyte count and T cell subsets were higher in malignant lymph nodes. The LASSO regression model identified short diameter, lymphocyte ratio, CD3+ T cell count, and CD4+ T cell ratio as significant predictors. The nomogram constructed based on these features demonstrated good predictive accuracy (AUC = 0.904).ConclusionsIn conclusion, our study developed a diagnostic nomogram based on clinical and ultrasound data to differentiate benign and malignant lymph nodes in HIV patients. This tool had diagnostic accuracy and offers practical guidance for clinical management of HIV patients with lymphadenopathy

Polarization-sensitive optical projection tomography for muscle fiber imaging

Optical projection tomography (OPT) is a tool used for three-dimensional imaging of millimeter-scale biological samples, with the advantage of exhibiting isotropic resolution typically in the micron range. OPT can be divided into two types: transmission OPT (tOPT) and emission OPT (eOPT). Compared with eOPT, tOPT discriminates different tissues based on their absorption coefficient, either intrinsic or after specific staining. However, it fails to distinguish muscle fibers whose absorption coefficients are similar to surrounding tissues. To circumvent this problem, in this article we demonstrate a polarization sensitive OPT system which improves the detection and 3D imaging of muscle fibers by using polarized light. We also developed image acquisition and processing protocols that, together with the system, enable the clear visualization of muscles. Experimental results show that the muscle fibers of diaphragm and stomach, difficult to be distinguished in regular tOPT, were clearly displayed in our system, proving its potential use. Moreover, polarization sensitive OPT was fused with tOPT to investigate the stomach tissue comprehensively. Future applications of polarization sensitive OPT could be imaging other fiberlike structures such as myocardium or other tissues presenting high optical anisotropy.This work is supported by the National Basic Research Program of China (973 Program) under Grant 2011CB707700, the National Natural Science Foundation of China under Grant No. 81227901, 61231004, 81501616, 81301346, 81527805 the Chinese Academy of Sciences Fellowship for Young Foreign Scientists under Grant No. 2010Y2GA03, 2013Y1GA0004, the Chinese Academy of Sciences Visiting Professorship for Senior International Scientists under Grant No. 2012T1G0036, 2013T1G0013, the Instrument Developing Project of the Chinese Academy of Sciences under Grant No. YZ201502, YZ201457 and the Strategic Priority Research Program (B) of Chinese Academy of Sciences (XDB02060010). A. Arranz acknowledges support from the Marie Curie Intra-European Fellowship program IEF-2010-275137. J.R. acknowledges support from EC FP7 IMI project PREDICT-TB, the EC FP7 CIG grant HIGH-THROUGHPUT TOMO, and the Spanish MINECO project grant FIS2013-41802-R MESO-IMAGING

Oral microbiome and risk of malignant esophageal lesions in a high-risk area of China: A nested case-control study.

OBJECTIVE: We aimed to prospectively evaluate the association of oral microbiome with malignant esophageal lesions and its predictive potential as a biomarker of risk. METHODS: We conducted a case-control study nested within a population-based cohort with up to 8 visits of oral swab collection for each subject over an 11-year period in a high-risk area for esophageal cancer in China. The oral microbiome was evaluated with 16S ribosomal RNA (rRNA) gene sequencing in 428 pre-diagnostic oral specimens from 84 cases with esophageal lesions of severe squamous dysplasia and above (SDA) and 168 matched healthy controls. DESeq analysis was performed to identify taxa of differential abundance. Differential oral species together with subject characteristics were evaluated for their potential in predicting SDA risk by constructing conditional logistic regression models. RESULTS: A total of 125 taxa including 37 named species showed significantly different abundance between SDA cases and controls (all P0.84. CONCLUSIONS: The oral microbiome may play an etiological and predictive role in esophageal cancer, and it holds promise as a non-invasive early warning biomarker for risk stratification for esophageal cancer screening programs

Unimodal productivity-biodiversity relationship along the gradient of multidimensional resources across Chinese grasslands

Resources can affect plant productivity and biodiversity simultaneously and thus are key drivers of their relationships in addition to plant-plant interactions. However, most previous studies only focused on a single resource while neglecting the nature of resource multidimensionality. Here we integrated four essential resources for plant growth into a single metric of resource diversity (RD) to investigate its effects on the productivity-biodiversity relationship (PBR) across Chinese grasslands. Results showed that habitats differing in RD have different PBRs − positive in low-resource habitats, but neutral in medium- and high-resource ones—while collectively, a weak positive PBR was observed. However, when excluding direct effects of RD on productivity and biodiversity, PBR in high-resource habitats became negative, which leads to a unimodal instead of a positive PBR along the RD gradient. By integrating resource effects and changing plant-plant interactions into a unified framework with the RD gradient, our work contributes to uncovering underlying mechanisms for inconsistent PBRs at large scales

Countering kernel malware in virtual execution environments

We present a rootkit prevention system, namely DARK that tracks suspicious Linux loadable kernel modules (LKM) at a granular level by using on-demand emulation, a technique that dynamically switches a running system between virtualized and emulated execution. Combining the strengths of emulation and virtualization, DARK is able to thoroughly capture the activities of the target module in a guest operating system (OS), while maintaining reasonable run-time performance. To address integrity-violation and confidentiality-violation rootkits, we create a group of security policies that can detect all available Linux rootkits. It is shown that normal guest OS performance is unaffected. The performance is only decreased when rootkits attempt to run, while most rootkits are detected at installation. Next, we present a sandbox-based malware analysis system called Rkprofiler that dynamically monitors and analyzes the behavior of Windows kernel malware. Kernel malware samples run inside a virtual machine (VM) that is supported and managed by a PC emulator. Rkprofiler provides several capabilities that other malware analysis systems do not have. First, it can detect the execution of malicious kernel code regardless of how the monitored kernel malware is loaded into the kernel and whether it is packed or not. Second, it captures all function calls made by the kernel malware and constructs call graphs from the trace files. Third, a technique called aggressive memory tagging (AMT) is proposed to track the dynamic data objects that the kernel malware visits. Last, Rkprofiler records and reports the hardware access events of kernel malware (e.g., MSR register reads and writes). Our evaluation results show that Rkprofiler can quickly expose the security-sensitive activities of kernel malware and thus reduces the effort exerted in conducting tedious manual malware analysis.Ph.D.Committee Chair: Copeland A. John; Committee Member: Alessandro Orso; Committee Member: Douglas M. Blough; Committee Member: George F. Riley; Committee Member: Raheem A. Beya

Countering kernel malware in virtual execution environments

We present a rootkit prevention system, namely DARK that tracks suspicious Linux loadable kernel modules (LKM) at a granular level by using on-demand emulation, a technique that dynamically switches a running system between virtualized and emulated execution. Combining the strengths of emulation and virtualization, DARK is able to thoroughly capture the activities of the target module in a guest operating system (OS), while maintaining reasonable run-time performance. To address integrity-violation and confidentiality-violation rootkits, we create a group of security policies that can detect all available Linux rootkits. It is shown that normal guest OS performance is unaffected. The performance is only decreased when rootkits attempt to run, while most rootkits are detected at installation. Next, we present a sandbox-based malware analysis system called Rkprofiler that dynamically monitors and analyzes the behavior of Windows kernel malware. Kernel malware samples run inside a virtual machine (VM) that is supported and managed by a PC emulator. Rkprofiler provides several capabilities that other malware analysis systems do not have. First, it can detect the execution of malicious kernel code regardless of how the monitored kernel malware is loaded into the kernel and whether it is packed or not. Second, it captures all function calls made by the kernel malware and constructs call graphs from the trace files. Third, a technique called aggressive memory tagging (AMT) is proposed to track the dynamic data objects that the kernel malware visits. Last, Rkprofiler records and reports the hardware access events of kernel malware (e.g., MSR register reads and writes). Our evaluation results show that Rkprofiler can quickly expose the security-sensitive activities of kernel malware and thus reduces the effort exerted in conducting tedious manual malware analysis.Ph.D

Noncoding RNA in Extracellular Vesicles Regulate Differentiation of Mesenchymal Stem Cells

To achieve the desired outcome in tissue engineering regeneration, mesenchymal stem cells need to undergo a series of biological processes, including differentiating into the ideal target cells. The extracellular vesicle (EV) in the microenvironment contributes toward determining the fate of the cells with epigenetic regulation, particularly from noncoding RNA (ncRNA), and exerts transportation and protective effects on ncRNAs. We focused on the components and functions of ncRNA (particularly microRNA) in the EVs. The EVs modified by the ncRNA favor tissue regeneration and pose a potential challenge.</jats:p