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Resolving the spatial distribution of RNA and protein in tissues at subcellular resolution is a challenge in the field of spatial biology. We describe spatial molecular imaging, a system that measures RNAs and proteins in intact biological samples at subcellular resolution by performing multiple cycles of nucleic acid hybridization of fluorescent molecular barcodes. We demonstrate that spatial molecular imaging has high sensitivity (one or two copies per cell) and very low error rate (0.0092 false calls per cell) and background (~0.04 counts per cell). The imaging system generates three-dimensional, super-resolution localization of analytes at ~2 million cells per sample. Cell segmentation is morphology based using antibodies, compatible with formalin-fixed, paraffin-embedded samples. We measured multiomic data (980 RNAs and 108 proteins) at subcellular resolution in formalin-fixed, paraffin-embedded tissues (nonsmall cell lung and breast cancer) and identified >18 distinct cell types, ten unique tumor microenvironments and 100 pairwise ligand–receptor interactions. Data on >800,000 single cells and ~260 million transcripts can be accessed at http://nanostring.com/CosMx-dataset . Hundreds of RNAs and proteins are imaged in fixed tissue at subcellular resolution.

Pathogenic Leptospira transmits from animals to humans, causing the zoonotic life-threatening infection called leptospirosis. This infection is reported worldwide with higher risk in tropical regions. Symptoms of leptospirosis range from mild illness to severe illness such as liver damage, kidney failure, respiratory distress, meningitis, and fatal hemorrhagic disease. Invasive species of Leptospira rapidly disseminate to multiple tissues where this bacterium damages host endothelial cells, increasing vascular permeability. Despite the burden in humans and animals, the pathogenic mechanisms of Leptospira infection remain to be elucidated. The pathogenic leptospires adhere to endothelial cells and permeabilize endothelial barriers in vivo and in vitro. In this study, human endothelial cells were infected with the pathogenic L. interrogans serovar Copenhageni or the saprophyte L. biflexa serovar Patoc to investigate morphological changes and other distinctive phenotypes of host cell proteins by fluorescence microscopy. Among those analyzed, 17 proteins from five biological classes demonstrated distinctive phenotypes in morphology and/or signal intensity upon infection with Leptospira. The affected biological groups include: 1) extracellular matrix, 2) intercellular adhesion molecules and cell surface receptors, 3) intracellular proteins, 4) cell-cell junction proteins, and 5) a cytoskeletal protein. Infection with the pathogenic strain most profoundly disturbed the biological structures of adherens junctions (VE-cadherin and catenins) and actin filaments. Our data illuminate morphological disruptions and reduced signals of cell-cell junction proteins and filamentous actin in L. interrogans-infected endothelial cells. In addition, Leptospira infection, regardless of pathogenic status, influenced other host proteins belonging to multiple biological classes. Our data suggest that this zoonotic agent may damage endothelial cells via multiple cascades or pathways including endothelial barrier damage and inflammation, potentially leading to vascular hyperpermeability and severe illness in vivo. This work provides new insights into the pathophysiological mechanisms of Leptospira infection.

Skin color is one of the colors we are most frequently exposed to. It contains information, such as ethnic group and health status, and numerous studies have demonstrated the influence of various facial attributes on the formation of impressions. However, no research has specifically explored the repercussions of treating changes in skin color as a singular variable. We cross-culturally examined skin color changes along with the red-yellow axis and how they influence facial impressions across six face shapes from three types of ethnicities. A 7-point scale was used for evaluation, and the observers evaluated the impression of face images according to the following six evaluation items: healthiness, preference, brightness, whiteness, transparency, and skin tone. The observers were divided into the following four groups: Japan, China, Thailand, and the Caucasus. Differences in the evaluation and association of skin color with various traits emerged between cultures. For instance, East Asian cultures associated positive attributes with reddish skin colors, whereas Caucasians often linked positive traits with yellowish skin colors. These cultural disparities emphasize the dynamic interplay between culture and perception in assessing facial impressions.

A number of studies on texture and ensemble perception have shown that humans can immediately estimate the average of spatially distributed visual information. The present study characterized mechanisms involved in estimating averages for information distributed over both space and time. Observers viewed a rapid sequence of texture patterns in which elements’ orientation were determined by dynamic Gaussian noise with variable spatial and temporal standard deviations (SDs). We found that discrimination thresholds increased beyond a certain spatial SD if temporal SD was small, but if temporal SD was large, thresholds remained nearly constant regardless of spatial SD. These data are at odds with predictions that threshold is uniquely determined by spatiotemporal SD. Moreover, a reverse correlation analysis revealed that observers judged the spatiotemporal average orientation largely depending on the spatial average orientation over the last few frames of the texture sequence – a recency effect widely observed in studies of perceptual decision making. Results are consistent with the notion that the visual system rapidly computes spatial ensembles and adaptively accumulates information over time to make a decision on spatiotemporal average. A simple computational model based on this notion successfully replicated observed data.

This study was aimed to evaluate whether the dose–response relationship of the sodium glucose co-transporter-2 inhibitors (SGLT2is) in patients with type 2 diabetes mellitus (T2DM)—canagliflozin, dapagliflozin, empagliflozin, ipragliflozin, luseogliflozin, and tofogliflozin—can be explained in a unified manner based on their ability to promote urinary glucose excretion (UGE). Information on HbA1c reduction at various doses of each SGLT2i was collected from literatures on randomized controlled trials and was normalized based on the daily UGE data from phase I studies. After normalizing doses, the dose–response relationship of HbA1c reduction of most of SGLT2is was represented by a unified nonlinear mixed-effect model, with the estimated maximum HbA1c (%) reduction (E max ) of 0.796 points, whereas covariate analysis showed that canagliflozin had a 1.33-fold higher E max than those of the other drugs. Other covariates included baseline HbA1c levels, body weight, disease duration, prior treatment, and renal function. Findings from this study would influence drug selection and adjustment in clinical practice. As with SGLT2is, in cases where the efficacy cannot be easily evaluated but an appropriate pharmacodynamic marker was assessed in early clinical trials, similar approaches for other drug classes can guide strategic and evidence-based dose selection in phase III trials.

Humans persist in their attempts to predict the future in spite of the fact that natural events often involve a fundamental element of uncertainty. The present study explored computational mechanisms underlying biases in prospective decision making by using a simple psychophysical task. Observers viewed a randomly moving Gabor target for T sec and anticipated its future position ΔT sec following stimulus offset. Applying reverse correlation analysis, we found that observer decisions focused heavily on the last part of target velocity and especially on velocity information following the last several direction reversals. If target random motion explicitly contained an additional linear trend, observers tended to utilize information of the linear trend as well. These behavioral data are well explained by a leaky-integrator model of perceptual decision making based on evidence accumulation with adaptive gain control. The results raise the possibility that prospective decision making toward future events follows principles similar to those involved in retrospective decision making toward past events.

As Parkinson's disease (PD) progresses, there are multiple biomarker changes, and sex and genetic variants may influence the rate of progression. Data‐driven, long‐term disease progression model analysis may provide precise knowledge of the relationships between these risk factors and progression and would allow for the selection of appropriate diagnosis and treatment according to disease progression. To construct a long‐term disease progression model of PD based on multiple biomarkers and evaluate the effects of sex and leucine‐rich repeat kinase 2 (LRRK2) mutations, a technique derived from the nonlinear mixed‐effects model (Statistical Restoration of Fragmented Time course [SReFT]) was applied to datasets of patients provided by the Parkinson's Progression Markers Initiative. Four biomarkers, including the Unified PD Rating Scale, were used, and a covariate analysis was performed to investigate the effects of sex and LRRK2‐related mutations. A model of disease progression over ~30 years was successfully developed using patient data with a median of 6 years. Covariate analysis suggested that female sex and LRRK2 G2019S mutations were associated with 21.6% and 25.4% significantly slower progression, respectively. LRRK2 rs76904798 mutation also tended to delay disease progression by 10.4% but the difference was not significant. In conclusion, a long‐term PD progression model was successfully constructed using SReFT from relatively short‐term individual patient observations and depicted nonlinear changes in relevant biomarkers and their covariates, including sex and genetic variants.

A hallmark of the progression of prostate cancer to advanced disease is the acquisition of androgen-independent growth. This malignant phenotype is characterized by resistance to conventional treatments and predisposes to formation of hypoxic regions containing stem-like cancer cells. Unfortunately, an effective therapy to target prostate cancer stem cells under hypoxia has not yet been established. In this report, we studied whether δ-tocotrienol (T3), a vitamin E family member that has exhibited the most potent anti-cancer activity, could suppress the survival of prostate cancer stem-like cells. PC3 stem-like cells were isolated from PC3 parental cells using a three-dimensional culture system. The stemness of the isolated PC3 stem-like cells was confirmed by evaluation of resistance to an anticancer agent (docetaxel) and tumor formation capacity in a xenograft model. The effects of δ-T3 on PC3 stem-like cells under a hypoxia condition were examined by WST-8 (cell viability), real-time reverse transcription-polymerase chain reaction (PCR) and western blotting. δ-T3 demonstrated a cytotoxic effect on prostate cancer stem-like cells in a dose dependent manner and a reduction in the protein levels of hypoxia-inducible factor (HIF)-1α and HIF-2α. Additionally, a specific inhibitor toward HIF-1α induced cytotoxicity on PC3 cells, but selective inhibition of HIF-2α had no effect. Overall, these results suggest that δ-T3 could inhibit the survival of prostate cancer stem-like cells under hypoxia, primarily through the inactivation of HIF-1α signaling.

Individual predictions of long‐term chronic disease progression from data of limited duration provide valuable insights into estimating patient outcomes and therapeutic needs. Statistical Restoration of Fragmented Time course (SReFT) was developed to address this challenge, yet it is computationally too intensive for large‐scale datasets. Although diabetes is a representative chronic disease with significant medical needs, it has been challenging to analyze long‐term changes using large‐scale patient data due to this limitation. In this study, we aimed to develop a new method (SReFT‐machine learning, SReFT‐ML) by applying machine learning to the concept of SReFT, and to confirm its performance using synthetic data and the data from a clinical trial, the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial (N = 10,251). SReFT‐ML has successfully analyzed both synthetic and clinical data, and reconstructed biomarker trajectories over a 30‐year period in patients with diabetes. Decreases in diastolic blood pressure and renal function may be important indicators of disease progression. Furthermore, although age and mortality data were not included in the model, survival analysis demonstrated a clear trend of hazard increases in mortality and diabetes‐related outcomes with disease progression. This study introduced machine learning to enhance long‐term disease progression modeling. The resulting model characterized a 30‐year trajectory of disease risk in diabetes. The results provide a clinically meaningful hypothesis that incorporating systemic factors such as renal function and blood pressure, in addition to classic glycemic control, may enhance comprehensive diabetes care. Trial Registration: ClinicalTrials.gov number: NCT00000620 From Study Observations to Disease‐Time Alignment: A Novel ML‐Based Reconstruction of Lifelong Trajectories and Risk Estimation — An Application to Type 2 Diabetes.

Background Mechanistic static pharmacokinetic (MSPK) models are simple, have fewer data requirements, and have broader applicability; however, they cannot use in vitro information and cannot distinguish the contributions of multiple cytochrome P450 (CYP) isoenzymes and the hepatic and intestinal first-pass effects appropriately. We aimed to establish a new MSPK analysis framework for the comprehensive prediction of drug interactions (DIs) to overcome these disadvantages. Methods Drug interactions that occurred by inhibiting CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A in the liver and CYP3A in the intestine were simultaneously analyzed for 59 substrates and 35 inhibitors. As in vivo information, the observed changes in the area under the concentration-time curve (AUC) and elimination half-life ( t 1/2 ), hepatic availability, and urinary excretion ratio were used. As in vitro information, the fraction metabolized (fm) and the inhibition constant (Ki) were used. The contribution ratio (CR) and inhibition ratio (IR) for multiple clearance pathways and hypothetical volume (V Hyp ) were inferred using the Markov Chain Monte Carlo (MCMC) method. Result Using in vivo information from 239 combinations and in vitro 172 fm and 344 Ki values, changes in AUC, and t 1/2 were estimated for all 2065 combinations, wherein the AUC was estimated to be more than doubled for 602 combinations. Intake-dependent selective intestinal CYP3A inhibition by grapefruit juice has been suggested. By separating the intestinal contributions, DIs after intravenous dosing were also appropriately inferred. Conclusion This framework would be a powerful tool for the reasonable management of various DIs based on all available in vitro and in vivo information.