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AlphaFold2 (AF2) is an artificial intelligence (AI) system developed by DeepMind that can predict three-dimensional (3D) structures of proteins from amino acid sequences with atomic-level accuracy. Protein structure prediction is one of the most challenging problems in computational biology and chemistry, and has puzzled scientists for 50 years. The advent of AF2 presents an unprecedented progress in protein structure prediction and has attracted much attention. Subsequent release of structures of more than 200 million proteins predicted by AF2 further aroused great enthusiasm in the science community, especially in the fields of biology and medicine. AF2 is thought to have a significant impact on structural biology and research areas that need protein structure information, such as drug discovery, protein design, prediction of protein function, et al. Though the time is not long since AF2 was developed, there are already quite a few application studies of AF2 in the fields of biology and medicine, with many of them having preliminarily proved the potential of AF2. To better understand AF2 and promote its applications, we will in this article summarize the principle and system architecture of AF2 as well as the recipe of its success, and particularly focus on reviewing its applications in the fields of biology and medicine. Limitations of current AF2 prediction will also be discussed.

Abstract Background: Spatiotemporal disparities exist in the disease burden of non-communicable diseases (NCDs) attributable to kidney dysfunction, which has been poorly assessed. The present study aimed to evaluate the spatiotemporal trends of the global burden of NCDs attributable to kidney dysfunction and to predict future trends. Methods: Data on NCDs attributable to kidney dysfunction, quantified using deaths and disability-adjusted life-years (DALYs), were extracted from the Global Burden of Diseases Injuries, and Risk Factors (GBD) Study in 2019. Estimated annual percentage change (EAPC) of age-standardized rate (ASR) was calculated with linear regression to assess the changing trend. Pearson’s correlation analysis was used to determine the association between ASR and sociodemographic index (SDI) for 21 GBD regions. A Bayesian age–period–cohort (BAPC) model was used to predict future trends up to 2040. Results: Between 1990 and 2019, the absolute number of deaths and DALYs from NCDs attributable to kidney dysfunction increased globally. The death cases increased from 1,571,720 (95% uncertainty interval [UI]: 1,344,420–1,805,598) in 1990 to 3,161,552 (95% UI: 2,723,363–3,623,814) in 2019 for both sexes combined. Both the ASR of death and DALYs increased in Andean Latin America, the Caribbean, Central Latin America, Southeast Asia, Oceania, and Southern Sub-Saharan Africa. In contrast, the age-standardized metrics decreased in the high-income Asia Pacific region. The relationship between SDI and ASR of death and DALYs was negatively correlated. The BAPC model indicated that there would be approximately 5,806,780 death cases and 119,013,659 DALY cases in 2040 that could be attributed to kidney dysfunction. Age-standardized death of cardiovascular diseases (CVDs) and CKD attributable to kidney dysfunction were predicted to decrease and increase from 2020 to 2040, respectively. Conclusion: NCDs attributable to kidney dysfunction remain a major public health concern worldwide. Efforts are required to attenuate the death and disability burden, particularly in low and low-to-middle SDI regions.

The remediation of heavy metal-contaminated soils has attracted increased attention worldwide. The immobilization of metals to prevent their uptake by plants is an efficient way to remediate contaminated soils. This work aimed to seek the immobilization of cadmium in contaminated soils via a combination method. Flask experiments were performed to investigate the effects of hydroxyapatite (HAP) and the Cupriavidus sp. strain ZSK on soil pH and DTPA-extractable cadmium. Pot experiments were carried out to study the effects of the combined amendment on three plant species. The results showed that HAP has no obvious influence on the growth of the strain. With increasing concentrations of HAP, the soil pH increased, and the DTPA-extractable Cd decreased. Via the combined amendment of the strain and HAP (SH), the DTPA-extractable Cd in the soil decreased by 58.2%. With the combined amendment of the SH, the cadmium accumulation in ramie, dandelion, and daisy decreased by 44.9%, 51.0%, and 38.7%, respectively. Moreover, the combined amendment somewhat benefitted the growth of the three plant species and significantly decreased the biosorption of cadmium. These results suggest that the immobilization by the SH combination is a potential method to decrease the available cadmium in the soil and the cadmium accumulation in plants.

Regular screening for the early detection of common chronic diseases might benefit from the use of deep-learning approaches, particularly in resource-poor or remote settings. Here we show that deep-learning models can be used to identify chronic kidney disease and type 2 diabetes solely from fundus images or in combination with clinical metadata (age, sex, height, weight, body-mass index and blood pressure) with areas under the receiver operating characteristic curve of 0.85–0.93. The models were trained and validated with a total of 115,344 retinal fundus photographs from 57,672 patients and can also be used to predict estimated glomerulal filtration rates and blood-glucose levels, with mean absolute errors of 11.1–13.4 ml min −1 per 1.73 m 2 and 0.65–1.1 mmol l −1 , and to stratify patients according to disease-progression risk. We evaluated the generalizability of the models for the identification of chronic kidney disease and type 2 diabetes with population-based external validation cohorts and via a prospective study with fundus images captured with smartphones, and assessed the feasibility of predicting disease progression in a longitudinal cohort. Deep-learning models trained on retinal fundus images can be used to identify chronic kidney disease and type 2 diabetes and to predict the risk of the progression of these diseases.

BackgroundSocioeconomic status (SES) has long been conjectured to be associated with the incidence and progression of chronic kidney disease (CKD), but few studies have examined this quantitatively. This meta-analysis aims to fill this gap.MethodsA systematic literature review was performed using Medline and EMBASE to identify observational studies on associations between SES and incidence and progression of CKD, published between 1974 and March 2017. Individual results were meta-analysed using a random effects model, in line with Meta-analysis of Observational Studies in Epidemiology guidelines.ResultsIn total, 43 articles met our inclusion criteria. CKD prevalence was associated with several indicators of SES, particularly lower income (OR 1.34, 95% CI (1.18 to 1.53), P<0.001; I2=73.0%, P=0.05); lower education (OR 1.21, 95% CI (1.11 to 1.32), P<0.001; I2=45.20%, P=0.034); and lower combined SES (OR 2.18, 95% CI (1.64 to 2.89), P<0.001; I2=0.0%, P=0.326). Lower levels of income, occupation and combined SES were also significantly associated with progression to end-stage renal disease (risk ratio (RR) 1.24, 95% CI (1.12 to 1.37), P<0.001; I2=66.6%, P=0.006; RR 1.05, 95% CI (1.01 to 1.09), P=0.012; I2=0.0%, P=0.796; and RR 1.39, 95% CI (1.09 to 1.79), P=0.009; I2=74.2%, P=0.009). Subgroup analyses generally confirmed these results, except in a few cases, such as an inverse association related to particular socioeconomic backgrounds and where results were adjusted by more disease-related risk factors.ConclusionLower income was most closely associated with prevalence and progression of CKD, and lower education was significantly associated with its prevalence. Evidence for other indicators was inconclusive.

Background Hyperuricemia has been reported to be associated with chronic kidney disease (CKD). However whether an elevated serum uric acid level is an independent risk factor for new-onset CKD remained controversial. Methods A systematic review and meta-analysis using a literature search of online databases including PubMed, Embase, Ovid and ISI Web/Web of Science was conducted. Summary adjusted odds ratios with corresponding 95% confidence intervals (95% CI) were calculated to evaluate the risk estimates of hyperuricemia for new-onset CKD. Results Thirteen studies containing 190,718 participants were included. A significant positive association was found between elevated serum uric acid levels and new-onset CKD at follow-up (summary OR, 1.15; 95% CI, 1.05–1.25). Hyperuricemia was found be an independent predictor for the development of newly diagnosed CKD in non-CKD patients (summary OR, 2.35; 95% CI, 1.59–3.46). This association increased with increasing length of follow-up. No significant differences were found for risk estimates of the associations between elevated serum uric acid levels and developing CKD between males and females. Conclusions With long-term follow-up of non-CKD individuals, elevated serum uric acid levels showed an increased risk for the development of chronic renal dysfunction.

Understanding intracellular phase separation is crucial for deciphering transcriptional control, cell fate transitions, and disease mechanisms. However, the key residues, which impact phase separation the most for protein phase separation function have remained elusive. We develop PSPHunter, which can precisely predict these key residues based on machine learning scheme. In vivo and in vitro validations demonstrate that truncating just 6 key residues in GATA3 disrupts phase separation, enhancing tumor cell migration and inhibiting growth. Glycine and its motifs are enriched in spacer and key residues, as revealed by our comprehensive analysis. PSPHunter identifies nearly 80% of disease-associated phase-separating proteins, with frequent mutated pathological residues like glycine and proline often residing in these key residues. PSPHunter thus emerges as a crucial tool to uncover key residues, facilitating insights into phase separation mechanisms governing transcriptional control, cell fate transitions, and disease development. Understanding intracellular phase separation is essential for transcriptional control, cell fate, and disease. Here the authors report PSPHunter which accurately predicts key residues, aiding in disease-associated protein identification and mechanistic insights.

The loss of skeletal muscle mass and function is defined as sarcopenia, which might develop in elderly patients with cancers. It has been indicated as a potential negative factor in the survival of patients with malignant tumours. The aim of this systematic review and meta‐analysis was to evaluate the associations between sarcopenia and survival outcomes or postoperative complications in patients with oesophageal cancer (EC). Web of Science, Embase, Medline, and Cochrane Library databases were searched until 10 May 2022, using keywords: sarcopenia, oesophageal cancer, and prognosis. Studies investigating the prognostic value of sarcopenia on EC survival were included. Forest plots and summary effect models were used to show the result of this meta‐analysis. The quality of included studies was evaluated with the Newcastle‐Ottawa Scale (NOS). A total of 1436 studies were identified from the initial search of four databases, and 41 studies were included for the final quantitative analysis. This meta‐analysis revealed a significant association between sarcopenia and overall survival (OS) [hazard ratios (HR):1.68, 95% confidence interval (CI):1.54–1.83, P = 0.004, I2 = 41.7%] or disease‐free survival (DFS) 1.97 (HR: 1.97, 95% CI: 1.44–2.69, P = 0.007, I2 = 61.9%) of EC patients. Subgroup analysis showed that sarcopenia remained a consistent negative predictor of survival when stratified by different treatment methods, populations, or sarcopenia measurements. Sarcopenia was also a risk factor for postoperative complications with a pooled odds ratio of 1.47 (95% CI: 1.21–1.77, P = 0.094, I2 = 32.7%). The NOS scores of all included studies were ≥6, and the quality of the evidence was relatively high. The results from the study suggested that sarcopenia was significantly associated with both survival outcomes and postoperative complications in EC patients. Sarcopenia should be appropriately diagnosed and treated for improving short‐term and long‐term outcomes of patients with EC.

AimsType 2 diabetes mellitus (T2DM) has a rising prevalence and gut microbiota involvement is increasingly recognized. Diabetic nephropathy (DN) is a major complication of T2DM. The aim of the study was to understand the gut–kidney axis by an analysis of gut microbiota composition among biopsy-proven DN, T2DM without kidney disease, and healthy control.MethodsFecal samples were collected from 14 DNs, 14 age/gender-matched T2DMs without renal diseases (DM), 14 age and gender-matched healthy controls (HC) and household contacts (HH) of DM group. The microbiota composition was analyzed by 16sRNA microbial profiling approach.ResultsSubstantial differences were found in the richness of gut microbiota and the variation of bacteria population in DM compared to HC, and DN compared to DM, respectively. DM could be accurately distinguished from age/gender-matched healthy controls by the variable of genus g_Prevotella_9 (AUC = 0.9), and DN patients could be accurately distinguished from age/gender-matched DM by the variables of two genera (g_Escherichia-Shigella and g_Prevotella_9, AUC = 0.86). The microbiota composition of HH group was close to that of HC group, and was different from DM group. Under the same diet, DM could be more accurately detected by the same genus (g_Prevotella_9, AUC = 0.92).ConclusionGut microbiota composition was explored to be related to the occurrence of biopsy-proven DN from DM. DM could be distinguished from HC by detecting g_Prevotella_9 level in feces, while DN was different from DM by the variables of g_Escherichia-Shigella and g_Prevotella_9, which potentially contributed to the physiopathological diagnosis of DN from DM.

The three-dimensional structure of chromatin plays a crucial role in development and disease, both of which are associated with transcriptional changes. However, given the heterogeneity in single-cell chromatin architecture and transcription, the regulatory relationship between the three-dimensional chromatin structure and gene expression is difficult to explain based on bulk cell populations. Here we develop a single-cell, multimodal, omics method allowing the simultaneous detection of chromatin architecture and messenger RNA expression by sequencing (single-cell transcriptome sequencing (scCARE-seq)). Applying scCARE-seq to examine chromatin architecture and transcription from 2i to serum single mouse embryonic stem cells, we observe improved separation of cell clusters compared with single-cell chromatin conformation capture. In addition, after defining the cell-cycle phase of each cell through chromatin architecture extracted by scCARE-seq, we find that periodic changes in chromatin architecture occur in parallel with transcription during the cell cycle. These findings highlight the potential of scCARE-seq to facilitate comprehensive analyses that may boost our understanding of chromatin architecture and transcription in the same single cell. Here the authors develop a single-cell multiomics sequencing method (scCARE-seq), which allows the simultaneous probing of 3D chromatin architecture and transcription for single cells. Using scCARE-seq they explore the relationship between the 3D genome and transcriptome in cell fate transitions and the cell cycle.