BertTCR: a Bert-based deep learning framework for predicting cancer-related immune status based on T cell receptor repertoire.

The T cell receptor (TCR) repertoire is pivotal to the human immune system, and understanding its nuances can significantly enhance our ability to forecast cancer-related immune responses. However, existing methods often overlook the intra- and inter-sequence interactions of T cell receptors (TCRs), limiting the development of sequence-based cancer-related immune status predictions. To address this challenge, we propose BertTCR, an innovative deep learning framework designed to predict cancer-related immune status using TCRs. BertTCR combines a pre-trained protein large language model with deep learning architectures, enabling it to extract deeper contextual information from TCRs. Compared to three state-of-the-art sequence-based methods, BertTCR improves the AUC on an external validation set for thyroid cancer detection by 21 percentage points. Additionally, this model was trained on over 2000 publicly available TCR libraries covering 17 types of cancer and healthy samples, and it has been validated on multiple public external datasets for its ability to distinguish cancer patients from healthy individuals. Furthermore, BertTCR can accurately classify various cancer types and healthy individuals. Overall, BertTCR is the advancing method for cancer-related immune status forecasting based on TCRs, offering promising potential for a wide range of immune status prediction tasks.

Meta-DHGNN: method for CRS-related cytokines analysis in CAR-T therapy based on meta-learning directed heterogeneous graph neural network.

Chimeric antigen receptor T-cell (CAR-T) immunotherapy, a novel approach for treating blood cancer, is associated with the production of cytokine release syndrome (CRS), which poses significant safety concerns for patients. Currently, there is limited knowledge regarding CRS-related cytokines and the intricate relationship between cytokines and cells. Therefore, it is imperative to explore a reliable and efficient computational method to identify cytokines associated with CRS. In this study, we propose Meta-DHGNN, a directed and heterogeneous graph neural network analysis method based on meta-learning. The proposed method integrates both directed and heterogeneous algorithms, while the meta-learning module effectively addresses the issue of limited data availability. This approach enables comprehensive analysis of the cytokine network and accurate prediction of CRS-related cytokines. Firstly, to tackle the challenge posed by small datasets, a pre-training phase is conducted using the meta-learning module. Consequently, the directed algorithm constructs an adjacency matrix that accurately captures potential relationships in a more realistic manner. Ultimately, the heterogeneous algorithm employs meta-photographs and multi-head attention mechanisms to enhance the realism and accuracy of predicting cytokine information associated with positive labels. Our experimental verification on the dataset demonstrates that Meta-DHGNN achieves favorable outcomes. Furthermore, based on the predicted results, we have explored the multifaceted formation mechanism of CRS in CAR-T therapy from various perspectives and identified several cytokines, such as IFNG (IFN-γ), IFNA1, IFNB1, IFNA13, IFNA2, IFNAR1, IFNAR2, IFNGR1 and IFNGR2 that have been relatively overlooked in previous studies but potentially play pivotal roles. The significance of Meta-DHGNN lies in its ability to analyze directed and heterogeneous networks in biology effectively while also facilitating CRS risk prediction in CAR-T therapy.

A next-generation intranasal trivalent MMS vaccine induces durable and broad protection against SARS-CoV-2 variants of concern.

As SARS-CoV-2 variants of concern (VoCs) that evade immunity continue to emerge, next-generation adaptable COVID-19 vaccines which protect the respiratory tract and provide broader, more effective, and durable protection are urgently needed. Here, we have developed one such approach, a highly efficacious, intranasally delivered, trivalent measles-mumps-SARS-CoV-2 spike (S) protein (MMS) vaccine candidate that induces robust systemic and mucosal immunity with broad protection. This vaccine candidate is based on three components of the MMR vaccine, a measles virus Edmonston and the two mumps virus strains [Jeryl Lynn 1 (JL1) and JL2] that are known to provide safe, effective, and long-lasting protective immunity. The six proline-stabilized prefusion S protein (preS-6P) genes for ancestral SARS-CoV-2 WA1 and two important SARS-CoV-2 VoCs (Delta and Omicron BA.1) were each inserted into one of these three viruses which were then combined into a trivalent "MMS" candidate vaccine. Intranasal immunization of MMS in IFNAR1-/- mice induced a strong SARS-CoV-2-specific serum IgG response, cross-variant neutralizing antibodies, mucosal IgA, and systemic and tissue-resident T cells. Immunization of golden Syrian hamsters with MMS vaccine induced similarly high levels of antibodies that efficiently neutralized SARS-CoV-2 VoCs and provided broad and complete protection against challenge with any of these VoCs. This MMS vaccine is an efficacious, broadly protective next-generation COVID-19 vaccine candidate, which is readily adaptable to new variants, built on a platform with a 50-y safety record that also protects against measles and mumps.

Functional near-infrared spectroscopy-based neurofeedback training regulates time-on-task effects and enhances sustained cognitive performance.

Time-on-task effect is a common consequence of long-term cognitive demand work, which reflects reduced behavioral performance and increases the risk of accidents. Neurofeedback is a neuromodulation method that can guide individuals to regulate their brain activity and manifest as changes in related symptoms and cognitive behaviors. This study aimed to examine the effects of functional near-infrared spectroscopy-based neurofeedback training on time-on-task effects and sustained cognitive performance. A randomized, single-blind, sham-controlled study was performed: 17 participants received feedback signals of their own dorsolateral prefrontal cortex activity (neurofeedback group), and 16 participants received feedback signals of dorsolateral prefrontal cortex activity from the neurofeedback group (sham-neurofeedback group). All participants received 5 neurofeedback training sessions and completed 2 sustained cognitive tasks, including a 2-back task and a psychomotor vigilance task, to evaluate behavioral performance changes following neurofeedback training. Results showed that neurofeedback relative to the sham-neurofeedback group exhibited increased dorsolateral prefrontal cortex activation, increased accuracy in the 2-back task, and decreased mean response time in the psychomotor vigilance task after neurofeedback training. In addition, the neurofeedback group showed slower decline performance during the sustained 2-back task after neurofeedback training compared with sham-neurofeedback group. These findings demonstrate that neurofeedback training could regulate time-on-task effects on difficult task and enhance performance on sustained cognitive tasks by increasing dorsolateral prefrontal cortex activity.

5-methylcytosine (m5C) RNA modification controls the innate immune response to virus infection by regulating type I interferons.

5-methylcytosine (m5C) is one of the most prevalent modifications of RNA, playing important roles in RNA metabolism, nuclear export, and translation. However, the potential role of RNA m5C methylation in innate immunity remains elusive. Here, we show that depletion of NSUN2, an m5C methyltransferase, significantly inhibits the replication and gene expression of a wide range of RNA and DNA viruses. Notably, we found that this antiviral effect is largely driven by an enhanced type I interferon (IFN) response. The antiviral signaling pathway is dependent on the cytosolic RNA sensor RIG-I but not MDA5. Transcriptome-wide mapping of m5C following NSUN2 depletion in human A549 cells revealed a marked reduction in the m5C methylation of several abundant noncoding RNAs (ncRNAs). However, m5C methylation of viral RNA was not noticeably altered by NSUN2 depletion. In NSUN2-depleted cells, the host RNA polymerase (Pol) III transcribed ncRNAs, in particular RPPH1 and 7SL RNAs, were substantially up-regulated, leading to an increase of unshielded 7SL RNA in cytoplasm, which served as a direct ligand for the RIG-I-mediated IFN response. In NSUN2-depleted cells, inhibition of Pol III transcription or silencing of RPPH1 and 7SL RNA dampened IFN signaling, partially rescuing viral replication and gene expression. Finally, depletion of NSUN2 in an ex vivo human lung model and a mouse model inhibits viral replication and reduces pathogenesis, which is accompanied by enhanced type I IFN responses. Collectively, our data demonstrate that RNA m5C methylation controls antiviral innate immunity through modulating the m5C methylome of ncRNAs and their expression.

PrCRS: a prediction model of severe CRS in CAR-T therapy based on transfer learning.

BACKGROUND: CAR-T cell therapy represents a novel approach for the treatment of hematologic malignancies and solid tumors. However, its implementation is accompanied by the emergence of potentially life-threatening adverse events known as cytokine release syndrome (CRS). Given the escalating number of patients undergoing CAR-T therapy, there is an urgent need to develop predictive models for severe CRS occurrence to prevent it in advance. Currently, all existing models are based on decision trees whose accuracy is far from meeting our expectations, and there is a lack of deep learning models to predict the occurrence of severe CRS more accurately. RESULTS: We propose PrCRS, a deep learning prediction model based on U-net and Transformer. Given the limited data available for CAR-T patients, we employ transfer learning using data from COVID-19 patients. The comprehensive evaluation demonstrates the superiority of the PrCRS model over other state-of-the-art methods for predicting CRS occurrence. We propose six models to forecast the probability of severe CRS for patients with one, two, and three days in advance. Additionally, we present a strategy to convert the model's output into actual probabilities of severe CRS and provide corresponding predictions. CONCLUSIONS: Based on our findings, PrCRS effectively predicts both the likelihood and timing of severe CRS in patients, thereby facilitating expedited and precise patient assessment, thus making a significant contribution to medical research. There is little research on applying deep learning algorithms to predict CRS, and our study fills this gap. This makes our research more novel and significant. Our code is publicly available at https://github.com/wzy38828201/PrCRS . The website of our prediction platform is: http://prediction.unicar-therapy.com/index-en.html .

Functional Three-Dimensional Zeolitic Imidazolate Framework with an Ordered Macroporous Structure for the Isolation of Extracellular Vesicles.

Extracellular vesicles (EVs) and their cargoes are increasingly being recognized as noninvasive diagnostic markers, necessitating the isolation of EVs from complex biological samples. Herein, a distearoyl phospholipid ethanolamine-functionalized single-crystal ordered macroporous three-dimensional zeolitic imidazolate framework (SOM-ZIF-8-DSPE) was developed, which combines the surface charge interaction of ZIF-8 with the synergistic effect of DSPE insertion into the phospholipid membrane of EVs to improve the isolating selectivity of EV capture. The materials have porous structures larger than 300 nm in diameter, providing enough space and active sites to trap EVs. Benefiting from this feature, the entire isolation process takes only 10 min and is well compatible with the subsequent analysis of RNA in EVs. Consequently, 10 upregulated miRNA of plasma EVs in the primary colorectal cancer (pCRC) patients is found over the healthy donors, and 6 upregulated miRNA of plasma EVs in the metastatic colorectal cancer (mCRC) patients over pCRC patients. These findings suggest that the isolation of EV-based SOM-ZIF-8-DSPE is a promising strategy to identify biomarkers for disease diagnosis, such as miRNAs in plasma EVs for the early detection of CRC.

A recombination-resistant genome for live attenuated and stable PEDV vaccines by engineering the transcriptional regulatory sequences.

IMPORTANCE: Coronaviruses are important pathogens of humans and animals, and vaccine developments against them are imperative. Due to the ability to induce broad and prolonged protective immunity and the convenient administration routes, live attenuated vaccines (LAVs) are promising arms for controlling the deadly coronavirus infections. However, potential recombination events between vaccine and field strains raise a safety concern for LAVs. The porcine epidemic diarrhea virus (PEDV) remodeled TRS (RMT) mutant generated in this study replicated efficiently in both cell culture and in pigs and retained protective immunogenicity against PEDV challenge in pigs. Furthermore, the RMT PEDV was resistant to recombination and genetically stable. Therefore, RMT PEDV can be further optimized as a backbone for the development of safe LAVs.

ILGBMSH: an interpretable classification model for the shRNA target prediction with ensemble learning algorithm.

Short hairpin RNA (shRNA)-mediated gene silencing is an important technology to achieve RNA interference, in which the design of potent and reliable shRNA molecules plays a crucial role. However, efficient shRNA target selection through biological technology is expensive and time consuming. Hence, it is crucial to develop a more precise and efficient computational method to design potent and reliable shRNA molecules. In this work, we present an interpretable classification model for the shRNA target prediction using the Light Gradient Boosting Machine algorithm called ILGBMSH. Rather than utilizing only the shRNA sequence feature, we extracted 554 biological and deep learning features, which were not considered in previous shRNA prediction research. We evaluated the performance of our model compared with the state-of-the-art shRNA target prediction models. Besides, we investigated the feature explanation from the model's parameters and interpretable method called Shapley Additive Explanations, which provided us with biological insights from the model. We used independent shRNA experiment data from other resources to prove the predictive ability and robustness of our model. Finally, we used our model to design the miR30-shRNA sequences and conducted a gene knockdown experiment. The experimental result was perfectly in correspondence with our expectation with a Pearson's coefficient correlation of 0.985. In summary, the ILGBMSH model can achieve state-of-the-art shRNA prediction performance and give biological insights from the machine learning model parameters.

C6orf15 promotes liver metastasis via WNT/ß-catenin signalling in colorectal cancer.

BACKGROUND: Colon cancer ranks third among global tumours and second in cancer-related mortality, prompting an urgent need to explore new therapeutic targets. C6orf15 is a novel gene that has been reported only in Sjogren's syndrome and systemic lupus erythematosus patients. We found a close correlation between increased C6orf15 expression and the occurrence of colon cancer. The aim of this study was to explore the potential of C6orf15 as a therapeutic target for colorectal cancer. METHOD: RNA-seq differential expression analysis of the TCGA database was performed using the R package 'limma.' The correlation between target genes and survival as well as tumour analysis was analysed using GEPIA. Western blot and PCR were used to assess C6orf15 expression in colorectal cancer tissue samples. Immunofluorescence and immunohistochemistry were used to assess C6orf15 subcellular localization and tissue expression. The role of C6orf15 in liver metastasis progression was investigated via a mouse spleen infection liver metastasis model. The association of C6orf15 with signalling pathways was assessed using the GSEA-Hallmark database. Immunohistochemistry (IHC), qPCR and western blotting were performed to assess the expression of related mRNAs or proteins. Biological characteristics were evaluated through cell migration assays, MTT assays, and Seahorse XF96 analysis to monitor fatty acid metabolism. RESULTS: C6orf15 was significantly associated with liver metastasis and survival in CRC patients as determined by the bioinformatic analysis and further verified by immunohistochemistry (IHC), qPCR and western blot results. The upregulation of C6orf15 expression in CRC cells can promote the nuclear translocation of ß-catenin and cause an increase in downstream transcription. This leads to changes in the epithelial-mesenchymal transition (EMT) and alterations in fatty acid metabolism, which together promote liver metastasis of CRC. CONCLUSION: Our study identified C6orf15 as a marker of liver metastasis in CRC. C6orf15 can activate the WNT/ß-catenin signalling pathway to promote EMT and fatty acid metabolism in CRC.

Associations between body mass index, sleep-disordered breathing, brain structure, and behavior in healthy children.

Pediatric overweight/obesity can lead to sleep-disordered breathing (SDB), abnormal neurological and cognitive development, and psychiatric problems, but the associations and interactions between these factors have not been fully explored. Therefore, we investigated the associations between body mass index (BMI), SDB, psychiatric and cognitive measures, and brain morphometry in 8484 children 9-11 years old using the Adolescent Brain Cognitive Development dataset. BMI was positively associated with SDB, and both were negatively correlated with cortical thickness in lingual gyrus and lateral orbitofrontal cortex, and cortical volumes in postcentral gyrus, precentral gyrus, precuneus, superior parietal lobule, and insula. Mediation analysis showed that SDB partially mediated the effect of overweight/obesity on these brain regions. Dimensional psychopathology (including aggressive behavior and externalizing problem) and cognitive function were correlated with BMI and SDB. SDB and cortical volumes in precentral gyrus and insula mediated the correlations between BMI and externalizing problem and matrix reasoning ability. Comparisons by sex showed that obesity and SDB had a greater impact on brain measures, cognitive function, and mental health in girls than in boys. These findings suggest that preventing childhood obesity will help decrease SDB symptom burden, abnormal neurological and cognitive development, and psychiatric problems.

Measuring Human Auditory Evoked Fields with a Flexible Multi-Channel OPM-Based MEG System.

BACKGROUND: Magnetoencephalography (MEG) is a non-invasive imaging technique for directly measuring the external magnetic field generated from synchronously activated pyramidal neurons in the brain. The optically pumped magnetometer (OPM) is known for its less expensive, non-cryogenic, movable and user-friendly custom-design provides the potential for a change in functional neuroimaging based on MEG. METHODS: An array of OPMs covering the opposite sides of a subject's head is placed inside a magnetically shielded room (MSR) and responses evoked from the auditory cortices are measured. RESULTS: High signal-to-noise ratio auditory evoked response fields (AEFs) were detected by a wearable OPM-MEG system in a MSR, for which a flexible helmet was specially designed to minimize the sensor-to-head distance, along with a set of bi-planar coils developed for background field and gradient nulling. Neuronal current sources activated in AEF experiments were localized and the auditory cortices showed the highest activities. Performance of the hybrid optically pumped magnetometer-magnetoencephalography/electroencephalography (OPM-MEG/EEG) system was also assessed. CONCLUSIONS: The multi-channel OPM-MEG system performs well in a custom built MSR equipped with bi-planar coils and detects human AEFs with a flexible helmet. Moreover, the similarities and differences of auditory evoked potentials (AEPs) and AEFs are discussed, while the operation of OPM-MEG sensors in conjunction with EEG electrodes provides an encouraging combination for the exploration of hybrid OPM-MEG/EEG systems.

[Effects of PM2.5 and O3 sub-chronic combined exposure on ATP amount and ATPase activities in rat nasal mucosa].

OBJECTIVE: To evaluate the effects of fine particle matter (PM2.5) and ozone (O3) combined exposure on adenosine triphosphate (ATP) amount and ATPase activities in nasal mucosa of Sprague Dawley (SD) rats. METHODS: Twenty male SD rats were divided into control group (n=10) and exposure group (n=10) by random number table method. The rats were fed in the conventional clean environment and the air pollutant exposure system established by our team, respectively, and exposed for 208 d. During the exposure period, the concentrations of PM2.5 and O3 in the exposure system were monitored, and a comprehensive assessment of PM2.5 and O3 in the exposure system was conducted by combining self-measurement and site data. On the 208 d of exposure, the core, liver, spleen, kidney, testis and other major organs and nasal mucosal tissues of the rats were harvested. Each organ was weighed and the organ coefficient calculated. The total amount of ATP was measured by bioluminescence, and the activities of Na+-K+ -ATPase and Ca2+ -ATPase were detected by spectrophotometry. The t test of two independent samples was used to compare the differences among the indicator groups. RESULTS: From the 3rd week to the end of exposure duration, the body weight of the rats in the exposure group was higher than that in the control group (P < 0.05), and there was no significant difference in organ coefficients between the two groups. The average daily PM2.5 concentration in the exposure group was (30.68±19.23) µg/m3, and the maximum 8 h ozone concentration (O3-8 h) was (82.45±35.81) µg/m3. The chemiluminescence value (792.4±274.1) IU/L of ATP in nasal mucosa of the rats in the exposure group was lower than that in the control group (1 126.8±218.1) IU/L. The Na+-K+-ATPase activity (1.53±0.85) U/mg in nasal mucosa of the rats in the exposure group was lower than that in the control group (4.31±1.60) U/mg (P < 0.05). The protein content of nasal mucosa in the control group and the exposure group were (302.14±52.51) mg/L and (234.58±53.49) mg/L, respectively, and the activity of Ca2+-ATPase was (0.81±0.27) U/mg and (0.99±0.73) U/mg, respectively. There was no significant difference between the groups. CONCLUSION: The ability of power capacity decreased in the rat nasal mucossa under the sub-chronic low-concentration exposure of PM2.5 and O3.

Green manufacturing for achieving carbon neutrality goal requires innovative technologies: A bibliometric analysis from 1991 to 2022.

Recent years have seen a significant increase in interest in green manufacturing as a key driver of global carbon-neutral efforts and sustainable development. To find the research hotspots of green manufacturing and reveal future research trends, this study reviewed and analyzed research articles from the Web of Science database on green manufacturing from 1991 to 2022 using a bibliometric method. The findings indicate a significant rise in the number of articles related to green manufacturing since the 2010s. Moreover, there has been an increase in the involvement of scholars from developing countries such as China and India in this field. Based on the literature review and bibliometric cluster analysis on green manufacturing, we believed that future research may continue following the lines of intelligent technology integration, adoption of frontier engineering techniques, and industry development in line with carbon reduction targets. A framework for future green manufacturing development is proposed, with a focus on Chinese policies. The framework could provide policy implications for developing countries looking to pursue opportunities for development in green manufacturing.

Relationships between blood chromium exposure and liver injury: Exploring the mediating role of systemic inflammation in a chromate-exposed population.

Hexavalent chromium and its compounds are prevalent pollutants, especially in the work environment, pose a significant risk for multisystem toxicity and cancers. While it is known that chromium accumulation in the liver can cause damage, the dose-response relationship between blood chromium (Cr) and liver injury, as well as the possible potential toxic mechanisms involved, remains poorly understood. To address this, we conducted a follow-up study of 590 visits from 305 participants to investigate the associations of blood Cr with biomarkers for liver injury, including serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (TBIL), and direct bilirubin (DBIL), and to evaluate the mediating effects of systemic inflammation. Platelet (PLT) and the platelet-to-lymphocyte ratio (PLR) were utilized as biomarkers of systemic inflammation. In the linear mixed-effects analyses, each 1-unit increase in blood Cr level was associated with estimated effect percentage increases of 0.82% (0.11%, 1.53%) in TBIL, 1.67% (0.06%, 3.28%) in DBIL, 0.73% (0.04%, 1.43%) in ALT and 2.08% (0.29%, 3.87%) in AST, respectively. Furthermore, PLT mediated 10.04%, 11.35%, and 10.77% increases in TBIL, DBIL, and ALT levels induced by chromate, respectively. In addition, PLR mediated 8.26% and 15.58% of the association between blood Cr and TBIL or ALT. These findings shed light on the mechanisms underlying blood Cr-induced liver injury, which is partly due to worsening systemic inflammation.

[Tacrolimus Induces Pain by Upregulating the Synaptic Expression of AMPA Receptors in the Spinal Cord Dorsal Horn]. / ä»–å ‹èŽ«å¸é€šè¿‡ä¸Šè°ƒè„Šé«“èƒŒè§’AMPA受体的突触表达诱发疼痛.

Objective: To investigate the effects of long-term administration of tacrolimus (also known as FK506) on the pain-related behaviors in mice and to study the underlying mechanism of pain induced by FK506 via measuring the effect of FK506 on the synaptic expression and phosphorylation of alpha-amino-3-hyroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor in the spinal cord dorsal horn of mice. Methods: 1) A total of 24 mice were evenly and randomly assigned to two groups, a FK506 group and a Saline group. The FK506 group was given daily intraperitoneal injection of FK506 and the Saline group received normal saline. Both groups received injection once a day for 7 days in a row. Some of the mice ( n=6 in each group) were monitored for the changes in the paw withdrawal threshold (PWT), the paw withdrawal latency (PWL), and the spontaneous pain behaviors to establish the pain model. The other mice ( n=6 in each group) of each group underwent isolation of the dorsal horn when obvious pain symptoms were induced on day 7 of injection. Then, immunoblotting was performed to determine the synaptic expression and phosphorylation levels of GluA1 and GluA2 subunits of AMPA receptors. 2) The mice were randomly divided into two groups, FK506+calcineurin (CaN) group and FK506+Saline group ( n=6 in each group). After the pain model was constructed, the mice were given intrathecal injection of recombinant CaN (also know as 33 U) or normal saline. Then, 60 minutes later, the PWT and the PWL of the mice were measured to investigate the role of CaN in FK506-induced pain. 3) Another18 mice were selected. The mice were randomly and evenly assigned to three groups, a control group (receiving intraperitoneal injection of normal saline followed by intrathecal injection of normal saline), FK506+Saline group (receiving intraperitoneal injection of FK506 followed by intrathecal injection of normal saline) and FK506+CaN group (receiving intraperitoneal injection of FK506 followed by intrathecal injection of CaN). Then, 60 minutes later, the spinal cords were isolated and subjected to immunoblotting assay to determine the role of CaN in FK506-induced AMPA receptor modification. Results: 1) After 7 consecutive days of intraperitoneal injection of FK506, the PWT and PWL of mice dropped significantly, reaching on day 7 as low as 22.3%±0.05% and 66.6%±0.05% of the control group, respectively ( P<0.01). The FK506-treated mice displayed evident spontaneous pain behavior, presenting significantly increased licking activities ( P<0.01). These results indicated that FK506-induced pain model was successfully established. Immunoblotting assay showed that the total expressions of GluA1 and GluA2 subunits in the spinal dorsal horn of the FK506 group remained unchanged in comparison with those of the Saline group. However, FK506 specifically induced an increase in the synaptic expression of GluA1. In addition, the phosphorylation levels of GluA1 at Ser845 and Ser831 in FK506-treated mice were significantly increased in comparison with those of the control group ( P<0.05). 2) Compared with those of the mice in the FK506+Saline group, the PWT and the PWL of mice in the FK506+CaN group were significantly increased ( P<0.05). 3) Compared with those of the FK506+Saline group, the synaptic expression of GluA1 were decreased in FK506+CaN group ( P<0.01) and the phosphorylation levels of GluA1 at Ser845 and Ser831 were significantly downregulated ( P<0.001). Conclusion: The hyper-expression and hyperphosphorylation of GluA1 subunit in the spinal cord dorsal horn resulting from CaN inhibition contributes to the FK506-induced pain syndrome. FK506 induces the synaptic hyper-expression and hyperphosphorylation of GluA1 in the dorsal horn of the spinal cord through CaN inhibition, thereby inducing pain.

A score-based method of immune status evaluation for healthy individuals with complete blood cell counts.

BACKGROUND: With the COVID-19 outbreak, an increasing number of individuals are concerned about their health, particularly their immune status. However, as of now, there is no available algorithm that effectively assesses the immune status of normal, healthy individuals. In response to this, a new score-based method is proposed that utilizes complete blood cell counts (CBC) to provide early warning of disease risks, such as COVID-19. METHODS: First, data on immune-related CBC measurements from 16,715 healthy individuals were collected. Then, a three-platform model was developed to normalize the data, and a Gaussian mixture model was optimized with expectation maximization (EM-GMM) to cluster the immune status of healthy individuals. Based on the results, Random Forest (RF), Light Gradient Boosting Machine (LightGBM) and Extreme Gradient Boosting (XGBoost) were used to determine the correlation of each CBC index with the immune status. Consequently, a weighted sum model was constructed to calculate a continuous immunity score, enabling the evaluation of immune status. RESULTS: The results demonstrated a significant negative correlation between the immunity score and the age of healthy individuals, thereby validating the effectiveness of the proposed method. In addition, a nonlinear polynomial regression model was developed to depict this trend. By comparing an individual's immune status with the reference value corresponding to their age, their immune status can be evaluated. CONCLUSION: In summary, this study has established a novel model for evaluating the immune status of healthy individuals, providing a good approach for early detection of abnormal immune status in healthy individuals. It is helpful in early warning of the risk of infectious diseases and of significant importance.

Revealing Intramolecular Isotope Effects with Chemical-Bond Precision.

Isotope substitution of a molecule not only changes its vibrational frequencies but also changes its vibrational distributions in real-space. Quantitatively measuring the isotope effects inside a polyatomic molecule requires both energy and spatial resolutions at the single-bond level, which has been a long-lasting challenge in macroscopic techniques. By achieving ångström resolution in tip-enhanced Raman spectroscopy (TERS), we record the corresponding local vibrational modes of pentacene and its fully deuterated form, enabling us to identify and measure the isotope effect of each vibrational mode. The measured frequency ratio νH/νD varies from 1.02 to 1.33 in different vibrational modes, indicating different isotopic contributions of H/D atoms, which can be distinguished from TERS maps in real-space and well described by the potential energy distribution simulations. Our study demonstrates that TERS can serve as a non-destructive and highly sensitive methodology for isotope detection and recognition with chemical-bond precision.

Mutations in Porcine Epidemic Diarrhea Virus nsp1 Cause Increased Viral Sensitivity to Host Interferon Responses and Attenuation In Vivo.

Coronavirus (CoV) nonstructural protein 1 (nsp1) inhibits cellular gene expression and antagonizes interferon (IFN) response. Porcine epidemic diarrhea virus (PEDV) infects pigs and causes high mortality in neonatal piglets. We hypothesized that a recombinant PEDV carrying mutations at the conserved residues N93 and N95 of nsp1 induces higher IFN responses and is more sensitive to IFN responses, leading to virus attenuation. We mutated PEDV nsp1 N93 and N95 to A93 and A95 to generate the recombinant N93/95A virus using the infectious clone of a highly virulent PEDV strain, PC22A (icPC22A), and evaluated N93/95A virus in vitro and in vivo. Compared with icPC22A, the N93/95A mutant replicated to significantly lower infectious titers, triggered stronger type I and III IFN responses, and was more sensitive to IFN treatment in vitro. To evaluate the pathogenicity and immunogenicity, 5-day-old gnotobiotic piglets were orally inoculated with the N93/95A or icPC22A strain or mock inoculated and then challenged at 22 days postinoculation (dpi) with icPC22A. icPC22A in all pigs (100% [5/5]) caused severe diarrhea and death within 6 dpi. Only one pig (25% [1/4]) died in the N93/95A group. Compared with the icPC22A group, significantly delayed and diminished fecal PEDV shedding was detected in the N93/95A group. Postchallenge, all piglets in N93/95A group were protected from severe diarrhea and death, whereas all pigs in the mock-challenged group developed severe diarrhea, and 25% (1/4) of them died. In summary, nsp1 N93A and N95A mutations attenuated PEDV but retained viral immunogenicity and can be targets for the development of live attenuated vaccines for PEDV. IMPORTANCE PEDV causes porcine epidemic diarrhea (PED) and remains a great threat to the swine industry worldwide because no effective vaccines are available yet. Safe and effective live attenuated vaccines can be designed using reverse genetics to induce lactogenic immunity in pregnant sows to protect piglets from the deadly PED. We found that an engineered PEDV mutant carrying N93A and N95A mutations of nsp1 was partially attenuated and remained immunogenic in neonatal pigs. Our study suggested that nsp1 N93 and N95 can be good targets for the rational design of live attenuated vaccines for PEDV using reverse genetics. Because CoV nsp1 is conserved among alphacoronaviruses (α-CoVs) and betacoronaviruses (ß-CoVs), it may be a good target for vaccine development for other α-CoVs or ß-CoVs.

Context-aware learning for cancer cell nucleus recognition in pathology images.

MOTIVATION: Nucleus identification supports many quantitative analysis studies that rely on nuclei positions or categories. Contextual information in pathology images refers to information near the to-be-recognized cell, which can be very helpful for nucleus subtyping. Current CNN-based methods do not explicitly encode contextual information within the input images and point annotations. RESULTS: In this article, we propose a novel framework with context to locate and classify nuclei in microscopy image data. Specifically, first we use state-of-the-art network architectures to extract multi-scale feature representations from multi-field-of-view, multi-resolution input images and then conduct feature aggregation on-the-fly with stacked convolutional operations. Then, two auxiliary tasks are added to the model to effectively utilize the contextual information. One for predicting the frequencies of nuclei, and the other for extracting the regional distribution information of the same kind of nuclei. The entire framework is trained in an end-to-end, pixel-to-pixel fashion. We evaluate our method on two histopathological image datasets with different tissue and stain preparations, and experimental results demonstrate that our method outperforms other recent state-of-the-art models in nucleus identification. AVAILABILITY AND IMPLEMENTATION: The source code of our method is freely available at https://github.com/qjxjy123/DonRabbit. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.