Affected by HIV Stigma: Interpreting Results from a Population Survey of an Urban Center in Guangxi, China

We aimed to identify factors related to HIV stigma in Liuzhou, Guangxi province, a city in southwest China with high HIV prevalence. We used a multi-stage cluster randomized sample of the general population to survey 852 adults. We conducted ordinal logistic regression analyses to test factors associated with punishment and isolation stigma. Eighteen percent of respondents agreed that people with HIV should be punished, and 40% agreed that people with HIV should be quarantined. Punishment stigma was associated with age, having three or more sexual partners, and TV watching. Isolation stigma was associated with age, urban residence and a history of STI. HIV transmission knowledge was low, and having correct knowledge attenuated the association with punishment and isolation stigma. Despite programs in China to provide care and treatment for PLHIV, HIV stigma is common in this region. Targeted interventions need to focus on fears related to HIV and PLHIV

Increased CD45RA+FoxP3low Regulatory T Cells with Impaired Suppressive Function in Patients with Systemic Lupus Erythematosus

BACKGROUND: The role of naturally occurring regulatory T cells (Treg) in the control of the development of systemic lupus erythematosus (SLE) has not been well defined. Therefore, we dissect the phenotypically heterogeneous CD4(+)FoxP3(+) T cells into subpopulations during the dynamic SLE development. METHODLOGY/PRINCIPAL FINDINGS: To evaluate the proliferative and suppressive capacities of different CD4(+) T cell subgroups between active SLE patients and healthy donors, we employed CD45RA and CD25 as surface markers and carboxyfluorescein diacetatesuccinimidyl ester (CFSE) dilution assay. In addition, multiplex cytokines expression in active SLE patients was assessed using Luminex assay. Here, we showed a significant increase in the frequency of CD45RA(+)FoxP3(low) naive Treg cells (nTreg cells) and CD45RA(-)FoxP3(low) (non-Treg) cells in patients with active SLE. In active SLE patients, the increased proportions of CD45RA(+)FoxP3(low) nTreg cells were positively correlated with the disease based on SLE disease activity index (SLEDAI) and the status of serum anti-dsDNA antibodies. We found that the surface marker combination of CD25(+)CD45RA(+) can be used to defined CD45RA(+)FoxP3(low) nTreg cells for functional assays, wherein nTreg cells from active SLE patients demonstrated defective suppression function. A significant correlation was observed between inflammatory cytokines, such as IL-6, IL-12 and TNFα, and the frequency of nTreg cells. Furthermore, the CD45RA(+)FoxP3(low) nTreg cell subset increased when cultured with SLE serum compared to healthy donor serum, suggesting that the elevated inflammatory cytokines of SLE serum may promote nTreg cell proliferation/expansion. CONCLUSIONS/SIGNIFICANCE: Our results indicate that impaired numbers of functional CD45RA(+)FoxP3(low) naive Treg cell and CD45RA(-)FoxP3(low) non-suppressive T cell subsets in inflammatory conditions may contribute to SLE development. Therefore, analysis of subsets of FoxP3(+) T cells, using a combination of FoxP3, CD25 and CD45RA, rather than whole FoxP3(+) T cells, will help us to better understand the pathogenesis of SLE and may lead to the development of new therapeutic strategies

Tai Chi Chuan in postsurgical non-small cell lung cancer patients: study protocol for a randomized controlled trial

Abstract Background Impairment of exercise capacity remains a common adverse effect of non-small cell lung cancer (NSCLC) survivors after surgery. Previous research has suggested that Tai Chi Chuan (TCC) offers an exercise capacity benefit in several types of cancers. This is a randomized trial to investigate the efficacy and safety of TCC in postoperative NSCLC patients over an observation period of 3 months and a 9-month follow-up. Methods/design Using a prospective, one center and randomized design, 120 subjects with histologically confirmed stage I–IIIA NSCLC following complete surgical resection will potentially be eligible for this trial. Following baseline assessments, eligible participants will be randomly assigned to one of two conditions: (1) TCC training, or (2) placebo control. The training sessions for both groups will last 60 min and take place three times a week for 3 months. The sessions will be supervised with target intensity of 60–80% of work capacity, dyspnea, and heart rate management. The primary study endpoint is peak oxygen consumption (VO2peak), and the secondary endpoints include: 6-min walk distance (6MWD), health-related quality of life (HRQoL), lung function, immunity function, and the state of depression and anxiety. All endpoints will be assessed at the baseline and postintervention (3 months). A follow-up period of 9 months will be included. The main time points for the evaluation of clinical efficacy and safety will be months 3, 6, 9, and 12 after enrollment. Discussion This study will assess the effect of group TCC in postsurgery NSCLC survivors on VO2peak, lung function, and other aspects. The results of this study will eventually provide clinical proof of the application of TCC as one kind of exercise training for patients across the entire NSCLC continuum, as well as information on the safety and feasibility of exercise. Trial Registration Chinese Clinical Trial Registry: ChiCTR-IOR-15006548 . Registered on 12 June 2015

A dosing strategy model of deep deterministic policy gradient algorithm for sepsis patients

Abstract Background A growing body of research suggests that the use of computerized decision support systems can better guide disease treatment and reduce the use of social and medical resources. Artificial intelligence (AI) technology is increasingly being used in medical decision-making systems to obtain optimal dosing combinations and improve the survival rate of sepsis patients. To meet the real-world requirements of medical applications and make the training model more robust, we replaced the core algorithm applied in an AI-based medical decision support system developed by research teams at the Massachusetts Institute of Technology (MIT) and IMPERIAL College London (ICL) with the deep deterministic policy gradient (DDPG) algorithm. The main objective of this study was to develop an AI-based medical decision-making system that makes decisions closer to those of professional human clinicians and effectively reduces the mortality rate of sepsis patients. Methods We used the same public intensive care unit (ICU) dataset applied by the research teams at MIT and ICL, i.e., the Multiparameter Intelligent Monitoring in Intensive Care III (MIMIC-III) dataset, which contains information on the hospitalizations of 38,600 adult sepsis patients over the age of 15. We applied the DDPG algorithm as a strategy-based reinforcement learning approach to construct an AI-based medical decision-making system and analyzed the model results within a two-dimensional space to obtain the optimal dosing combination decision for sepsis patients. Results The results show that when the clinician administered the exact same dose as that recommended by the AI model, the mortality of the patients reached the lowest rate at 11.59%. At the same time, according to the database, the baseline mortality rate of the patients was calculated as 15.7%. This indicates that the patient mortality rate when difference between the doses administered by clinicians and those determined by the AI model was zero was approximately 4.2% lower than the baseline patient mortality rate found in the dataset. The results also illustrate that when a clinician administered a different dose than that recommended by the AI model, the patient mortality rate increased, and the greater the difference in dose, the higher the patient mortality rate. Furthermore, compared with the medical decision-making system based on the Deep-Q Learning Network (DQN) algorithm developed by the research teams at MIT and ICL, the optimal dosing combination recommended by our model is closer to that given by professional clinicians. Specifically, the number of patient samples administered by clinicians with the exact same dose recommended by our AI model increased by 142.3% compared with the model based on the DQN algorithm, with a reduction in the patient mortality rate of 2.58%. Conclusions The treatment plan generated by our medical decision-making system based on the DDPG algorithm is closer to that of a professional human clinician with a lower mortality rate in hospitalized sepsis patients, which can better help human clinicians deal with complex conditional changes in sepsis patients in an ICU. Our proposed AI-based medical decision-making system has the potential to provide the best reference dosing combinations for additional drugs

Additional file 2: of Tai Chi Chuan in postsurgical non-small cell lung cancer patients: study protocol for a randomized controlled trial

Informed Consent form - Informed notice page (Chinese version). (DOC 44 kb

Additional file 1: of Tai Chi Chuan in postsurgical non-small cell lung cancer patients: study protocol for a randomized controlled trial

SPIRIT 2013 Checklist: recommended items to address in a clinical trial protocol and related documents. (DOC 128 kb

Data_Sheet_1_Abnormal functional connectivity of the frontostriatal circuits in type 2 diabetes mellitus.PDF

BackgroundType 2 diabetes mellitus (T2DM) is a metabolic disorder associated with an increased incidence of cognitive and emotional disorders. Previous studies have indicated that the frontostriatal circuits play a significant role in brain disorders. However, few studies have investigated functional connectivity (FC) abnormalities in the frontostriatal circuits in T2DM.ObjectiveWe aimed to investigate the abnormal functional connectivity (FC) of the frontostriatal circuits in patients with T2DM and to explore the relationship between abnormal FC and diabetes-related variables.MethodsTwenty-seven patients with T2DM were selected as the patient group, and 27 healthy peoples were selected as the healthy controls (HCs). The two groups were matched for age and sex. In addition, all subjects underwent resting-state functional magnetic resonance imaging (rs-fMRI) and neuropsychological evaluation. Seed-based FC analyses were performed by placing six bilateral pairs of seeds within a priori defined subdivisions of the striatum. The functional connection strength of subdivisions of the striatum was compared between the two groups and correlated with each clinical variable.ResultsPatients with T2DM showed abnormalities in the FC of the frontostriatal circuits. Our findings show significantly reduced FC between the right caudate nucleus and left precentral gyrus (LPCG) in the patients with T2DM compared to the HCs. The FC between the prefrontal cortex (left inferior frontal gyrus, left frontal pole, right frontal pole, and right middle frontal gyrus) and the right caudate nucleus has a significant positive correlation with fasting blood glucose (FBG).ConclusionThe results showed abnormal FC of the frontostriatal circuits in T2DM patients, which might provide a new direction to investigate the neuropathological mechanisms of T2DM.</p

Prediction models constructed for Hashimoto’s thyroiditis risk based on clinical and laboratory factors

BackgroundHashimoto's thyroiditis (HT) frequently occurs among autoimmune diseases and may simultaneously appear with thyroid cancer. However, it is difficult to diagnose HT at an early stage just by clinical symptoms. Thus, it is urgent to integrate multiple clinical and laboratory factors for the early diagnosis and risk prediction of HT.MethodsWe recruited 1,303 participants, including 866 non-HT controls and 437 diagnosed HT patients. 44 HT patients also had thyroid cancer. Firstly, we compared the difference in thyroid goiter degrees between controls and patients. Secondly, we collected 15 factors and analyzed their significant differences between controls and HT patients, including age, body mass index, gender, history of diabetes, degrees of thyroid goiter, UIC, 25-(OH)D, FT3, FT4, TSH, TAG, TC, FPG, low-density lipoprotein cholesterol, and high-density lipoprotein cholesterol. Thirdly, logistic regression analysis demonstrated the risk factors for HT. For machine learning modeling of HT and thyroid cancer, we conducted the establishment and evaluation of six models in training and test sets.ResultsThe degrees of thyroid goiter were significantly different among controls, HT patients without cancer (HT-C), and HT patients with thyroid cancer (HT+C). Most factors had significant differences between controls and patients. Logistic regression analysis confirmed diabetes, UIC, FT3, and TSH as important risk factors for HT. The AUC scores of XGBoost, LR, SVM, and MLP models indicated appropriate predictive power for HT. The features were arranged by their importance, among which, 25-(OH)D, FT4, and TSH were the top three high-ranking factors.ConclusionsWe firstly analyzed comprehensive factors of HT patients. The proposed machine learning modeling, combined with multiple factors, are efficient for thyroid diagnosis. These discoveries will extensively promote precise diagnosis, personalized therapies, and reduce unnecessary cost for thyroid diseases