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

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.

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

Background: 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. Method: 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). Result: 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. Conclusion: 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.

Epidemiology of extrapulmonary tuberculosis in central Guangxi from 2016 to 2021.

The burden of extrapulmonary tuberculosis (EPTB) has gradually increased in recent years, but not enough epidemiological data is available from central Guangxi. To better understand the epidemiology of EPTB in central Guangxi and identify risk factors associated with them, we retrospectively investigated the epidemiology of tuberculosis (TB), especially EPTB, among patients admitted to the Chest Hospital of Guangxi Zhuang Autonomous Region between 2016 and 2021. We excluded those infected with both pulmonary tuberculosis (PTB) and EPTB, reported the proportion and incidence of PTB or EPTB, and compared the demographic characteristics and risk factors of EPTB and PTB cases using univariate and multivariate logistic regression models. Among 30,893 TB patients, 67.25% (20,774) had PTB and 32.75% (10,119) had EPTB. Among EPTB, pleural, skeletal, lymphatic, pericardial, meningeal, genitourinary, intestinal, and peritoneal TB accounted for 49.44%, 27.20%, 8.55%, 4.39%, 3.36%, 1.48%, 0.87%, and 0.79%, respectively. Patients who were younger (age < 25), from rural areas, Zhuang and other ethnic groups, and diagnosed with anemia and HIV infection were more likely to develop EPTB. However, patients with diabetes and COPD were less likely to have EPTB. From 2016 to 2021, the proportion of PTB cases decreased from 69.73 to 64.07%. The percentage of EPTB cases increased from 30.27 to 35.93%, with the largest increase in skeletal TB from 21.48 to 34.13%. The epidemiology and risk factors of EPTB in central Guangxi are different from those of PTB. The incidence of EPTB is increasing and further studies are needed to determine the reasons for it.

Anemia and opportunistic infections in hospitalized people living with HIV: a retrospective study.

BACKGROUND: There is a high prevalence of anemia among people living with HIV in Guangxi, China. Therefore, we investigated anemia and opportunistic infections in hospitalized people living with HIV and explored the risk factors related to anemia in people living with HIV to actively prevent anemia in people living with HIV. METHODS: We retrospectively studied people living with HIV admitted to Guangxi Chest Hospital from June 2016 to October 2021. Detailed information on the sociodemographic and clinical features of the participants was collected. The X2 test was used to compare the prevalence between the anemic and non-anemic groups. The logistic regression analysis was applied to exclude confounding factors and identify factors related to anemia. RESULTS: Among 5645 patients with HIV, 1525 (27.02%) had anemia. The overall prevalence of mild, moderate, and severe anemia was 4.66%, 14.08%, and 8.27%, respectively. The factors significantly related to increased risk of anemia were CD4 count < 50 cells/µl (aOR = 2.221, 95% CI = [1.775, 2.779]), CD4 count 50-199 cells/µl (aOR = 1.659, 95% CI = [1.327, 2. 073]), female (aOR = 1.644, 95% CI = [1.436, 1.881]) co-infected with HCV (aOR = 1.465, 95% CI = [1.071, 2.002]), PM (aOR = 2.356, 95% CI = [1.950, 2.849]), or TB (aOR = 1.198, 95% CI = [1.053, 1.365]). CONCLUSIONS: Within Guangxi of China, 27.02% of hospitalized people living with HIV presented with anemia. Most patients with anemia were in the mild to moderate stage. The low CD4 count, female gender, and concomitant infection with Penicillium marneffei, Hepatitis C virus, or Tuberculosis were independent correlates of anemia. Thus, these findings would be helpful to clinicians in preventing and intervening in anemia in people living with HIV.

Lncap-AI prostate cancer cell line establishment by Flutamide and androgen-free environment to promote cell adherent.

BACKGROUND: To establish castration-resistant prostate cancer (CRPC) - Lncap androgen-independent (AI) cell line from Lncap androgen-dependent (AD) cell line, and explore the different molecular biological between these two cell lines. METHODS: The Lncap-AD cell line was cultured and passaged 60 times over 16 months. The morphology of the Lncap-AI cell line was observed. AR levels identification were detected in qRT-PCR and Western Blot assay. CCK-8, EdU assay, wound healing assay and cell adhesion assays were used to observe the ability of proliferation, migration, and adhesion. SEM and TEM were used to observe microculture structure. At last, the PSA secrete ability was evaluated by Elisa assay. RESULTS: The Lncap-AD cell line was cultured and passaged 60 times over 16 months. The Lncap-AI cell line showed a morphologic change at the end stage of culture, the cells turned slender and cell space turned separated compared to the Lncap-AD cell line. The relative levels of AR-related genes in the Lncap-AI cell line were up-regulation compared to the Lncap-AD cell line both in mRNA and protein levels. The expression of AR and HK2 proteins were influenced and down-regulation by Enzalutamide in the Lncap-AD cell line, but no obvious difference in Lncap-AI cell lines. Lncap-AI cell line showed strong viability of proliferation, migration, and adhesion by CCK-8, EdU assay, wound healing assay, and adhesion assay. The microstructure of Scanning Electron Microscopy (SEM) showed many synapses in the Lncap-AI cell line and PC3 cell line, but not in the Lncap-AD cell line. At last, the PSA secrete ability was evaluated by Elisa assay, and PCa cell lines showed no significant difference. CONCLUSION: Simulation of CRPC progression, Lncap-AD cell line turned to Lncap-AI cell line with androgen deprivation therapy.

Influence of sleep disruption on inflammatory bowel disease and changes in circadian rhythm genes.

According to clinical investigations, sleep disruption (SD) can influence the immune system and cause inflammatory bowel disease (IBD). However, the detailed effects of sleep on IBD development and progression have not been clarified. Here, we used dextran sulfate sodium (DSS) to induce colitis in mice, and then interfered with SD (day-time 8:00 a.m. to 5:00 p.m.) to explore the influence of sleep on colitis by analyzing colon length, mouse body weight, disease activity index (DAI) score, pathology detection, and infiltration of inflammatory cells with LCA immunohistochemistry analysis. Next, we detected the mRNA levels of circadian genes and related inflammatory factors, including Bmal1, CLOCK, Cry1, Cry2, Per1, Per2, Timeless, Rev-erbα, TNF-α, IL-6, and IFN-γ. Additionally, we conducted a sleep survey in IBD patients and collected colon lesion sites to detect the mRNA levels of those eight circadian genes and three inflammatory factors. We found that SD promoted the body weight decrease, increased inflammation as shown with pathological staining of the DSS animal model, and increased expression of the clock gene Cry2 in DSS-induced colitis mice. In IBD patients with active disease, the mRNA level of circadian genes Bmal1, Cry1, Cry2, and Rev-erbα in inflammatory tissues decreased significantly compared with non-inflammatory tissues.

Screening and Identification of Human Endogenous Retrovirus-K mRNAs for Breast Cancer Through Integrative Analysis of Multiple Datasets.

Objective: Human endogenous retroviruses (HERVs) make up 8% of the human genome. HERVs are biologically active elements related to multiple diseases. HERV-K, a subfamily of HERVs, has been associated with certain types of cancer and suggested as an immunologic target in some tumors. The expression levels of HERV-K in breast cancer (BCa) have been studied as biomarkers and immunologic therapeutic targets. However, HERV-K has multiple copies in the human genome, and few studies determined the transcriptional profile of HERV-K copies across the human genome for BCa. Methods: Ninety-one HERV-K indexes with entire proviral sequences were used as the reference database. Nine raw sequencing datasets with 243 BCa and 137 control samples were mapped to this database by Salmon software. The differential proviral expression across several groups was analyzed by DESeq2 software. Results: First, the clustering of each dataset demonstrated that these 91 HERV-K proviruses could well cluster the BCa and control samples when the normal controls were normal cells or healthy donor tissues. Second, several common HERV-K proviruses that are closely related with BCa risk were significantly differentially expressed (p adj < 0.05 and absolute log2FC > 1.5) in the tissues and cell lines. Additionally, almost all the HERV-K proviruses had higher expression in BCa tissue than in healthy donor tissue. Notably, we first found the expression of 17p13.1 provirus that located with TP53 should regulate TP53 expression in ER+ and HER2+ BCa. Conclusion: The expression profiling of these 91 HERV-K proviruses can be used as biomarkers to distinguish individuals with BCa and healthy controls. Some proviruses, especially 17p13.1, were strongly associated with BCa risk. The results suggest that HERV-K expression profiles may be appropriate biomarkers and targets for BCa.

Long noncoding RNA HCP5 suppresses skin cutaneous melanoma development by regulating RARRES3 gene expression via sponging miR-12.

OBJECTIVE: This research aimed to investigate the role and mechanism of long noncoding RNA (lncRNA) HCP5 in skin cutaneous melanoma (SKCM). MATERIALS AND METHODS: Survival analysis was performed using The Cancer Genome Atlas (TCGA)-SKCM data and SKCM patients' clinical data. Primary SKCM cells were derived from patients' pathologic tissue specimens. HCP5 overexpression was achieved by lentiviral transduction. Malignancy of SKCM cells was evaluated in vitro by cell proliferation, colony formation, apoptosis and transwell invasion assays. RARRES3 knockdown was achieved by siRNA transfection. DIANA microT-CDS algorithm was used to predict miRNAs that might interact with HCP5 and 3' untranslated region of RARRES3 mRNA. microRNA target luciferase reporter assay and AGO2-RNA immunoprecipitation were used to verify the interaction between HCP5, 3' UTR of RARRES3 mRNA and miR-1286. RESULTS: HCP5 level was decreased in SKCM tissue specimens compared to noncancerous counterparts. Low expression of HCP5 was associated with SKCM patients' poor overall survival and disease progression. HCP5 overexpression significantly reduced the malignancy of primary SKCM cells in vitro. RARRES3 was found as a HCP5-co-expressing gene in SKCM cells. HCP5 overexpression significantly increased RARRES3 expression in SKCM cells. RARRES3 knockdown partially abolished the anti-SKCM effect of HCP5 overexpression. MiR-1286 was found interacting with both HCP5 and 3' UTR of RARRES3 mRNA. CONCLUSION: HCP5 is a cancer-suppressive lncRNA in SKCM. HCP5 overexpression decreased SKCM cell malignancy in vitro by upregulating RARRES3, possibly via sponging miR-1286.