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Abstract Background Suboptimal health status (SHS) is an intermediate status between ideal heath and illness, it is characterized by the perception of health complaints, general weakness, decreased immunity and low energy. An increasing number of Chinese middle school students are suffering from psychological symptoms (PS), particularly anxiety and depression. The relationship between SHS and PS is unclear in adolescents. This study aimed to investigate the prevalence of SHS and the correlation between SHS and PS among Chinese middle school students and to identify the SHS-related risk factors from the perspective of public health. Methods A cross-sectional study was conducted with the cluster sampling method among 1955 middle school students in Shantou, China. SHS was assessed by Suboptimal Health Status Questionnaire-25 (SHSQ-25). And the PS of anxiety and depression were assessed with Generalized Anxiety Disorder Scale (GAD-7) and Beck Depression Inventory-II Scale (BDI-II) self-assessment questionnaires. Variate logistic analysis was applied to explore risk factors of SHS. The relationship between SHS and PS among Chinese middle school students was subsequently analyzed. Results Among the 1955 participants, 1904 middle school students were finally included in the analysis, the effective response rate was 97.39%. The prevalence of SHS was 10.3% (197/1904) while the prevalence of anxiety and depression was 30.7% (585/1904) and 34.1% (649/1904), respectively. A strong correlation was identified between SHS and PS among middle school students. With the aggravation of anxiety and depression, the probability of suffering from SHS increased (both P

Abstract Deep learning models adapted from natural language processing offer new opportunities for the prediction of active compounds via machine translation of sequential molecular data representations. For example, chemical language models are often derived for compound string transformation. Moreover, given the principal versatility of language models for translating different types of textual representations, off-the-beaten-path design tasks might be explored. In this work, we have investigated generative design of active compounds with desired potency from target sequence embeddings, representing a rather provoking prediction task. Therefore, a dual-component conditional language model was designed for learning from multimodal data. It comprised a protein language model component for generating target sequence embeddings and a conditional transformer for predicting new active compounds with desired potency. To this end, the designated “biochemical” language model was trained to learn mappings of combined protein sequence and compound potency value embeddings to corresponding compounds, fine-tuned on individual activity classes not encountered during model derivation, and evaluated on compound test sets that were structurally distinct from training sets. The biochemical language model correctly reproduced known compounds with different potency for all activity classes, providing proof-of-concept for the approach. Furthermore, the conditional model consistently reproduced larger numbers of known compounds as well as more potent compounds than an unconditional model, revealing a substantial effect of potency conditioning. The biochemical language model also generated structurally diverse candidate compounds departing from both fine-tuning and test compounds. Overall, generative compound design based on potency value-conditioned target sequence embeddings yielded promising results, rendering the approach attractive for further exploration and practical applications. Scientific contribution The approach introduced herein combines protein language model and chemical language model components, representing an advanced architecture, and is the first methodology for predicting compounds with desired potency from conditioned protein sequence data.

Abstract For many machine learning applications in drug discovery, only limited amounts of training data are available. This typically applies to compound design and activity prediction and often restricts machine learning, especially deep learning. For low-data applications, specialized learning strategies can be considered to limit required training data. Among these is meta-learning that attempts to enable learning in low-data regimes by combining outputs of different models and utilizing meta-data from these predictions. However, in drug discovery settings, meta-learning is still in its infancy. In this study, we have explored meta-learning for the prediction of potent compounds via generative design using transformer models. For different activity classes, meta-learning models were derived to predict highly potent compounds from weakly potent templates in the presence of varying amounts of fine-tuning data and compared to other transformers developed for this task. Meta-learning consistently led to statistically significant improvements in model performance, in particular, when fine-tuning data were limited. Moreover, meta-learning models generated target compounds with higher potency and larger potency differences between templates and targets than other transformers, indicating their potential for low-data compound design.

Abstract Compound potency prediction is a major task in medicinal chemistry and drug design. Inspired by the concept of activity cliffs (which encode large differences in potency between similar active compounds), we have devised a new methodology for predicting potent compounds from weakly potent input molecules. Therefore, a chemical language model was implemented consisting of a conditional transformer architecture for compound design guided by observed potency differences. The model was evaluated using a newly generated compound test system enabling a rigorous assessment of its performance. It was shown to predict known potent compounds from different activity classes not encountered during training. Moreover, the model was capable of creating highly potent compounds that were structurally distinct from input molecules. It also produced many novel candidate compounds not included in test sets. Taken together, the findings confirmed the ability of the new methodology to generate structurally diverse highly potent compounds.

BackgroundPreclinical studies demonstrated that immune checkpoint inhibitors combined with antiangiogenic drugs have a synergistic anti-tumor effect. This present phase II trial aimed to evaluate the efficacy and safety of apatinib combined with camrelizumab in patients with recurrent/metastatic nasopharyngeal carcinoma (RM-NPC).MethodsPatients with RM-NPC were administered with apatinib at 250 mg orally once every day and with camrelizumab at 200 mg via intravenous infusion every 2 weeks until the disease progressed or toxicity became unacceptable. The objective response rate (ORR) was the primary endpoint, assessed using RECIST version 1.1. Progression-free survival (PFS), overall survival (OS), disease control rate (DCR) and safety were the key secondary endpoints. This study was registered with ClinicalTrials.gov, NCT04350190.ResultsThis study enrolled 26 patients with RM-NPC between January 14, 2021 and September 15, 2021. At data cutoff (March 31, 2023), the median duration of follow-up was 16 months (ranging from 1 to 26 months). The ORR was 38.5% (10/26), the disease control rate (DCR) was 61.5% (16/26), and the median PFS was 6 months (IQR 3.0-20.0). The median OS was 14 months (IQR 6.0-21.25). Treatment-related grade 3 or 4 adverse events occurred in seven (26.9%) patients, and comprised anemia (7.7%), stomatitis (3.8%), headache (3.8%), pneumonia (7.7%), and myocarditis (3.8%). There were no serious treatment-related adverse events or treatment-related deaths.ConclusionIn patients with RM-NPC, apatinib plus camrelizumab showed promising antitumor activity and manageable toxicities.

While synergistic drug combinations are more effective at fighting tumors with complex pathophysiology, preference compensating mechanisms, and drug resistance, the identification of novel synergistic drug combinations, especially complex higher-order combinations, remains challenging due to the size of combination space. Even though certain computational methods have been used to identify synergistic drug combinations in lieu of traditional in vitro and in vivo screening tests, the majority of previously published work has focused on predicting synergistic drug pairs for specific types of cancer and paid little attention to the sophisticated high-order combinations. The main objective of this study is to develop a deep learning-based approach that integrated multi-omics data to predict novel synergistic multi-drug combinations (DeepMDS) in a given cell line. To develop this approach, we firstly created a dataset comprising of gene expression profiles of cancer cell lines, target information of anti-cancer drugs, and drug response against a large variety of cancer cell lines. Based on the principle of a fully connected feed forward Deep Neural Network, the proposed model was constructed using this dataset, which achieved a high performance with a Mean Square Error (MSE) of 2.50 and a Root Mean Squared Error (RMSE) of 1.58 in the regression task, and gave the best classification accuracy of 0.94, an area under the Receiver Operating Characteristic curve (AUC) of 0.97, a sensitivity of 0.95, and a specificity of 0.93. Furthermore, we utilized three breast cancer cell subtypes (MCF-7, MDA-MD-468 and MDA-MB-231) and one lung cancer cell line A549 to validate the predicted results of our model, showing that the predicted top-ranked multi-drug combinations had superior anti-cancer effects to other combinations, particularly those that were widely used in clinical treatment. Our model has the potential to increase the practicality of expanding the drug combinational space and to leverage its capacity to prioritize the most effective multi-drug combinational therapy for precision oncology applications.

Abstract Background Accumulating evidence from human trials and rodent studies has indicated that modulation of gut microbiota affects host physiological homeostasis and behavioral characteristics. Similarly, alterations in gut microbiota could be a feasible strategy for reducing aggressive behavior and improving health in chickens. The study was conducted to determine the effects of early-life cecal microbiota transplantation (CMT) on cecal microbial composition, brain serotonergic activity, and aggressive behavior of recipient chickens. Methods Chicken lines 63 and 72 with nonaggressive and aggressive behavior, respectively, were used as donors and a commercial strain Dekalb XL was used as recipients for CMT. Eighty-four 1-d-old male chicks were randomly assigned to 1 of 3 treatments with 7 cages per treatment and 4 chickens per cage (n = 7): saline (control, CTRL), cecal solution of line 63 (63-CMT), and cecal solution of line 72 (72-CMT). Transplantation was conducted via oral gavage once daily from d 1 to 10, and then boosted once weekly from week 3 to 5. At weeks 5 and 16, home-cage behavior was recorded, and chickens with similar body weights were assigned to paired aggression tests between the treatments. Samples of blood, brain, and cecal content were collected from the post-tested chickens to detect CMT-induced biological and microbiota changes. Results 63-CMT chickens displayed less aggressive behavior with a higher hypothalamic serotonergic activity at week 5. Correspondingly, two amplicon sequence variants (ASVs) belonging to Lachnospiraceae and one Ruminococcaceae UCG-005 ASV were positively correlated with the levels of brain tryptophan and serotonin, respectively. 72-CMT chickens had lower levels of brain norepinephrine and dopamine at week 5 with higher levels of plasma serotonin and tryptophan at week 16. ASVs belonging to Mollicutes RF39 and GCA-900066225 in 72-CMT chickens were negatively correlated with the brain 5-hydroxyindoleacetic acid (5-HIAA) at week 5, and one Bacteroides ASV was negatively correlated with plasma serotonin at week 16. Conclusion Results indicate that CMT at an early age could regulate aggressive behavior via modulating the cecal microbial composition, together with central serotonergic and catecholaminergic systems in recipient chickens. The selected CMT could be a novel strategy for reducing aggressive behavior through regulating signaling along the microbiota-gut-brain axis.

Abstract Background The excessive use of antibiotics in the livestock feed industry caused inevitable side effects of microbial resistance. Besides this residual antibiotics in animal-derived foodstuff imposed serious health problems for humans. So this study aimed to investigate the potential use of Bacillus velezensis to substitute antibiotics for poultry production. A total of 468, 49-week-old Hy-Line Brown chickens, were randomly divided into four groups the control group (regular diet), experiment group I (0.1% B. veleznesis), experiment group II (0.2% B. veleznesis), and antibiotic group (50 mg/kg flavomycin), with three replicates per group and trial period consisted on 42 days. Results The results showed that, compared with the control group, the average egg production rate and daily feed intake of experimental groups I and II increased significantly (P 0.05) in group (II) Egg quality parameters such as yolk weight of the experimental group II was increased, but that of the antibiotic group and experiment group I was decreased, neither significant (P > 0.05). Moreover, the eggshell strength, yolk color, albumen height, and Haugh unit were significantly increased (P 0.05) but decrease the secretin and cholecystokinin in the blood plasma (P > 0.05). Conclusions This study suggested that B. velezensis can substitute in-feed-antibiotics and improved most of the study parameters significantly. Which suggested that B. velezensis has potential future application value to replace the feed antibiotics.

This study was to investigate the effects of Bacillus subtilis on production performance and bone pathophysiological characteristics of layers. Twenty-four 48-week-old Lohmann Pink-shell laying hens were randomly divided into two groups: a basic diet (control) and the basic diet mixed with Bacillus subtilis (0.5 g/kg) for a 60-day trial. Statistically, independent-sample t-test was used to assess the treatment differences. The results showed that Bacillus subtilis supplementation improved the percent of marketable eggs (p < 0.05) with reduced numbers of broken and soft-shelled eggs but had no effects on egg weight, height of albumen, yolk color, and Haugh unit (p > 0.05). Bacillus subtilis supplement also elevated maximum load (p = 0.06), maximum stress (p = 0.01), stiffness (p < 0.01), and Young’s modulus (p < 0.01) but suppressed maximum strain (p = 0.06) in the femur. In addition, compared with control birds, phosphorous concentration (p < 0.01) was reduced in serum at day 61 but increased in the femur (p < 0.05) in Bacillus subtilis fed birds. Bacillus subtilis fed birds also had lower magnesium concentrations in both femur (p = 0.04) and feces (p = 0.09). Furthermore, Bacillus subtilis increased plasma estrogen concentration (p = 0.01) and femur TNF receptor superfamily member 11b (OPG) expression (p < 0.05) but reduced plasma IL-1 (p < 0.01) and TNF-α (p < 0.01) concentrations. These results indicate that Bacillus subtilis could be used as a health promotor to reduce overproduction-induced inflammation and associated bone damage and to increase marketable egg production. The data provide evidence for developing a management strategy to use Bacillus subtilis as a feed additive to improve marketable egg production and health and welfare status of laying hens.