MedComm - Future medicine最新文献

Chronic obstructive pulmonary disease (COPD) stands as a global health crisis, responsible for substantial morbidity and mortality on a worldwide scale. Its insidious nature underscores the importance of early detection and accurate diagnosis. While spirometry has been the cornerstone for COPD diagnosis, the role of computed tomography (CT) imaging has evolved, offering a valuable avenue for early detection and subtype classification. Recently, the advent of artificial intelligence (AI) has brought forth the potential to revolutionize the accuracy and efficiency of COPD diagnosis, with a specific focus on CT images. This intersection of healthcare and technology signifies a paradigm shift in the way we approach COPD management. The transformative capacity of AI positions it as a vital instrument for early detection and precise subtype classification of COPD. Moreover, the synergistic relationship between medical imaging and AI paves the way for more precise and efficient disease management. Therefore, in this perspective, we tend to offer a comprehensive exploration of the latest breakthroughs in the field of CT-based AI in COPD diagnosis, aiming to demonstrate the promise and potential of AI in refining the accuracy of COPD classification and to illuminate the evolving landscape of AI's impact on COPD management.

A research by Bod et al.¹ has been published recently in Nature entitled “B-cell-specific checkpoint molecules that regulate anti-tumor immunity‘.” It revealed that T-cell immunoglobulin and mucin domain protein 1 (TIM-1) blockade can strikingly reduce the melanoma size in mouse models.

In the field of antitumor immunotherapy, researchers have been extensively studying the role of T cells in fighting against tumors. However, the function of B cells in antitumor immunity has remained unclear or controversial. To address this gap, this collaboration study among Harvard, MIT, and many others has shed light on a crucial molecule called TIM-1, which for the first time, has been claimed to act as a specific checkpoint on B cells.¹

TIM-1 is also known as hepatitis A virus cellular receptor 1 (HAVCR1) and kidney injury molecule 1 as well. It is a membrane glycoprotein encoded by the Havcr1 gene. Expressed on various cells as a receptor for many viruses and ligands, TIM-1 has been reported to involve in kidney diseases, atopic diseases, T-cell activation and cancers.^2-4 Antibody–drug targeting TIM-1 has also been developed for treating cancers with high TIM-1 expression.⁴ In Phase I clinical trial, CDX-014, an antibody–drug conjugate against TIM-1, exhibited a manageable toxicity profile and early signs of activity in 16 patients with advanced refractory renal cell cancer.⁴ However, no scholar considers the role of TIM-1 on B cells in these studies, not to speak of as a B-cell-specific checkpoint molecule.

Immune checkpoints are host molecules that have the ability to suppress immune responses.⁵ Their expression will suppress antitumor immune responses in cancer. In past studies, researchers have primarily focused on enhancing the immune response of T cells, particularly cytotoxic T lymphocytes, in the fight against cancer. The discovery of immune checkpoints, such as the PD-1/PD-L1 and the CTLA4/B7 pathways sparked a wave of interest in T-cell-specific immune checkpoint inhibitors for cancer treatment. However, the authors of this study sought to investigate B cells, as they are one of the most abundant cell types that infiltrate tumors, especially in melanoma.

To understand the role of B cell subsets in melanoma, the researchers employed the well-established B16F10 melanoma mouse model and collected immune cells at 7, 10, and 16 days after tumor cell injection. Subsequently, bulk messenger RNA-sequencing was performed on the tumor group along with draining lymph nodes (dLNs), nondraining lymph nodes (ndLNs), and spleen groups at Day 16 postinjection to examine the expression profiles of B cells across different groups. To further comprehend the heterogeneity of B cells within tumor infiltration, single-cell RNA sequencing (scRNA-seq) combined with B-cell receptor sequenci

As myopia develops and progresses at an accelerated pace during adolescence, a timely diagnosis is beneficial for preventing its further progression. Optical coherence tomography angiography (OCTA) can visualize distinct layers of retinal microvessels, offering valuable insights into structural changes associated with myopia. This capability facilitates the early detection and monitoring of myopia-related complications, such as choroidal neovascularization and myopic maculopathy. Previous research suggests that alterations in the superficial capillary plexus (SCP) vessel density and deep capillary plexus (DCP) of OCTA images occur in myopic eyes, but few studies have focused on myopia in younger children.¹ A recent study compared retinal microvasculature in the SCP of children and adolescents using OCTA imaging, which indicated that there were no obvious variations in microvessel density, perfusion density (PD), and the size of the foveal avascular zone within the SCP between groups with mild and moderate/high myopia.² Conversely, another study demonstrated a negative correlation between children's myopia diopter and the microvessel density of both the superficial and deep retinal capillary plexus in the macula, as well as retinal thickness.³ Nonetheless, these studies only scrutinized a limited number of OCTA image parameters in the macular region and involved a relatively small number of subjects. Further research is warranted to fully understand the potential of OCTA images in assessing myopia during adolescence.

Deep learning can extract high-dimensional features from images through its multilayer network architecture, leading to improved task performance. However, to our knowledge, there is limited research on the use of artificial intelligence for analyzing OCTA images related to myopia. Our study contributes to addressing this research gap by highlighting the potential of deep learning in OCTA image analysis for myopia assessment. In this study, we aimed to employ end-to-end deep learning models to classify children with mild versus severe myopia. This study aimed to evaluate the potential of deep and superficial blood vessels in the macula and optic disc as indicators of myopia severity in children, utilizing a classification task based on OCTA images.

Initially, we collected four images from both the superficial and deep retinal capillary plexus in the macula, as well as from the optic disc, of children aged 8–16. Exclusion criteria included poor quality OCTA images, patients with other ocular conditions, or those who had undergone eye surgery. Ultimately, a total of 129 children (242 eyes) were included in this study. The subjects were divided into two groups based on their degree of refractive error: emmetropia/mild myopia (177 eyes, with a mean spherical equivalent between −3.00 and ≤0.50 D) and moderate/high myopia (65 eyes, with a mean spherical e

In a recent study published in Science,¹ Cheng and colleagues developed a highly accurate protein structuring model named AlphaMissense, which can predict and characterize the pathogenicity of all possible missense variants in the human genome at a single amino acid substitution level. As a community resource, AlphaMissense is absolutely helping us to gain better insights into the functional consequences of genetic variation.

Despite the identification of over 4 million missense variants in the human genome, only approximately 2% are definitively annotated as pathogenic or benign, and the significance of the large proportion of missense variants is unknown. As such, there has been a push to search for highly effective methods to accurately predict the variants' clinical implications.

Presently, four primary methodologies have been used to predict the pathogenicity of genetic variations. The first class of methods is known as “database-driven approaches,” which rely extensively on meticulously curated databases. Such strategies suffer from data leakage caused by unintended information transfer between the training and test halves, posing a significant challenge to reliability and accuracy.^{1, 2} The second class of methods is referred to as “weak-labeling approaches,” which circumvent circularity concerns by eliminating human annotations. However, such models often encounter false labels in the training data, necessitating the use of more reliable labels for accurate evaluation. A third class of approaches focuses on the recognition of naturally evolved amino acid sequence distributions and hidden structures of proteins, providing insights into the evolutionary patterns and functional characteristics of proteins.¹ Such models, however, do not possess the advanced understanding of protein structure achieved by AlphaFold (AF).³ A fourth approach utilizes protein structure information to improve the assessment of genetic constraints. However, this approach encounters a new challenge in the accuracy of predicting variant pathogenicity based solely on structural features. The limited performance of the structure-based approach in predicting pathogenicity in ClinVar variations suggests that additional factors, such as functional annotations as well as population frequencies and clinical evidence, play a crucial role in determining the pathogenicity of a genetic variant.¹

AlphaMissense, constructed upon the protein structure prediction model of AF, is a machine-learning model that utilizes advancements in unsupervised protein language modeling (Figure 1). AF represents a groundbreaking method that enables the prediction of a protein's three-dimensional structure solely from its amino acid sequence.³ By incorporating the structural insights provided by AF, researchers have achieved notabl

Traditional Chinese medicine (TCM) has the potential to contribute to the treatment of tumor patients. The integration of TCM with fields such as bioinformatics is crucial to gaining insights into the active compounds, target genes, and therapeutic mechanisms of TCM. In this study, our objective was to investigate the active compounds, target genes, and therapeutic mechanisms of rhubarb in the treatment of papillary thyroid carcinoma (PTC) using network pharmacology and bioinformatics approaches. By integrating multiple omics data sources and analysis methods, we identified 37 highly confident rhubarb target genes for the treatment of PTC. Subsequently, six genes (TP53, JUN, ESR1, CDKN1A, TNF, and MYC) were identified as core targets in the protein-protein interaction (PPI) network. The herb-component-target network suggested that β-sitosterol played a key role in the antitumor effect of rhubarb, while docking analysis revealed that TP53, JUN, and ESR1 were core target genes binding with β-sitosterol. Additionally, functional annotation implicated that Hepatitis B and P53 signaling were key pathways in the rhubarb therapy for PTC. This study provides insights into the pharmacology of rhubarb and facilitates research on its potential in the treatment of PTC.

The enormous incidence and dire consequences of liver fibrosis highlight the need for biologically plausible markers as readouts of pathology and drug efficacy. The classic reference standard for diagnosis of liver fibrosis is histopathological examinations. However, it is burdened by time-consuming steps, subjectivity, and the static nature of information. Raman spectroscopy is a rapid, objective, and label-free analytical tool that provides numerous molecular information. Thus, we utilized Raman spectroscopy to characterize the pathological features of liver fibrosis and to evaluate the efficacy of drugs (obeticholic acid [OCA] and gigantol [YS30]). Principal component analysis with subsequent linear discriminant analysis could basically discriminate between fibrotic mice and healthy controls and showed that animals with OCA or YS30 treatment resembled those with vehicle treatment. Moreover, we identified several potential biomarkers, including cytochrome C, for liver fibrosis development based on their Raman “fingerprints.” Raman imaging provided quantitative and spatial distribution of collagen and cytochrome C in situ. In addition, we demonstrated the role of OCA or YS30 in revitalizing mitochondrial function to alleviate liver fibrosis using Raman spectroscopy. Collectively, Raman spectroscopy succeeded in tracking liver fibrosis progression, assessing drug efficacy, and yielding significant molecular information useful for quantitatively linking pathological assessment and mechanism of drug action.

The transfusion of red blood cells (RBCs) is crucial in current medicinal research. The shelf-life of donated RBCs preserved by the normal method is very short, limiting their clinical application. Cryopreservation is a reliable and effective technology for the long-term storage of RBCs. During the process, ice formation will cause irreversible injuries to RBCs. Glycerol is used as a cryoprotectant (CPA) for RBCs to mitigate cryoinjuries. But it severely induces RBC hemolysis and deformation. This review introduces some biocompatible and effective CPAs used for RBC cryopreservation, outlining recent advances. Currently, there is no uniform standard to determine whether a CPA is suitable for RBCs. According to previous studies, we summarize for the first time comprehensive methods to evaluate the performance of CPAs by ensuring their safety and effectiveness. The safety of CPAs is defined as the degree of damage to RBCs, while the effectiveness is demonstrated by the properties of thawed RBCs, including membrane properties, protein activities, rheological properties, and metabolites levels. A novel CPA that is confirmed suitable for RBCs by these methods may be applied to other cells. We believe comprehensive methods can thoroughly evaluate the performance of CPAs and promote the development of transfusion therapy in clinic.

An uncontrolled case-series reported a benefit of haloperidol in rheumatoid arthritis (RA).¹ A study of the FDA Adverse Events Reporting System reported that among RA patients there were fewer users of antipsychotic drugs, including haloperidol, and concluded that antipsychotic drugs could reduce the risk of developing RA.² However, these results only suggest the converse: that RA is associated with a reduced risk of antipsychotic use; they do not indicate that antipsychotics reduce the risk of RA. The same study also examined a small employee insurance database in Japan and reported an inverse temporal relationship between RA and antipsychotic drugs, including haloperidol. Limitations of these two studies include small size, unverified and incomplete records, lack of correction for sociodemographic or clinical confounders, changing time-trends in prescriptions, and selective loss to follow-up. Therefore, we sought to undertake a robust pharmacoepidemiologic study of whether haloperidol, an FDA-approved drug for the treatment of schizophrenia or Tourette disorder, affects the risk of incident RA in three large nationwide health insurance databases that comprise most of the United States population. We did so with the goal of determining whether an inexpensive drug already in clinical use could be repurposed for RA, a disease that affects tens of millions of people worldwide.

We studied three health insurance databases: PearlDiver Mariner (2010–2021), IBM Marketscan Research Databases (2006–2020), and Veterans Administration (VA) Health database (2001–2021) (Supporting Information: Tables S1–S4). Inclusion criteria were at least 6 months follow-up, at least 18 years of age, schizophrenia or Tourette disorder diagnosed on two separate occasions, and treatment with antipsychotics. RA before diagnosis of schizophrenia or Tourette disorder was exclusionary (Supporting Information: Figure S1). Disease claims were identified by ICD-9-CM and ICD-10-CM codes. Drug exposure was determined by prescriptions for generic or brand versions. Time from first prescription of antipsychotics to diagnosis of RA was the dependent variable. Sensitivity analyses were performed using diagnosis of schizophrenia or Tourette disorder as the index date and flagging treatment with antipsychotics within 60 days of schizophrenia or Tourette disorder diagnosis. Patients were censored when they developed RA, unenrolled, died, or switched between study medication groups. Cox proportional hazards regression analyses were performed to estimate the hazard of RA in relation to haloperidol use. We performed propensity score matching to create cohorts with similar baseline characteristics, reducing possible bias in estimating treatment effects. Additionally, to control for any residual covariate imbalance, we adjusted for RA-associated confounders: age, sex, smoking, body mass index, Charlson comorbidity index, and database entry

In a recent study, Zani et al. published an article entitled “The dietary sweetener sucralose is a negative modulator of T cell-mediated responses” in Nature.¹ They reported that the intake of high amounts of sucralose, a calorie-free sugar substitute, can suppress autoimmunity and promote tumor growth by suppressing the proliferation and function of effector T cells in mice.

With the deepening of research, more and more studies have proved that excessive intake of sugar could cause a variety of diseases, including inflammatory disorders and tumors.^{2, 3} On the other hand, there are some studies suggesting that calorie-free sugar substitutes may also have some adverse health effects, such as glucose intolerance, by affecting the gut microbiome.⁴ However, the mainstream view is still that non-caloric sugar substitutes are harmless to humans. As a calorie-free sugar substitute, sucralose was also considered to be safe for people. Therefore, the consumption of sucralose has increased significantly in the past decades. These findings reported by Zani et al. almost upend the way people think about sucralose.¹

They first treated the wild-type mice with 0.17 or 0.72 mg/mL sucralose to determine whether sucralose can affect the immune system. They did not identify any detectable effect on the homeostatic levels of immune cells, showing that sucralose had no significant effect on the immune system under immune homeostasis conditions. Then, they investigated whether a high dose of sucralose could affect immune responses in different kinds of immune challenge conditions; they found that high sucralose exposure decreases cell proliferation in Rag2^−/− mice transferred with naïve T cells. However, the immune challenges did not affect B cells or macrophages. After that, they found high sucralose exposure suppressed cell proliferation and differentiation of CD4⁺ and CD8⁺ T cells in the in vitro cultures, and the suppression is dose-dependent. To find the target of sucralose-mediated limitation of T cell proliferation and T cell differentiation, the authors used Jurkat T cells in the absence of T cell receptor (TCR) stimulation to first determine that the sucralose effect is not mediated by the sweet taste receptor (STR). Given this, the authors conducted an RNA-sequencing analysis to explore the alternative mechanism. Principal component analysis (PCA) identified that T cell subsets exposed to sucralose displayed a unique expression profile compared with control cells, and enrichment analysis of the RNA-seq data identified a number of pathways affected by sucralose, including those associated with proliferation. After further evaluation, the authors found that sucralose specifically impedes TCR-dependent T cell proliferation.

Subsequently, the authors conducted experiments to test at which level(s) suc