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Background Chronic obstructive pulmonary disease (COPD) is a common respiratory disease, whose pathogenetic complexity was strongly associated with aging/smoking and poorly understood. Methods Here we performed single-cell RNA sequencing (scRNA-seq) analysis of 66,610 cells from COPD and age-stratified control lung tissues of donors with different smoking histories to prioritize cell types most perturbed in COPD lungs in aging/smoking dependent or independent manner. By performing an array of advanced bioinformatic analyses, such as gene set enrichment analysis, trajectory analysis, cell–cell interactions analysis, regulatory potential analysis, weighted correlation network analysis, functional interaction analysis, and gene set variation analysis, we integrated cell-type-level alterations into a system-level malfunction and provided a more clarified COPD pathological model containing specific mechanisms by which aging and smoking facilitate COPD development. Finally, we integrated the publicly available scRNA-seq data of 9 individuals, resulting in a total of 110,931 cells, and replicated the analyses to enhance the credibility of our findings. Results Our study pointed to enrichment of COPD molecular alteration in monocytes, which further induced a previously unrecognized pro-inflammatory effect on alveolar epithelial cells. In addition, aged monocytes and club cells facilitated COPD development via maintaining an autoimmune airway niche. Unexpectedly, macrophages, whose defect to resolve inflammation was long-recognized in COPD pathogenesis, primarily induced an imbalance of sphingolipids rheostat in a smoking-dependent way. These findings were validated in a meta-analysis including other public single-cell transcriptomic data. Conclusions In sum, our study provided a clarified view of COPD pathogenesis and demonstrated the potential of targeting monocytes in COPD diagnosis and treatment.

The canonical Alzheimer’s Disease (AD) pathological cascade posits that the accumulation of amyloid beta (Aβ) is the initiating event, accelerating the accumulation of tau in the entorhinal cortex (EC), which subsequently spreads into the neocortex. Here in a multi-cohort study (ADNI, A4, HABS-HD) of 1354 participants with multimodal imaging and genetic information we queried how genetic variation affects these stages of the AD cascade. We observed that females and APOE -ε4 homozygotes are more susceptible to the effects of Aβ on the primary accumulation of tau, with greater EC tau for a given level of Aβ. Furthermore, we observed for individuals who have rare risk variants in TREM2 and/or APOE -ε4 homozygotes there was a greater spread of primary tau from the EC into the neocortex. These findings offer insights into the function of sex, APOE and microglia in AD progression and have implications for determining personalised treatment with drugs targeting Aβ and tau. In Alzheimer’s disease cohorts with whole genome sequencing and tau and amyloid-β, the authors show that sex, APOE-ε4 and TREM2 impact tau via both Aβ and tau pathways. APOE-ε4 homozygotes and TREM2 carriers showed greater neocortical tau pathology.

Multi-organ fibrosis among end stage renal disease (ESRD) patients cannot be explained by uremia alone. Despite mitigation of thrombosis during hemodialysis (HD), subsequent platelet dysfunction and tissue dysregulation are less understood. We comprehensively profiled plasma and platelets from ESRD patients before and after HD to examine HD-modulation of platelets beyond thrombotic activation. Basal plasma levels of proteolytic regulators and fibrotic factors were elevated in ESRD patients compared to healthy controls, with isoform-specific changes during HD. Platelet lysate (PL) RNA transcripts for growth and coagulative factors were elevated post-HD, with upregulation correlated to HD vintage. Platelet secretome correlations to plasma factors reveal acutely induced pro-fibrotic platelet phenotypes in ESRD patients during HD characterized by preferentially enhanced proteolytic enzyme translation and secretion, platelet contribution to inflammatory response, and increasing platelet dysfunction with blood flow rate (BFR) and Vintage. Compensatory mechanisms of increased platelet growth factor synthesis with acute plasma matrix metalloproteinase (MMP) and tissue inhibitor of MMPs (TIMP) increases show short-term mode-switching between dialysis sessions leading to long-term pro-fibrotic bias. Chronic pro-fibrotic adaptation of platelet synthesis were observed through changes in differential secretory kinetics of heterogenous granule subtypes. We conclude that chronic and acute platelet responses to HD contribute to a pro-fibrotic milieu in ESRD.

Despite antiretroviral therapy (ART), HIV persists in latently-infected cells (the HIV reservoir) which decay slowly over time. Here, leveraging >500 longitudinal samples from 67 people living with HIV (PLWH) treated during acute infection, we developed a mathematical model to predict reservoir decay from peripheral CD4 + T cells. Nonlinear generalized additive models demonstrated rapid biphasic decay of intact DNA (week 0-5: t 1/2  ~ 2.83 weeks; week 5-24: t 1/2  ~ 15.4 weeks) that extended out to 1 year. These estimates were ~5-fold faster than prior decay estimates among chronic treated PLWH. Defective DNA had a similar biphasic pattern, but data were more variable. Predicted intact and defective decay rates were faster for PLWH with earlier timing of ART initiation, higher initial CD4 + T cell count, and lower pre-ART viral load. In this study, we advanced our limited understanding of HIV reservoir decay at the time of ART initiation, informing future curative strategies targeting this critical time. HIV reservoir decay is less well studied in acute infection. Here, the authors show that reservoir decay rates are biphasic and 5x faster in people initiating antiretroviral therapy during acute HIV than prior estimates for chronic HIV. Higher initial CD4+ counts and lower viral loads predicted faster decay.

The differences in protein expression of calcium sensitive receptor (CaSR) and claudin-14 in a kidney stone model established by nanobacteria (NB) and ethylene glycol (EG) were compared. Ninety Wistar male rats were randomly divided into the NB group, the EG group, and the blank control group (NC group), with 30 rats in each group. Three rats of each group were sacrificed every week after injection. Histopathology was used to evaluate the stone formation of each group. The expression of CaSR and claudin-14 protein was detected by immunohistochemistry every week. There was formation of bright crystals in the kidneys of the EG group and the NB group, but not the NC group. At the 3rd week, the expression of CaSR and claudin-14 in the kidney tissue of the EG group began to increase while that in the NB group increased at the 4 week. The expression of CaSR and claudin-14 protein in the EG group was stronger than that in the NB group. Meanwhile, CaSR was expressed in the NC group but did not change significantly. Claudin-14 was not expressed in the NC group. Our results indicate that the traditional EG kidney stone modeling method is more rapid than the NB kidney stone modeling method, with a high stone formation rate, and the CaSR and claudin-14 protein expression levels are higher. Meanwhile, the NB used to establish the kidney stone model was isolated from patients with kidney stones, which may imitate the process of natural formation of kidney stones of patients. Therefore, the results of our research are more conducive to related research on the etiology of stones.

ObjectivesThis study aimed to develop a machine learning (ML) model to predict disengagement from HIV care, high viral load or death among people living with HIV (PLHIV) with the goal of enabling proactive support interventions in Tanzania. The algorithm addressed common challenges when applying ML to electronic medical record (EMR) data: (1) imbalanced outcome distribution; (2) heterogeneity across multisite EMR data and (3) evolving virological suppression thresholds.DesignObservational study using a national EMR database.SettingConducted in two regions in Tanzania, using data from the National HIV Care database.ParticipantsThe study included over 6 million HIV care visit records from 295 961 PLHIV in two regions in Tanzania’s National HIV Care database from January 2015 to May 2023.ResultsOur ML model effectively identified PLHIV at increased risk of adverse outcomes. Key predictors included past disengagement from care, antiretroviral therapy (ART) status (which tracks a patient’s engagement with ART across visits), age and time on ART. The downsampling approach we implemented effectively managed imbalanced data to reduce prediction bias. Site-specific algorithms performed better compared with a universal approach, highlighting the importance of tailoring ML models to local contexts. A sensitivity analysis confirmed the model’s robustness to changes in viral load suppression thresholds.ConclusionsML models leveraging large-scale databases of patient data offer significant potential to identify PLHIV for interventions to enhance engagement in HIV care in resource-limited settings. Tailoring algorithms to local contexts and flexibility towards evolving clinical guidelines are essential for maximising their impact.

Background Economic incentives can improve clinical outcomes among in-care people living with HIV (PLHIV), but evidence is limited for their effectiveness among out-of-care PLHIV or those at risk of disengagement. We propose a type 1 hybrid effectiveness-implementation study to advance global knowledge about the use of economic incentives to strengthen the continuity of HIV care and accelerate global goals for HIV epidemic control. Methods The Rudi Kundini, Pamoja Kundini study will evaluate two implementation models of an economic incentive strategy for supporting two groups of PLHIV in Tanzania. Phase 1 of the study consists of a two-arm, cluster randomized trial across 32 health facilities to assess the effectiveness of a home visit plus one-time economic incentive on the proportion of out-of-care PLHIV with viral load suppression (< 1000 copies/ml) 6 months after enrollment ( n  = 640). Phase 2 is an individual 1:1 randomized controlled trial designed to determine the effectiveness of a short-term counseling and economic incentive program offered to in-care PLHIV who are predicted through machine learning to be at risk of disengaging from care on the outcome of viral load suppression at 12 months ( n  = 692). The program includes up to three incentives conditional upon visit attendance coupled with adapted counselling sessions for this population of PLHIV. Consistent with a hybrid effectiveness-implementation study design, phase 3 is a mixed methods evaluation to explore barriers and facilitators to strategy implementation in phases 1 and 2. Results will be used to guide optimization and scale-up of the incentive strategies, if effective, to the larger population of Tanzanian PLHIV who struggle with continuity of HIV care. Discussion Innovative strategies that recognize the dynamic process of lifelong retention in HIV care are urgently needed. Strategies such as conditional economic incentives are a simple and effective method for improving many health outcomes, including those on the HIV continuum. If coupled with other supportive services such as home visits (phase 1) or with tailored counselling (phase 2), economic incentives have the potential to strengthen engagement among the subpopulation of PLHIV who struggle with retention in care and could help to close the gap towards reaching global “95–95-95” goals for ending the AIDS epidemic. Trial registration Phase 1: ClinicalTrials.gov, NCT05248100 , registered 2/21/2022. Phase 2: ClinicalTrials.gov, NCT05373095 , registered 5/13/2022.

Introduction Several Mendelian randomization studies have been conducted that identified multiple risk factors for Alzheimer's disease (AD). However, they typically focus on a few pre‐selected risk factors. Methods A two‐sample Mendelian randomization (MR) study was used to systematically examine the potential causal associations of 1037 risk factors/medical conditions and 31 drugs with the risk of late‐onset AD. To correct for multiple comparisons, the false discovery rate was set at < 0.05. Results There was strong evidence of a causal association between glioma risk, reduced trunk fat‐free mass, lower education levels, lower intelligence and a higher risk of AD. For 31 investigated treatments (such as antihypertensive drugs), we found limited evidence for their associations. Discussion MR found robust evidence of causal associations between glioma, trunk fat‐free, and AD. Our study also confirms that higher educational attainment and higher intelligence are associated with a reduced risk of AD.

This article concerns the potential bias in statistical inference on treatment effects when a large number of covariates are present in a linear or partially linear model. While the estimation bias in an under-fitted model is well understood, we address a lesser-known bias that arises from an over-fitted model. The over-fitting bias can be eliminated through data splitting at the cost of statistical efficiency, and we show that smoothing over random data splits can be pursued to mitigate the efficiency loss. We also discuss some of the existing methods for debiased inference and provide insights into their intrinsic bias-variance trade-off, which leads to an improvement in bias controls. Under appropriate conditions, we show that the proposed estimators for the treatment effects are asymptotically normal and their variances can be well estimated. We discuss the pros and cons of various methods both theoretically and empirically, and show that the proposed methods are valuable options in post-selection inference. Supplementary materials for this article are available online.

Although many cognitive measures have been developed to assess cognitive decline due to Alzheimer's disease (AD), there is little consensus on optimal measures, leading to varied assessments across research cohorts and clinical trials making it difficult to pool cognitive measures across studies. We used a two-stage approach to harmonize cognitive data across cohorts and derive a cross-cohort score of cognitive impairment due to AD. First, we pool and harmonize cognitive data from international cohorts of varying size and ethnic diversity. Next, we derived cognitive composites that leverage maximal data from the harmonized dataset. We show that our cognitive composites are robust across cohorts and achieve greater or comparable sensitivity to AD-related cognitive decline compared to the Mini-Mental State Examination and Preclinical Alzheimer Cognitive Composite. Finally, we used an independent cohort validating both our harmonization approach and composite measures. Our easy to implement and readily available pipeline offers an approach for researchers to harmonize their cognitive data with large publicly available cohorts, providing a simple way to pool data for the development or validation of findings related to cognitive decline due to AD.