Deletion variants calling in third-generation sequencing data based on a dual-attention mechanism.

Deletion is a crucial type of genomic structural variation and is associated with numerous genetic diseases. The advent of third-generation sequencing technology has facilitated the analysis of complex genomic structures and the elucidation of the mechanisms underlying phenotypic changes and disease onset due to genomic variants. Importantly, it has introduced innovative perspectives for deletion variants calling. Here we propose a method named Dual Attention Structural Variation (DASV) to analyze deletion structural variations in sequencing data. DASV converts gene alignment information into images and integrates them with genomic sequencing data through a dual attention mechanism. Subsequently, it employs a multi-scale network to precisely identify deletion regions. Compared with four widely used genome structural variation calling tools: cuteSV, SVIM, Sniffles and PBSV, the results demonstrate that DASV consistently achieves a balance between precision and recall, enhancing the F1 score across various datasets. The source code is available at https://github.com/deconvolution-w/DASV.

ViroISDC: a method for calling integration sites of hepatitis B virus based on feature encoding.

BACKGROUND: Hepatitis B virus (HBV) integrates into human chromosomes and can lead to genomic instability and hepatocarcinogenesis. Current tools for HBV integration site detection lack accuracy and stability. RESULTS: This study proposes a deep learning-based method, named ViroISDC, for detecting integration sites. ViroISDC generates corresponding grammar rules and encodes the characteristics of the language data to predict integration sites accurately. Compared with Lumpy, Pindel, Seeksv, and SurVirus, ViroISDC exhibits better overall performance and is less sensitive to sequencing depth and integration sequence length, displaying good reliability, stability, and generality. Further downstream analysis of integrated sites detected by ViroISDC reveals the integration patterns and features of HBV. It is observed that HBV integration exhibits specific chromosomal preferences and tends to integrate into cancerous tissue. Moreover, HBV integration frequency was higher in males than females, and high-frequency integration sites were more likely to be present on hepatocarcinogenesis- and anti-cancer-related genes, validating the reliability of the ViroISDC. CONCLUSIONS: ViroISDC pipeline exhibits superior precision, stability, and reliability across various datasets when compared to similar software. It is invaluable in exploring HBV infection in the human body, holding significant implications for the diagnosis, treatment, and prognosis assessment of HCC.

DeepHipp: accurate segmentation of hippocampus using 3D dense-block based on attention mechanism.

BACKGROUND: The hippocampus is a key area of the brain responsible for learning, memory, and other abilities. Accurately segmenting the hippocampus and precisely calculating the volume of the hippocampus is of great significance for predicting Alzheimer's disease and amnesia. Most of the segmentation algorithms currently involved are based on templates, such as the more popular FreeSufer. METHODS: This study proposes Deephipp, a deep learning network based on a 3D dense block using an attention mechanism for accurate segmentation of the hippocampus. DeepHipp is based on the following novelties: (i) DeepHipp adopts powerful data augmentation schemes to enhance the segmentation ability. (ii) DeepHipp is designed to incorporate 3D dense-block to capture multiple-scale features of the hippocampus. (iii) DeepHipp creatively uses the attention mechanism in the field of hippocampal image segmentation, extracting useful hippocampus information in a massive feature map, and improving the accuracy and sensitivity of the model. CONCLUSIONS: We describe the illustrative results and show extensive qualitative and quantitative comparisons with other methods. Our achievement demonstrates that the accuracy of DeepHipp can reach 83.63%, which is superior to most existing methods in terms of accuracy and efficiency of hippocampus segmentation. It is noticeable that deep learning can potentially lead to an effective segmentation of medical images.

Enhancing maize's nitrogen-fixing potential through ZmSBT3, a gene suppressing mucilage secretion.

Maize (Zea mays) requires substantial amounts of nitrogen, posing a challenge for its cultivation. Recent work discovered that some ancient Mexican maize landraces harbored diazotrophic bacteria in mucilage secreted by their aerial roots. To see if this trait is retained in modern maize, we conducted a field study of aerial root mucilage (ARM) in 258 inbred lines. We observed that ARM secretion is common in modern maize, but the amount significantly varies, and only a few lines have retained the nitrogen-fixing traits found in ancient landraces. The mucilage of the high-ARM inbred line HN5-724 had high nitrogen-fixing enzyme activity and abundant diazotrophic bacteria. Our genome-wide association study identified 17 candidate genes associated with ARM across three environments. Knockouts of one candidate gene, the subtilase family gene ZmSBT3, confirmed that it negatively regulates ARM secretion. Notably, the ZmSBT3 knockout lines had increased biomass and total nitrogen accumulation under nitrogen-free culture conditions. High ARM was associated with three ZmSBT3 haplotypes that were gradually lost during maize domestication, being retained in only a few modern inbred lines such as HN5-724. In summary, our results identify ZmSBT3 as a potential tool for enhancing ARM, and thus nitrogen fixation, in maize.

An efficient scRNA-seq dropout imputation method using graph attention network.

BACKGROUND: Single-cell sequencing technology can address the amount of single-cell library data at the same time and display the heterogeneity of different cells. However, analyzing single-cell data is a computationally challenging problem. Because there are low counts in the gene expression region, it has a high chance of recognizing the non-zero entity as zero, which are called dropout events. At present, the mainstream dropout imputation methods cannot effectively recover the true expression of cells from dropout noise such as DCA, MAGIC, scVI, scImpute and SAVER. RESULTS: In this paper, we propose an autoencoder structure network, named GNNImpute. GNNImpute uses graph attention convolution to aggregate multi-level similar cell information and implements convolution operations on non-Euclidean space on scRNA-seq data. Distinct from current imputation tools, GNNImpute can accurately and effectively impute the dropout and reduce dropout noise. We use mean square error (MSE), mean absolute error (MAE), Pearson correlation coefficient (PCC) and Cosine similarity (CS) to measure the performance of different methods with GNNImpute. We analyze four real datasets, and our results show that the GNNImpute achieves 3.0130 MSE, 0.6781 MAE, 0.9073 PCC and 0.9134 CS. Furthermore, we use Adjusted rand index (ARI) and Normalized mutual information (NMI) to measure the clustering effect. The GNNImpute achieves 0.8199 (ARI) and 0.8368 (NMI), respectively. CONCLUSIONS: In this investigation, we propose a single-cell dropout imputation method (GNNImpute), which effectively utilizes shared information for imputing the dropout of scRNA-seq data. We test it with different real datasets and evaluate its effectiveness in MSE, MAE, PCC and CS. The results show that graph attention convolution and autoencoder structure have great potential in single-cell dropout imputation.

Depletion of hepatic forkhead box O1 does not affect cholelithiasis in male and female mice.

Cholelithiasis is one of the most prevalent gastroenterological diseases and is characterized by the formation of gallstones in the gallbladder. Both clinical and preclinical data indicate that obesity, along with comorbidity insulin resistance, is a predisposing factor for cholelithiasis. Forkhead box O1 (FoxO1) is a key transcription factor that integrates insulin signaling with hepatic metabolism and becomes deregulated in the insulin-resistant liver, contributing to dyslipidemia in obesity. To gain mechanistic insights into how insulin resistance is linked to cholelithiasis, here we determined FoxO1's role in bile acid homeostasis and its contribution to cholelithiasis. We hypothesized that hepatic FoxO1 deregulation links insulin resistance to impaired bile acid metabolism and cholelithiasis. To address this hypothesis, we used the FoxO1LoxP/LoxP-Albumin-Cre system to generate liver-specific FoxO1-knockout mice. FoxO1-knockout mice and age- and sex-matched WT littermates were fed a lithogenic diet, and bile acid metabolism and gallstone formation were assessed in these animals. We showed that FoxO1 affected bile acid homeostasis by regulating hepatic expression of key enzymes in bile acid synthesis and in biliary cholesterol and phospholipid secretion. Furthermore, FoxO1 inhibited hepatic expression of the bile acid receptor farnesoid X receptor and thereby counteracted hepatic farnesoid X receptor signaling. Nonetheless, hepatic FoxO1 depletion neither affected the onset of gallstone disease nor impacted the disease progression, as FoxO1-knockout and control mice of both sexes had similar gallstone weights and incidence rates. These results argue against the notion that FoxO1 is a link between insulin resistance and cholelithiasis.

InvBFM: finding genomic inversions from high-throughput sequence data based on feature mining.

BACKGROUND: Genomic inversion is one type of structural variations (SVs) and is known to play an important biological role. An established problem in sequence data analysis is calling inversions from high-throughput sequence data. It is more difficult to detect inversions because they are surrounded by duplication or other types of SVs in the inversion areas. Existing inversion detection tools are mainly based on three approaches: paired-end reads, split-mapped reads, and assembly. However, existing tools suffer from unsatisfying precision or sensitivity (eg: only 50~60% sensitivity) and it needs to be improved. RESULT: In this paper, we present a new inversion calling method called InvBFM. InvBFM calls inversions based on feature mining. InvBFM first gathers the results of existing inversion detection tools as candidates for inversions. It then extracts features from the inversions. Finally, it calls the true inversions by a trained support vector machine (SVM) classifier. CONCLUSIONS: Our results on real sequence data from the 1000 Genomes Project show that by combining feature mining and a machine learning model, InvBFM outperforms existing tools. InvBFM is written in Python and Shell and is available for download at https://github.com/wzj1234/InvBFM.

DeepSV: accurate calling of genomic deletions from high-throughput sequencing data using deep convolutional neural network.

BACKGROUND: Calling genetic variations from sequence reads is an important problem in genomics. There are many existing methods for calling various types of variations. Recently, Google developed a method for calling single nucleotide polymorphisms (SNPs) based on deep learning. Their method visualizes sequence reads in the forms of images. These images are then used to train a deep neural network model, which is used to call SNPs. This raises a research question: can deep learning be used to call more complex genetic variations such as structural variations (SVs) from sequence data? RESULTS: In this paper, we extend this high-level approach to the problem of calling structural variations. We present DeepSV, an approach based on deep learning for calling long deletions from sequence reads. DeepSV is based on a novel method of visualizing sequence reads. The visualization is designed to capture multiple sources of information in the sequence data that are relevant to long deletions. DeepSV also implements techniques for working with noisy training data. DeepSV trains a model from the visualized sequence reads and calls deletions based on this model. We demonstrate that DeepSV outperforms existing methods in terms of accuracy and efficiency of deletion calling on the data from the 1000 Genomes Project. CONCLUSIONS: Our work shows that deep learning can potentially lead to effective calling of different types of genetic variations that are complex than SNPs.

Key pathway and gene alterations in the gastric mucosa associated with obesity and obesity-related diabetes.

BACKGROUND AND OBJECTIVE: The stomach plays an important role in obesity and obesity-related diabetes; yet, little is known about key pathways in the gastric mucosa associated with obesity and diabetes. METHODS: We performed gene microarray and real time-polymerase chain reaction (RT-PCR) on gut mucosa samples from control subjects (CON), patients with simple obesity (OB), and patients with obesity and comorbid diabetes (OD) (n = 3 per group). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were used to predict the functional significance of differentially expressed genes. RESULTS: In total, 262 genes were upregulated and 265 genes were downregulated in the OB group whereas 1756 genes were upregulated and 1053 genes were downregulated in the OD group compared with the CON group. Of these, 23 were co-regulated in both comparisons. Seven differentially expressed genes were validated by RT-PCR (NRIP3, L1CAM, TPO, P2RY1, OR8A1, ADAMTS19, and ASIC3). A functional analysis revealed that genes differentially expressed between the OB or OD and CON groups played crucial roles in metabolic, T cell, and G-protein coupled receptor biological processes, and primarily participated in the PI3K-Akt and AGE-RAGE signaling pathways. CONCLUSIONS: Obesity and obesity-related diabetes are associated with important gene expression and pathway alterations in the stomach.

Linkage mapping and genome-wide association reveal candidate genes conferring thermotolerance of seed-set in maize.

It is predicted that high-temperature stress will increasingly affect crop yields worldwide as a result of climate change. In order to determine the genetic basis of thermotolerance of seed-set in maize under field conditions, we performed mapping of quantitative trait loci (QTLs) in a recombinant inbred line (RIL) population using a collection of 8329 specifically developed high-density single-nucleotide polymorphism (SNP) markers, combined with a genome-wide association study (GWAS) of 261 diverse maize lines using 259 973 SNPs. In total, four QTLs and 17 genes associated with 42 SNPs related to thermotolerance of seed-set were identified. Among them, four candidate genes were found in both linkage mapping and GWAS. Thermotolerance of seed-set was increased significantly in near-isogenic lines (NILs) that incorporated the four candidate genes in a susceptible parent background. The expression profiles of two of the four genes showed that they were induced by high temperatures in the maize tassel in a tolerant parent background. Our results indicate that thermotolerance of maize seed-set is regulated by multiple genes each of which has minor effects, with calcium signaling playing a central role. The genes identified may be exploited in breeding programs to improve seed-set and yield of maize under heat stress.

Genetic analysis of cob resistance to F. verticillioides: another step towards the protection of maize from ear rot.

KEY MESSAGE: We lay the foundation for further research on maize resistance to Fusarium verticillioides cob rot by identifying a candidate resistance gene. Fusarium verticillioides ear rot is the most common type of maize ear rot in the Huanghuaihai Plain of China. Ear rot resistance includes cob and kernel resistance. Most of the current literature concentrates on kernel resistance, and genetic studies on cob resistance are scarce. We aimed on identifying the QTLs responsible for F. verticillioides cob rot (FCR) resistance. Twenty-eight genes associated with 48 single nucleotide polymorphisms (SNPs) were identified (P < 10-4) to correlate with FCR resistance using a whole-genome association study. The major quantitative trait locus, qRcfv2, for FCR resistance was identified on chromosome 2 through linkage mapping and was validated in near-isogenic line populations. Two candidate genes associated with two SNPs were detected in the qRcfv2 region with a lower threshold (P < 10-3). Through real-time fluorescence quantitative PCR, one candidate gene was found to have no expression in the cob but the other was expressed in response to F. verticillioides. These results lay a foundation for research on the resistance mechanisms of cob and provide resources for marker-assisted selection.

Classification of G-protein coupled receptors based on a rich generation of convolutional neural network, N-gram transformation and multiple sequence alignments.

Sequence classification is crucial in predicting the function of newly discovered sequences. In recent years, the prediction of the incremental large-scale and diversity of sequences has heavily relied on the involvement of machine-learning algorithms. To improve prediction accuracy, these algorithms must confront the key challenge of extracting valuable features. In this work, we propose a feature-enhanced protein classification approach, considering the rich generation of multiple sequence alignment algorithms, N-gram probabilistic language model and the deep learning technique. The essence behind the proposed method is that if each group of sequences can be represented by one feature sequence, composed of homologous sites, there should be less loss when the sequence is rebuilt, when a more relevant sequence is added to the group. On the basis of this consideration, the prediction becomes whether a query sequence belonging to a group of sequences can be transferred to calculate the probability that the new feature sequence evolves from the original one. The proposed work focuses on the hierarchical classification of G-protein Coupled Receptors (GPCRs), which begins by extracting the feature sequences from the multiple sequence alignment results of the GPCRs sub-subfamilies. The N-gram model is then applied to construct the input vectors. Finally, these vectors are imported into a convolutional neural network to make a prediction. The experimental results elucidate that the proposed method provides significant performance improvements. The classification error rate of the proposed method is reduced by at least 4.67% (family level I) and 5.75% (family Level II), in comparison with the current state-of-the-art methods. The implementation program of the proposed work is freely available at: https://github.com/alanFchina/CNN .

Gender difference in the relationship between serum uric acid reduction and improvement in body fat distribution after laparoscopic sleeve gastrectomy in Chinese obese patients: a 6-month follow-up.

BACKGROUND: Hyperuricemia is related to obesity and fat accumulation. This study aimed to observe the effects of laparoscopic sleeve gastrectomy (LSG) on serum uric acid (sUA) level and body fat distribution in obese patients. The relationships between post-LSG improvement in sUA levels and body fat distribution changes, as well as their sex-related differences, were also explored. METHODS: In total, 128 obese patients (48 men; 80 women) who underwent LSG were enrolled. Anthropometric indicators, glucose and lipid metabolic indicators, and sUA levels were measured pre-LSG and 6 months post-LSG. The body compositions were measured via dual-energy X-ray absorptiometry. The patients were divided into normal-sUA (NUA) and high-sUA (HUA) groups based on preoperative sUA levels. RESULTS: Compared with the NUA group, the reduction of sUA levels 6 months post-LSG was more significant in the HUA group. In addition, sUA reduction in the female HUA group was more significant than that of the male HUA group (P < 0.01). Changes in serum uric acid levels (ΔsUA) in the male HUA group was positively correlated with changes in body weight, body mass index, neck circumference, and hip circumference (r = 0.618, 0.653, 0.716, and 0.501, respectively; P < 0.05 in all cases). It was also positively correlated with changes in fat mass in the gynoid region, android region, and legs, (r = 0.675, 0.551, and 0.712, respectively; P < 0.05 in all cases), and negatively correlated with changes in total testosterone (ΔTT) (r = - 0.517; P = 0.040). Furthermore, ΔTT in this group was closely associated with the improved sex-related fat distribution. The ΔsUA in the female HUA group was positively correlated with changes in fasting serum C peptide and ΔLNIR (r = 0.449 and 0.449, respectively; P < 0.05 in both cases). In addition, it was also positively correlated with changes in visceral adipose tissue (VAT) fat mass, VAT fat volume, and VAT fat area (r = 0.749, 0.749, and 0.747, respectively; P < 0.01 in all cases). CONCLUSIONS: sUA levels of obese patients with hyperuricemia improved 6 months after LSG. Reduction of sUA after LSG was correlated with improved body fat distribution, and the relationships also displayed sex-based differences. Uric acid might be an important metabolic regulator associated with fat distribution and sex hormones.

New association of bone morphogenetic protein 4 concentrations with fat distribution in obesity and Exenatide intervention on it.

BACKGROUND: Bone morphogenetic protein 4 (BMP-4) has been proven to regulate white adipogensis. We aimed to demonstrate the correlation of BMP-4 with fat distribution and Exenatide treatment on it. METHODS: We enrolled 69 obese patients. Anthropometric and metabolic indexes were collected. Fat distribution was measured by dual-energy X-ray absorptiometry. BPM-4 levels were assessed using enzyme-link immunosorbent assay kit. 30 obese patients were treated with Exenatide twice a day. Change in body weight, metabolic-related indices and BPM-4 levels were evaluated after 18 weeks. RESULTS: 1) The mean(±SD) BMP-4 levels were 763.98 ± 324.11 pg/ml in the obese. BPM-4 levels were significantly positively correlated with estimated visceral adipose tissue mass in all subjects and also in females (r = 0.377, r = 0.625, respectively,all P < 0.05). BPM-4 levels were also significantly positively correlated with body mass index, hip circumference and total fat% in females (r = 0.375,r = 0.429,r = 0.493,respectively, all P < 0.05). BPM-4 levels were negatively correlated with total cholesterol(TC) in all subjects and males also (r = -0.373,r = -0.332,respectively, all P < 0.05). BPM-4 levels were also significantly positively correlated with free triiodothyronine in males (r = 0.441, P < 0.05). 3) Multivariate analyses showed that TC was risk factor of BMP-4 concentration in males and Est.VAT Area was risk factor of BMP-4 levels in females. 4) BMP-4 levels were significantly higher in the obesity with slightly increased thyroid stimulating hormone(TSH) than the obesity without slightly increased TSH (902.08 ± 354.74 pg/ml vs. 720.24 ± 306.41 pg/ml, P < 0.05). 5) Exenatide treatment leads to a significant decreased in BMP-4 from 860.05 ± 352.65 pg/ml to 649.44 + 277.49 pg/ml independent of weight loss(P < 0.05). CONCLUSION: BMP-4 levels were associated with the visceral adipose tissue and may play a certain role in fat distribution and subclinical hypothyroidism in obesity. Exenatide treatment reduced BMP-4 levels independent of weight loss. TRIAL REGISTRATION: Clinicaltrials.gov Identifier: NCT02118376 , Registered 16 April.

scIALM: A method for sparse scRNA-seq expression matrix imputation using the Inexact Augmented Lagrange Multiplier with low error.

Single-cell RNA sequencing (scRNA-seq) is a high-throughput sequencing technology that quantifies gene expression profiles of specific cell populations at the single-cell level, providing a foundation for studying cellular heterogeneity and patient pathological characteristics. It is effective for developmental, fertility, and disease studies. However, the cell-gene expression matrix of single-cell sequencing data is often sparse and contains numerous zero values. Some of the zero values derive from noise, where dropout noise has a large impact on downstream analysis. In this paper, we propose a method named scIALM for imputation recovery of sparse single-cell RNA data expression matrices, which employs the Inexact Augmented Lagrange Multiplier method to use sparse but clean (accurate) data to recover unknown entries in the matrix. We perform experimental analysis on four datasets, calling the expression matrix after Quality Control (QC) as the original matrix, and comparing the performance of scIALM with six other methods using mean squared error (MSE), mean absolute error (MAE), Pearson correlation coefficient (PCC), and cosine similarity (CS). Our results demonstrate that scIALM accurately recovers the original data of the matrix with an error of 10e-4, and the mean value of the four metrics reaches 4.5072 (MSE), 0.765 (MAE), 0.8701 (PCC), 0.8896 (CS). In addition, at 10%-50% random masking noise, scIALM is the least sensitive to the masking ratio. For downstream analysis, this study uses adjusted rand index (ARI) and normalized mutual information (NMI) to evaluate the clustering effect, and the results are improved on three datasets containing real cluster labels.

Preparation and Properties of Starch-Cellulose Composite Aerogel.

In this study, we conducted research on the preparation of aerogels using cellulose and starch as the primary materials, with the addition of N,N'-methylenebisacrylamide (MBA) as a cross-linking agent. The chemical, morphological and textural characteristics of the aerogels were found to be influenced by the proportions of cellulose, starch, and cross-linking agent that were utilized. An increase in the proportion of cellulose led to stronger adsorption forces within the aerogel structure. The aerogel showed a fine mesh internal structure, but the pores gradually increased with the further increase in cellulose. Notably, when the mass fractions of starch and cellulose were 5 wt% and 1 wt% respectively, the aerogels exhibited the smallest pore size and largest porosity. With an increase in the crosslinking agent, the internal structure of the aerogel first became dense and then loose, and the best internal structure was displayed at the addition of 3 wt%. Through texture analysis and the swelling test, the impact of the proportion of cellulose and MBA on the aerogel structure was significant. Dye adsorption experiments indicated that MBA affected the water absorption and expansion characteristics of the aerogel by improving the pore structure. Lastly, in tests involving the loading of vitamin E, the aerogels exhibited a higher capacity for incorporating vitamin E compared to native starch.

Predictive Factors of Menstrual Recovery After Laparoscopic Sleeve Gastrectomy in Polycystic Ovary Syndrome Women with Obesity.

Objective: To explore the predictors of menstrual recovery in polycystic ovary syndrome (PCOS) women with obesity following laparoscopic sleeve gastrectomy (LSG). Methods: A total of 88 PCOS patients with obesity and 76 control patients with obesity aged 18-45 years were enrolled between May 2013 and December 2020. PCOS was diagnosed using the Rotterdam diagnostic criteria (2003). Anthropometric measurements, biochemical parameters, sex hormones, and circulating fibrinogen-like protein 1 (FGL-1) levels were collected before and six-month after LSG. The data on postoperative menstrual status, body weight, and fertility were obtained through telephone follow-ups for all individuals with PCOS. Results: Patients with PCOS were followed up for at least six months after surgery, and the mean follow-up time was 3.23 years. At 6 months after LSG, circulating total testosterone (TT), calculated free testosterone (cFT), and FGL-1 levels declined significantly. The mean percent excess weight loss (%EWL) and percent total weight loss (%TWL) in PCOS patients at the final follow-up was 97.52% ± 33.90% and 31.65% ± 10.31%, respectively. The proportion of regular menstruation in PCOS patients significantly increased within six months (75.86% vs 0.03% at baseline). In the logistic regression analysis, time from PCOS diagnosis (P=0.007), body mass index (BMI) (P=0.007), TT (P=0.038) at baseline were demonstrated to be independent predictive factors for the regular menstruation in women with PCOS and obesity within 6 months after LSG. Conclusion: In PCOS patients with obesity, time from PCOS diagnosis, BMI, and TT levels at baseline were independently and negatively associated with menstrual recovery within 6 months after LSG, which could be applied in preoperative evaluation.

Harnessing ultrasound in photocatalysis: Synthesis and piezo-enhanced effect: A review.

The photocatalytic technique has drawn far-ranging interests in addressing the current issues; however, its property suffers from the limited visible light response and rapid recombination of carriers. To address these issues, two specific approaches have been proposed to enhance the photocatalytic activity: (1) ultrasound-assisted synthesis has been utilized to prepare photocatalysts, resulting in refined grain size, increased specific surface area, and reduced photogenerated carrier recombination; (2) sonophotocatalysis and piezoelectric enhanced photocatalysis have been developed to accelerate the reaction, which utilizes the synergism between ultrasound and light. On one side, sonophotocatalysis generates cavitation bubbles which induce more reactive radicals for redox reactions. On the other side, ultrasound induces deformation of the piezoelectric material structure, which changes the internal piezoelectric potential and improves the photocatalytic performance. Currently, intensive efforts have been devoted to related research and great progress has been reached with applications in pollutant degradation, new energy production, and other fields. This work starts by elucidating the fundamental concept of ultrasound-assisted photocatalyst synthesis and photocatalysis. Then, the synergistic behavior between ultrasonic and light in ultrasonic-assisted photocatalysis has been thoroughly discussed, including pollutant degradation, water splitting, and bacterial sterilization. Finally, the challenge and outlook are investigated and proposed.

CTCs Detection and Whole-exome Sequencing Might Be Used to Differentiate Benign and Malignant Pulmonary Nodules.

BACKGROUND: Low-density computed tomography (LDCT) improved early lung cancer diagnosis but introduces an excess of false-positive pulmonary nodules data. Hence, accurate diagnosis of early-stage lung cancer remains challenging. The purpose of the study was to assess the feasibility of using circulating tumour cells (CTCs) to differentiate malignant from benign pulmonary nodules. METHODS: 122 patients with suspected malignant pulmonary nodules detected on chest CT in preparation for surgery were prospectively recruited. Peripheral blood samples were collected before surgery, and CTCs were identified upon isolation by size of epithelial tumour cells and morphological analysis. Laser capture microdissection, MALBAC amplification, and whole-exome sequencing were performed on 8 samples. The diagnostic efficacy of CTCs counting, and the genomic variation profile of benign and malignant CTCs samples were analysed. RESULTS: Using 2.5 cells/5 mL as the cut-off value, the area under the receiver operating characteristic curve was of 0.651 (95% confidence interval: 0.538-0.764), with a sensitivity and specificity of 0.526 and 0.800, respectively, and positive and negative predictive values of 91.1% and 30.3%, respectively. Distinct sequence variations differences in DNA damage repair-related and driver genes were observed in benign and malignant samples. TP53 mutations were identified in CTCs of four malignant cases; in particular, g.7578115T>C, g.7578645C>T, and g.7579472G>C were exclusively detected in all four malignant samples. CONCLUSIONS: CTCs play an ancillary role in the diagnosis of pulmonary nodules. TP53 mutations in CTCs might be used to identify benign and malignant pulmonary nodules.

Relationship of Vitamin A and Thyroid Function in Individuals With Obesity and After Laparoscopic Sleeve Gastrectomy.

Vitamin A deficiency (VAD) occurs in obesity and may be associated with thyroid dysfunction. We aimed to investigate the association of VA with thyroid function in obesity and after laparoscopic sleeve gastrectomy (LSG). Nine hundred and seventy-six obese subjects were enrolled for this study and were divided into VAD, marginal vitamin A deficiency (MVAD), and vitamin A normal (NVA) groups. VAD was defined as VA ≤ 200 ng/ml, MVAD was defined as VA > 200 but <300 ng/ml, and NVA was defined as VA ≥ 300 ng/ml. Thyroid function was compared among groups and the relationship of VA and thyroid function was analyzed. Two hundred and forty-four of the 976 obese subjects underwent LSG, and the change in thyroid function and VA at 3, 6, and 12 months after surgery was measured. Results showed that 37% of all the subjects had subclinical hypothyroidism (SH), and the SH group had lower VA levels than the non-SH group (P = 0.008). Forty-nine percent of all the subjects had MVAD, 9% had VAD, while the MVAD or VAD group had lower FT4 than the NVA group (P = 0.005 and P = 0.001). The VAD group also had higher TSH than NVA group (P = 0.037). VA was significantly negatively associated with TSH (r = -0.151, P = 0.006) and positively associated with FT4 (r = 0.228, P < 0.001). TSH was significantly decreased at 3, 6, and 12 months (3M: from 4.43 ± 2.70 to 2.63 ± 1.46 mU/l, P < 0.001; 6M: from 4.43 ± 2.70 to 3.84 ± 2.34 mU/l, P = 0.041; 12M: from 4.43 ± 2.70 to 2.85 ± 1.68 mU/l, P = 0.024). After LSG surgery, VA levels were slightly increased, when compared to pre-surgery levels, at 3, 6, and 12 months (3M: from 262.57 ± 68.19 to 410.33 ± 76.55 ng/ml, P = 0.065; 6M: from 262.57 ± 68.19 to 281.36 ± 93.23 ng/ml, P = 0.343; 12M: from 262.57 ± 68.19 to 300.37 ± 86.03 ng/ml, P = 0.083). SH group also had lower TSH and higher VA than the non-SH group at 3 months post-surgery [TSH: -1.4(-2.3, -0.3) vs. -0.2(-0.8, -0.2) mU/l, P < 0.001; VA: 163.99 ± 32.58 vs. 121.69 ± 27.59 ng/ml, P = 0.044]. In conclusion VA, which is related to thyroid hormone production, protects against thyroid dysfunction in obese subjects. The improvement of thyroid function in subjects with SH after LSG may be related to the increased VA levels observed post-surgery. Clinical Trial Registration: ClinicalTrial.gov ID: NCT04548232.