MolMVC: Enhancing molecular representations for drug-related tasks through multi-view contrastive learning.

MOTIVATION: Effective molecular representation is critical in drug development. The complex nature of molecules demands comprehensive multi-view representations, considering 1D, 2D, and 3D aspects, to capture diverse perspectives. Obtaining representations that encompass these varied structures is crucial for a holistic understanding of molecules in drug-related contexts. RESULTS: In this study, we introduce an innovative multi-view contrastive learning framework for molecular representation, denoted as MolMVC. Initially, we use a Transformer encoder to capture 1D sequence information and a Graph Transformer to encode the intricate 2D and 3D structural details of molecules. Our approach incorporates a novel attention-guided augmentation scheme, leveraging prior knowledge to create positive samples tailored to different molecular data views. To align multi-view molecular positive samples effectively in latent space, we introduce an adaptive multi-view contrastive loss (AMCLoss). In particular, we calculate AMCLoss at various levels within the model to effectively capture the hierarchical nature of the molecular information. Eventually, we pre-train the encoders via minimizing AMCLoss to obtain the molecular representation, which can be used for various down-stream tasks. In our experiments, we evaluate the performance of our MolMVC on multiple tasks, including molecular property prediction (MPP), drug-target binding affinity (DTA) prediction and cancer drug response (CDR) prediction. The results demonstrate that the molecular representation learned by our MolMVC can enhance the predictive accuracy on these tasks and also reduce the computational costs. Furthermore, we showcase MolMVC's efficacy in drug repositioning across a spectrum of drug-related applications. AVAILABILITY AND IMPLEMENTATION: The code and pre-trained model are publicly available at https://github.com/Hhhzj-7/MolMVC.

HOLI-1-to-3: Transient-Enhanced Holistic Image-to-3D Generation.

We introduce HOLI-1-to-3, a novel technique for holistic 3D shape recovery from a single-viewpoint input, by effectively combining line-of-sight (LOS) and non-line-of-sight (NLOS) imaging. We leverage advancements in ultrafast time-of-flight (ToF) sensors and learning-based 3D shape inference techniques, such as diffusion models. HOLI-1-to-3 employs a new neural plenoptic representation, which unifies radiance fields (for LOS RGB images) and transient fields (for NLOS transients). HOLI-1-to-3 is optimized through a two-stage pipeline involving diffusion priors and transients prior. Our technique allows for accurate and continuous reconstruction of both visible and invisible parts of objects from a single view. Comprehensive experiments on both simulated and real-world datasets demonstrate the effectiveness of HOLI-1-to-3 in resolving ambiguities in invisible parts of objects and significantly improving overall generation quality. The datasets used in our experiments will be made available to the research community to facilitate further achievements in holistic 3D shape recovery.

InDel mutations within the bovine PER2 gene are significantly associated with reproductive traits.

Functioning as a key regulator of circadian rhythms, the PER2 gene exerts a substantial impact on the reproductive traits of animals. However, the effect of the PER2 gene on ovarian development remains unclear. In order to examine the relationship between bovine reproductive trait and the PER2 gene, a total of 901 ovarian samples were collected, categorized into different oestrus cycles (proestrus, oestrus, post-oestrus, anoestrous), and subjected to analysis for two potential insertion/deletions (InDels) in the PER2 gene. Through agarose gel electrophoresis and DNA sequencing, two polymorphic deletion mutations (P2-D5-bp, P3-D13-bp) were identified. Furthermore, a significant association between mature follicle diameter and P2-D5-bp was found (P < 0.05). Additionally, several significant correlations with ovarian length, width, height, and white body diameter were found for P3-D13-bp (P < 0.05). These findings suggested that the bovine PER2 gene plays an important role in above-mentioned reproductive traits, offering new avenues for improving cow fertility through marker-assisted selection (MAS).

Utilization of Reimbursed Acupuncture Therapy for Low Back Pain.

Importance: Treating low back pain (LBP) often involves a combination of pharmacologic, nonpharmacologic, and interventional treatments; one approach is acupuncture therapy, which is safe, effective, and cost-effective. How acupuncture is used within pain care regimens for LBP has not been widely studied. Objective: To document trends in reimbursed acupuncture between 2010 and 2019 among a large sample of patients with LBP, focusing on demographic, socioeconomic, and clinical characteristics associated with acupuncture use and the nonpharmacologic, pharmacologic, and interventional treatments used by patients who utilize acupuncture. Design, Setting, and Participants: This cross-sectional study included insurance claims of US adults in a deidentified database. The study sample included patients diagnosed with LBP between 2010 and 2019. Data were analyzed between September 2023 and June 2024. Main Outcomes and Measures: Changes in rates of reimbursed acupuncture utilization between 2010 and 2019, including electroacupuncture use, which involves the electrical stimulation of acupuncture needles. Covariates included age, sex, race and ethnicity, income, educational attainment, region, and a chronic LBP indicator. Secondary analyses tracked other nonpharmacologic treatments (eg, physical therapy, chiropractic care), pharmacologic treatments (eg, opioids, gabapentinoids), and interventional treatments (eg, epidural steroid injections). Results: The total sample included 6 840 497 adults with LBP (mean [SD] age, 54.6 [17.8] years; 3 916 766 female [57.3%]; 802 579 Hispanic [11.7%], 258 087 non-Hispanic Asian [3.8%], 804 975 non-Hispanic Black [11.8%], 4 974 856 non-Hispanic White [72.7%]). Overall, 106 485 (1.6%) had 1 or more acupuncture claim, while 61 503 (0.9%) had 1 or more electroacupuncture claim. The rate of acupuncture utilization increased consistently, from 0.9% in 2010 to 1.6% in 2019; electroacupuncture rates were relatively stable. Patients who were female (male: odds ratio [OR], 0.68; 99% CI, 0.67-0.70), Asian (OR, 3.26; 99% CI, 3.18-3.35), residing in the Pacific region (New England: OR, 0.26; 99% CI, 0.25-0.28), earning incomes of over $100 000 (incomes less than $40 000: OR, 0.59; 99% CI, 0.57-0.61), college educated (high school or less: OR, 0.32; 99% CI, 0.27-0.35), and with chronic LBP (OR, 2.39; 99% CI, 2.35-2.43) were more likely to utilize acupuncture. Acupuncture users were more likely to engage in other nonpharmacologic pain care like physical therapy (39.2%; 99% CI, 38.9%-39.5% vs 29.3%; 99% CI, 29.3%-29.3%) and less likely to utilize prescription drugs, including opioids (41.4%; 99% CI, 41.1%-41.8% vs 52.5%; 99% CI, 52.4%-52.5%), compared with nonusers. Conclusions and Relevance: In this cross-sectional study, we found that acupuncture utilization among patients with LBP was rare but increased over time. Demographic, socioeconomic, and clinical characteristics were associated with acupuncture utilization, and acupuncture users were more likely to utilize other nonpharmacologic treatments and less likely to utilize pharmacologic treatments.

Entropy drives the ligand recognition in G-protein-coupled receptor subtypes.

Achieving ligand subtype selectivity within highly homologous subtypes of G-protein-coupled receptor (GPCR) is critical yet challenging for GPCR drug discovery, primarily due to the unclear mechanism underlying ligand subtype selectivity, which hampers the rational design of subtype-selective ligands. Herein, we disclose an unusual molecular mechanism of entropy-driven ligand recognition in cannabinoid (CB) receptor subtypes, revealed through atomic-level molecular dynamics simulations, cryoelectron microscopy structure, and mutagenesis experiments. This mechanism is attributed to the distinct conformational dynamics of the receptor's orthosteric pocket, leading to variations in ligand binding entropy and consequently, differential binding affinities, which culminate in specific ligand recognition. We experimentally validated this mechanism and leveraged it to design ligands with enhanced or ablated subtype selectivity. One such ligand demonstrated favorable pharmacokinetic properties and significant efficacy in rodent inflammatory analgesic models. More importantly, it is precisely due to the high subtype selectivity obtained based on this mechanism that this ligand does not show addictive properties in animal models. Our findings elucidate the unconventional role of entropy in CB receptor subtype selectivity and suggest a strategy for rational design of ligands to achieve entropy-driven subtype selectivity for many pharmaceutically important GPCRs.

Structure-based identification of a G protein-biased allosteric modulator of cannabinoid receptor CB1.

Cannabis sativa is known for its therapeutic benefit in various diseases including pain relief by targeting cannabinoid receptors. The primary component of cannabis, Δ9-tetrahydrocannabinol (THC), and other agonists engage the orthosteric site of CB1, activating both Gi and ß-arrestin signaling pathways. The activation of diverse pathways could result in on-target side effects and cannabis addiction, which may hinder therapeutic potential. A significant challenge in pharmacology is the design of a ligand that can modulate specific signaling of CB1. By leveraging insights from the structure-function selectivity relationship (SFSR), we have identified Gi signaling-biased agonist-allosteric modulators (ago-BAMs). Further, two cryoelectron microscopy (cryo-EM) structures reveal the binding mode of ago-BAM at the extrahelical allosteric site of CB1. Combining mutagenesis and pharmacological studies, we elucidated the detailed mechanism of ago-BAM-mediated biased signaling. Notably, ago-BAM CB-05 demonstrated analgesic efficacy with fewer side effects, minimal drug toxicity and no cannabis addiction in mouse pain models. In summary, our finding not only suggests that ago-BAMs of CB1 provide a potential nonopioid strategy for pain management but also sheds light on BAM identification for GPCRs.

Structural basis of neuropeptide Y signaling through Y1 and Y2 receptors.

Neuropeptide Y (NPY), a 36-amino-acid peptide, functions as a neurotransmitter in both the central and peripheral nervous systems by activating the NPY receptor subfamily. Notably, NPY analogs display varying selectivity and exert diverse physiological effects through their interactions with this receptor family. [Pro34]-NPY and [Leu31, Pro34]-NPY, mainly acting on Y1R, reportedly increases blood pressure and postsynaptically potentiates the effect of other vasoactive substances above all, while N-terminal cleaved NPY variants in human body primary mediates angiogenesis and neurotransmitter release inhibition through Y2R. However, the recognition mechanisms of Y1R and Y2R with specific agonists remain elusive, thereby hindering subtype receptor-selective drug development. In this study, we report three cryo-electron microscopy (cryo-EM) structures of Gi2-coupled Y1R and Y2R in complexes with NPY, as well as Y1R bound to a selective agonist [Leu31, Pro34]-NPY. Combined with cell-based assays, our study not only reveals the conserved peptide-binding mode of NPY receptors but also identifies an additional sub-pocket that confers ligand selectivity. Moreover, our analysis of Y1R evolutionary dynamics suggests that this sub-pocket has undergone functional adaptive evolution across different species. Collectively, our findings shed light on the molecular underpinnings of neuropeptide recognition and receptor activation, and they present a promising avenue for the design of selective drugs targeting the NPY receptor family.

Incorporating clinician insight and care plans into an audit and feedback initiative for antipsychotic prescribing to Medicaid-enrolled youth in Philadelphia.

BACKGROUND: Audit and feedback (A/F), which include initiatives like report cards, have an inconsistent impact on clinicians' prescribing behavior. This may be attributable to their focus on aggregate prescribing measures, a one-size-fits-all approach, and the fact that A/F initiatives rarely engage with the clinicians they target. METHODS: In this study, we describe the development and delivery of a report card that summarized antipsychotic prescribing to publicly-insured youth in Philadelphia, which was introduced by a Medicaid managed care organization in 2020. In addition to measuring aggregate prescribing behavior, the report card included different elements of care plans, including whether youth were receiving polypharmacy, proper medication management, and the concurrent use of behavioral health outpatient services. The A/F initiative elicited feedback from clinicians, which we refer to as an "audit and feedback loop." We also evaluate the impact of the report card by comparing pre-post differences in prescribing measures for clinicians who received the report card with a group of clinicians who did not receive the report card. RESULTS: Report cards indicated that many youth who were prescribed antipsychotics were not receiving proper medication management or using behavioral health outpatient services alongside the antipsychotic prescription, but that polypharmacy was rare. In their feedback, clinicians who received report cards cited several challenges related to antipsychotic prescribing, such as the logistical difficulties of entering lab orders and family members' hesitancy to change care plans. The impact of the report card was mixed: there was a modest reduction in the share of youth receiving polypharmacy following the receipt of the report card, while other measures did not change. However, we documented a large reduction in the number of youth with one or more antipsychotic prescription fill among clinicians who received a report card. CONCLUSIONS: A/F initiatives are a common approach to improving the quality of care, and often target specific practices such as antipsychotic prescribing. Report cards are a low-cost and feasible intervention but there is room for quality improvement, such as adding measures that track medication management or eliciting feedback from clinicians who receive report cards. To ensure that the benefits of antipsychotic prescribing outweigh its risks, it is important to promote quality and safety of antipsychotic prescribing within a broader care plan.

Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning.

Global fine particulate matter (PM2.5) assessment is impeded by a paucity of monitors. We improve estimation of the global distribution of PM2.5 concentrations by developing, optimizing, and applying a convolutional neural network with information from satellite-, simulation-, and monitor-based sources to predict the local bias in monthly geophysical a priori PM2.5 concentrations over 1998-2019. We develop a loss function that incorporates geophysical a priori estimates and apply it in model training to address the unrealistic results produced by mean-square-error loss functions in regions with few monitors. We introduce novel spatial cross-validation for air quality to examine the importance of considering spatial properties. We address the sharp decline in deep learning model performance in regions distant from monitors by incorporating the geophysical a priori PM2.5. The resultant monthly PM2.5 estimates are highly consistent with spatial cross-validation PM2.5 concentrations from monitors globally and regionally. We withheld 10% to 99% of monitors for testing to evaluate the sensitivity and robustness of model performance to the density of ground-based monitors. The model incorporating the geophysical a priori PM2.5 concentrations remains highly consistent with observations globally even under extreme conditions (e.g., 1% for training, R2 = 0.73), while the model without exhibits weaker performance (1% for training, R2 = 0.51).

Development of a Nomogram Model Based on Lactate-To-Albumin Ratio for Prognostic Prediction in Hospitalized Patients with Intracerebral Hemorrhage.

OBJECTIVE: This study aims to develop a nomogram model incorporating lactate-to-albumin ratio (LAR) to predict the prognosis of hospitalized patients with intracerebral hemorrhage (ICH) and demonstrate its excellent predictive performance. METHODS: A total of 226 patients with ICH from the Medical information mart for intensive care III (MIMIC Ⅲ) database were randomly split into 8:2 ratio training and experimental groups, and 38 patients from the eICU-CRD for external validation. Univariate and multivariate Cox proportional hazards regression analysis was performed to identify independent factors associated with ICH, and multivariate Cox regression was used to construct nomograms for 7-day and 14-day overall survival (OS). The performance of nomogram was verified by the calibration curves, decision curves, and receiver operating characteristic (ROC) curves. RESULTS: Our study identified LAR, glucose, mean blood pressure, sodium, and ethnicity as independent factors influencing in-hospital prognosis. The predictive performance of our nomogram model for predicting 7-day and 14 -day OS (AUCs: 0.845 and 0.830 respectively) are both superior to Oxford Acute Severity of Illness Score, Simplified acute physiology score II, and SIRS (AUCs: 0.617, 0.620 and 0.591 and AUCs: 0.709, 0.715 and 0.640, respectively) in internal validation, and also demonstrate favorable predictive performance in external validation (AUCs: 0.778 and 0.778 respectively). CONCLUSIONS: LAR as a novel biomarker is closely associated with an increased risk of in-hospital mortality of patients with ICH. The nomogram model incorporating LAR along with glucose, mean blood pressure, sodium, and ethnicity demonstrate excellent predictive performance for predicting the prognosis of 7- and 14-day OS of hospitalized patients with ICH.

Exploration of the Polymorphism Distribution of Bovine HMGA2 Gene in Worldwide Breeds and Its Associations with Ovarian Traits.

The high-mobility group AT-hook 2(HMGA2) gene has been widely studied in the context of cancer and animal growth. However, recently, several studies have uncovered its critical role in cell proliferation. A genome-wide association study (GWAS) further suggests that the HMGA2 gene is a candidate gene in fertility, indicating its connection not only to growth traits but also to reproduction, specifically ovarian traits. Thus, this study aimed to analyze the distribution of the HMGA2 gene in 54 bovine breeds worldwide, identify important short fragment variants (indels), and investigate the relationship between HMGA2 and ovarian development. The dataset included genotypic information from a bovine population of 634 individuals (n = 634). After genotyping and analyzing four selected loci, we found that one out of four loci, rs133750033 (P4-D22-bp), was polymorphic. Our results also reveal that this indel of HMGA2 is significantly associated with certain ovarian traits (p < 0.05). Specifically, it has connection with ovarian length (p = 0.004) and ovarian height (p = 0.026) during diestrus. Additionally, we discovered a higher expression of the HMGA2 gene in Asian cattle breeds. In summary, this study suggests that HMGA2 has the potential to serve as an animal fertility testing marker gene. Moreover, these findings contribute to a more promising outlook for the bovine industry.

Bovine FRAS1: mRNA Expression Profile, Genetic Variations, and Significant Correlations with Ovarian Morphological Traits, Mature Follicle, and Corpus Luteum.

The amelioration of bovine fertility caused by a multi-factorial problem has always been a hot topic, among which the detection of available target genes is the most crucial. It was hypothesized that the Fraser extracellular matrix complex subunit 1 (FRAS1) gene detected by GWAS is involved in physiological activities such as ovarian development. Herein, unilateral ovaries from 2111 cows were used to examine the mRNA expression profile and polymorphisms of bovine FRAS1 and their associations with fertility-related characteristics. Firstly, it was confirmed that FRAS1 gene transcripts are expressed in various bovine tissues. Then, among five potential insertion-deletion (indel) loci, the 20 bp (named P3-D20-bp) and 15 bp (P4-D15-bp) deletion mutations were confirmed to be polymorphic with linkage equilibrium. Secondly, the P3-D20-bp polymorphism was significantly associated with ovarian weight and corpus luteum diameter in the metaestrus phase and ovarian length in the dioestrum stage. Additionally, both ovarian length and mature follicle diameter in metaestrus are significantly correlated with different genotypes of P4-D15-bp. Thirdly, the transcriptional expression of the FRAS1 gene in groups with a minimum value of ovarian weight or volume was significantly higher than the expression in groups with a maximum value. Instead of that, the more corpus luteum and mature follicles there are, the higher the transcription expression of the FRAS1 gene is. Furthermore, FRAS1 expression in cows with a heterozygous genotype (ID) of P3-D20-bp was significantly higher than others. Eventually, P3-D20-bp deletion could disturb the binding efficiency of WT1-I and Sox2 to FRAS1 sequence according to binding prediction, indicating that mutation may affect gene expression and traits by influencing the binding of transcription factors. Overall, the polymorphisms of P3-D20-bp and P4-D15-bp of the bovine FRAS1 gene significantly correlated to follicle or ovarian traits that could be applied in optimizing female fertility in cow MAS breeding programs.

Ligand recognition and G-protein coupling of trace amine receptor TAAR1.

Trace-amine-associated receptors (TAARs), a group of biogenic amine receptors, have essential roles in neurological and metabolic homeostasis1. They recognize diverse endogenous trace amines and subsequently activate a range of G-protein-subtype signalling pathways2,3. Notably, TAAR1 has emerged as a promising therapeutic target for treating psychiatric disorders4,5. However, the molecular mechanisms underlying its ability to recognize different ligands remain largely unclear. Here we present nine cryo-electron microscopy structures, with eight showing human and mouse TAAR1 in a complex with an array of ligands, including the endogenous 3-iodothyronamine, two antipsychotic agents, the psychoactive drug amphetamine and two identified catecholamine agonists, and one showing 5-HT1AR in a complex with an antipsychotic agent. These structures reveal a rigid consensus binding motif in TAAR1 that binds to endogenous trace amine stimuli and two extended binding pockets that accommodate diverse chemotypes. Combined with mutational analysis, functional assays and molecular dynamic simulations, we elucidate the structural basis of drug polypharmacology and identify the species-specific differences between human and mouse TAAR1. Our study provides insights into the mechanism of ligand recognition and G-protein selectivity by TAAR1, which may help in the discovery of ligands or therapeutic strategies for neurological and metabolic disorders.

Building an Integrated Data Infrastructure to Examine the Spectrum of Suicide Risk Factors in Philadelphia Medicaid.

While there are many data-driven approaches to identifying individuals at risk of suicide, they tend to focus on clinical risk factors, such as previous psychiatric hospitalizations, and rarely include risk factors that occur in nonclinical settings, such as jails or emergency shelters. A better understanding of system-level encounters by individuals at risk of suicide could help inform suicide prevention efforts. In Philadelphia, we built a community-level data infrastructure that encompassed suicide death records, behavioral health claims, incarceration episodes, emergency housing episodes, and involuntary commitment petitions to examine a broader spectrum of suicide risk factors. Here, we describe the development of the data infrastructure, present key trends in suicide deaths in Philadelphia, and, for the Medicaid-eligible population, determine whether suicide decedents were more likely to interact with the behavioral health, carceral, and housing service systems compared to Medicaid-eligible Philadelphians who did not die by suicide. Between 2003 and 2018, there was an increase in the number of annual suicide deaths among Medicaid-eligible individuals, in part due to changes in Medicaid eligibility. There were disproportionately more suicide deaths among Black and Hispanic individuals who were Medicaid-eligible, who were younger on average, compared to suicide decedents who were never Medicaid-eligible. However, when we accounted for the racial and ethnic composition of the Medicaid population at large, we found that White individuals were four times as likely to die by suicide, while Asian, Black, Hispanic, and individuals of other races were less likely to die by suicide. Overall, 58% of individuals who were Medicaid-eligible and died by suicide had at least one Medicaid-funded behavioral health claim, 10% had at least one emergency housing episode, 25% had at least one incarceration episode, and 22% had at least one involuntary commitment. By developing a data infrastructure that can incorporate a broader spectrum of risk factors for suicide, we demonstrate how communities can harness administrative data to inform suicide prevention efforts. Our findings point to the need for suicide prevention in nonclinical settings such as jails and emergency shelters, and demonstrate important trends in suicide deaths in the Medicaid population.

High-efficiency broadband achromatic metalenses for visible full-stokes polarization imaging.

Polarization-imaging technology has important applications in target detection, communication, biomedicine, and other fields. A polarization imaging system based on metalenses, which provides new possibilities for the realization of highly integrated full-Stokes polarization imaging systems, can solve the problems of traditional polarization imaging systems, such as complex structures, large volumes, and the inability to simultaneously obtain linear and circular polarization states. However, currently designed metalens arrays that can achieve real-time full-Stokes polarization imaging can generally only be used for monochromatic detection, which significantly limits the amount of measured information of the object. Broad-spectrum polarization color imaging allows more image degrees of freedom, enabling more accurate characterization of polarization for multi-target object scenes in complex environments. To achieve broad-spectrum polarization imaging, we propose and design a metalens array that can achieve full-Stokes polarization imaging in the broadband visible range, in which the design process of metalenses for splitting and focusing broadband orthogonal circularly polarized light is emphasized. To design metalenses that can achieve polarization splitting and efficient focusing, we simulate and optimize the height and period of the nano-units and show that smaller periods and larger heights do not always result in higher-performance devices when designing multifunctional metalenses. The designed metalens array can split and diffraction-limited focus the orthogonal polarized incident light to the designated position with average focusing efficiencies of 59.2% under 460-680 nm TM linearly polarized light, 53.1% under TE linearly polarized light, 58.8% under left-handed circularly polarized light, and 52.7% under right-handed circularly polarized light. The designed metalenses can be applied to imaging systems, such as polarization imaging and polarization light-field imaging systems.

Understanding the "individual drug reaction" from the perspective of the interaction between probiotics and lovastatin in vitro and in vivo.

BACKGROUND: The existence of the gut microbiota produces an "individual drug reaction." As members of the intestinal microbiota, probiotics, although they have prebiotic functions, may accelerate the degradation of drugs, thereby affecting drug efficacy. Lovastatin is one of the well-recognized lipid-lowering drugs. Its main action site is the liver. Therefore, if it is degraded in advance by gastrointestinal probiotics, its efficacy may be reduced. RESULTS: Here, we designed a two-stage experiment in vitro and in vivo to explore the degradation of lovastatin by probiotics. In vitro, the degradation of lovastatin by 83 strains of Lactiplantibacillus plantarum and the "star strain" Lacticaseibacillus paracasei strain Shirota was investigated by high-performance liquid chromatography (HPLC). The results showed that probiotics could degrade lovastatin to varying degrees. Subsequently, we selected Lactiplantibacillus plantarum A5 (16.87%) with the strongest ability to degrade lovastatin, Lactiplantibacillus plantarum C3 (4.61%) with the weakest ability to degrade lovastatin and Lacticaseibacillus paracasei strain Shirota (17.6%) as representative probiotics for in vivo experiments. In vivo, the therapeutic effect of lovastatin combined with probiotics on golden hamsters with mixed hyperlipidemia was evaluated by measuring blood indicators, intestinal microbiota metagenomic sequencing, and the liver transcriptome. The results showed that the intake of probiotics did not affect the efficacy of lovastatin and could slow the inflammatory reaction of the liver. CONCLUSIONS: The supplementation of probiotics produced beneficial metabolites in the intestine by promoting beneficial microbes. Intestinal metabolites affected the expression of the liver genes through the gut-liver axis, increased the relative content of the essential amino acids, and finally improved the liver inflammatory response of the host. This study aims to reveal the impact of probiotics on the human body from a unique perspective, suggesting the impact of taking probiotics while taking drugs. Video Abstract.

LINC00426, a novel m6A-regulated long non-coding RNA, induces EMT in cervical cancer by binding to ZEB1.

PURPOSE: To explore the function and molecular mechanism of LINC00426 in Cervical Cancer (CC), and to explore the clinical treatment strategy of LINC00426 for CC. METHODS: Bioinformatics analysis was used to explore the expression of LINC00426 and patient prognosis of CC. Cell function experiments were conducted to explore the potential effect of LINC00426 on CC malignant phenotypes. The difference in m6A modification level between the high and low expression groups of LINC00426 was analyzed by detecting the total m6A level. The luciferase reporter assay was used to confirm the binding of miR-200a-3p to LINC00426. The RIP assay was used to confirm the binding of LINC00426 to ZEB1. Cell viability assay was performed to detect the effect of LINC00426 on cellular drug resistance. RESULTS: LINC00426 is up-regulated in CC, which can enhance the proliferation, migration and invasion of CC cells. METTL3 promotes the expression of LINC00426 by m6A methylation modification. In addition, the LINC00426/miR-200a-3p/ZEB1 axis affects the proliferation, migration, and invasion of CC by regulating the expression of EMT markers. Through the detection of cell viability, we observed that overexpression LINC00426 in cells resulted in resistance to cisplatin and bleomycin, and more sensitive to imatinib. CONCLUSION: LINC00426 is a cancer-promoting lncRNA related to m6A modification. The process of EMT in CC is regulated by the LINC00426/miR-200a/3p/ZEB1 axis. LINC00426 can affect the sensitivity of CC cells to chemotherapy drugs, and is expected to become a therapeutic target for CC.

Association of lactate-to-albumin ratio with in-hospital and intensive care unit mortality in patients with intracerebral hemorrhage.

Background: Intracerebral hemorrhage (ICH) is a severe stroke subtype with a high mortality rate; the lactate-to-albumin ratio (LAR) is a new biomarker for predicting clinical outcomes in patients with ICH. However, the relationship between LAR and mortality in patients with ICH treated in the intensive care unit (ICU) remains controversial. Therefore, in this study, we aimed to investigate the association between LAR and in-hospital and ICU mortality in patients with ICH. Methods: Patients with ICH were selected from the Medical Information Mart for Intensive Care III (MIMIC-III) database; their clinical information, including baseline characteristics, vital signs, comorbidities, laboratory test results, and scoring systems, was extracted. Univariate and multivariate Cox proportional hazards analyses were used to investigate the association of LAR with in-hospital and ICU mortality. The maximum selection statistical method and subgroup analysis were used to investigate these relationships further. Kaplan-Meier (KM) analysis was used to draw survival curves. Results: This study enrolled 237 patients with ICH whose lactate and albumin levels, with median values of 1.975 and 3.6 mg/dl, respectively, were measured within the first 24 h after ICU admission. LAR had an association with increased risk of in-hospital mortality [unadjusted hazards ratio (HR), 1.79; 95% confidence interval (CI), 1.32-2.42; p < 0.001] and ICU mortality (unadjusted HR, 1.88; 95% CI, 1.38-2.55; p < 0.001). A cut-off value of 0.963 mg/dl was used to classify patients into high LAR (≥0.963) and low LAR (<0.963) groups, and survival curves suggested that those two groups had significant survival differences (p = 0.0058 and 0.0048, respectively). Furthermore, the high LAR group with ICH had a significantly increased risk of in-hospital and ICU mortality compared to the low LAR group. Conclusion: Our study suggests that a high LAR is associated with an increased risk of in-hospital and ICU mortality in patients with ICH. Thus, the LAR is a useful prognostic predictor of clinical outcomes in patients with ICH.

An improved UAV target detection algorithm based on ASFF-YOLOv5s.

Object detection in drone-captured scenarios is a recent popular task. Due to the high flight altitude of unmanned aerial vehicle (UAV), the large variation of target scales, and the existence of dense occlusion of targets, in addition to the high requirements for real-time detection. To solve the above problems, we propose a real-time UAV small target detection algorithm based on improved ASFF-YOLOv5s. Based on the original YOLOv5s algorithm, the new shallow feature map is passed into the feature fusion network through multi-scale feature fusion to improve the extraction capability for small target features, and the Adaptively Spatial Feature Fusion (ASFF) is improved to improve the multi-scale information fusion capability. To obtain anchor frames for the VisDrone2021 dataset, we improve the K-means algorithm to obtain four different scales of anchor frames on each prediction layer. The Convolutional Block Attention Module (CBAM) is added in front of the backbone network and each prediction network layer to improve the capture capability of important features and suppress redundant features. Finally, to address the shortcomings of the original GIoU loss function, the SIoU loss function is used to accelerate the convergence of the model and improve accuracy. Extensive experiments conducted on the dataset VisDrone2021 show that the proposed model can detect a wide range of small targets in various challenging environments. At a detection rate of 70.4 FPS, the proposed model obtained a precision value of 32.55%, F1-score of 39.62%, and a mAP value of 38.03%, which improved 2.77, 3.98, and 5.1%, respectively, compared with the original algorithm, for the detection performance of small targets and to meet the task of real-time detection of UAV aerial images. The current work provides an effective method for real-time detection of small targets in UAV aerial photography in complex scenes, and can be extended to detect pedestrians, cars, etc. in urban security surveillance.