MedComm - Future medicine最新文献

We aimed to evaluate the posterior corneal and anterior chamber changes following small incision lenticule extraction (SMILE) and femtosecond laser-assisted laser in situ keratomileusis (FS-LASIK). This retrospective study included 93 eyes for SMILE and 228 eyes for FS-LASIK. Posterior corneal curvature, anterior chamber biometrics, and posterior corneal aberrations preoperatively and at 1 week and 3 months after surgery were evaluated. The correlation between surgical-related factors and posterior corneal morphology changes, posterior corneal morphology changes, and posterior corneal aberration alterations were determined. There was an increase in mean posterior corneal radius (PCC R) and flat posterior corneal radius (PCC R1) and decrease in anterior chamber depth and anterior chamber volume in the SMILE and FS-LASIK group (all p < 0.05). A significant difference was observed in posterior corneal higher-order aberrations before and after the FS-LASIK (p = 0.006). A negative correlation was found between alterations of PCC R and changes of posterior corneal spherical aberrations in the SMILE (r = −0.289) and FS-LASIK group (r = −0.383). In conclusion, posterior corneal and anterior chamber biometrics are changed after SMILE and FS-LASIK. Innovatively, we find that changes in posterior corneal morphology are correlated with posterior corneal aberration alterations.

The aim of this study was to investigate whether the use of nucleoside reverse transcriptase inhibitors (NRTIs) impacts the incidence of prediabetes or type 2 diabetes mellitus (T2DM) or the progression from prediabetes to T2DM in people living with HIV (PLWH). We conducted a retrospective cohort study using the US Veterans Health Administration database among adult patients with an HIV diagnosis from the year 2000 until 2021 to determine the incidence of prediabetes and further progression to T2DM among NRTI exposed and unexposed patients. A multistate model was used to evaluate progression from normoglycemia to prediabetes and then to T2DM, and covariate adjustment with the Cox proportional hazards model was used to estimate the hazard ratios (HRs). Among 32,240 veterans diagnosed with HIV, prediabetes and T2DM were observed among 20.2% and 20.7% of patients, respectively. Among those diagnosed with prediabetes, 31.8% progressed to T2DM. Patients exposed to NRTIs at any time (86.6%), had a reduced risk of prediabetes [HR: 0.50 (95% confidence interval, CI: 0.47–0.53)] and among prediabetics, a lower risk of progression to T2DM [HR: 0.73 (95% CI: 0.63–0.85)] when compared to patients who never used NRTIs. In summary, NRTIs may reduce the risk of developing prediabetes and the progression from prediabetes to T2DM in PLWH.

Recent works have shown that Transformer's excellent performances on natural language processing tasks can be maintained on natural image analysis tasks. However, the complicated clinical settings in medical image analysis and varied disease properties bring new challenges for the use of Transformer. The computer vision and medical engineering communities have devoted significant effort to medical image analysis research based on Transformer with especial focus on scenario-specific architectural variations. In this paper, we comprehensively review this rapidly developing area by covering the latest advances of Transformer-based methods in medical image analysis of different settings. We first give introduction of basic mechanisms of Transformer including implementations of selfattention and typical architectures. The important research problems in various medical image data modalities, clinical visual tasks, organs and diseases are then reviewed systemically. We carefully collect 276 very recent works and 76 public medical image analysis datasets in an organized structure. Finally, discussions on open problems and future research directions are also provided. We expect this review to be an up-to-date roadmap and serve as a reference source in pursuit of boosting the development of medical image analysis field.

The increased transmissibility of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Delta and Omicron variants due to their complex mutations has been confirmed by many studies, but the disease severity caused by these two variants and vaccine efficacy have not been clearly concluded. This meta-analysis evaluated and compared disease severity and vaccine efficacy for infection with these two variants. Databases were searched on July 29, 2022, for original studies. Data extracted included participants, hospitalization rate, intensive care unit (ICU) admission rate, mortality rate, and vaccination status. Total 15 studies (570,713 subjects) were included. The risk of hospitalization, ICU admission and death in patients with Delta were increased compared with patients with Omicron, especially hospitalization (Log risk ratio [RR]: 1.09, 95% confidence interval [CI]: 0.65–1.53). The Delta was associated with a higher risk of death in the unvaccinated population (Log RR: 0.48, 95% CI: 0.04–0.93). No significant differences in the risk of severe illness between BA.1 and BA.2 were observed. All three risks were reduced in fully vaccinated populations infected with Delta and Omicron. The existing evidence shows that the inherent virulence and disease severity of Omicron are reduced compared with Delta, and fully vaccination is effective in preventing severe infection of both variants.

According to a number of studies, the occurrence and progression clear cell renal cell carcinoma (ccRCC), among the most prevalent cancerous tumors of the urinary bladder, may be inextricable from metabolism and immunology. The recent discovery of cuproptosis revealed a novel cell death mechanism based on mitochondrial respiration and the tricarboxylic acid cycle, and cuproptosis is strongly linked to the metabolic process. The cuproptosis process is different from the previously revealed cell death processes such as pyroptosis, apoptosis and ferroptosis, which is expected to provide a new perspective in the study of tumor mechanism. Here we aim to explore the important role of key regulators of cuproptosis in renal cancer using a combination of bioinformatics and experimental validation. We found that Ferredoxin 1 (FDX1) was related to the infiltration of various immune cells in the tumor microenvironment and the response to immunotherapy in ccRCC. At the same time, the experiment confirmed that FDX1 was significantly lower in ccRCC, which was consistent with the previous analysis results. In conclusions, FDX1 was expected to be an important marker of immune infiltration, immunotherapy, and tumor prognosis in ccRCC.

Cancer is one of the leading causes of death worldwide. The proliferation, survival, and metastasis of cancer cells are governed by a complex series of intracellular and extracellular signaling pathways. Therefore, efficient cancer therapy often requires the modulation of multiple targets. Besides, the modulation of several biological targets with a single drug can lead to synergistic therapeutic effects and avoid side effects associated with combination therapy. Oridonin, an ent-kaurane type diterpenoid isolated from Rabdosia rubescens, has been reported to possess potent anti-inflammatory, antioxidant, anticancer, and neuroprotective activities. Among its many benefits, the anticancer effects have attracted the most attention because of its effectiveness in many tumor cells and its potential to overcome therapeutic resistance. Recently, several proteins involved in oncogenic signaling, including CRM1, PHGDH, HSP70, AML1-ETO, and STAT3, were identified as functional targets of oridonin and its analogs. The binding mechanism and the consequence of oridonin binding to these oncogenic proteins are summarized and discussed. These advances suggest that oridonin exhibits broad spectrum anticancer effects by targeting multiple oncogenic proteins mainly via Michael addition between its unsaturated ketone and the cysteines in the target proteins. Therefore, oridonin and its derivatives hold the potential to be developed as multitargeting anticancer drugs.

Recently, two companion papers published in Nature by Timon Heide et al.¹ and Jacob Househam et al.² suggested that phenotypic characteristics can vary without heritable (epi)genetic alteration to drive gene expression change, namely phenotypic plastic, which could take part in intratumor heterogeneity in colorectal cancer (CRC) evolution.

As in other cancer types, intratumor heterogeneity represents a challenge in the facets of tumorigenesis, evolution, and therapy response in CRC.³ However, the information on how genomes and epigenomes contribute to intratumor heterogeneity is limited. What's more, although consensus molecular subtypes (CMS) and CRC intrinsic subtypes (CRIS) are approaches used to classify CRC cases by gene expression patterns,⁴ Househam et al.² found that very few tumors with sufficient samples could be homogeneously classified by these classifiers, indicating intratumor heterogeneity of molecular subtypes in CRC and the discrepancy between CMS and CRIS classifications. Therefore, the researchers collected a large number of samples from a multiregion of carcinoma, concomitant adenoma if present, and a distant region of the normal epithelium to integrate their spatially resolved mutiomics analysis with single gland profiling data set, and combine with computational modeling to understand the cancer cell biology and assess the functional impact of altered gene expression on the evolution of CRC, due to intratumor heterogeneity is a significant confounding factor in bulk-tumor profiling (Figure 1). As for spatially resolved multiomics analysis, it presents a new avenue to reveal tumors and microenvironments co-evolution, which could be used to clarify heterogeneity.⁵ In detail, it contains a strategy for the spatial sampling of tumor tissue to implement a series of new spatial genomic, transcriptomic, and proteomic technologies.⁵

Heide et al.¹ first looked forward to measuring genome–epigenome co-evaluation in a quantitative manner and gained some evidence. First of all, it was confirmed that there were recurrent cancer driver mutation events in CRC. After examining somatic mutations in chromatin modifier genes and assessing the evolutionary selection by the ratio of nonsynonymous to synonymous substitutions (dN/dS), they identified clear evidence of clonal truncating mutations of chromatin modifiers genes, which indicated that somatic mutations affect the epigenome. Besides, somatic chromatin accessibility alterations (SCAAs), which were found in known driver genes without accompanying mutations, were a substitute pattern for driver gene (in)activation. Subsequently, Heide et al. found that most SCAAs occurred at the onset of the adenoma-carcinoma transition. And SCAAs were indeed changes that originated

Angiogenesis is the process of new blood vessel growth from pre-existing ones involving vascular endothelial cell activation, proliferation, migration, and tube formation. The vascular endothelial growth factor A (VEGF-A) is known to be a key factor that promotes angiogenesis. Pathological angiogenesis is a key and common feature of numerous ocular neovascular diseases, such as wet age-related macular degeneration, diabetic retinopathy, retinopathy of prematurity, retinal vein occlusion, and corneal neovascularization. Although anti-VEGF treatment has been a great success for the treatment of such ocular neovascular diseases, many challenges exist, such as limited efficacy, unresponsiveness in many patients, drug resistance, treatment burden due to repeated intravitreous injection, all of which incent much effort and enthusiasm to find new and better treatment for ocular neovascular diseases. In recent years, new antiangiogenic drug, targets and drug delivery methods have been developed. This perspective discusses the status of currently available therapies, challenges, opportunities, and potential new directions toward better therapies for ocular neovascular diseases.

Virus infection is one of the major diseases that seriously threaten human health. To meet the growing demand for mining and sharing data resources related to antiviral drugs and to accelerate the design and discovery of new antiviral drugs, we presented an open-access antiviral drug resource and machine learning platform (VDDB), which, to the best of our knowledge, is the first comprehensive dedicated resource for experimentally verified potential drugs/molecules based on manually curated data. Currently, VDDB highlights 848 clinical vaccines and 199 clinical antibodies, as well as over 710,000 small molecules targeting 39 medically important viruses including severe acute respiratory syndrome coronavirus 2. Furthermore, VDDB stores approximately three million records of pharmacological data for these collected potential antiviral drugs/molecules, involving 314 cell infection-based phenotypic and 234 target-based genotypic assays. Based on these annotated pharmacological data, VDDB allows users to browse, search, and download reliable information about these collects for various viruses of interest. In particular, VDDB also integrates 57 cell infection- and 117 target-based associated high-accuracy machine learning models to support various antivirals identification-related tasks, such as compound activity prediction, virtual screening, drug repositioning, and target fishing. VDDB is freely accessible at https://vddb.idruglab.cn.