Beyond stiffness: Multiscale viscoelastic features as biomechanical markers for assessing cell types and states.

Cell mechanics are pivotal in regulating cellular activities, diseases progression, and cancer development. However, the understanding of how cellular viscoelastic properties vary in physiological and pathological stimuli remains scarce. Here, we develop a hybrid self-similar hierarchical theory-microrheology approach to accurately and efficiently characterize cellular viscoelasticity. Focusing on two key cell types associated with livers fibrosis-the capillarized liver sinusoidal endothelial cells and activated hepatic stellate cells-we uncover a universal two-stage power-law rheology characterized by two distinct exponents, αshort and αlong. The mechanical profiles derived from both exponents exhibit significant potential for discriminating among diverse cells. This finding suggests a potential common dynamic creep characteristic across biological systems, extending our earlier observations in soft tissues. Using a tailored hierarchical model for cellular mechanical structures, we discern significant variations in the viscoelastic properties and their distribution profiles across different cell types and states from the cytoplasm (elastic stiffness E1 and viscosity η), to a single cytoskeleton fiber (elastic stiffness E2), and then to the cell level (transverse expansion stiffness E3). Importantly, we construct a logistic-regression-based machine-learning model using the dynamic parameters that outperforms conventional cell-stiffness-based classifiers in assessing cell states, achieving an area under the curve of 97% vs. 78%. Our findings not only advance a robust framework for monitoring intricate cell dynamics but also highlight the crucial role of cellular viscoelasticity in discerning cell states across a spectrum of liver diseases and prognosis, offering new avenues for developing diagnostic and therapeutic strategies based on cellular viscoelasticity.

Common dyadic coping and its congruence in couples facing breast cancer: The impact on couples' psychological distress.

OBJECTIVE: Psychological distress is prevalent in couples facing breast cancer. Couples often deal with breast cancer as a unit instead of as individuals. Couple's dyadic coping is important for their adjustment to breast cancer; however, little is known about how couple's coping congruence influences their distress. This study examined how common dyadic coping (CDC) and coping congruence impact psychological distress in couples facing breast cancer. METHODS: Baseline data were analyzed from 343 women with recently diagnosed early-stage breast cancer and their partners who participated in a randomized clinical trial. Psychological distress was indicated by depressed mood and state anxiety. Common dyadic coping was measured by a self-report scale. Coping congruence was assessed by the absolute difference between a woman's and her partner's CDC scores. RESULTS: Higher CDC scores were associated with lower psychological distress in both women and partners. In the CDC subscales, women who suffered less scored higher on open communication, sharing a positive outlook, and lower on avoidance coping. Partners who suffered less scored higher on open communication, sharing a positive outlook, spending time talking, and lower on avoidance coping. Greater congruence in CDC was associated with lower psychological distress in women and their partners. Congruence in sharing a positive outlook benefited both members of the dyad; congruence in avoidance coping significantly benefited patients; congruence in open communication significantly benefited partners. CONCLUSIONS: CDC and its congruence in specific areas have potential benefit to couple's psychological distress when facing breast cancer. Health care providers could consider enhancing couple's CDC and coping congruence to improve their adjustment.

GENETIC FACTORS AND CHARACTERISTICS ON SPECTRAL-DOMAIN OPTICAL COHERENCE TOMOGRAPHY ARE ASSOCIATED WITH CHOROIDAL THICKNESS IN ABCA4 -RELATED RETINOPATHY.

PURPOSE: To investigate the possible correlation factors of choroidal thickness in ABCA4 -related retinopathy. METHODS: A total of 66 patients were included in the cohort. It is a retrospective, cross-sectional laboratory investigation. The patients were tested using whole-exon sequencing and ophthalmic examinations, including slit-lamp examinations, best-corrected visual acuity, spectral-domain optical coherence tomography, fundus photograph, and fundus autofluorescence. RESULTS: Besides demographic characteristics (age, onset age, duration), we selected genetic factors and ocular characteristics on spectral-domain optical coherence tomography as the candidates related to choroidal thickness. Mutation type (inframe mutation or premature termination codon), epiretinal membrane, retinal pigment epithelium- Bruch membrane integrity, and macular curvature changes were identified as related factors to choroidal thickness in ABCA4 -related retinopathy after the adjustment of Logistic LASSO regression. CONCLUSION: Mutation type, epiretinal membrane, retinal pigment epithelium-Bruch membrane integrity, and macular curvature changes are related factors to choroidal thinning. These findings could provide us a further understanding for the pathological process and clinical features of ABCA4 mutation.

Viscoelastic Multiscale Mechanical Indexes for Assessing Liver Fibrosis and Treatment Outcomes.

Understanding liver tissue mechanics, particularly in the context of liver pathologies like fibrosis, cirrhosis, and carcinoma, holds pivotal significance for assessing disease severity and prognosis. Although the static mechanical properties of livers have been gradually studied, the intricacies of their dynamic mechanics remain enigmatic. Here, we characterize the dynamic creep responses of healthy, fibrotic, and mesenchymal stem cells (MSCs)-treated fibrotic lives. Strikingly, we unearth a ubiquitous two-stage power-law rheology of livers across different time scales with the exponents and their distribution profiles highly correlated to liver status. Moreover, our self-similar hierarchical theory effectively captures the delicate changes in the dynamical mechanics of livers. Notably, the viscoelastic multiscale mechanical indexes (i.e., power-law exponents and elastic stiffnesses of different hierarchies) and their distribution characteristics prominently vary with liver fibrosis and MSCs therapy. This study unveils the viscoelastic characteristics of livers and underscores the potential of proposed mechanical criteria for assessing disease evolution and prognosis.

Development of a common dyadic coping scale in couples facing breast cancer: the importance of open communication.

OBJECTIVE: Couples' joint coping is important in managing the impact of breast cancer. However, measures assessing couples' communication as a way of coping are insufficient. This study aimed to generate a self-report valid and reliable measure of couples' coping with a particular focus on communication. METHOD: We used baseline data of 343 couple dyads who participated in a randomized clinical trial targeting marital communication. Women were diagnosed with early-stage breast cancer in the past eight months; couples were married or in an intimate relationship for at least six months, could read and write English, and lived within 100 miles of the study center. An expert panel selected items with conceptual fit from the Mutuality and Interpersonal Sensitivity Scale (MIS) that was originally designed to evaluate marital communication about breast cancer. RESULTS: Exploratory and confirmatory factor analyses supported a 12-item measurement model with four factors: Keeping the communication open with each other about breast cancer (4 items), Sharing a positive outlook on breast cancer (2 items), Avoiding discussion of negative thoughts and feelings about breast cancer (3 items), and Spending sufficient time together talking about breast cancer (3 items). Reliability ranged from 0.76 to 0.87 for women and 0.70 to 0.83 for spouses. CONCLUSION: This new measure has potential application in clinical practice and future research to assess couple's joint coping efforts especially through communication.

A rapid method for assembly of single chromosome and identification of sex determination region based on single-chromosome sequencing.

The sex-determining-region (SDR) may offer the best prospects for studying sex-determining gene, recombination suppression, and chromosome heteromorphism. However, current progress of SDR identification and cloning showed following shortcomings: large near-isogenic lines need to be constructed, and a relatively large population is needed; the cost of whole-genome sequencing and assembly is high. Herein, the X/Y chromosomes of Spinacia oleracea L. subsp. turkestanica were successfully microdissected and assembled using single-chromosome sequencing. The assembly length of X and Y chromosome is c. 192.1 and 195.2 Mb, respectively. Three large inversions existed between X and Y chromosome. The SDR size of X and Y chromosome is c. 13.2 and 24.1 Mb, respectively. MSY region and six male-biased genes were identified. A Y-chromosome-specific marker in SDR was constructed and used to verify the chromosome assembly quality at cytological level via fluorescence in situ hybridization. Meanwhile, it was observed that the SDR located on long arm of Y chromosome and near the centromere. Overall, a technical system was successfully established for rapid cloning the SDR and it is also applicable to rapid assembly of specific chromosome in other plants. Furthermore, this study laid a foundation for studying the molecular mechanism of sex chromosome evolution in spinach.

Machine Learning Model for Screening Thyroid Stimulating Hormone Receptor Agonists Based on Updated Datasets and Improved Applicability Domain Metrics.

Machine learning (ML) models for screening endocrine-disrupting chemicals (EDCs), such as thyroid stimulating hormone receptor (TSHR) agonists, are essential for sound management of chemicals. Previous models for screening TSHR agonists were built on imbalanced datasets and lacked applicability domain (AD) characterization essential for regulatory application. Herein, an updated TSHR agonist dataset was built, for which the ratio of active to inactive compounds greatly increased to 1:2.6, and chemical spaces of structure-activity landscapes (SALs) were enhanced. Resulting models based on 7 molecular representations and 4 ML algorithms were proven to outperform previous ones. Weighted similarity density (ρs) and weighted inconsistency of activities (IA) were proposed to characterize the SALs, and a state-of-the-art AD characterization methodology ADSAL{ρs, IA} was established. An optimal classifier developed with PubChem fingerprints and the random forest algorithm, coupled with ADSAL{ρs ≥ 0.15, IA ≤ 0.65}, exhibited good performance on the validation set with the area under the receiver operating characteristic curve being 0.984 and balanced accuracy being 0.941 and identified 90 TSHR agonist classes that could not be found previously. The classifier together with the ADSAL{ρs, IA} may serve as efficient tools for screening EDCs, and the AD characterization methodology may be applied to other ML models.

A Study on Autophagy Related Biomarkers in Alzheimer's Disease Based on Bioinformatics.

Alzheimer's disease (AD) is a neurodegenerative disease with an annual incidence increase that poses significant health risks to people. However, the pathogenesis of AD is still unclear. Autophagy, as an intracellular mechanism can degrade damaged cellular components and abnormal proteins, which is closely related to AD pathology. The goal of this work is to uncover the intimate association between autophagy and AD, and to mine potential autophagy-related AD biomarkers by identifying key differentially expressed autophagy genes (DEAGs) and exploring the potential functions of these genes. GSE63061 and GSE140831 gene expression profiles of AD were downloaded from the Gene Expression Omnibus (GEO) database. R language was used to standardize and differentially expressed genes (DEGs) of AD expression profiles. A total of 259 autophagy-related genes were discovered through the autophagy gene databases ATD and HADb. The differential genes of AD and autophagy genes were integrated and analyzed to screen out DEAGs. Then the potential biological functions of DEAGs were predicted, and Cytoscape software was used to detect the key DEAGs. There were ten DEAGs associated with the AD development, including nine up-regulated genes (CAPNS1, GAPDH, IKBKB, LAMP1, LAMP2, MAPK1, PRKCD, RAB24, RAF1) and one down-regulated gene (CASP1). The correlation analysis reveals the potential correlation among 10 core DEAGs. Finally, the significance of the detected DEAGs expression was verified, and the value of DEAGs in AD pathology was detected by the receiver operating characteristic curve. The area under the curve values indicated that ten DEAGs are potentially valuable for the study of the pathological mechanism and may become biomarkers of AD. This pathway analysis and DEAG screening in this study found a strong association between autophagy-related genes and AD, providing new insights into the pathological progression of AD. Exploring the relationship between autophagy and AD: analysis of genes associated with autophagy in pathological mechanisms of AD using bioinformatics. 10 autophagy-related genes play an important role in the pathological mechanisms of AD.

Applicability Domains Based on Molecular Graph Contrastive Learning Enable Graph Attention Network Models to Accurately Predict 15 Environmental End Points.

In silico models for predicting physicochemical properties and environmental fate parameters are necessary for the sound management of chemicals. This study employed graph attention network (GAT) algorithms to construct such models on 15 end points. The results showed that the GAT models outperformed the previous state-of-the-art models, and their performance was not influenced by the presence or absence of compounds with certain structures. Molecular similarity density (ρs) was found to be a key metrics characterizing data set modelability, in addition to the proportion of compounds at activity cliffs. By introducing molecular graph (MG) contrastive learning, MG-based ρs and molecular inconsistency in activities (IA) were calculated and employed for characterizing the structure-activity landscape (SAL)-based applicability domain ADSAL{ρs, IA}. The GAT models coupled with ADSAL{ρs, IA} significantly improved the prediction coefficient of determination (R2) on all the end points by an average of 14.4% and enabled all the end points to have R2 > 0.9, which could hardly be achieved previously. The models were employed to screen persistent, mobile, and/or bioaccumulative chemicals from inventories consisting of about 106 chemicals. Given the current state-of-the-art model performance and coverage of the various environmental end points, the constructed models with ADSAL{ρs, IA} may serve as benchmarks for future efforts to improve modeling efficacy.

High-altitude exposure duration dependent global and regional gray matter volume decrease in healthy immigrants: a cross-sectional study.

BACKGROUND: The correlation between brain injury and high-altitude (HA) exposure duration (Dur_HA) as well as peripheral oxygen saturation (SpO2) remains unclear. PURPOSE: To evaluate the global and regional brain volume differences between HA immigrants and sea-level residents, and the relationship between brain volume with Dur_HA and SpO2. MATERIAL AND METHODS: Structural magnetic resonance imaging (MRI) scans were acquired in 33 healthy male HA immigrants (HA group) and 33 matched sea-level male residents (SL group). Differences in global gray matter volume (GMV), white matter volume (WMV), brain parenchyma volume (BV), total intracranial volume (TIV), and the volume-fraction (the ratio of GMV/TIV, WMV/TIV, BV/TIV) were assessed. Regional gray matter differences were investigated using voxel-based morphology analysis. The volume of clusters with GM loss were calculated as the volume of volume of interest (V_VOI). Student's t-test and partial correlation were adopted for statistic calculation. RESULTS: Compared to the SL group, the HA immigrants had larger WMV (P = 0.015), smaller ratio of GMV/WMV (P = 0.022), and regional gray matter loss in bilateral basal ganglion, limbic system, midbrain, and vermis (cluster size >100 voxels, family-wise error corrected at P = 0.01). The global GMV, BV, and V_VOI confined to vermis had negative correlations with the Dur_HA (r = -0.369, P = 0.049; r = -0.380, P = 0.042; and r = -0.471, P = 0.010. Neither global nor regional brain volume correlated with SpO2. CONCLUSION: Global and regional brain are affected by long-term HA exposure, and global and regional gray matter have a time-dependent volume loss.

Implementation fidelity to HIV assisted partner services (aPS) during scale-up in western Kenya: a convergent mixed methods study.

BACKGROUND: HIV assisted partner services (aPS) is an intervention to improve HIV status awareness among sex and drug-injecting partners of people newly diagnosed with HIV (index clients). Implementation fidelity-the degree to which an intervention is conducted as intended - is critical to effectiveness, but there are limited data about aPS fidelity when delivered by HIV testing service (HTS) providers. We explored factors affecting implementation fidelity to aPS in two high-HIV prevalence counties in western Kenya. METHODS: We used convergent mixed methods adapting the conceptual framework for implementation fidelity within the aPS scale-up project. This was an implementation study examining scale-up of APS within HTS programs in Kisumu and Homa Bay counties that recruited male sex partners (MSPs) of female index clients. We defined implementation fidelity as the extent to which HTS providers followed the protocol for phone and in-person participant tracing at six expected tracing attempts. Quantitative data were collected from tracing reports in 31 facilities between November 2018 and December 2020, and in-depth interviews (IDIs) were conducted with HTS providers. Descriptive statistics were used to describe tracing attempts. IDIs were analyzed using thematic content analysis. RESULTS: Overall, 3017 MSPs were mentioned of whom 98% (2969/3017) were traced, with most tracing attempts being successful (2831/2969, 95%). Fourteen HTS providers participated in the IDIs-mostly females (10/14, 71%) with a median age of 35 years (range 25-52), who all had post-secondary education (14/14, 100%). The proportion of tracing attempts occurring by phone ranged from 47 to 66%, with the highest proportion occurring on the first attempt and lowest on the sixth attempt. Contextual factors either enhanced or impeded implementation fidelity to aPS. Positive provider attitudes towards aPS and conducive work environment factors promoted implementation fidelity, while negative MSP responses and challenging tracing conditions impeded it. CONCLUSION: Interactions at the individual (provider), interpersonal (client-provider), and health systems (facility) levels affected implementation fidelity to aPS. As policymakers prioritize strategies to reduce new HIV infections, our findings highlight the importance of conducting fidelity assessments to better anticipate and mitigate the impact of contextual factors during the scale-up of interventions.

Current knowledge and potential intervention of hexosamine biosynthesis pathway in lung cancer.

Lung cancer is a highly prevalent malignancy characterized by significant metabolic alterations. Understanding the metabolic rewiring in lung cancer is crucial for the development of effective therapeutic strategies. The hexosamine biosynthesis pathway (HBP) is a metabolic pathway that plays a vital role in cellular metabolism and has been implicated in various cancers, including lung cancer. Abnormal activation of HBP is involved in the proliferation, progression, metastasis, and drug resistance of tumor cells. In this review, we will discuss the function and regulation of metabolic enzymes related to HBP in lung cancer. Furthermore, the implications of targeting the HBP for lung cancer treatment are also discussed, along with the challenges and future directions in this field. This review provides a comprehensive understanding of the role and intervention of HBP in lung cancer. Future research focusing on the HBP in lung cancer is essential to uncover novel treatment strategies and improve patient outcomes.

Implementation of the Surgical Apgar Score in Laboratory Animal Science: A Showcase Pilot Study in a Porcine Model and a Review of the Literature.

INTRODUCTION: In an attempt to further improve surgical outcomes, a variety of outcome prediction and risk-assessment tools have been developed for the clinical setting. Risk scores such as the surgical Apgar score (SAS) hold promise to facilitate the objective assessment of perioperative risk related to comorbidities of the patients or the individual characteristics of the surgical procedure itself. Despite the large number of scoring models in clinical surgery, only very few of these models have ever been utilized in the setting of laboratory animal science. The SAS has been validated in various clinical surgical procedures and shown to be strongly associated with postoperative morbidity. In the present study, we aimed to review the clinical evidence supporting the use of the SAS system and performed a showcase pilot trial in a large animal model as the first implementation of a porcine-adapted SAS (pSAS) in an in vivo laboratory animal science setting. METHODS: A literature review was performed in the PubMed and Embase databases. Study characteristics and results using the SAS were reported. For the in vivo study, 21 female German landrace pigs have been used either to study bleeding analogy (n = 9) or to apply pSAS after abdominal surgery in a kidney transplant model (n = 12). The SAS was calculated using 3 criteria: (1) estimated blood loss during surgery; (2) lowest mean arterial blood pressure; and (3) lowest heart rate. RESULTS: The SAS has been verified to be an effective tool in numerous clinical studies of abdominal surgery, regardless of specialization confirming independence on the type of surgical field or the choice of surgery. Thresholds for blood loss assessment were species specifically adjusted to >700 mL = score 0; 700-400 mL = score 1; 400-55 mL score 2; and <55 mL = score 3 resulting in a species-specific pSAS for a more precise classification. CONCLUSION: Our literature review demonstrates the feasibility and excellent performance of the SAS in various clinical settings. Within this pilot study, we could demonstrate the usefulness of the modified SAS (pSAS) in a porcine kidney transplantation model. The SAS has a potential to facilitate early veterinary intervention and drive the perioperative care in large animal models exemplified in a case study using pigs. Further larger studies are warranted to validate our findings.

Comprehensive analysis of the PRPF31 gene in retinitis pigmentosa patients: Four novel Alu-mediated copy number variations at the PRPF31 locus.

Retinitis pigmentosa (RP) is a monogenic disease characterized by irreversible degeneration of the retina. PRPF31, the second most common causative gene of autosomal dominant RP, frequently harbors copy number variations (CNVs), but the underlying mechanism is unclear. In this study, we summarized the phenotypic and genotypic characteristics of 18 RP families (F01-F18) with variants in PRPF31. The prevalence of PRPF31 variants in our cohort of Chinese RP families was 1.7% (18/1024). Seventeen different variants in PRPF31 were detected, including eight novel variants. Notably, four novel CNVs encompassing PRPF31, with a proportion of 22.2% (4/18), were validated to harbor gross deletions involving Alu/Alu-mediated rearrangements (AAMRs) in the same orientation. Among a total of 12 CNVs of PRPF31 with breakpoints mapped on nucleotide-resolution, 10 variants (83.3%) were presumably mediated by Alu elements. Furthermore, we described the correlation between the genotypes and phenotypes in PRPF31-related RP. Our findings expand the mutational spectrum of the PRPF31 gene and provide strong evidence that Alu elements of PRPF31 probably contribute to the susceptibility to genomic rearrangement in this locus.

MiR-942-3p as a Potential Prognostic Marker of Gastric Cancer Associated with AR and MAPK/ERK Signaling Pathway.

Gastric cancer is a common tumor with high morbidity and mortality. MicroRNA (miRNA) can regulate gene expression at the translation level and various tumorigenesis processes, playing an important role in tumor occurrence and prognosis. This study aims to screen miRNA associated with gastric cancer prognosis as biomarkers and explore the regulatory genes and related signaling pathways. In this work, R language was used for the standardization and differential analysis of miRNA and mRNA expression profiles. Samples were randomly divided into a testing group and a training group. Subsequently, we built the five miRNAs (has-miR-9-3p, has-miR-135b-3p, has-miR-143-5p, has-miR-942-3p, has-miR-196-3p) prognostic modules, verified and evaluated their prediction ability by the Cox regression analysis. They can be used as an independent factor in the prognosis of gastric cancer. By predicting and analyzing potential biological functions of the miRNA target genes, this study found that the AR gene was not only a hub gene in the PPI network, but also associated with excessive survival of patients. In conclusion, this study demonstrated that hsa-miR-942-3p could be a potential prognostic marker of gastric cancer associated with the AR and MAPK/ERK signaling pathways. The results of this study provide insights into the occurrence and development of gastric cancer.

A Tumor-Targeting Metal-Organic Nanoparticle Constructed by Dynamic Combinatorial Chemistry toward Accurately Redressing Carcinogenic Wnt Cascade.

Targeted and immunological therapy have revolutionized the malignancy treatment, but is suffering from the dose-limiting side effects and inadequate responsiveness. The emerging nanoscale infinite coordination polymers provide a feasible strategy for tumor targeting and immune sensitization. Herein, a "one-pot" self-assembled strategy based on dynamic combinatorial chemistry (DCC) principle is designed to construct a tumor-targeting metal-organic nanoparticle (MOICP) through a spontaneous co-assembling among three metal-organic coordination polymers tuned by a Wnt-inhibitor carnosic acid (CA). Responding to the tumor microenvironment, MOICP presents an optimized tumor-preferential accumulation and the satisfactory biosafety. MOICP is more active in vitro and in vivo than CA in suppressing of Wnt signaling pathway, and potently inhibits tumor growth in a patient-derived xenograft model of Wnt-activated pancreatic carcinoma. Moreover, MOICP reverses the lack of intratumoral infiltration of T lymphocytes, and hence augments the action of Anti-PD1 (programmed cell death protein 1) immunotherapy in B16F10 melanoma allograft mice model. This clinically viable MOICP can not only be applied to Wnt inhibition for cancer targeted therapy and immunotherapeutic sensitization, but also provides a de novo pattern for nanomedicine architecture with cargo-initiated co-self-assembly guided by DCC, thereby bringing new inspiration in general for disease intervention.

Three-stimulus control ultrasensitive Dirac point modulator using an electromagnetically induced transparency-like terahertz metasurface with graphene.

This letter presents a fabricated Dirac point modulator of a graphene-based terahertz electromagnetically induced transparency (EIT)-like metasurface (GrE & MS). Dynamic modulation is realized by applying three stimulus modes of optical pump, bias voltage, and optical pump-bias voltage combination. With increasing luminous flux or bias voltage, the transmission amplitude undergoes two stages: increasing and decreasing, because the graphene Fermi level shifts between the valence band, Dirac point, and conduction band. Thus, an approximate position of the Dirac point can be evaluated by the transmission spectrum fluctuation. The maximum modulation depth is measured to be 182% under 1 V. These findings provide a method for designing ultrasensitive terahertz modulation devices.

Graph Attention Network Model with Defined Applicability Domains for Screening PBT Chemicals.

In silico models for screening environmentally persistent, bio-accumulative, and toxic (PBT) substances are necessary for sound management of chemicals. Due to the complex structure-activity landscapes (SALs) on the PBT attributes, previous models for screening PBT chemicals lack either applicability domain (AD) characterizations or interpretability, restricting their applications. Herein, graph attention networks (GATs), a novel neural network architecture, were introduced to construct models for screening PBT chemicals. Results show that the GAT model not only outperformed those in previous studies but also exhibited interpretability since it optimizes attention weight parameters (PAW) that indicate contributions of each atom to the PBT attributes. An AD characterization termed ADFP-AC, which considers both molecular fingerprint (FP) similarities and compounds at activity cliffs (ACs) of SALs, was proposed to describe the ADs, which further assured the performance of the GAT model. Eight previously unidentified classes of compounds were identified as PBT chemicals from the Inventory of Existing Chemical Substances in China. The GAT model together with the ADFP-AC characterization may serve as efficient tools for screening PBT chemicals, and the modeling methodology can be applied to other physicochemical, environmental, behavioral, and toxicological parameters of chemicals that are necessary for their risk assessment and management.

Potential Application of Machine-Learning-Based Quantum Chemical Methods in Environmental Chemistry.

It is an important topic in environmental sciences to understand the behavior and toxicology of chemical pollutants. Quantum chemical methodologies have served as useful tools for probing behavior and toxicology of chemical pollutants in recent decades. In recent years, machine learning (ML) techniques have brought revolutionary developments to the field of quantum chemistry, which may be beneficial for investigating environmental behavior and toxicology of chemical pollutants. However, the ML-based quantum chemical methods (ML-QCMs) have only scarcely been used in environmental chemical studies so far. To promote applications of the promising methods, this Perspective summarizes recent progress in the ML-QCMs and focuses on their potential applications in environmental chemical studies that could hardly be achieved by the conventional quantum chemical methods. Potential applications and challenges of the ML-QCMs in predicting degradation networks of chemical pollutants, searching global minima for atmospheric nanoclusters, discovering heterogeneous or photochemical transformation pathways of pollutants, as well as predicting environmentally relevant end points with wave functions as descriptors are introduced and discussed.

A deep learning-based model improves diagnosis of early gastric cancer under narrow band imaging endoscopy.

BACKGROUND: Diagnosis of early gastric cancer (EGC) under narrow band imaging endoscopy (NBI) is dependent on expertise and skills. We aimed to elucidate whether artificial intelligence (AI) could diagnose EGC under NBI and evaluate the diagnostic assistance of the AI system. METHODS: In this retrospective diagnostic study, 21,785 NBI images and 20 videos from five centers were divided into a training dataset (13,151 images, 810 patients), an internal validation dataset (7057 images, 283 patients), four external validation datasets (1577 images, 147 patients), and a video validation dataset (20 videos, 20 patients). All the images were labeled manually and used to train an AI system using You look only once v3 (YOLOv3). Next, the diagnostic performance of the AI system and endoscopists were compared and the diagnostic assistance of the AI system was assessed. The accuracy, sensitivity, specificity, and AUC were primary outcomes. RESULTS: The AI system diagnosed EGCs on validation datasets with AUCs of 0.888-0.951 and diagnosed all the EGCs (100.0%) in video dataset. The AI system achieved better diagnostic performance (accuracy, 93.2%, 95% CI, 90.0-94.9%) than senior (85.9%, 95% CI, 84.2-87.4%) and junior (79.5%, 95% CI, 77.8-81.0%) endoscopists. The AI system significantly enhanced the performance of endoscopists in senior (89.4%, 95% CI, 87.9-90.7%) and junior (84.9%, 95% CI, 83.4-86.3%) endoscopists. CONCLUSION: The NBI AI system outperformed the endoscopists and exerted potential assistant impact in EGC identification. Prospective validations are needed to evaluate the clinical reinforce of the system in real clinical practice.