Factors associated with immune-related thyroid dysfunction induced by PD-1/PD-L1 inhibitors in cancer patients: an observational study.

This study aimed to identify the potential factors associated with immune thyroid dysfunction caused by programmed death 1 (PD-1)/programmed death ligand 1 (PD-L1) inhibitors in cancer patients. We conducted a retrospective study of thyroid immune-related adverse events (irAEs) in cancer patients treated with PD-1/PD-L1 inhibitors at Tianjin First Central Hospital from January 2020 to March 2023. Thyroid irAEs were characterized as hypothyroidism, hyperthyroidism and thyrotoxicosis followed by hypothyroidism. A total of 175 patients were screened in the study, of whom 48 patients (27%) developed thyroid irAEs (including 24 hypothyroidism, 11 hyperthyroidism and 13 thyrotoxicosis followed by hypothyroidism) following PD-1/PD-L1 inhibitors treatment. Multivariate logistic regression analysis showed that combination therapy with PD-1/PD-L1 inhibitors and tyrosine kinase inhibitors (lenvatinib/regorafenib) and high baseline anti-TPO level were associated with the development of thyroid irAEs caused by PD-1/PD-L1 inhibitors. The nomogram models showed good discriminant ability and could bring net benefits for more patients according to the decision curve analysis. However, the model needs to be further validated in other large cohorts.

Cinnamaldehyde Alleviates Bone Loss by Targeting Oxidative Stress and Mitochondrial Damage via the Nrf2/HO-1 Pathway in BMSCs and Ovariectomized Mice.

Osteoporosis (OP) is typically brought on by disruption of bone homeostasis. Excessive oxidative stress and mitochondrial dysfunction are believed to be the primary mechanisms underlying this disorder. Therefore, in order to restore bone homeostasis effectively, targeted treatment of oxidative stress and mitochondrial dysfunction is necessary. Cinnamaldehyde (CIN), a small molecule that acts as an agonist for the nuclear factor erythroid 2-related factor (Nrf2), has been found to possess antiapoptotic, anti-inflammatory, and antioxidant properties. We found that CIN, while rescuing apoptosis, can also reduce the accumulation of reactive oxygen species (ROS) to improve mitochondrial dysfunction and thus restore the osteogenic differentiation potential of BMSCs disrupted by hydrogen peroxide (H2O2) exposure. The role of CIN was preliminarily considered to be a consequence of Nrf2/HO-1 axis activation. The ovariectomized mice model further demonstrated that CIN treatment ameliorated oxidative stress in vivo, partially reversing OVX-induced bone loss. This improvement was seen in the trabecular microarchitecture and bone biochemical indices. However, when ML385 was concurrently injected with CIN, the positive effects of CIN were largely blocked. In conclusion, this study sheds light on the intrinsic mechanisms by which CIN regulates BMSCs and highlights the potential therapeutic applications of these findings in the treatment of osteoporosis.

Bone mineral density saturation as influenced by the visceral adiposity index in adults older than 20 years: a population-based study.

OBJECTIVE: The goal of this research was to determine whether or not there is a saturation effect and whether or not the visceral adiposity index (VAI) correlates with bone mineral density (BMD) in adult Americans. METHODS: This study used multivariate logistic regression models to examine the association between VAI and total femur BMD, drawing on the most up-to-date data from the National Health and Nutrition Examination Survey (NHANES) between 2007 and 2018. Saturation levels and non-linear connections were calculated using a smooth curve-fitting algorithm and an investigation of saturation effects. Subgroup analyses and interaction tests were also conducted. RESULTS: This study ultimately recruited 6257 individuals aged 20 years or older. According to multivariate regression analysis, those with high VAI scores exhibited higher total femur BMD. Total femur BMD was greater in the highest VAI quartile (Q4: 0.060 g/cm2) after adjustment than in the lowest VAI quartile (Q1) (P < 0.05). After controlling for variables, subgroup analysis failed to reveal any significant interaction effects. Furthermore, the study determined that VAI and BMD exhibited a specific saturation effect through the investigation of the saturation effect and the fitting of smooth curves. Saturation effect investigation of total femur BMD using VAI revealed a saturation value of 3.3. CONCLUSION: The present study uncovered a non-linear relationship between VAI and total femur BMD, which exhibited a saturation effect.

Comprehensive analysis of m6A regulators characterized by the immune microenvironment in Duchenne muscular dystrophy.

BACKGROUND: Duchenne muscular dystrophy (DMD) is an X-linked, incurable, degenerative neuromuscular disease that is exacerbated by secondary inflammation. N6-methyladenosine (m6A), the most common base modification of RNA, has pleiotropic immunomodulatory effects in many diseases. However, the role of m6A modification in the immune microenvironment of DMD remains elusive. METHODS: Our study retrospectively analyzed the expression data of 56 muscle tissues from DMD patients and 26 from non-muscular dystrophy individuals. Based on single sample gene set enrichment analysis, immune cells infiltration was identified and the result was validated by flow cytometry analysis and immunohistochemical staining. Then, we described the features of genetic variation in 26 m6A regulators and explored their relationship with the immune mircoenvironment of DMD patients through a series of bioinformatical analysis. At last, we determined subtypes of DMD patients by unsupervised clustering analysis and characterized the molecular and immune characteristics in different subgroups. RESULTS: DMD patients have a sophisticated immune microenvironment that is significantly different from non-DMD controls. Numerous m6A regulators were aberrantly expressed in the muscle tissues of DMD and inversely related to most muscle-infiltrating immune cell types and immune response-related signaling pathways. A diagnostic model involving seven m6A regulators was established using LASSO. Furthermore, we determined three m6A modification patterns (cluster A/B/C) with distinct immune microenvironmental characteristics. CONCLUSION: In summary, our study demonstrated that m6A regulators are intimately linked to the immune microenvironment of muscle tissues in DMD. These findings may facilitate a better understanding of the immunomodulatory mechanisms in DMD and provide novel strategies for the treatment.

Aquaporin 4-positive neuromyelitis optica spectrum disorder with meningoencephalitis-like onset: A case report.

Neuromyelitis optica spectrum disease (NMOSD) is a debilitating autoimmune inflammatory demyelinating disease of the central nervous system. The relationship between harboring an infection and NMOSD is currently unclear and needs further investigation. This article reports meningoencephalitis-like manifestations, including fever, headache, neck resistance, seizures, and pleocytosis, accompanied by nausea and vomiting, in a patient with serum AQP4 antibody-positive area postrema syndrome (APS). In the presence of aseptic meningitis combined with clinical symptoms such as optic neuritis and myelitis, the possibility of NMOSD diagnosis can be considered. However, for patients with unknown causes, especially combined with aseptic meningitis, a probable differential diagnosis of NMOSD is considered.

Further divided gender gaps in research productivity and collaboration during the COVID-19 pandemic: Evidence from coronavirus-related literature.

Based on publication data on coronavirus-related fields, this study applies a difference in differences approach to explore the evolution of gender inequalities before and during the COVID-19 pandemic by comparing the differences in the numbers and shares of authorships, leadership in publications, gender composition of collaboration, and scientific impacts. We find that, during the pandemic: (1) females' leadership in publications as the first author was negatively affected; (2) although both females and males published more papers relative to the pre-pandemic period, the gender gaps in the share of authorships have been strengthened due to the larger increase in males' authorships; (3) the share of publications by mixed-gender collaboration declined; (4) papers by teams in which females play a key role were less cited in the pre-pandemic period, and this citation disadvantage was exacerbated during the pandemic; and (5) gender inequalities regarding authorships and collaboration were enhanced in the initial stage of COVID-19, widened with the increasing severity of COVID-19, and returned to the pre-pandemic level in September 2020. This study shows that females' lower participation in teams as major contributors and less collaboration with their male colleagues also reflect their underrepresentation in science in the pandemic period. This investigation significantly deepens our understanding of how the pandemic influenced academia, based on which science policies and gender policy changes are proposed to mitigate the gender gaps.

Pathological Clinical Analysis and Imaging Manifestations for Spinal Bone Tumors Based on Cement Injection.

In order to explore the imaging manifestations and pathological characteristics of spine tumors, this article explores the clinical diagnosis and treatment methods through multi-sample case analysis with the support of imaging, and proposes a targeted treatment method that uses a special PVP needle with a beveled puncture surface for puncture. Moreover, this article uses the supporting PVP syringe for bone cement injection, develops a health status questionnaire, and adopts a scoring method for comprehensive assessment. The purpose of this article is to show that through the combination of preoperative radiotherapy and postoperative bracing, bone cement injection to treat vertebral tumors can immediately obtain satisfactory pain relief. Finally, through case analysis and image performance, we can see that the method proposed in this article has a certain effect.

Pandemics are catalysts of scientific novelty: Evidence from COVID-19.

Scientific novelty drives the efforts to invent new vaccines and solutions during the pandemic. First-time collaboration and international collaboration are two pivotal channels to expand teams' search activities for a broader scope of resources required to address the global challenge, which might facilitate the generation of novel ideas. Our analysis of 98,981 coronavirus papers suggests that scientific novelty measured by the BioBERT model that is pretrained on 29 million PubMed articles, and first-time collaboration increased after the outbreak of COVID-19, and international collaboration witnessed a sudden decrease. During COVID-19, papers with more first-time collaboration were found to be more novel and international collaboration did not hamper novelty as it had done in the normal periods. The findings suggest the necessity of reaching out for distant resources and the importance of maintaining a collaborative scientific community beyond nationalism during a pandemic.

[The Screening Approaches for 34 Common Drugs and Metabolites in Biological Samples by Liquid Chromatography Orbital Trap Mass Spectrometry].

Objective: To establish a high-performance liquid chromatography orbital trap mass spectrometry (HPLC-Obitrap MS) method for screening 34 common drugs and metabolites in biological samples. Methods: The target analytes in urine and blood samples were extracted with ethyl acetate, concentrated by nitrogen blowing and redissolved. The hair samples were washed with water and acetone, dried and cut into bits of about 1 mm, and then crushed in a freezing grinder. The analytes were extracted with methanol, and after filtration, the filtrate was used for instrumental analysis. Hypersil Gold PFP (2.1 mm×100 mm, 3 µm) column was used for chromatographic separation. Methanol and 5 mmol/L ammonium acetate solution were used as mobile phase with gradient elution at a flow rate of 400 µL/min. Mass spectrometry was done by electrospray positive and negative ion alternation mode. The data were collected using Full MS and Full MS/dd-MS2 mode. Xcalibur 4.0 software was used to control instruments and to collect data, and TraceFinder 3.3 was used for screening and identification. Results: The method's detection limits for 34 drugs and their metabolites in blood, urine and hair samples were 3.30-10700 ng/L, 4.43-5440 ng/L, 0.0350-4.21 µg/kg, respectively. The intra-day and inter-day precisions of the spiked samples at the levels of 5.0, 10, and 20 µg/L were 3.50%-6.00% and 4.18%-9.90%, respectively. A total of 1125 biological samples of urine, blood and hair were collected and screened. The results showed that 96.7% of the drug users were taking a single drug, while 3.3% were mixed drug users. The main types of drug of abuse were methamphetamine (75.8%), heroin (18.5%), ketamine (2.4%) and other drugs (3.3%), and 87.9% of the positive samples were from male users. Compared with the results of high-performance liquid chromatography triple quadrupole mass spectrometry, this method can be used to identify more types of drugs in one run and to conduct retrospective analysis. Conclusion: The method established in the study is simple and sensitive and is well suited for the screening of common drugs and metabolites in biological samples.

Kininogen-Nitric Oxide Signaling at Nearby Nonexcited Acupoints after Long-Term Stimulation.

Acupuncture treatment is based on acupoint stimulation; however, the biological basis is not understood. We stimulated one acupoint with catgut embedding for 8 weeks and then used isobaric tags for relative and absolute quantitation to screen proteins with altered expression in adjacent acupoints of Sprague Dawley rats. We found that kininogen expression was significantly upregulated in the stimulated and the nonstimulated adjacent acupoints along the same meridian. The enhanced kininogen expression was meridian dependent and was most apparent among small vessels in the subcutaneous layer. Enhanced signals of nitric oxide synthases, cGMP-dependent protein kinase, and myosin light chain were also observed at the nonstimulated adjacent acupoints along the same meridian. These findings uncover biological changes at acupoints and suggest the critical role of the kininogen-nitric oxide signaling pathway in acupoint activation.

Toward a Coronavirus Knowledge Graph.

This study builds a coronavirus knowledge graph (KG) by merging two information sources. The first source is Analytical Graph (AG), which integrates more than 20 different public datasets related to drug discovery. The second source is CORD-19, a collection of published scientific articles related to COVID-19. We combined both chemo genomic entities in AG with entities extracted from CORD-19 to expand knowledge in the COVID-19 domain. Before populating KG with those entities, we perform entity disambiguation on CORD-19 collections using Wikidata. Our newly built KG contains at least 21,700 genes, 2500 diseases, 94,000 phenotypes, and other biological entities (e.g., compound, species, and cell lines). We define 27 relationship types and use them to label each edge in our KG. This research presents two cases to evaluate the KG's usability: analyzing a subgraph (ego-centered network) from the angiotensin-converting enzyme (ACE) and revealing paths between biological entities (hydroxychloroquine and IL-6 receptor; chloroquine and STAT1). The ego-centered network captured information related to COVID-19. We also found significant COVID-19-related information in top-ranked paths with a depth of three based on our path evaluation.

A patient with acute myeloid leukemia presented with a superior sagittal sinus thrombosis as the first manifestation of Trousseau syndrome.

This paper reports a case of Trousseau syndrome with intracranial venous sinus thrombosis as the first manifestation, which is relatively rare in the clinic. A 44-year-old female patient presented with a blurred vision of the visual substance for 2 months, and the condition was aggravated with a headache for 10 days. The final diagnosis was intracranial venous sinus thrombosis and acute myeloid leukemia subtype M2. Anticoagulant + intra-arterial regimen (cytarabine + igdabistar) was given, and the patient's headache and blurred vision were gradually restored. After 2 courses of chemotherapy, acute myeloid leukemia subtype M2 was in complete remission. After 6 months of follow-up, headache and the blurred vision disappeared, leukemia did not recur, limb vascular ultrasound was screened regularly, and no new vascular embolism disease occurred.

Analyzing knowledge entities about COVID-19 using entitymetrics.

COVID-19 cases have surpassed the 109 + million markers, with deaths tallying up to 2.4 million. Tens of thousands of papers regarding COVID-19 have been published along with countless bibliometric analyses done on COVID-19 literature. Despite this, none of the analyses have focused on domain entities occurring in scientific publications. However, analysis of these bio-entities and the relations among them, a strategy called entity metrics, could offer more insights into knowledge usage and diffusion in specific cases. Thus, this paper presents an entitymetric analysis on COVID-19 literature. We construct an entity-entity co-occurrence network and employ network indicators to analyze the extracted entities. We find that ACE-2 and C-reactive protein are two very important genes and that lopinavir and ritonavir are two very important chemicals, regardless of the results from either ranking.

Therapeutic Effect of Idebenone on Rats with Vascular Dementia via the MicroRNA-216a/RSK2/NF-κB Axis.

BACKGROUND: Vascular dementia (VD) is a brain disease featured by cognitive impairment and cerebrovascular pathologies. Idebenone can treat neurodegenerative diseases. This study evaluated the mechanism of Idebenone in VD. METHODS: The VD rat model was established by permanent occlusion of bilateral common carotid arteries, followed by intragastrical administration of Idebenone. The learning and spatial memory abilities, and the levels of MDA, SOD, IL-6 and TNF-α were measured. Histological staining was adopted to observe the damage of neurons in the hippocampal cortex and to quantitatively analyze the neuronal damage in CA1 area of hippocampus. Microarray analysis was performed to find out the effect of Idebenone treatment on microRNA (miR) expression in hippocampus of rats. The potential target genes of miR and the pathways regulated by target genes were searched by bioinformatics analysis, and verified by experiments. The mechanism of action behind Idebenone in VD rats was proved by rescue experiment. RESULTS: Idebenone treatment improved the learning and spatial memory abilities of VD rats, inhibited neuroinflammation and oxidative stress, and prevented neuronal apoptosis. Idebenone treatment elevated miR-216a expression in hippocampus of rats, but the therapeutic effect of Idebenone was averted by lentivirus inhibition of miR-216a. miR-216a targeted RSK2. Overexpression of RSK2 annulled the therapeutic effect of Idebenone on VD rats by activating the IκBα/NF-κB axis. CONCLUSION: Idebenone inhibits the activation of RSK2/IκBα/NF-κB axis by increasing miR-216a, thus alleviating oxidative stress and neuroinflammation in VD rats.

Building a PubMed knowledge graph.

PubMed® is an essential resource for the medical domain, but useful concepts are either difficult to extract or are ambiguous, which has significantly hindered knowledge discovery. To address this issue, we constructed a PubMed knowledge graph (PKG) by extracting bio-entities from 29 million PubMed abstracts, disambiguating author names, integrating funding data through the National Institutes of Health (NIH) ExPORTER, collecting affiliation history and educational background of authors from ORCID®, and identifying fine-grained affiliation data from MapAffil. Through the integration of these credible multi-source data, we could create connections among the bio-entities, authors, articles, affiliations, and funding. Data validation revealed that the BioBERT deep learning method of bio-entity extraction significantly outperformed the state-of-the-art models based on the F1 score (by 0.51%), with the author name disambiguation (AND) achieving an F1 score of 98.09%. PKG can trigger broader innovations, not only enabling us to measure scholarly impact, knowledge usage, and knowledge transfer, but also assisting us in profiling authors and organizations based on their connections with bio-entities.

Improving the Named Entity Recognition of Chinese Electronic Medical Records by Combining Domain Dictionary and Rules.

Electronic medical records are an integral part of medical texts. Entity recognition of electronic medical records has triggered many studies that propose many entity extraction methods. In this paper, an entity extraction model is proposed to extract entities from Chinese Electronic Medical Records (CEMR). In the input layer of the model, we use word embedding and dictionary features embedding as input vectors, where word embedding consists of a character representation and a word representation. Then, the input vectors are fed to the bidirectional long short-term memory to capture contextual features. Finally, a conditional random field is employed to capture dependencies between neighboring tags. We performed experiments on body classification task, and the F1 values reached 90.65%. We also performed experiments on anatomic region recognition task, and the F1 values reached 93.89%. On both tasks, our model had higher performance than state-of-the-art models, such as Bi-LSTM-CRF, Bi-LSTM-Attention, and Vote. Through experiments, our model has a good effect when dealing with small frequency entities and unknown entities; with a small training dataset, our method showed 2-4% improvement on F1 value compared to the basic Bi-LSTM-CRF models. Additionally, on anatomic region recognition task, besides using our proposed entity extraction model, 12 rules we designed and domain dictionary were adopted. Then, in this task, the weighted F1 value of the three specific entities extraction reached 84.36%.

Effects of atomic species and interatomic distance on the interactions in one-dimensional nanomaterials.

Noncovalent van der Waals (vdW) interactions are significant for the constitution of nanomaterials; however, they are not well understood in one-dimensional materials. Herein, we employ density functional theory (DFT) methods to address this issue and find that the many-body effects of vdW interactions within the one-dimensional wires composed of atoms chosen from the second period (B, C, N, O, F) vary with the interatomic distance of the wires. Furthermore, the atomic species effectively regulate the transition threshold of the many-body effects of vdW interactions. In the case of the adsorption of n-heptane (C7H16) on the wires, the atomic species alters the interactions between the wires and the molecule by modulating the coupling vibration between wires and C7H16 molecules. Correspondingly, replacing a portion of Pb with Tl atoms could contribute to the stability of the organic-inorganic hybrid halide perovskites with one-dimensional structures. Our findings not only contribute to the understanding of vdW interactions in one-dimensional structures with second-period atoms (B, C, N, O, F) but also provide clues for improving the stability of perovskites with one-dimensional structures.

Predicting biomedical relationships using the knowledge and graph embedding cascade model.

Advances in machine learning and deep learning methods, together with the increasing availability of large-scale pharmacological, genomic, and chemical datasets, have created opportunities for identifying potentially useful relationships within biochemical networks. Knowledge embedding models have been found to have value in detecting knowledge-based correlations among entities, but little effort has been made to apply them to networks of biochemical entities. This is because such networks tend to be unbalanced and sparse, and knowledge embedding models do not work well on them. However, to some extent, the shortcomings of knowledge embedding models can be compensated for if they are used in association with graph embedding. In this paper, we combine knowledge embedding and graph embedding to represent biochemical entities and their relations as dense and low-dimensional vectors. We build a cascade learning framework which incorporates semantic features from the knowledge embedding model, and graph features from the graph embedding model, to score the probability of linking. The proposed method performs noticeably better than the models with which it is compared. It predicted links and entities with an accuracy of 93%, and its average hits@10 score has an average of 8.6% absolute improvement compared with original knowledge embedding model, 1.1% to 9.7% absolute improvement compared with other knowledge and graph embedding algorithm. In addition, we designed a meta-path algorithm to detect path relations in the biomedical network. Case studies further verify the value of the proposed model in finding potential relationships between diseases, drugs, genes, treatments, etc. Amongst the findings of the proposed model are the suggestion that VDR (vitamin D receptor) may be linked to prostate cancer. This is backed by evidence from medical databases and published research, supporting the suggestion that our proposed model could be of value to biomedical researchers.

Metabolic Engineering of Escherichia coli for Producing Astaxanthin as the Predominant Carotenoid.

Astaxanthin is a carotenoid of significant commercial value due to its superior antioxidant potential and wide applications in the aquaculture, food, cosmetic and pharmaceutical industries. A higher ratio of astaxanthin to the total carotenoids is required for efficient astaxanthin production. ß-Carotene ketolase and hydroxylase play important roles in astaxanthin production. We first compared the conversion efficiency to astaxanthin in several ß-carotene ketolases from Brevundimonas sp. SD212, Sphingomonas sp. DC18, Paracoccus sp. PC1, P. sp. N81106 and Chlamydomonas reinhardtii with the recombinant Escherichia coli cells that synthesize zeaxanthin due to the presence of the Pantoea ananatis crtEBIYZ. The B. sp. SD212 crtW and P. ananatis crtZ genes are the best combination for astaxanthin production. After balancing the activities of ß-carotene ketolase and hydroxylase, an E. coli ASTA-1 that carries neither a plasmid nor an antibiotic marker was constructed to produce astaxanthin as the predominant carotenoid (96.6%) with a specific content of 7.4 ± 0.3 mg/g DCW without an addition of inducer.