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Abstract The health of honey bee queens is crucial for colony success, particularly during stressful periods like overwintering. To accompany a previous longitudinal study of colony and worker health, we explored niche-specific gut microbiota, host gene expression, and pathogen prevalence in honey bee queens overwintering in a warm southern climate. We found differential gene expression and bacterial abundance with respect to various pathogens throughout the season. Biologically older queens had larger microbiotas, particularly enriched in Bombella and Bifidobacterium. Both Deformed Wing Virus A and B subtypes were highest in the fat body tissue in January, correlating with colony Varroa levels, and Deformed Wing Virus titers in workers. High viral titers in queens were associated with decreased vitellogenin expression, suggesting a potential trade-off between immune function and reproductive capacity. Additionally, we found a complex and dynamic relationship between these viral loads and immune gene expression, indicating a possible breakdown in the coordinated immune response as the season progressed. Our study also revealed a potential link between Nosema and Melissococcus plutonius infections in queens, demonstrating that seasonal opportunism is not confined to just workers. Overall, our findings highlight the intricate interplay between pathogens, metabolic state, and immune response in honey bee queens. Combined with worker and colony-level metrics from the same colonies, our findings illustrate the social aspect of queen health and resilience over the winter dearth.

The microbiome of the honey bee worker hindgut has been explored thoroughly with culturing and next-generation sequencing revealing both composition and function. However, less effort has been devoted to the aerobic social niches associated with the hive environment and colony process. We performed a meta-analysis of 3,800+ publicly available 16S rRNA gene sequence libraries examining the hypothesis of a native aerobic microbiota associated with social interaction and colony resources. We selected high-throughput studies to represent tissue-specific samples, including nine distinct aerobic niches throughout the colony and hive, defined by social nutrient processing. These included queen and worker gut tissues, foregut, midgut, ileum, rectum, mouthparts, worker social glands, developing larvae, and secreted and stored nutrition. We found that the aerobic mouthparts, foregut and midgut niches of queens and workers share a significant portion of their microbiome with that of larval rearing and nutrient secretion and storage, defining the microbiota of the social resource niche. Characterized by species dominance and rapid growth, the social resource microbiota functions primarily in disease prevention at both the individual and colony level and may also function in social communication and gut microbiome resilience. Defining the microbiota of social function contributes to a systems-level understanding of host–microbial interactions in the honey bee.

Abstract Probiotics are widely used in agriculture including commercial beekeeping, but there is little evidence supporting their effectiveness. Antibiotic treatments can greatly distort the gut microbiome, reducing its protective abilities and facilitating the growth of antibiotic resistant pathogens. Commercial beekeepers regularly apply antibiotics to combat bacterial infections, often followed by an application of non-native probiotics advertised to ease the impact of antibiotic-induced gut dysbiosis. We tested whether probiotics affect the gut microbiome or disease prevalence, or rescue the negative effects of antibiotic induced gut dysbiosis. We found no difference in the gut microbiome or disease markers by probiotic application or antibiotic recovery associated with probiotic treatment. A colony-level application of the antibiotics oxytetracycline and tylosin produced an immediate decrease in gut microbiome size, and over the longer-term, very different and persistent dysbiotic effects on the composition and membership of the hindgut microbiome. Our results demonstrate the lack of probiotic effect or antibiotic rescue, detail the duration and character of dysbiotic states resulting from different antibiotics, and highlight the importance of the gut microbiome for honeybee health.

Abstract As essential pollinators of ecosystems and agriculture, honey bees (Apis mellifera) are host to a variety of pathogens that result in colony loss. Two highly prevalent larval diseases are European foulbrood (EFB) attributed to the bacterium Melissococcus plutonius, and Varroosis wherein larvae can be afflicted by one or more paralytic viruses. Here we used high-throughput sequencing and qPCR to detail microbial succession of larval development from six diseased, and one disease-free apiary. The disease-free larval microbiome revealed a variety of disease-associated bacteria in early larval instars, but later developmental stages were dominated by beneficial symbionts. Microbial succession associated with EFB pathology differed by apiary, characterized by associations with various gram-positive bacteria. At one apiary, diseased larvae were uniquely described as “melting and deflated”, symptoms associated with Varroosis. We found that Acute Bee Paralysis Virus (ABPV) levels were significantly associated with these symptoms, and various gram-negative bacteria became opportunistic in the guts of ABPV afflicted larvae. Perhaps contributing to disease progression, the ABPV associated microbiome was significantly depleted of gram-positive bacteria, a likely result of recent antibiotic application. Our results contribute to the understanding of brood disease diagnosis and treatment, a growing problem for beekeeping and agriculture worldwide.

Honey bees exhibit an elaborate social structure based in part on an age-related division of labor. Young workers perform tasks inside the hive, while older workers forage outside the hive, tasks associated with distinct diets and metabolism. Critical to colony fitness, the work force can respond rapidly to changes in the environment or colony demography and assume emergency tasks, resulting in young foragers or old nurses. We hypothesized that both task and age affect the gut microbiota consistent with changes to host diet and physiology. We performed two experiments inducing precocious foragers and reverted nurses, then quantified tissue-specific gut microbiota and host metabolic state associated with nutrition, immunity and oxidative stress. In the precocious forager experiment, both age and ontogeny explained differences in midgut and ileum microbiota, but host gene expression was best explained by an interaction of these factors. Precocious foragers were nutritionally deficient, and incurred higher levels of oxidative damage relative to age-matched nurses. In the oldest workers, reverted nurses, the oxidative damage associated with age and past foraging was compensated by high Vitellogenin expression, which exceeded that of young nurses. Host-microbial interactions were evident throughout the dataset, highlighted by an age-based increase of Gilliamella abundance and diversity concurrent with increased carbonyl accumulation and CuZnSOD expression. The results in general contribute to an understanding of ecological succession of the worker gut microbiota, defining the species-level transition from nurse to forager.

ABSTRACT The highly social honey bee has dense populations but a significantly reduced repertoire of immune genes relative to solitary species, suggesting a greater reliance on social immunity. Here we investigate immune gene expression and gut microbial succession in queens during colony introduction. Recently mated queens were placed into an active colony or a storage hive for multiple queens: a queen-bank. Feeding intensity, social context, and metabolic demand differ greatly between the two environments. After 3 weeks, we examined gene expression associated with oxidative stress and immunity and performed high-throughput sequencing of the queen gut microbiome across four alimentary tract niches. Microbiota and gene expression in the queen hindgut differed by time, queen breeder source, and metabolic environment. In the ileum, upregulation of most immune and oxidative stress genes occurred regardless of treatment conditions, suggesting postmating effects on gut gene expression. Counterintuitively, queens exposed to the more social colony environment contained significantly less bacterial diversity indicative of social immune factors shaping the queens microbiome. Queen bank queens resembled much older queens with decreased Alpha 2.1, greater abundance of Lactobacillus firm5 and Bifidobacterium in the hindgut, and significantly larger ileum microbiotas, dominated by blooms of Snodgrassella alvi. Combined with earlier findings, we conclude that the queen gut microbiota experiences an extended period of microbial succession associated with queen breeder source, postmating development, and colony assimilation. IMPORTANCE In modern agriculture, honey bee queen failure is repeatedly cited as one of the major reasons for yearly colony loss. Here we discovered that the honey bee queen gut microbiota alters according to early social environment and is strongly tied to the identity of the queen breeder. Like human examples, this early life variation appears to set the trajectory for ecological succession associated with social assimilation and queen productivity. The high metabolic demand of natural colony assimilation is associated with less bacterial diversity, a smaller hindgut microbiome, and a downregulation of genes that control pathogens and oxidative stress. Queens placed in less social environments with low metabolic demand (queen banks) developed a gut microbiota that resembled much older queens that produce fewer eggs. The queens key reproductive role in the colony may rely in part on a gut microbiome shaped by social immunity and the early queen rearing environment.

Abstract Background In social insects, identical genotypes can show extreme lifespan variation providing a unique perspective on age-associated microbial succession. In honey bees, short- and long-lived host phenotypes are polarized by a suite of age-associated factors including hormones, nutrition, immune senescence, and oxidative stress. Similar to other model organisms, the aging gut microbiota of short-lived (worker) honey bees accrue Proteobacteria and are depleted of Lactobacillus and Bifidobacterium, consistent with a suite of host senescence markers. In contrast, long-lived (queen) honey bees maintain youthful cellular function with much lower expression of oxidative stress genes, suggesting a very different host environment for age-associated microbial succession. Results We sequenced the microbiota of 63 honey bee queens exploring two chronological ages and four alimentary tract niches. To control for genetic and environmental variation, we quantified carbonyl accumulation in queen fat body tissue as a proxy for biological aging. We compared our results to the age-specific microbial succession of worker guts. Accounting for queen source variation, two or more bacterial species per niche differed significantly by queen age. Biological aging in queens was correlated with microbiota composition highlighting the relationship of microbiota with oxidative stress. Queens and workers shared many major gut bacterial species, but differ markedly in community structure and age succession. In stark contrast to aging workers, carbonyl accumulation in queens was significantly associated with increased Lactobacillus and Bifidobacterium and depletion of various Proteobacteria. Conclusions We present a model system linking changes in gut microbiota to diet and longevity, two of the most confounding variables in human microbiota research. The pattern of age-associated succession in the queen microbiota is largely the reverse of that demonstrated for workers. The guts of short-lived worker phenotypes are progressively dominated by three major Proteobacteria, but these same species were sparse or significantly depleted in long-lived queen phenotypes. More broadly, age-related changes in the honey bee microbiota reflect the regulatory anatomy of reproductive host metabolism. Our synthesis suggests that the evolution of colony-level reproductive physiology formed the context for host-microbial interactions and age-related succession of honey bee microbiota.

European honey bees (Apis mellifera Linnaeus) are beneficial insects that provide essential pollination services for agriculture and ecosystems worldwide. Modern commercial beekeeping is plagued by a variety of pathogenic and environmental stressors often confounding attempts to understand colony loss. European foulbrood (EFB) is considered a larval-specific disease whose causative agent, Melissococcus plutonius, has received limited attention due to methodological challenges in the field and laboratory. Here, we improve the experimental and informational context of larval disease with the end goal of developing an EFB management strategy. We sequenced the bacterial microbiota associated with larval disease transmission, isolated a variety of M.plutonius strains, determined their virulence against larvae in vitro, and explored the potential for probiotic treatment of EFB disease. The larval microbiota was a low diversity environment similar to honey, while worker mouthparts and stored pollen contained significantly greater bacterial diversity. Virulence of M. plutonius against larvae varied markedly by strain and inoculant concentration. Our chosen probiotic, Parasaccharibacter apium strain C6, did not improve larval survival when introduced alone, or in combination with a virulent EFB strain. We discuss the importance of positive and negative controls for in vitro studies of the larval microbiome and disease.

Small nucleolar RNAs (snoRNAs), a distinct class of noncoding RNAs, encompass highly diverse structures and have a range of 60 to 300 nucleotides in length. About 90% of human snoRNAs are intronic and embedded within introns of their host gene transcripts. Most snoRNAs enriched in specific tissue correlate in abundance with their parental host genes. Advancements in high-throughput sequencing have facilitated the discovery of dysregulated snoRNA expression in numerous human malignancies including head and neck squamous cell carcinoma (HNSCC). Hundreds of differentially expressed snoRNAs have been identified in HNSCC tissues. Among 1,524 snoRNA genes in a 567 HNSCC cohort, 113 snoRNAs were found to be survival related. As for snoRNA's roles in HNSCC, based on the available evidence, dysregulated snoRNAs are closely associated with the carcinogenesis and development of HNSCC. Upregulated snoRNAs have been shown to augment the expression of other oncogenes or activate the Wnt/β-catenin signaling pathway, thereby promoting tumor cell viability, glycolysis, migration, and the epithelial-mesenchymal transition while inhibiting apoptosis in vitro. In vivo animal studies have further elucidated the functional roles of snoRNAs. Knockdown of host genes of these snoRNAs suppressed the Wnt/β-catenin signaling pathway and restrained tumor proliferation and aggressiveness in mice. The putative mechanisms underlying these observations are associated with the biological functions of snoRNAs, primarily involving microRNA-like functions through the generation of microRNA-like fragments and regulation of alternative splicing to yield diverse transcripts. While most of the snoRNAs are upregulated in HNSCC, 4 downregulated snoRNAs have been identified and annotated. SNORA36B (implicated in the regulation of DNA templates) and U3 (chr17, influencing cell proliferation) may serve as protective factors associated with prolonged overall survival. This review describes the viable structures of snoRNAs, endeavors to refine snoRNA sequencing technology, and summarizes snoRNAs' expression profile as well as their role in HNSCC progression for potential diagnostic and therapeutic strategies for HNSCC management. [ABSTRACT FROM AUTHOR]

Sirui Fu,1,2,&ast; Meiqing Pan,2,3,&ast; Jie Zhang,4,&ast; Hui Zhang,2,3,5,&ast; Zhenchao Tang,2,3,5 Yong Li,1 Wei Mu,2,3,5 Jianwen Huang,1 Di Dong,3 Chongyang Duan,6 Xiaoqun Li,7 Shuo Wang,2,3 Xudong Chen,8 Xiaofeng He,9 Jianfeng Yan,10 Ligong Lu,1 Jie Tian2,3,5,11 1Zhuhai Interventional Medical Centre, Zhuhai People’s Hospital (Zhuhai Hospital Affiliated with Jinan University), Zhuhai, People’s Republic of China; 2Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, People’s Republic of China; 3CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, People’s Republic of China; 4Department of Radiology, Zhuhai People’s Hospital (Zhuhai Hospital Affiliated with Jinan University), Zhuhai, People’s Republic of China; 5Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology, Beijing, People’s Republic of China; 6Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, People’s Republic of China; 7Department of Interventional Treatment, Zhongshan City People’s Hospital, Zhongshan, People’s Republic of China; 8Department of Radiology, Shenzhen People’s Hospital, Shenzhen, People’s Republic of China; 9Interventional Diagnosis and Treatment Department, Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China; 10Department of Radiology, Yangjiang People’s Hospital, Yangjiang, People’s Republic of China; 11University of Chinese Academy of Sciences, Beijing, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Jie TianBeijing Advanced Innovation Centre for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, 100191, People’s Republic of ChinaEmail tian@ieee.orgLigong LuZhuhai Interventional Medical Centre, Zhuhai People’s Hospital (Zhuhai Hospital Affiliated with Jinan University), No. 79 Kangning Road, Zhuhai, 519000, Guangdong Province, People’s Republic of ChinaEmail llg0902@sina.comPurpose: For timely treatment of extrahepatic metastasis and macrovascular invasion (aggressive progressive disease [PD]) in hepatocellular carcinoma, models aimed at stratifying the risks of subsequent aggressive PD should be constructed.Patients and Methods: After dividing 332 patients from five hospitals into training (n = 236) and validation (n = 96) datasets, non-invasive models, including clinical/semantic factors (ModelCS), deep learning radiomics (ModelD), and both (ModelCSD), were constructed to stratify patients according to the risk of aggressive PD. We examined the discrimination and calibration; similarly, we plotted a decision curve and devised a nomogram. Furthermore, we performed analyses of subgroups who received different treatments or those in different disease stages and compared time to aggressive PD and overall survival in the high- and low-risk subgroups.Results: Among the constructed models, ModelCSD, combining clinical/semantic factors and deep learning radiomics, outperformed ModelCS and ModelD (areas under the curve [AUCs] for the training dataset: 0.741, 0.815, and 0.856; validation dataset: 0.780, 0.836, and 0.862), with statistical difference per the net reclassification improvement, the integrated discrimination improvement, and/or the DeLong test in both datasets. Besides, ModelCSD had the best calibration and decision curves. The performance of ModelCSD was not affected by treatment types (AUC: resection = 0.839; transarterial chemoembolization = 0.895; p = 0.183) or disease stages (AUC: BCLC [Barcelona Clinic Liver Cancer] stage 0 and A = 0.827; BCLC stage AB &B = 0.861; p = 0.537). Moreover, the high-risk group had a significantly shorter median time to aggressive PD than the low-risk group (training dataset hazard ratio [HR] = 0.108, p < 0.001; validation dataset HR = 0.058, p < 0.001) and poorer overall survival (training dataset HR = 0.357, p < 0.001; validation dataset HR = 0.204, p < 0.001).Conclusion: Our deep learning-based model successfully stratified the risks of aggressive PD. In the high-risk population, current guideline indicates that first-line treatments are insufficient to prevent extrahepatic metastasis and macrovascular invasion and ensure survival benefits, so more therapies may be explored for these patients.Keywords: aggressive disease progression, deep learning radiomics, clinical factors, high-risk, risk prediction

Current interventions for oral/dental diseases heavily rely on operative/surgical procedures, while the discovery of novel drug targets may enable access to noninvasive pharmacotherapy. Therefore, this study aims to leverage large-scale data and Mendelian randomization (MR) techniques, utilizing genetic variants as instruments, to identify potential therapeutic targets for oral and dental diseases supported by genetic evidence. By intersecting 4,302 druggable genes with expression quantitative trait loci from 31,684 blood samples, we identified 2,580 druggable targets as exposures. Single nucleotide polymorphisms associated with dental disease/symptom traits were collected from FinnGen R9, the Gene–Lifestyle Interactions in Dental Endpoints consortium, and the UK Biobank to serve as outcomes for both discovery and replication purposes. Through MR analysis, we identified 43 druggable targets for various dental disease/symptom traits. To evaluate the viability of these targets, we replicated the analysis using circulating protein quantitative trait loci as exposures. Additionally, we conducted sensitivity, colocalization, Gene Ontology/Kyoto Encyclopedia of Genes and Genomes annotation, protein–protein interaction analyses, and validated dental trait–associated druggable gene expression in animal models. Among these targets, IL12RB1 (odds ratio [OR], 1.01; 95% confidence interval [CI], 1.01–1.01) and TNF (OR, 0.98; 95% CI, 0.97–0.99) exhibited therapeutic promise for oral ulcers, whereas CXCL10 (OR, 0.84; 95% CI, 0.76–0.91) was for periodontitis. Through a rigorous quality control and validation pipeline, our study yields compelling evidence for these druggable targets, which may enhance the clinical prognosis by developing novel drugs or repurposing existing ones. [ABSTRACT FROM AUTHOR]

Zhengguo Qiu,1 Qianhe Shen,2 Chao Jiang,3 Li Yao,4 Xiaopeng Sun,5 Jing Li,6 Chongzhen Duan,1 Rui Li,1 Xiuli Li,1 Subash CB Gopinath,7,8 Periasamy Anbu,9 Thangavel Lakshmipriya,8 Xu Li10 1Department of Anesthesiology, Affiliated Hospital of Shaanxi University of Traditional Chinese Medicine, Xianyang, Shaanxi, 712000, People’s Republic of China; 2Department of Anesthesiology, Xi’an GemFlower Changqing Hospital, Xi’an, Shaanxi, 710200, People’s Republic of China; 3The Third Department of Neurology, The Second Affiliated Hospital of Xi’an Medical University, Xi’an, Shaanxi, 710038, People’s Republic of China; 4Department of Neurology, The Hospital of Xidian Group, Xi’an, Shaanxi, 710077, People’s Republic of China; 5Department of Otolaryngology, The Second Affiliated Hospital of Xi’an Medical University, Xi’an, Shaanxi, 710038, People’s Republic of China; 6Department of Anesthesiology, The Second Affiliated Hospital of Xi’an Medical University, Xi’an, Shaanxi, 710038, People’s Republic of China; 7Faculty of Chemical Engineering Technology, Universiti Malaysia Perlis (UniMAP), Arau, Perlis, 02600, Malaysia; 8Institute of Nano Electronic Engineering, Universiti Malaysia Perlis (UniMAP), Kangar, 01000, Malaysia; 9Department of Biological Engineering, College of Engineering, Inha University, Incheon, 402-751, Republic of Korea; 10Department of Surgery and Anesthesiology, Xi’an Hospital of Traditional Chinese Medicine, Xi’an, Shaanxi, People’s Republic of ChinaCorrespondence: Xu Li Email qzg2020@sina.comBackground: Alzheimer’s disease (AD) is a neurodegenerative chronic disorder that causes dementia and problems in thinking, cognitive impairment and behavioral changes. Amyloid-beta (Aβ) is a peptide involved in AD progression, and a high level of Aβ is highly correlated with severe AD. Identifying and quantifying Aβ levels helps in the early treatment of AD and reduces the factors associated with AD.Materials and Methods: This research introduced a dual probe detection system involving aptamers and antibodies to identify Aβ. Aptamers and antibodies were attached to the gold (Au) urchin and hybrid on the carbon nanohorn-modified surface. The nanohorn was immobilized on the sensor surface by using an amine linker, and then a Au urchin dual probe was immobilized.Results: This dual probe-modified surface enhanced the current flow during Aβ detection compared with the surface with antibody as the probe. This dual probe interacted with higher numbers of Aβ peptides and reached the detection limit at 10 fM with R2=0.992. Furthermore, control experiments with nonimmune antibodies, complementary aptamer sequences and control proteins did not display the current responses, indicating the specific detection of Aβ.Conclusion: Aβ-spiked artificial cerebrospinal fluid showed a similar response to current changes, confirming the selective identification of Aβ.Keywords: amyloid-beta, biomarker, nanomaterial, aptamer, antibody

Jianyuan Song,1 Cuicui Duan,2 Wangyu Cai,1 Wei Wu,1 Houyi Lv,2 Jian Xu1 1The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang 322000, People’s Republic of China; 2Department of Assisted Reproduction, Women’s Hospital School of Medicine Zhejiang University, Hangzhou, Zhejiang 310006, People’s Republic of ChinaCorrespondence: Jian XuThe Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang 322000, People’s Republic of ChinaEmail xuj@zju.edu.cnPurpose: Gonadotrophin releasing hormone agonist (GnRH-a) is widely used for pituitary down-regulation and recruiting more follicles in assisted reproduction. However, no information is available on its value for patients with thin endometrial thickness.Patients and Methods: This was a retrospective cohort study of 302 patients with endometrium < 8 mm undergoing fresh embryo transfer at a fertility center of a university hospital from January 2016 and December 2018. In 148 cycles of the GnRH-a prolonged protocol, one depot of 3.75 mg GnRH-a was injected on day 2 of the menstrual cycle, while in 154 cycles of the short GnRH-a long protocol, 0.1 mg of GnRH-a was injected daily from the mid-luteal phase. The live birth rate and clinical pregnancy rate were compared between the two groups. Other outcome measures included the implantation rate, miscarriage rate, and characteristics of stimulation procedures.Results: Live birth rates and clinical pregnancy rates were significantly higher in the GnRH-a prolonged protocol group than in the other group (36.5% vs 20.8%, P=0.002; 43.9% vs 28.2%, P=0.006, respectively). The live birth rate was significantly increased in the prolonged protocol group (crude OR: 2.190, 95% CI: 1.311, 3.660; adjusted OR: 2.458, 95% CI: 1.430, 4.224) compared with that in the reference group. The implantation rate of the former group was also significantly higher than that of the latter group (35.4% vs 15.9%, P=0.000). There was no significant difference in miscarriage rates between the two protocols. In terms of stimulation procedures, the GnRH-a prolonged protocol group required significantly higher Gn time (10.9 vs 9.5 days, P=0.000) and Gn consumption (2625.0 vs 2047.5 IU, P=0.000) than the short GnRH-a long protocol group.Conclusion: The GnRH-a prolonged protocol in fresh embryo transfer cycles yielded better clinical outcomes of patients with thin endometrium than the short GnRH-a long protocol.Keywords: ovarian stimulation, GnRH-a prolonged protocol, thin endometrium, IVF, COH

Junling Yang,1 Zaijun Hao,1 Wei Li,1 Caiping Duan,1 Xiujuan Fan,1 Jing Xin,1 Chunguang Ren2 1Department of Anesthesiology, Ordos Central Hospital, Ordos, People’s Republic of China; 2Department of Anesthesiology, Liaocheng People’s Hospital, Liaocheng, Shandong, People’s Republic of ChinaCorrespondence: Chunguang Ren Email weiyichunguang@163.comBackground: Although video-assisted thoracoscopic surgery (VATS) is increasingly used, the optimal analgesia strategy is still unknown. We explore the efficacy and safety of preemptive ultrasound-guided paravertebral block (PVB) combined with parecoxib during VATS.Methods: Seventy-four patients were divided into two groups. PVBs were performed before anesthesia induction under real-time ultrasound guidance. Visual analog scale (VAS) score with coughing at 48 h after surgery, postoperative sufentanil consumption and level of sedation (LOS) at 1, 4, 8, 12, 24, and 48 h postoperatively, intraoperative hemodynamics, satisfaction scores of patients and surgeons, remedial measures, time to chest tube removed and mobilization, adverse effects and hospital stay length were recorded. We also recorded inflammatory markers, respiratory function and the prevalence of chronic pain after surgery.Results: VAS scores at rest and with coughing during the first 24 h after surgery were significantly lower in the P group (P< 0.05). Consumption of sevoflurane, remifentanil, and dexmedetomidine was all significantly reduced in the P group (P< 0.05). The consumption of sufentanil within 48 h after surgery, time to first dose and total dose of rescue ketorolac was significantly lower in the P group (P< 0.05). The FEV1/FVC ratio was significantly higher in the P group at 1 and 3 d after surgery (P< 0.05). Times to chest tube removal and mobilization were significantly shorter in the P group (P< 0.05). Compared with the C group, the level of both ACTH and cortisol was significantly reduced in the P group at 1 and 3 d after surgery (P< 0.05).Conclusion: PVB combined with parecoxib was associated with better pain relief, decreased sufentanil and ketorolac consumption, less hemodynamic instability, and a lower surgery-related stress response. However, the incidences of chronic pain 3 and 6 months after surgery and the risk of complication except urinary retention were not significantly different between groups.Keywords: paravertebral block, parecoxib, video-assisted thoracic surgery, preemptive analgesia

The health of honey bee queens is crucial for colony success, particularly during stressful periods like overwintering. To accompany a previous longitudinal study of colony and worker health, we explored niche-specific gut microbiota, host gene expression, and pathogen prevalence in honey bee queens overwintering in a warm southern climate. We found differential gene expression and bacterial abundance with respect to various pathogens throughout the season. Biologically older queens had larger microbiotas, particularly enriched in Bombella and Bifidobacterium. Both Deformed Wing Virus A and B subtypes were highest in the fat body tissue in January, correlating with colony Varroa levels, and Deformed Wing Virus titers in workers. High viral titers in queens were associated with decreased vitellogenin expression, suggesting a potential trade-off between immune function and reproductive capacity. Additionally, we found a complex and dynamic relationship between these viral loads and immune gene expression, indicating a possible breakdown in the coordinated immune response as the season progressed. Our study also revealed a potential link between Nosema and Melissococcus plutonius infections in queens, demonstrating that seasonal opportunism is not confined to just workers. Overall, our findings highlight the intricate interplay between pathogens, metabolic state, and immune response in honey bee queens. Combined with worker and colony-level metrics from the same colonies, our findings illustrate the social aspect of queen health and resilience over the winter dearth. [ABSTRACT FROM AUTHOR]