VIBRANT: spectral profiling for single-cell drug responses.

High-content cell profiling has proven invaluable for single-cell phenotyping in response to chemical perturbations. However, methods with improved throughput, information content and affordability are still needed. We present a new high-content spectral profiling method named vibrational painting (VIBRANT), integrating mid-infrared vibrational imaging, multiplexed vibrational probes and an optimized data analysis pipeline for measuring single-cell drug responses. Three infrared-active vibrational probes were designed to measure distinct essential metabolic activities in human cancer cells. More than 20,000 single-cell drug responses were collected, corresponding to 23 drug treatments. The resulting spectral profile is highly sensitive to phenotypic changes under drug perturbation. Using this property, we built a machine learning classifier to accurately predict drug mechanism of action at single-cell level with minimal batch effects. We further designed an algorithm to discover drug candidates with new mechanisms of action and evaluate drug combinations. Overall, VIBRANT has demonstrated great potential across multiple areas of phenotypic screening.

Pharmacological expansion of type 2 alveolar epithelial cells promotes regenerative lower airway repair.

Type 2 alveolar epithelial cells (AEC2s) are stem cells in the adult lung that contribute to lower airway repair. Agents that promote the selective expansion of these cells might stimulate regeneration of the compromised alveolar epithelium, an etiology-defining event in several pulmonary diseases. From a high-content imaging screen of the drug repurposing library ReFRAME, we identified that dipeptidyl peptidase 4 (DPP4) inhibitors, widely used type 2 diabetes medications, selectively expand AEC2s and are broadly efficacious in several mouse models of lung damage. Mechanism of action studies revealed that the protease DPP4, in addition to processing incretin hormones, degrades IGF-1 and IL-6, essential regulators of AEC2 expansion whose levels are increased in the luminal compartment of the lung in response to drug treatment. To selectively target DPP4 in the lung with sufficient drug exposure, we developed NZ-97, a locally delivered, lung persistent DPP4 inhibitor that broadly promotes efficacy in mouse lung damage models with minimal peripheral exposure and good tolerability. This work reveals DPP4 as a central regulator of AEC2 expansion and affords a promising therapeutic approach to broadly stimulate regenerative repair in pulmonary disease.

Machine Learning Reveals Serum Glycopatterns as Potential Biomarkers for the Diagnosis of Nonalcoholic Fatty Liver Disease (NAFLD).

Nonalcoholic fatty liver disease (NAFLD) has emerged as the predominant chronic liver condition globally, and underdiagnosis is common, particularly in mild cases, attributed to the asymptomatic nature and traditional ultrasonography's limited sensitivity to detect early-stage steatosis. Consequently, patients may experience progressive liver pathology. The objective of this research is to ascertain the efficacy of serum glycan glycopatterns as a potential diagnostic biomarker, with a particular focus on the disease's early stages. We collected a total of 170 serum samples from volunteers with mild-NAFLD (Mild), severe-NAFLD (Severe), and non-NAFLD (None). Examination via lectin microarrays has uncovered pronounced disparities in serum glycopatterns identified by 19 distinct lectins. Following this, we employed four distinct machine learning algorithms to categorize the None, Mild, and Severe groups, drawing on the alterations observed in serum glycopatterns. The gradient boosting decision tree (GBDT) algorithm outperformed other models in diagnostic accuracy within the validation set, achieving an accuracy rate of 95% in differentiating the None group from the Mild group. Our research indicates that employing lectin microarrays to identify alterations in serum glycopatterns, when integrated with advanced machine learning algorithms, could constitute a promising approach for the diagnosis of NAFLD, with a special emphasis on its early detection.

Using new indices to predict metabolism dysfunction-associated fatty liver disease (MAFLD): analysis of the national health and nutrition examination survey database.

BACKGROUND: Metabolism dysfunction-associated fatty liver disease (MAFLD), is the most common chronic liver disease. Few MAFLD predictions are simple and accurate. We examined the predictive performance of the albumin-to-glutamyl transpeptidase ratio (AGTR), plasma atherogenicity index (AIP), and serum uric acid to high-density lipoprotein cholesterol ratio (UHR) for MAFLD to design practical, inexpensive, and reliable models. METHODS: The National Health and Nutrition Examination Survey (NHANES) 2007-2016 cycle dataset, which contained 12,654 participants, was filtered and randomly separated into internal validation and training sets. This study examined the relationships of the AGTR and AIP with MAFLD using binary multifactor logistic regression. We then created a MAFLD predictive model using the training dataset and validated the predictive model performance with the 2017-2018 NHANES and internal datasets. RESULTS: In the total population, the predictive ability (AUC) of the AIP, AGTR, UHR, and the combination of all three for MAFLD showed in the following order: 0.749, 0.773, 0.728 and 0.824. Further subgroup analysis showed that the AGTR (AUC1 = 0.796; AUC2 = 0.690) and the combination of the three measures (AUC1 = 0.863; AUC2 = 0.766) better predicted MAFLD in nondiabetic patients. Joint prediction outperformed the individual measures in predicting MAFLD in the subgroups. Additionally, the model better predicted female MAFLD. Adding waist circumference and or BMI to this model improves predictive performance. CONCLUSION: Our study showed that the AGTR, AIP, and UHR had strong MAFLD predictive value, and their combination can increase MAFLD predictive performance. They also performed better in females.

DARS-AS1: A Vital Oncogenic LncRNA Regulator with Potential for Cancer Prognosis and Therapy.

DARS-AS1, short for Aspartyl-tRNA synthetase antisense RNA 1, has emerged as a pivotal player in cancers. Upregulation of this lncRNA is a recurrent phenomenon observed across various cancer types, where it predominantly assumes oncogenic roles, exerting influence on multiple facets of tumor cell biology. This aberrant expression of DARS-AS1 has triggered extensive research investigations, aiming to unravel its roles and clinical values in cancer. In this review, we elucidate the significant correlation between dysregulated DARS-AS1 expression and adverse survival prognoses in cancer patients, drawing from existing literature and pan-cancer analyses from The Cancer Genome Atlas (TCGA). Additionally, we provide comprehensive insights into the diverse functions of DARS-AS1 in various cancers. Our review encompasses the elucidation of the molecular mechanisms, ceRNA networks, functional mediators, and signaling pathways, as well as its involvement in therapy resistance, coupled with the latest advancements in DARS-AS1-related cancer research. These recent updates enrich our comprehensive comprehension of the pivotal role played by DARS-AS1 in cancer, thereby paving the way for future applications of DARS-AS1-targeted strategies in tumor prognosis evaluation and therapeutic interventions. This review furnishes valuable insights to advance the ongoing efforts in combating cancer effectively.

Survival analysis of patients with extrahepatic cholangiocarcinoma: a nomogram for clinical and MRI features.

BACKGROUND: This study aimed to establish a predictive model to estimate the postoperative prognosis of patients with extrahepatic cholangiocarcinoma (ECC) based on preoperative clinical and MRI features. METHODS: A total of 104 patients with ECC confirmed by surgery and pathology were enrolled from January 2013 to July 2021, whose preoperative clinical, laboratory, and MRI data were retrospectively collected and examined, and the effects of clinical and imaging characteristics on overall survival (OS) were analyzed by constructing Cox proportional hazard regression models. A nomogram was constructed to predict OS, and calibration curves and time-dependent receiver operating characteristic (ROC) curves were employed to assess OS accuracy. RESULTS: Multivariate regression analyses revealed that gender, DBIL, ALT, GGT, tumor size, lesion's position, the signal intensity ratio of liver to paraspinal muscle (SIRLiver/Muscle), and the signal intensity ratio of spleen to paraspinal muscle (SIRSpleen/Muscle) on T2WI sequences were significantly associated with OS, and these variables were included in a nomogram. The concordance index of nomogram for predicting OS was 0.766, and the AUC values of the nomogram predicting 1-year and 2-year OS rates were 0.838 and 0.863, respectively. The calibration curve demonstrated good agreement between predicted and observed OS. 5-fold and 10-fold cross-validation show good stability of nomogram predictions. CONCLUSIONS: Our nomogram based on clinical, laboratory, and MRI features well predicted OS of ECC patients, and could be considered as a convenient and personalized prediction tool for clinicians to make decisions.

Alterations of mannosylated glycopatterns recognized by Hippeastrum hybrid lectin in saliva of patients with lung cancer.

OBJECTIVES: Lung cancer (LC) is the malignant tumor with the highest mortality rate worldwide, and precise early diagnosis can improve patient prognosis. The purpose of this study was to investigate whether alterations in the glycopatterns recognized by the Hippeastrum hybrid lectin (HHL) in salivary proteins are associated with the development of LC. MATERIALS AND METHODS: First, we collected saliva samples from LC (15 lung adenocarcinoma (ADC); 15 squamous cell carcinoma (SCC); 15 small cell lung cancer (SCLC)) and 15 benign pulmonary disease (BPD) for high-throughput detection of abundance levels of HHL-recognized glycopatterns using protein microarrays, and then validated the pooled samples from each group with lectin blotting analysis. Finally, the N-glycan profiles of salivary glycoproteins isolated from the pooled samples using HHL-magnetic particle conjugates were characterized separately using MALDI-TOF/TOF-MS. RESULTS: The results showed that the abundance level of glycopatterns recognized by HHL in salivary proteins was elevated in LC compared to BPD. The proportion of mannosylated N-glycans was notably higher in ADC (31.7%), SCC (39.0%), and SCLC (46.6%) compared to BPD (23.3%). CONCLUSIONS: The altered salivary glycopatterns such as oligomannose, Manα1-3Man, or Manα1-6Man N-glycans recognized by HHL might serve as potential biomarkers for the diagnosis of LC patients. CLINICAL RELEVANCE: This study provides crucial information for studying changes in salivary to differentiate between BPD and LC and facilitate the discovery of biomarkers for LC diagnosis based on precise alterations of mannosylated N-glycans in saliva.

Application of Facial Expression Analysis Technology in Violence Risk Assessment of Individuals with Mental Disorders in Supervised Settings.

OBJECTIVES: To explore the association between violent behaviors and emotions in individuals with mental disorders, to evaluate the application value of facial expression analysis technology in violence risk assessment of individuals with mental disorders in supervised settings, and to provide a reference for violence risk assessment. METHODS: Thirty-nine male individuals with mental disorders in supervised settings were selected, the participant risk of violence, cognitive function, psychiatric symptoms and severity were assessed using the Modified Overt Aggression Scale (MOAS), the Historical, Clinical, Risk Management-Chinese version(HCR-CV), the Positive and Negative Syndrome Scale (PANSS) and the Brief Psychiatric Rating Scale (BPRS). An emotional arousal was performed on the participants and the intensity of their emotions and facial expression action units was recorded before, during and after the arousal. One-way analysis of variance (ANOVA) was used to compare the differences in the intensity of emotions and facial expression action units before, during and after the arousal. Pearson correlation analysis was used to calculate the correlations between the intensity of the seven basic emotional facial expressions and the scores of the assessment scales. RESULTS: The intensity difference of sadness, surprise and fear in different time periods was statistically significant (P<0.05). The intensity of the left medial eyebrow lift action unit was found significantly different before and after the emotional arousal (P<0.05). The intensity of anger was positively correlated with the Modified Overt Aggression Scale score throughout the experiment (P<0.05). CONCLUSIONS: Eye action units such as eyebrow lifting, eyelid tightening and upper eyelid lifting can be used as effective action units to identify sadness, anger and other negative emotions associated with violent behaviors. Facial expression analysis technology can be used as an auxiliary tool to assess the potential risk of violence in individuals with mental disorders in supervised settings.

Studies on the Biomimetic Synthesis of Marine Ladder Polyethers via Endo-Selective Epoxide-to-Epoxonium Ring-Opening Cascades.

Marine ladder polyethers have attracted the attention of chemists and biologists because of their potent biological activities. Synthetic chemists have attempted to construct their polyether frameworks by epoxide ring-opening cascades, as Nakanishi hypothesis describes. However, Baldwin's rules of ring closure state that exo-selective intramolecular cyclization of epoxy alcohols is preferred over endo-selective cyclization. Herein, we investigated epoxide ring-opening cascades of polyepoxy alcohols in [EMIM]BF4/PFTB (1-ethyl-3-methylimidazolium tetrafluoroborate /perfluoro-tert-butyl alcohol) and found that all-endo products were formed via epoxide-to-epoxonium ring-opening cyclizations (not restricted by Baldwin's rules, which only apply to intramolecular hydroxyl-to-epoxide cyclizations). We determined that the key factor enabling polyepoxy alcohols to undergo a high proportion of all-endo-selective cyclization was inhibition of exo-selective hydroxyl-to-epoxide cyclization starting from the terminal hydroxyl group of a polyepoxy alcohol. By introducing a slow-release protecting group to the terminal hydroxyl group, we could markedly increase the cyclization yields of polyether fragments with hydrogen atoms at the ring junctions. For the first time, we constructed consecutively fused six-membered-ring and fused seven-, eight-, and nine-membered-ring polyether fragments by epoxide-to-epoxonium ring-opening cyclizations through the addition of a suitable Lewis acid. We also suggest that the biosynthesis of marine ladder polyethers may proceed via epoxide-to-epoxonium ring-opening cyclization of polyepoxide.

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Neonatal sepsis is a common and severe infectious disease with a high mortality rate. Its pathogenesis is complex, lacks specific manifestations, and has a low positive culture rate, making early diagnosis and personalized treatment still a challenge for clinicians. Epidemiological studies on twins have shown that genetic factors are associated with neonatal sepsis. Gene polymorphisms are closely related to susceptibility, disease development, and prognosis. This article provides a review of gene polymorphisms related to neonatal sepsis, including interleukins, tumor necrosis factor, Toll-like receptors, NOD-like receptors, CD14, triggering receptor expressed on myeloid cells-1, mannose-binding lectin, and other immune proteins, aiming to promote precision medicine for this disease.

Immunotherapy and targeted therapy for cholangiocarcinoma: Artificial intelligence research in imaging.

Cholangiocarcinoma (CCA) is a highly aggressive hepatobiliary malignancy, second only to hepatocellular carcinoma in prevalence. Despite surgical treatment being the recommended method to achieve a cure, it is not viable for patients with advanced CCA. Gene sequencing and artificial intelligence (AI) have recently opened up new possibilities in CCA diagnosis, treatment, and prognosis assessment. Basic research has furthered our understanding of the tumor-immunity microenvironment and revealed targeted molecular mechanisms, resulting in immunotherapy and targeted therapy being increasingly employed in the clinic. Yet, the application of these remedies in CCA is a challenging endeavor due to the varying pathological mechanisms of different CCA types and the lack of expressed immune proteins and molecular targets in some patients. AI in medical imaging has emerged as a powerful tool in this situation, as machine learning and deep learning are able to extract intricate data from CCA lesion images while assisting clinical decision making, and ultimately improving patient prognosis. This review summarized and discussed the current immunotherapy and targeted therapy related to CCA, and the research progress of AI in this field.

Predictive Factor of Tumor Aggressiveness in Patients with Extrahepatic Cholangiocarcinoma Based on Diffusion-weighted MRI.

BACKGROUND: Extrahepatic cholangiocarcinoma (EHCC), an exceedingly malignant neoplasm, often eludes early detection, culminating in a dire prognosis. Accurate cancer staging systems and pathological differentiation are designed to guide adjuvant interventions and predict postoperative prognoses. OBJECTIVE: This study sought to investigate the predictive capacity of DW-MRI in discerning T stages, lymph node metastasis, and pathological differentiation grades in patients with EHCC. METHODS: Eighty-five patients were pathologically diagnosed with EHCC and underwent abdominal MRI within two weeks before surgery at our hospital from Aug 2011 to Aug 2021. Tumor axial maximum area (AMA) and apparent diffusion coefficient (ADC) values for diverse T stages, N stages, and differentiation grades were retrospectively analyzed. RESULTS: The Mann-Whitney U test displayed significantly higher lesion AMA values (P =0.006) and lower tumor ADC values (P = 0.001) in the nodepositive group (median ADC and AMA value: 1.220×10-3 mm2/s, 82.231 mm2) than in the node-negative group (median ADC and AMA value: 1.316×10-3 mm2/s, 51.174 mm2). A tumor ADC value<1.249×10-3 mm2/s from the receiver operating characteristic curve (AUC=0.725, P=0.001) exhibited the capability to predict node-positive EHCC with a sensitivity of 64.29%, and specificity of 73.68%. Furthermore, a progressive decrease in the degree of EHCC differentiation was associated with a reduction in the tumor ADC value (P=0.000). CONCLUSION: The N stage and differentiation of EHCC can be evaluated non-invasively using diffusion-weighted MRI.

Predicting potential SARS-CoV-2 spillover and spillback in animals.

BACKGROUND: The COVID-19 pandemic is spreading rapidly around the world, causing countries to impose lockdowns and efforts to develop vaccines on a global scale. However, human-to-animal and animal-to-human transmission cannot be ignored, as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can spread rapidly in farmed and wild animals. This could create a worrying cycle of SARS-CoV-2 spillover from humans to animals and spillback of new strains back into humans, rendering vaccines ineffective. METHOD: This study provides a key indicator of animals that may be potential susceptible hosts for SARS-CoV-2 and coronavirus infections by analysing the phylogenetic distance between host angiotensin-converting enzyme 2 and the coronavirus spike protein. Crucially, our analysis identifies animals that are at elevated risk from a spillover and spillback incident. RESULTS: One group of animals has been identified as potentially susceptible to SARS-CoV-2 by harbouring a parasitic coronavirus spike protein similar to the SARS-CoV-2 spike protein. These animals may serve as amplification hosts in spillover events from zoonotic reservoirs. This group consists of a mixture of animals infected internally and naturally: minks, dogs, cats, tigers. Additionally, no internal or natural infections have been found in masked palm civet. CONCLUSION: Tracing interspecies transmission in multi-host environments based solely on in vitro and in vivo examinations of animal susceptibility or serology is a time-consuming task. This approach allows rapid identification of high-risk animals to prioritize research and assessment of the risk of zoonotic disease transmission in the environment. It is a tool to rapidly identify zoonotic species that may cause outbreaks or participate in expansion cycles of coexistence with their hosts. This prevents the spread of coronavirus infections between species, preventing spillover and spillback incidents from occurring.

The value of an MRI-based radiomics model in predicting the survival and prognosis of patients with extrahepatic cholangiocarcinoma.

OBJECTIVES: The study aimed to establish radiomics models based on magnetic resonance imaging (MRI) multiparameter images to predict the survival and prognosis of patients with extrahepatic cholangiocarcinoma (ECC). METHODS: Seventy-eight patients with ECC confirmed by pathology were collected retrospectively. The radiomics model_a/b/c were constructed based on the 1/2/3-year survival of patients with ECC. The best texture features were selected according to postoperative survival time and ECC patient status to calculate the radiomics score (Rad-score). A cutoff value was selected, and patients were divided into high-risk and low-risk groups. RESULTS: Model_a, model_b, and model_c were used to predict 1-, 2-, and 3-year postoperative survival rates, respectively. The area under the curve values in the training and test groups were 1.000 and 0.933 for model_a, 0.909 and 0.907 for model_b, 1.000 and 0.975 for model_c, respectively. The survival prediction model based on the Rad-score showed that the postoperative mortality risk differed significantly between risk groups (p < 0.0001). CONCLUSIONS: The MRI radiomics model could be used to predict the survival and prognosis of patients with ECC.

Laser-Scribed Graphene for Human Health Monitoring: From Biophysical Sensing to Biochemical Sensing.

Laser-scribed graphene (LSG), a classic three-dimensional porous carbon nanomaterial, is directly fabricated by laser irradiation of substrate materials. Benefiting from its excellent electrical and mechanical properties, along with flexible and simple preparation process, LSG has played a significant role in the field of flexible sensors. This review provides an overview of the critical factors in fabrication, and methods for enhancing the functionality of LSG. It also highlights progress and trends in LSG-based sensors for monitoring physiological indicators, with an emphasis on device fabrication, signal transduction, and sensing characteristics. Finally, we offer insights into the current challenges and future prospects of LSG-based sensors for health monitoring and disease diagnosis.

Evaluation of salivary glycopatterns based diagnostic models for prediction of diabetic vascular complications.

Diabetic vascular complications (DVC) are the main cause of death in diabetic patients. However, there is a lack of effective biomarkers or convenient methods for early diagnosis of DVC. In this study, the salivary glycopatterns from 130 of healthy volunteers (HV), 139 patients with type 2 diabetes mellitus (T2DM) and 167 patients with DVC were case-by-case analyzed by using lectin microarrays. Subsequently, diagnostic models were developed using logistic regression and machine learning algorithms based on the data of lectin microarrays in training set. The performance of diagnostic models was evaluated in an independent blind cohort. The results of lectin microarrays indicated that the glycopatterns identified by 16 lectins (e.g. BS-I, PWM and EEL) were significantly altered in DVC patients compared with patients with T2DM, which suggested the alterations in salivary glycopatterns could reflect onset of DVC. Notably, K-Nearest Neighbor (KNN) model exhibited better performance for distinguishing DVC (accuracy: 0.939) than other models in blind cohort. The integrated classifier, which combined three machine learning models, exhibited a higher overall accuracy (≥ 0.933) than other models in blind cohort. Our study provided a cost-effective and non-invasive method for auxiliary diagnosis DVC based on the combination of salivary glycopatterns and machine learning algorithms.

N-Glycan Profiles of Neuraminidase from Avian Influenza Viruses.

The cleavage of sialic acids by neuraminidase (NA) facilitates the spread of influenza A virus (IV) descendants. Understanding the enzymatic activity of NA aids research into the transmission of IVs. An effective method for purifying NA was developed using p-aminophenyloxamic acid-modified functionalized hydroxylated magnetic particles (AAMPs), and from 0.299 to 0.401 mg of NA from eight IV strains was isolated by 1 mg AAMP. A combination of lectin microarrays and MALDI-TOF/TOF-MS was employed to investigate the N-glycans of isolated NAs. We found that more than 20 N-glycans were identified, and 16 glycan peaks were identical in the strains derived from chicken embryo cultivation. Multi-antennae, bisected, or core-fucosylated N-glycans are common in all the NAs. The terminal residues of N-glycans are predominantly composed of galactose and N-acetylglucosamine residues. Meanwhile, sialic acid residue was uncommon in these N-glycans. Further computational docking analysis predicted the interaction mechanism between NA and p-aminophenyloxamic acid.

DMGM: deformable-mechanism based cervical cancer staging via MRI multi-sequence.

Objective.This study aims to leverage a deep learning approach, specifically a deformable convolutional layer, for staging cervical cancer using multi-sequence MRI images. This is in response to the challenges doctors face in simultaneously identifying multiple sequences, a task that computer-aided diagnosis systems can potentially improve due to their vast information storage capabilities.Approach.To address the challenge of limited sample sizes, we introduce a sequence enhancement strategy to diversify samples and mitigate overfitting. We propose a novel deformable ConvLSTM module that integrates a deformable mechanism with ConvLSTM, enabling the model to adapt to data with varying structures. Furthermore, we introduce the deformable multi-sequence guidance model (DMGM) as an auxiliary diagnostic tool for cervical cancer staging.Main results.Through extensive testing, including comparative and ablation studies, we validate the effectiveness of the deformable ConvLSTM module and the DMGM. Our findings highlight the model's ability to adapt to the deformation mechanism and address the challenges in cervical cancer tumor staging, thereby overcoming the overfitting issue and ensuring the synchronization of asynchronous scan sequences. The research also utilized the multi-modal data from BraTS 2019 as an external test dataset to validate the effectiveness of the proposed methodology presented in this study.Significance.The DMGM represents the first deep learning model to analyze multiple MRI sequences for cervical cancer, demonstrating strong generalization capabilities and effective staging in small dataset scenarios. This has significant implications for both deep learning applications and medical diagnostics. The source code will be made available subsequently.

Knowledge, attitude, and practice of medication and its influencing factors among residents in western China: a large-scale cross-sectional study.

Background: This study aimed to understand the knowledge, attitude, and practice (KAP) of drug use among residents in western China and its influencing factors for accurately designing the knowledge, contents, and methods of popular science activities for safe drug use among residents to provide a reference for conducting rational drug use educational activities and improving residents' level of safe drug use. Methods: A cross-sectional questionnaire survey was conducted to investigate the KAP of medication among western China residents and its influencing factors from March to April 2023. Each question option was assigned a score according to logic, and the risk factors for resident medication safety KAP were explored through univariate and logistic regression analyses. Results: A total of 7,557 valid questionnaires were collected, with an effective recovery rate of 96.7%. The average scores of KAP were 72.77 ± 22.91, 32.89 ± 10.64, and 71.27 ± 19.09, respectively. In the evaluation criteria of the questionnaire, the score of medication knowledge reached "good," and the score of attitude and practice was "average." Multiple linear regression analysis indicated that male sex and low education level were significant factors affecting the lack of drug knowledge among residents. Old age and low education level were the factors of poor attitude toward medication. The low condition of medical security was a factor in residents' irregular drug use behavior. Conclusion: The overall level of rational drug use among residents in western China is good, but there are still some inconsistencies. Rational drug use education should be conducted according to the risk points of residents in drug safety KAP to further improve the level of rational drug use of residents.

Prediction of single-cell RNA expression profiles in live cells by Raman microscopy with Raman2RNA.

Single-cell RNA sequencing and other profiling assays have helped interrogate cells at unprecedented resolution and scale, but are inherently destructive. Raman microscopy reports on the vibrational energy levels of proteins and metabolites in a label-free and nondestructive manner at subcellular spatial resolution, but it lacks genetic and molecular interpretability. Here we present Raman2RNA (R2R), a method to infer single-cell expression profiles in live cells through label-free hyperspectral Raman microscopy images and domain translation. We predict single-cell RNA sequencing profiles nondestructively from Raman images using either anchor-based integration with single molecule fluorescence in situ hybridization, or anchor-free generation with adversarial autoencoders. R2R outperformed inference from brightfield images (cosine similarities: R2R >0.85 and brightfield <0.15). In reprogramming of mouse fibroblasts into induced pluripotent stem cells, R2R inferred the expression profiles of various cell states. With live-cell tracking of mouse embryonic stem cell differentiation, R2R traced the early emergence of lineage divergence and differentiation trajectories, overcoming discontinuities in expression space. R2R lays a foundation for future exploration of live genomic dynamics.