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An autochthonous model of pancreatic ductal adenocarcinoma (PDA) permitted the analysis of why immunotherapy is ineffective in this human disease. Despite finding that PDA-bearing mice had cancer cell-specific CD8 ⁺ T cells, the mice, like human patients with PDA, did not respond to two immunological checkpoint antagonists that promote the function of T cells: anti-cytotoxic T-lymphocyte-associated protein 4 (α-CTLA-4) and α-programmed cell death 1 ligand 1 (α-PD-L1). Immune control of PDA growth was achieved, however, by depleting carcinoma-associated fibroblasts (CAFs) that express fibroblast activation protein (FAP). The depletion of the FAP ⁺ stromal cell also uncovered the antitumor effects of α-CTLA-4 and α-PD-L1, indicating that its immune suppressive activity accounts for the failure of these T-cell checkpoint antagonists. Three findings suggested that chemokine (C-X-C motif) ligand 12 (CXCL12) explained the overriding immunosuppression by the FAP ⁺ cell: T cells were absent from regions of the tumor containing cancer cells, cancer cells were coated with the chemokine, CXCL12, and the FAP ⁺ CAF was the principal source of CXCL12 in the tumor. Administering AMD3100, a CXCL12 receptor chemokine (C-X-C motif) receptor 4 inhibitor, induced rapid T-cell accumulation among cancer cells and acted synergistically with α-PD-L1 to greatly diminish cancer cells, which were identified by their loss of heterozygosity of Trp53 gene. The residual tumor was composed only of premalignant epithelial cells and inflammatory cells. Thus, a single protein, CXCL12, from a single stromal cell type, the FAP ⁺ CAF, may direct tumor immune evasion in a model of human PDA.

Multimorbidity clinical risk scores allow clinicians to quickly assess their patients' health for decision making, often for recommendation to care management programs. However, these scores are limited by several issues: existing multimorbidity scores (1) are generally limited to one data group (eg, diagnoses, labs) and may be missing vital information, (2) are usually limited to specific demographic groups (eg, age), and (3) do not formally provide any granularity in the form of more nuanced multimorbidity risk scores to direct clinician attention. Using diagnosis, lab, prescription, procedure, and demographic data from electronic health records (EHRs), we developed a physiologically diverse and generalizable set of multimorbidity risk scores. Using EHR data from a nationwide cohort of patients, we developed the total health profile, a set of six integrated risk scores reflecting five distinct organ systems and overall health. We selected the occurrence of an inpatient hospital visitation over a 2-year follow-up window, attributable to specific organ systems, as our risk endpoint. Using a physician-curated set of features, we trained six machine learning models on 794,294 patients to predict the calibrated probability of the aforementioned endpoint, producing risk scores for heart, lung, neuro, kidney, and digestive functions and a sixth score for combined risk. We evaluated the scores using a held-out test cohort of 198,574 patients. Study patients closely matched national census averages, with a median age of 41 years, a median income of $66,829, and racial averages by zip code of 73.8% White, 5.9% Asian, and 11.9% African American. All models were well calibrated and demonstrated strong performance with areas under the receiver operating curve (AUROCs) of 0.83 for the total health score (THS), 0.89 for heart, 0.86 for lung, 0.84 for neuro, 0.90 for kidney, and 0.83 for digestive functions. There was consistent performance of this scoring system across sexes, diverse patient ages, and zip code income levels. Each model learned to generate predictions by focusing on appropriate clinically relevant patient features, such as heart-related hospitalizations and chronic hypertension diagnosis for the heart model. The THS outperformed the other commonly used multimorbidity scoring systems, specifically the Charlson Comorbidity Index (CCI) and the Elixhauser Comorbidity Index (ECI) overall (AUROCs: THS=0.823, CCI=0.735, ECI=0.649) as well as for every age, sex, and income bracket. Performance improvements were most pronounced for middle-aged and lower-income subgroups. Ablation tests using only diagnosis, prescription, social determinants of health, and lab feature groups, while retaining procedure-related features, showed that the combination of feature groups has the best predictive performance, though only marginally better than the diagnosis-only model on at-risk groups. Massive retrospective EHR data sets have made it possible to use machine learning to build practical multimorbidity risk scores that are highly predictive, personalizable, intuitive to explain, and generalizable across diverse patient populations.

Although multiple CHCHD10 mutations are associated with the spectrum of familial and sporadic frontotemporal dementia–amyotrophic lateral sclerosis (FTD–ALS) diseases, neither the normal function of endogenous CHCHD10 nor its role in the pathological milieu (that is, TDP-43 pathology) of FTD/ALS have been investigated. In this study, we made a series of observations utilizing Caenorhabditis elegans models, mammalian cell lines, primary neurons and mouse brains, demonstrating that CHCHD10 normally exerts a protective role in mitochondrial and synaptic integrity as well as in the retention of nuclear TDP-43, whereas FTD/ALS-associated mutations (R15L and S59L) exhibit loss of function phenotypes in C. elegans genetic complementation assays and dominant negative activities in mammalian systems, resulting in mitochondrial/synaptic damage and cytoplasmic TDP-43 accumulation. As such, our results provide a pathological link between CHCHD10-associated mitochondrial/synaptic dysfunction and cytoplasmic TDP-43 inclusions. Mutations in CHCHD10 have been recently associated with frontotemporal dementia and amyotrophic lateral sclerosis. Here the authors study the functions of endogenous CHCHD10 in Caenorhabditis elegans , primary neurons, and mouse, and show that it normally protects mitochondria and synaptic integrity, and retains TDP-43 in the nucleus.

SKN-1/Nrf are the primary antioxidant/detoxification response transcription factors in animals and they promote health and longevity in many contexts. SKN-1/Nrf are activated by a remarkably broad-range of natural and synthetic compounds and physiological conditions. Defining the signaling mechanisms that regulate SKN-1/Nrf activation provides insights into how cells coordinate responses to stress. Nrf2 in mammals is regulated in part by the redox sensor repressor protein named Keap1. In C. elegans, the p38 MAPK cascade in the intestine activates SKN-1 during oxidative stress by promoting its nuclear accumulation. Interestingly, we find variation in the kinetics of p38 MAPK activation and tissues with SKN-1 nuclear accumulation among different pro-oxidants that all trigger strong induction of SKN-1 target genes. Using genome-wide RNAi screening, we identify new genes that are required for activation of the core SKN-1 target gene gst-4 during exposure to the natural pro-oxidant juglone. Among 10 putative activators identified in this screen was skr-1/2, highly conserved homologs of yeast and mammalian Skp1, which function to assemble protein complexes. Silencing of skr-1/2 inhibits induction of SKN-1 dependent detoxification genes and reduces resistance to pro-oxidants without decreasing p38 MAPK activation. Global transcriptomics revealed strong correlation between genes that are regulated by SKR-1/2 and SKN-1 indicating a high degree of specificity. We also show that SKR-1/2 functions upstream of the WD40 repeat protein WDR-23, which binds to and inhibits SKN-1. Together, these results identify a novel p38 MAPK independent signaling mechanism that activates SKN-1 via SKR-1/2 and involves WDR-23.

ObjectivePrior studies examining diabetes prevalence in India have found that nearly 50% of the diabetes population remains undiagnosed; however, the specific populations at risk are unclear.Research design and methodsFirst, we estimated the prevalence of undiagnosed diabetes in India for 750 924 persons between the ages of 15 years and 50 years who participated in the National Family Health Survey (NFHS-4)/Demographic Health Survey (2015–2016), a cross-sectional survey of all 29 states and 7 union territories of India. We defined ‘undiagnosed diabetes’ as individuals who did not know about their diabetes status but had high random (≥200 mg/dL) or fasting (≥126 mg/dL) blood glucose levels. Second, using Poisson regression, we associated 10 different factors, including the role of healthcare access, and undiagnosed diabetes. Third, we examined the association of undiagnosed diabetes with other potential comorbid conditions.ResultsThe crude prevalence of diabetes for women and men aged 15–50 years was 2.9%, 95% CI 2.9% to 3.1%, with self-reported diabetes prevalence at 1.7%, 95% CI 1.6 to 1.8. The overall prevalence of undiagnosed diabetes for 15–50 year olds was at 1.2%, 95% CI 1.2% to 1.3%. Forty-two per cent, 95% CI 40.7% to 43.4% of the individuals with high glucose levels were unaware of their diabetes status. Approximately 45%, 95% CI 42.9% to 46.4% of undiagnosed diabetes population had access to healthcare. Men, younger individuals, and those with lower levels of education were most at risk of being undiagnosed. Geographically, the Southern states in India had a significantly higher prevalence of undiagnosed diabetes despite having nearly universal access to healthcare. Risk factors combined with random glucose could predict undiagnosed diabetes (area under the curve of 97.8%, 95% CI 97.7% to 97.8%), Nagelkerke R2 of 66%).ConclusionClose to half (42%) of the people with diabetes in India are not aware of their disease status, and a large subset of these people are at risk of poor detection, despite having health insurance and/or having access to healthcare. Younger age groups and men are the most vulnerable.

High-throughput screening (HTS) is a powerful approach to drug discovery, but many lead compounds are found to be unsuitable for use in vivo after initial screening. Screening in small animals like C. elegans can help avoid these problems, but this system has been limited to screens with low-throughput or no specific molecular target. We report the first in vivo 1536-well plate assay for a specific genetic pathway in C. elegans. Our assay measures induction of a gene regulated by SKN-1, a master regulator of detoxification genes. SKN-1 inhibitors will be used to study and potentially reverse multidrug resistance in parasitic nematodes. Screens of two small commercial libraries and the full Molecular Libraries Small Molecule Repository (MLSMR) of ∼364,000 compounds validate our platform for ultra HTS. Our platform overcomes current limitations of many whole-animal screens and can be widely adopted for other inducible genetic pathways in nematodes and humans.

Background: Although it is well-known that older individuals with certain comorbidities are at the highest risk for complications related to COVID-19 including hospitalization and death, we lack tools to identify communities at the highest risk with fine-grained spatial resolution. Information collected at a county level obscures local risk and complex interactions between clinical comorbidities, the built environment, population factors, and other social determinants of health. Objective: This study aims to develop a COVID-19 community risk score that summarizes complex disease prevalence together with age and sex, and compares the score to different social determinants of health indicators and built environment measures derived from satellite images using deep learning. Methods: We developed a robust COVID-19 community risk score (COVID-19 risk score) that summarizes the complex disease co-occurrences (using data for 2019) for individual census tracts with unsupervised learning, selected on the basis of their association with risk for COVID-19 complications such as death. We mapped the COVID-19 risk score to corresponding zip codes in New York City and associated the score with COVID-19–related death. We further modeled the variance of the COVID-19 risk score using satellite imagery and social determinants of health. Results: Using 2019 chronic disease data, the COVID-19 risk score described 85% of the variation in the co-occurrence of 15 diseases and health behaviors that are risk factors for COVID-19 complications among ~28,000 census tract neighborhoods (median population size of tracts 4091). The COVID-19 risk score was associated with a 40% greater risk for COVID-19–related death across New York City (April and September 2020) for a 1 SD change in the score (risk ratio for 1 SD change in COVID-19 risk score 1.4; P<.001) at the zip code level. Satellite imagery coupled with social determinants of health explain nearly 90% of the variance in the COVID-19 risk score in the United States in census tracts (r2=0.87). Conclusions: The COVID-19 risk score localizes risk at the census tract level and was able to predict COVID-19–related mortality in New York City. The built environment explained significant variations in the score, suggesting risk models could be enhanced with satellite imagery.

Exposure of C. elegans to hypertonic stress-induced water loss causes rapid and widespread cellular protein damage. Survival in hypertonic environments depends critically on the ability of worm cells to detect and degrade misfolded and aggregated proteins. Acclimation of C. elegans to mild hypertonic stress suppresses protein damage and increases survival under more extreme hypertonic conditions. Suppression of protein damage in acclimated worms could be due to 1) accumulation of the chemical chaperone glycerol, 2) upregulation of protein degradation activity, and/or 3) increases in molecular chaperoning capacity of the cell. Glycerol and other chemical chaperones are widely thought to protect proteins from hypertonicity-induced damage. However, protein damage is unaffected by gene mutations that inhibit glycerol accumulation or that cause dramatic constitutive elevation of glycerol levels. Pharmacological or RNAi inhibition of proteasome and lyosome function and measurements of cellular protein degradation activity demonstrated that upregulation of protein degradation mechanisms plays no role in acclimation. Thus, changes in molecular chaperone capacity must be responsible for suppressing protein damage in acclimated worms. Transcriptional changes in chaperone expression have not been detected in C. elegans exposed to hypertonic stress. However, acclimation to mild hypertonicity inhibits protein synthesis 50-70%, which is expected to increase chaperone availability for coping with damage to existing proteins. Consistent with this idea, we found that RNAi silencing of essential translational components or acute exposure to cycloheximide results in a 50-80% suppression of hypertonicity-induced aggregation of polyglutamine-YFP (Q35::YFP). Dietary changes that increase protein production also increase Q35::YFP aggregation 70-180%. Our results demonstrate directly for the first time that inhibition of protein translation protects extant proteins from damage brought about by an environmental stressor, demonstrate important differences in aging- versus stress-induced protein damage, and challenge the widely held view that chemical chaperones are accumulated during hypertonic stress to protect protein structure/function.

Metallic structures can be used for the localized heating of fluid and the controlled generation of microfluidic currents. Carefully designed currents can move and trap small particles and cells. Here we demonstrate a new bi-metallic substrate that allows much more powerful micro-scale manipulation. We show that there are multiple regimes of opto-fluidic manipulation that can be controlled by an external laser power. While the lowest power does not affect even small objects, medium power can be used for efficiently capturing and trapping particles and cells. Finally, the high-power regime can be used for 3D levitation that, for the first time, has been demonstrated in this paper. Additionally, we demonstrate opto-fluidic manipulation for an extraordinarily dynamic range of masses extending eight orders of magnitude: from 80 fg nano-wires to 5.4 µg live worms.

Analysis of individual cells at the subcellular level is important for understanding diseases and accelerating drug discovery. Nanoscale endoscopes allow minimally invasive probing of individual cell interiors. Several such instruments have been presented previously, but they are either too complex to fabricate or require sophisticated external detectors because of low signal collection efficiency. Here we present a nanoendoscope that can locally excite fluorescence in labelled cell organelles and collect the emitted signal for spectral analysis. Finite Difference Time Domain (FDTD) simulations have shown that with an optimized nanoendoscope taper profile, the light emission and collection was localized within ~100 nm. This allows signal detection to be used for nano-photonic sensing of the proximity of fluorophores. Upon insertion into the individual organelles of living cells, the nanoendoscope was fabricated and resultant fluorescent signals collected. This included the signal collection from the nucleus of Acridine orange labelled human fibroblast cells, the nucleus of Hoechst stained live liver cells and the mitochondria of MitoTracker Red labelled MDA-MB-231 cells. The endoscope was also inserted into a live organism, the yellow fluorescent protein producing nematode Caenorhabditis elegans and a fluorescent signal was collected. To our knowledge this is the first demonstration of in vivo , local fluorescence signal collection on the sub-organelle level.