Exploring spatial heterogeneity in factors associated with injury severity in speeding-related crashes: An integrated machine learning and spatial modeling approach.

Speeding, a risky act of driving a vehicle at a speed exceeding the posted limit, has consistently emerged as a leading contributor to traffic fatalities. Identifying the risk factors associated with injury severity in speeding-related crashes is essential for implementing countermeasures aimed at preventing severe injury incidents and achieving Vision Zero goals. With the wealth of traffic crash data collected by various agencies, researchers have a valuable opportunity to conduct data-driven studies and employ various modeling methods to gain insights into the correlated factors affecting injury severity in traffic crashes. Machine learning models, owing to their superior predictive power compared to statistical models, are increasingly being adopted by researchers. These models, in conjunction with interpretation techniques, can reveal potential relationships between crash injury severity and contributing factors. Traffic crashes are inherently tied to geographic locations, distributed across road networks influenced by diverse socioeconomic and geographical factors. Recognizing spatial heterogeneity in traffic safety is crucial for tailored safety measures to address speeding-related crashes, as a one-size-fits-all approach may not work effectively everywhere. However, most existing machine learning models are unable to incorporate the spatial dependency among observations, such as traffic crashes, which hinders their ability to uncover spatial heterogeneity in traffic safety. To address this gap, this study introduces the Geographically Weighted Neural Network (GWNN) model, a spatial machine-learning model that integrates neural network (NN) and geographically weighted modeling approaches to investigate spatial heterogeneity in speeding-related crashes. Unlike the traditional NN model, which trains a single set of model parameters for all observations, the GWNN trains a local NN model for each crash location using a spatially weighted subsample of nearby crashes, allowing for the quantification of corresponding local effects of features through calculating local marginal effects. To understand the spatial heterogeneity in speeding-related crashes, this study extracted two years (2020 and 2021) of speeding-related crash data from Alabama for the development of the GWNN local models. The modeling results show significant spatial variability among several factors contributing to injury severity in speeding-related crashes. These factors include driver condition, vehicle type, crash type, speed limit, weather, crash time and location, roadway alignment, and traffic volume. Based on the GWNN modeling results, this study identified three types of spatial variations in relationships between contributing factors and crash injury severity: consistent positive associations, consistent negative associations, and inverse associations (i.e., marginal effects can vary between positive and negative depending on the location). This study contributes by integrating advanced machine learning and spatial modeling approaches to uncover intricate spatial patterns and factors influencing injury severity in speeding-related crashes, thereby facilitating the development of targeted policy implementations and safety interventions.

A novel low-background nitroreductase fluorescent probe for real-time fluorescence imaging and surgical guidance of thyroid cancer resection.

Thyroid cancer always appears insidiously with few noticeable clinical symptoms. Due to its limitations, conventional ultrasound imaging can lead to missed or misdiagnosed cases. Surgery is still the primary treatment method of thyroid cancer, but removal of surrounding healthy tissues to minimize recurrence leads to overtreatment and added patient suffering. To address this challenge, herein, a nitroreductase (NTR) fluorescent probe, Ox-NTR, has been developed for detecting thyroid cancer and tracking the surgical removal of thyroid tumors by fluorescence imaging. The conjugated structure of oxazine 1 was disrupted, significantly reducing the issue of high background signals, thus effectively achieving low background fluorescence. Under hypoxic conditions, the nitro group of Ox-NTR can be reduced to an amine and subsequently decomposed into oxazine 1, emitting intense red fluorescence. Ox-NTR has a low detection limit of 0.09 µg/mL for NTR with excellent photostability and selectivity. Cellular studies show that Ox-NTR can effectively detect NTR levels in hypoxic thyroid cancer cells. Moreover, the ability of Ox-NTR of rapid response to thyroid cancer in vivo is confirmed by fluorescence imaging in mice, distinguishing tumors from normal tissues due to its superior low background fluorescence. Utilizing this fluorescence imaging method during surgical resection can guide the removal of tumors, preventing both missed tumor tissues and accidental removal of healthy tissue. In summary, the novel Ox-NTR offers precise detection capabilities that provide significant advantages over traditional imaging methods for thyroid cancer diagnosis and treatment, making it a valuable tool to guide tumor removal in surgical procedures.

Highly Stable 72-Nuclearity Nanocages for Efficient Synthesis of Aryl Nitriles via Ni/Cu Synergistic Catalysis.

Seeking noble-metal-free catalysts for efficient synthesis of aryl nitriles under mild conditions poses a significant challenge due to the use of hypertoxic cyanides or high-pressure/temperature NH3/O2 in conventional synthesis processes. Herein, we developed a novel framework 1 assembled by [Ni72] nanocages with excellent solvents/pH stability. To investigate the structure-activity relationship of catalytic performance, several isostructural MOFs with different molar ratios of Ni/Cu by doping Cu2+ into framework 1 (Ni0.59Cu0.41 (2), Ni0.81Cu0.19 (3), Ni0.88Cu0.12 (4), and Ni0.92Cu0.08 (5)) were prepared. Catalytic studies revealed that catalyst 3 exhibited remarkable performance in the synthesis of aryl nitriles, utilizing a formamide alternative to hypertoxic NaCN/KCN. Notably, catalyst 3 achieved an excellent TOF value of 9.8 h-1. Furthermore, catalyst 3 demonstrated its applicability in a gram-scale experiment and maintained its catalytic performance even after six recycling cycles, owing to its high stability resulting from significant electrostatic and orbital interactions between the Ni center and ligands as well as a large SOMO-LUMO energy gap supported by DFT calculations. Control experiments and DFT calculations further revealed that the excellent catalytic performance of catalyst 3 originated from the synergistic effect of Ni/Cu. Importantly, this work not only provides a highly feasible method to construct highly stable MOFs containing multinuclear nanocages with exceptional catalytic performance but also represents the first example of a heterogeneous catalyst for the synthesis of aryl nitriles using formamide as the cyanide source.

"Being a Good Parent" During Times of Illness as Defined by Chinese Children With Cancer, Their Parents, and Providers.

BACKGROUND: Identifying the definition of "being a good parent" facilitates the understanding of parents' personal beliefs and deeds regarding their ill child. OBJECTIVE: The aim of this study was to explore the concept of "being a good parent to my ill child" during pediatric cancer treatment from the perspective of Chinese children, parents, and providers. METHODS: A descriptive qualitative study was conducted with 6 children, 18 parents, 5 doctors, 19 nurses, and 3 social workers by semistructured interviews at 3 Chinese hospitals. RESULTS: Except for "letting the Lord lead," 7 themes from the original conceptual model were validated, for example, "being there for my child" (n = 51, 100.0%); "doing right by my child" (n = 38, 74.5%), "being an advocate for my child" (n = 27, 52.9%), "conveying love to my child" (n = 26, 51.0%), "making my child healthy" (n = 18, 35.3%), "being a good life example" (n = 13, 25.5%), and "not allowing suffering" (n = 13, 25.5%). A new theme, "rebuilding myself" (n = 39, 76.5%), emerged in the Chinese context. "Being a good parent to my ill child" is perceived differently among stakeholders. Healthcare professionals' facilitation to fulfill the concept included "recognizing the individualized good-parent definition," "providing best available care" and "establishing a supportive environment." CONCLUSION: "Being a good parent to my ill child" is meaningfully expressed by Chinese parents and recognized by children and providers during pediatric cancer treatment. IMPLICATIONS FOR PRACTICE: It is important to support parents in conveying their internal good parent definition and sharing it with stakeholders. Attention should be paid to related cultural influencers, a supportive family-friendly environment, and shared decision making involving the child's voice.

Correlations between root phosphorus acquisition and foliar phosphorus allocation reveal how grazing promotes plant phosphorus utilization.

Overgrazing and phosphorus (P) deficiency are two major factors limiting the sustainable development of grassland ecosystems. Exploring plant P utilization and acquisition strategies under grazing can provide a solid basis for determining a reasonable grazing intensity. Both foliar P allocation and root P acquisition are crucial mechanisms for plants to adapt to environmental P availability; however, their changing characteristics and correlation under grazing remain unknown. Here, we investigated foliar P fractions, root P-acquisition traits and gene expression, as well as rhizosphere and bulk soil properties of two dominant plant species, Leymus chinensis (a rhizomatous grass) and Stipa grandis (a bunchgrass), in a field grazing intensity gradient site in Inner Mongolia. Grazing induced different degrees of compensatory growth in the two dominant plant species, increased rhizosphere P availability, and alleviated plant P limitation. Under grazing, the foliar metabolite P of L. chinensis increased, whereas the nucleic acid P of S. grandis increased. Increased P fractions in L. chinensis were positively correlated with increased root exudates and rapid inorganic P absorption. For S. grandis, increased foliar P fractions were positively correlated with more fine roots, more root exudates, and up-regulated expression of genes involved in defense and P metabolism. Overall, efficient root P mobilization and uptake traits, as well as increases in leaf metabolic activity-related P fractions, supported plant compensatory growth under grazing, a process that differed between tiller types. The highest plant productivity and leaf metabolic activity-related P concentrations under medium grazing intensity clarify the underlying basis for sustainable livestock production.

Can energy internet improve corporate ESG performance? -- Evidence from Chinese high energy-consuming companies.

Under the challenges of global crises such as climate warming, ESG performance, which represents sustainable development, has received widespread attention at home and abroad. Using the panel data from 2011 to 2020, comprising 726 high energy-consuming companies listed on the Shanghai and Shenzhen A-shares, this paper takes Energy Internet demonstration project in 2016 as a quasi-natural experiment and builds a difference-in-difference model to study its microscopic policy effects. The study found that, firstly, Energy Internet can markedly enhance ESG performance of high energy-consuming companies. Secondly, the mechanism test finds that Energy Internet can facilitate high energy-consuming enterprises to enhance their ESG performance through three mechanisms: increasing government subsidies for enterprises in energy conservation and environmental protection, absorbing talent employment and improving information environment within the enterprises. Finally, the heterogeneity analysis proves that Energy Internet's policy effects are more pronounced in regions with higher financial expenditures on local science undertakings and among state-owned enterprises. China ought to persist in advancing the development of Energy Internet and provide companies with adequate support on finance, talent and technology. Meanwhile, during the construction of Energy Internet, attention should be paid to adapting to local conditions and enterprises.

Nerve injury disrupts temporal processing in the spinal cord dorsal horn through alterations in PV+ interneurons.

How mechanical allodynia following nerve injury is encoded in patterns of neural activity in the spinal cord dorsal horn (DH) remains incompletely understood. We address this in mice using the spared nerve injury model of neuropathic pain and in vivo electrophysiological recordings. Surprisingly, despite dramatic behavioral over-reactivity to mechanical stimuli following nerve injury, an overall increase in sensitivity or reactivity of DH neurons is not observed. We do, however, observe a marked decrease in correlated neural firing patterns, including the synchrony of mechanical stimulus-evoked firing, across the DH. Alterations in DH temporal firing patterns are recapitulated by silencing DH parvalbumin+ (PV+) interneurons, previously implicated in mechanical allodynia, as are allodynic pain-like behaviors. These findings reveal decorrelated DH network activity, driven by alterations in PV+ interneurons, as a prominent feature of neuropathic pain and suggest restoration of proper temporal activity as a potential therapeutic strategy to treat chronic neuropathic pain.

Thymol targeting interleukin 4 induced 1 expression reshapes the immune microenvironment to sensitize the immunotherapy in lung adenocarcinoma.

Immune checkpoint blockades are the most promising therapy in lung adenocarcinoma (LUAD). However, the response rate remains limited, underscoring the urgent need for effective sensitizers. Interleukin 4 induced 1 (IL4I1) is reported to have immunoinhibitory and tumor-promoting effects in several cancers. However, the targetable value of IL4I1 in sensitizing the immunotherapy is not clear, and there is a lack of effective small molecules that specifically target IL4I1. Here, we show that silencing IL4I1 significantly remodels the immune microenvironment via inhibiting aryl hydrocarbon receptor (AHR) signaling, thereby enhancing the efficacy of anti-PD-1 antibody in LUAD, which suggests that IL4I1 is a potential drug target for the combination immunotherapy. We then identify thymol as the first small molecule targeting IL4I1 transcription through a drug screening. Thymol inhibits the IL4I1 expression and blocks AHR signaling in LUAD cells. Thymol treatment restores the antitumor immune response and suppresses the progression of LUAD in an orthotopic mouse model. Strikingly, the combination treatment of thymol with anti-PD-1 antibody shows significant tumor regression in LUAD mice. Thus, we demonstrate that thymol is an effective small molecule to sensitize the PD-1 blockade in LUAD via targeting IL4I1, which provides a novel strategy for the immunotherapy of LUAD.

Case Report: ALK rearranged locally advanced lung adenocarcinoma showing inconsistent radiographic findings and pathological responses during neoadjuvant alectinib therapy.

Alectinib has been approved as first-line treatment for anaplastic lymphoma kinase (ALK)-positive non-small cell lung carcinoma. Oncologists are also exploring the possibility of applying alectinib in the perioperative period. Here, we present a patient with locally advanced lung adenocarcinoma associated with EML4-ALK fusion mutation, who received neoadjuvant chemotherapy and alectinib treatment, and then underwent thoracoscopic left lower lung lobectomy. The patient initially received eight chemotherapy cycles and achieved partial remission. After eight cycles of chemotherapy, the lymph nodes in the hilar region again enlarged. The patient was then switched to 4 months of alectinib therapy, but no significant lesion changes were detected on imaging during this period. This raised the question of whether the patient developed alectinib resistance. The pathological findings of the postoperative lung lobe specimens indicated extensive necrosis in the tumor area with no residual tumor cells and massive chronic inflammatory cell infiltration around the tumor area, confirming inconsistency between the imaging findings and pathological results. Multi-point tumor specimen sampling was postoperatively performed. Tumor immune-related gene expression was detected in the sample with the help of the PanCancer IO360™ panel based on the nCounter platform. This is a rare case of a patient who was treated with neoadjuvant alectinib and had paradoxical radiographic findings and pathological responses. The possibility that intratumoral immune heterogeneity was responsible for this phenomenon has been discussed. Based on the findings, it is argued that the pathological response should be an important basis for assessing the effectiveness of neoadjuvant alectinib therapy.

Soil phosphorus availability alters the correlations between root phosphorus-uptake rates and net photosynthesis of dominant C3 and C4 species in a typical temperate grassland of Northern China.

Phosphorus (P) fertilization can alleviate a soil P deficiency in grassland ecosystems. Understanding plant functional traits that enhance P uptake can improve grassland management. We measured impacts of P addition on soil chemical and microbial properties, net photosynthetic rate (Pn ) and nonstructural carbohydrate concentrations ([NSC]), and root P-uptake rate (PUR), morphology, anatomy, and exudation of two dominant grass species: Leymus chinensis (C3 ) and Cleistogenes squarrosa (C4 ). For L. chinensis, PUR and Pn showed a nonlinear correlation. Growing more adventitious roots compensated for the decrease in P transport per unit root length, so that it maintained a high PUR. For C. squarrosa, PUR and Pn presented a linear correlation. Increased Pn was associated with modifications in root morphology, which further enhanced its PUR and a greater surplus of photosynthate and significantly stimulated root exudation (proxied by leaf [Mn]), which had a greater impact on rhizosheath micro-environment and microbial PLFAs. Our results present correlations between the PUR and the Pn of L. chinensis and C. squarrosa and reveal that NSC appeared to drive the modifications of root morphology and exudation; they provide more objective basis for more efficient P-input in grasslands to address the urgent problem of P deficiency.

The oncomodulin receptor ArmC10 enables axon regeneration in mice after nerve injury and neurite outgrowth in human iPSC-derived sensory neurons.

Oncomodulin (Ocm) is a myeloid cell-derived growth factor that enables axon regeneration in mice and rats after optic nerve injury or peripheral nerve injury, yet the mechanisms underlying its activity are unknown. Using proximity biotinylation, coimmunoprecipitation, surface plasmon resonance, and ectopic expression, we have identified armadillo-repeat protein C10 (ArmC10) as a high-affinity receptor for Ocm. ArmC10 deletion suppressed inflammation-induced axon regeneration in the injured optic nerves of mice. ArmC10 deletion also suppressed the ability of lesioned sensory neurons to regenerate peripheral axons rapidly after a second injury and to regenerate their central axons after spinal cord injury in mice (the conditioning lesion effect). Conversely, Ocm acted through ArmC10 to accelerate optic nerve and peripheral nerve regeneration and to enable spinal cord axon regeneration in these mouse nerve injury models. We showed that ArmC10 is highly expressed in human-induced pluripotent stem cell-derived sensory neurons and that exposure to Ocm altered gene expression and enhanced neurite outgrowth. ArmC10 was also expressed in human monocytes, and Ocm increased the expression of immune modulatory genes in these cells. These findings suggest that Ocm acting through its receptor ArmC10 may be a useful therapeutic target for nerve repair and immune modulation.

Changes in plant phosphorus demand and supply relationships in response to different grazing intensities affect the soil organic carbon stock of a temperate steppe.

Ongoing climate change and long-term overgrazing are the main causes of grassland degradation worldwide. Phosphorus (P) is typically a limiting nutrient in degraded grassland soils, and its dynamics may play a crucial role in the responses of carbon (C) feedback to grazing. Yet how multiple P processes respond to a multi-level of grazing and its impact on soil organic carbon (SOC), which is critical for sustainable grassland development in the face of climate change, remains inadequately understood. Here, we investigated P dynamics at the ecosystem level in a 7-year-long multi-level grazing field experiment and analyzed their relation to SOC stock. The results showed that, due to the greater P demand for compensatory plant growth, grazing by sheep increased the aboveground plants' P supply (by 70 % at most) while decreasing their relative P limitation. The increase in P in aboveground tissue was associated with changes in plant root-shoot P allocation and P resorption, and the mobilization of moderately labile organic P in soil. Affected by the altered P supply under grazing, corresponding changes to root C stock and soil total P were two major factors impacting SOC. Compensatory growth-induced P demand and P supply processes responded differently to grazing intensity, resulting in differential effects on SOC. Unlike the light and heavy grazing levels, which reduced the SOC stock, moderate grazing was capable of maintaining maximal vegetation biomass, total plant biomass P, and SOC stock, mainly by promoting biologically- and geochemically-driven plant-soil P turnover. Our findings have important implications for addressing future soil C losses and mitigating higher atmospheric CO2 threats, as well as maintaining high productivity in temperate grasslands.

Nerve injury disrupts temporal processing in the spinal cord dorsal horn through alterations in PV + interneurons.

How mechanical allodynia following nerve injury is encoded in patterns of neural activity in the spinal cord dorsal horn (DH) is not known. We addressed this using the spared nerve injury model of neuropathic pain and in vivo electrophysiological recordings. Surprisingly, despite dramatic behavioral over-reactivity to mechanical stimuli following nerve injury, an overall increase in sensitivity or reactivity of DH neurons was not observed. We did, however, observe a marked decrease in correlated neural firing patterns, including the synchrony of mechanical stimulus-evoked firing, across the DH. Alterations in DH temporal firing patterns were recapitulated by silencing DH parvalbumin + (PV + ) inhibitory interneurons, previously implicated in mechanical allodynia, as were allodynic pain-like behaviors in mice. These findings reveal decorrelated DH network activity, driven by alterations in PV + interneurons, as a prominent feature of neuropathic pain, and suggest that restoration of proper temporal activity is a potential treatment for chronic neuropathic pain.

Localizing safety performance functions for two-way STOP-controlled (TWST) three-leg intersections on rural two-lane two-way (TLTW) roadways in Alabama: A geospatial modeling approach with clustering analysis.

Safety Performance Functions (SPFs) can be used to predict the number of crashes for highway facilities by site characteristics, including traffic exposures and other specific site factors. The traditional approach to developing SPFs relies on factors that are observed in the data and has an unstated assumption that the relationships between safety performance and observed factors are stationary. However, there might be factors that are not captured by the data but also have significant impacts on roadway safety performance. These factors can lead to significant unobserved heterogeneity in safety performance at different sites. Failure to capture such unobserved heterogeneity in developing SPFs may result in biases and decrease the predictive accuracy. Given the interactions between highway traffic and roadway environments, the unobserved heterogeneity is likely related to the geographic space of the highway network. This study employs a spatial modeling approach, namely Geographically Weighted Negative Binomial Regression (GWNBR), to incorporate spatial heterogeneity into SPF model estimation. The GWNBR model can generate a local SPF for every site instead of a global SPF for one entire jurisdiction (e.g., a state) from the traditional approach. Local SPFs (or l-SPFs) are high-resolution and may be difficult for practitioners to use. To support the implementation of l-SPFs, this study proposes a method to aggregate l-SPFs to various geographic levels. This study first uses the 2014-2018 geo-referenced crash data from Alabama to develop l-SPFs for two-way STOP-controlled (TWST) three-leg intersections on rural two-lane two-way (TLTW) roadways in the state. The results show that l-SPFs vary substantially across Alabama. For example, the coefficients of traffic volume (AADT) on major roads range from 0.126 to 1.203 across different areas of the state. Then, an aggregation method based on K-means clustering is demonstrated to aggregate l-SPFs to various geographic levels of interest. The l-SPFs and their aggregation provide geographic flexibility in developing countermeasures and allocating funds to improve traffic safety considering local conditions.

Development and Validation of a Combined Hypoxia and Immune Prognostic Classifier for Lung Adenocarcinoma.

Lung cancer is the leading cause of cancer-related deaths worldwide. Hypoxia is a crucial microenvironmental factor in lung adenocarcinoma (LUAD). However, the prognostic value based on hypoxia and immune in LUAD remains to be further clarified. The hypoxia-related genes (HRGs) and immune-related genes (IRGs) were downloaded from the public database. The RNA-seq expression and matched complete clinical data for LUAD were retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. The least absolute shrinkage and selection operator (LASSO) Cox regression analysis was applied to model construction. Hypoxia expression profiles, immune cell infiltration, functional enrichment analysis, Tumor Immune Dysfunction and Exclusion (TIDE) score and the somatic mutation status were analyzed and compared based on the model. Moreover, immunofluorescence (IF) staining in human LUAD cases to explore the expression of hypoxia marker and immune checkpoint. A prognostic model of 9 genes was established, which can divide patients into two subgroups. There were obvious differences in hypoxia and immune characteristics in the two groups, the group with high-risk score value showed significantly high expression of hypoxia genes and programmed death ligand-1 (PD-L1), and maybe more sensitive to immunotherapy. Patients in the high-risk group had shorter overall survival (OS). This model has a good predictive value for the prognosis of LUAD. We constructed a new HRGs and IRGs model for prognostic prediction of LUAD. This model may benefit future immunotherapy for LUAD.

N addition rebalances the carbon and nitrogen metabolisms of Leymus chinensis through leaf N investment.

Intensifying nitrogen (N) deposition disturbs the growth of grassland plants due to an imbalance between their carbon (C) and N metabolism. However, it's unclear how plant physiological strategies restore balance. We investigated the effects of multiple N addition levels (0-25 g N m-2 yr-1) on the coordination of C and N metabolism in a dominant grass (Leymus chinensis) in a semiarid grassland in northern China. To do so, we evaluated photosynthetic parameters, leaf N allocation, C- and N-based metabolites, and metabolic enzymes. We found that a moderate N level (10 g N m-2 yr-1) promoted carboxylation and electron transport by allocating more N to the photosynthetic apparatus and increasing ribulose bisphosphate carboxylase/oxygenase activity, thereby increasing photosynthetic capacity. The highest N level (25 g N m-2 yr-1) promoted N investment in nonphotosynthetic pathways and increased the free amino acids in the leaves. N addition stimulated the accumulation of C and N compounds across organs by activating sucrose phosphate synthase, nitrate reductase, and glutamine synthetase. This enhancement triggered a transformation of primary metabolites (nonstructural carbohydrates, proteins, amino acids) to secondary metabolites (flavonoids, phenols, and alkaloids) for temporary storage or as defense compounds. Citric acid, as the C skeleton for enhanced N metabolism, decreased significantly, and malic acid increased by catalysis of phosphoenolpyruvate carboxylase. Our findings show the adaptability of L. chinensis to different N-addition levels by adjusting its allocations of C and N metabolic compounds and confirm the roles of C and N coordination by grassland plants in these adaptations.

Automated preclinical detection of mechanical pain hypersensitivity and analgesia.

ABSTRACT: The lack of sensitive and robust behavioral assessments of pain in preclinical models has been a major limitation for both pain research and the development of novel analgesics. Here, we demonstrate a novel data acquisition and analysis platform that provides automated, quantitative, and objective measures of naturalistic rodent behavior in an observer-independent and unbiased fashion. The technology records freely behaving mice, in the dark, over extended periods for continuous acquisition of 2 parallel video data streams: (1) near-infrared frustrated total internal reflection for detecting the degree, force, and timing of surface contact and (2) simultaneous ongoing video graphing of whole-body pose. Using machine vision and machine learning, we automatically extract and quantify behavioral features from these data to reveal moment-by-moment changes that capture the internal pain state of rodents in multiple pain models. We show that these voluntary pain-related behaviors are reversible by analgesics and that analgesia can be automatically and objectively differentiated from sedation. Finally, we used this approach to generate a paw luminance ratio measure that is sensitive in capturing dynamic mechanical hypersensitivity over a period and scalable for high-throughput preclinical analgesic efficacy assessment.

Synergistic Effects of Fluconazole Combined with Doxycycline Against Dual-Species Cultures of Candida albicans and Staphylococcus epidermidis and the Mechanisms of Action.

Bacterial and fungal coinfections have posed great clinical challenges in recent years, and combination therapy may be a useful way to treat these mixed infections. The objective of this study was to find an effective drug combination to treat dual-species cultures of fungi and bacteria. In this study, we focused on poorly investigated mixed cultures of Candida albicans and Staphylococcus epidermidis. In this research, we investigated the effects of fluconazole (FLC) and doxycycline (DOX) against dual-species cultures of C. albicans and S. epidermidis. Both the fractional inhibitory concentration index model and ΔE model revealed a synergistic antimicrobial effect between FLC and DOX against the four groups of dual-species cultures. Mechanistic studies revealed that the synergism of FLC and DOX against dual-species cultures may be associated with the inhibition of biofilms and calcium dysregulation. Fluconazole+doxycycline appears to be a potential drug combination for the treatment of bacterial and fungal coinfections. These findings are of great significance for overcoming clinical bacterial and fungal coinfections and might provide novel insights into drug discovery for combination therapy.

Combining EGFR-TKI With SAHA Overcomes EGFR-TKI-Acquired Resistance by Reducing the Protective Autophagy in Non-Small Cell Lung Cancer.

Nowadays, lung cancer has the highest mortality worldwide. The emergence of epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) has greatly improved the survival of patients with non-small cell lung cancer (NSCLC) having EGFR-TKI-sensitive mutations. Unfortunately, acquired resistance happens for most patients. In the present research, we found that EGFR-TKIs (such as gefitinib and osimertinib) can induce autophagy in NSCLC cell lines. Compared with parental sensitive cells, drug-resistant cells have higher autophagy activity. The use of an autophagy inhibitor could enhance the toxicity of gefitinib and osimertinib, which indicates that the enhancement of protective autophagy might be one of the mechanisms of EGFR-TKI resistance in NSCLC. In addition, increased autophagy activity is associated with decreased enhancer of zeste homolog 2 (EZH2) expression. Knockdown of EZH2 or EZH2 inhibitor treatment could lead to increased autophagy in NSCLC cells, indicating that EZH2 is a negative regulator of autophagy. We revealed that the increase in autophagy caused by the reduction of EZH2 was reversed in vitro and in vivo when combining gefitinib or osimertinib with suberoylanilide hydroxamic acid (SAHA), a broad-spectrum histone deacetylase inhibitor (HDACi). In conclusion, our results indicated that the combination of EGFR-TKIs and SAHA may be a new strategy to overcome EGFR-TKIs acquired resistance.

DualMMP-GAN: Dual-scale multi-modality perceptual generative adversarial network for medical image segmentation.

Multi-modality magnetic resonance imaging (MRI) can reveal distinct patterns of tissue in the human body and is crucial to clinical diagnosis. But it still remains a challenge to obtain diverse and plausible multi-modality MR images due to expense, noise, and artifacts. For the same lesion, different modalities of MRI have big differences in context information, coarse location, and fine structure. In order to achieve better generation and segmentation performance, a dual-scale multi-modality perceptual generative adversarial network (DualMMP-GAN) is proposed based on cycle-consistent generative adversarial networks (CycleGAN). Dilated residual blocks are introduced to increase the receptive field, preserving structure and context information of images. A dual-scale discriminator is constructed. The generator is optimized by discriminating patches to represent lesions with different sizes. The perceptual consistency loss is introduced to learn the mapping between the generated and target modality at different semantic levels. Moreover, generative multi-modality segmentation (GMMS) combining given modalities with generated modalities is proposed for brain tumor segmentation. Experimental results show that the DualMMP-GAN outperforms the CycleGAN and some state-of-the-art methods in terms of PSNR, SSMI, and RMSE in most tasks. In addition, dice, sensitivity, specificity, and Hausdorff95 obtained from segmentation by GMMS are all higher than those from a single modality. The objective index obtained by the proposed methods are close to upper bounds obtained from real multiple modalities, indicating that GMMS can achieve similar effects as multi-modality. Overall, the proposed methods can serve as an effective method in clinical brain tumor diagnosis with promising application potential.