Improving deep learning protein monomer and complex structure prediction using DeepMSA2 with huge metagenomics data.

Leveraging iterative alignment search through genomic and metagenome sequence databases, we report the DeepMSA2 pipeline for uniform protein single- and multichain multiple-sequence alignment (MSA) construction. Large-scale benchmarks show that DeepMSA2 MSAs can remarkably increase the accuracy of protein tertiary and quaternary structure predictions compared with current state-of-the-art methods. An integrated pipeline with DeepMSA2 participated in the most recent CASP15 experiment and created complex structural models with considerably higher quality than the AlphaFold2-Multimer server (v.2.2.0). Detailed data analyses show that the major advantage of DeepMSA2 lies in its balanced alignment search and effective model selection, and in the power of integrating huge metagenomics databases. These results demonstrate a new avenue to improve deep learning protein structure prediction through advanced MSA construction and provide additional evidence that optimization of input information to deep learning-based structure prediction methods must be considered with as much care as the design of the predictor itself.

A classic screening marker does not affect antennal electrophysiology but strongly regulates reproductive behaviours in Bactrocera dorsalis.

The key phenotype white eye (white) has been used for decades to selectively remove females before release in sterile insect technique programs and as an effective screening marker in genetic engineering. Bactrocera dorsalis is a representative tephritid pest causing damage to more than 150 fruit crops. Yet, the function of white in important biological processes remains unclear in B. dorsalis. In this study, the impacts of the white gene on electrophysiology and reproductive behaviour in B. dorsalis were tested. The results indicated that knocking out Bdwhite disrupted eye pigmentation in adults, consistent with previous reports. Bdwhite did not affect the antennal electrophysiology response to 63 chemical components with various structures. However, reproductive behaviours in both males and females were significantly reduced in Bdwhite-/- . Both pre-copulatory and copulation behaviours were significantly reduced in Bdwhite-/- , and the effect was male-specific. Mutant females significantly delayed their oviposition towards γ-octalactone, and the peak of oviposition behaviour towards orange juice was lost. These results show that Bdwhite might not be an ideal screening marker in functional gene research aiming to identify molecular targets for behaviour-modifying chemicals. Instead, owing to its strong effect on B. dorsalis sexual behaviours, the downstream genes regulated by Bdwhite or the genes from white-linked areas could be alternate molecular targets that promote the development of better behavioural modifying chemical-based pest management techniques.

Performance of MAST, FAST, and MEFIB in predicting metabolic dysfunction-associated steatohepatitis.

BACKGROUND AND AIM: To identify individuals with metabolic dysfunction-associated steatohepatitis (MASH) or "at-risk" MASH among patients with metabolic dysfunction-associated steatotic liver disease (MASLD), three noninvasive models are available with satisfactory efficiency, which include magnetic resonance imaging [MRI]- AST (MAST), FibroScan-AST (FAST score), and magnetic resonance elastography [MRE] plus FIB-4 (MEFIB). We aimed to evaluate the most accurate approach for diagnosing MASH or "at-risk" MASH. METHODS: We included 108 biopsy-proven MASLD patients who underwent simultaneous assessment of MRE, MRI proton density fat fraction (MRI-PDFF), and FibroScan scans. Compared with the histological diagnosis, we analyzed the AUC of each model and assessed the accuracy. RESULTS: Our study cohort consisted of 64.8% of MASH and 25.9% of "at-risk" MASH. When analyzing the performance of each model for the diagnostic accuracy of MASH, we found that the AUC [95% CI] of MAST was comparable to FAST (0.803 [0.719-0.886] vs 0.799 [0.707-0.891], P = 0.930) and better than MEFIB (0.671 [0.571-0.772], P = 0.005). Similar findings were observed in the "at-risk" MASH patients. The AUCs [95% CI] for MAST, FAST, and MEFIB were 0.810 [0.719-0.900], 0.782 [0.689-0.874], and 0.729 [0.619-0.838], respectively. The models of MAST and FAST had comparable AUCs (P = 0.347), which were statistically significantly higher than that of MEFIB (P = 0.041). Additionally, the cutoffs for diagnosis of MASH were lower than "at-risk" MASH. CONCLUSION: MAST and FAST performed better than MEFIB in diagnosing "at-risk" MASH and MASH using lower cutoff values. Our findings provided evidence for selecting the most accurate noninvasive model to identify patients with MASH or at-risk MASH.

LOMETS3: integrating deep learning and profile alignment for advanced protein template recognition and function annotation.

Deep learning techniques have significantly advanced the field of protein structure prediction. LOMETS3 (https://zhanglab.ccmb.med.umich.edu/LOMETS/) is a new generation meta-server approach to template-based protein structure prediction and function annotation, which integrates newly developed deep learning threading methods. For the first time, we have extended LOMETS3 to handle multi-domain proteins and to construct full-length models with gradient-based optimizations. Starting from a FASTA-formatted sequence, LOMETS3 performs four steps of domain boundary prediction, domain-level template identification, full-length template/model assembly and structure-based function prediction. The output of LOMETS3 contains (i) top-ranked templates from LOMETS3 and its component threading programs, (ii) up to 5 full-length structure models constructed by L-BFGS (limited-memory Broyden-Fletcher-Goldfarb-Shanno algorithm) optimization, (iii) the 10 closest Protein Data Bank (PDB) structures to the target, (iv) structure-based functional predictions, (v) domain partition and assembly results, and (vi) the domain-level threading results, including items (i)-(iii) for each identified domain. LOMETS3 was tested in large-scale benchmarks and the blind CASP14 (14th Critical Assessment of Structure Prediction) experiment, where the overall template recognition and function prediction accuracy is significantly beyond its predecessors and other state-of-the-art threading approaches, especially for hard targets without homologous templates in the PDB. Based on the improved developments, LOMETS3 should help significantly advance the capability of broader biomedical community for template-based protein structure and function modelling.

Analysis of correlation between BMI and TWL% outcome following metabolic and bariatric surgery: a retrospective study using restricted cubic spline.

OBJECTIVE: This study aimed to examine the correlation between preoperative body mass index (BMI) and adequate percentage of total weight loss (TWL%) outcome and present evidence of tiered treatment for patients with obesity in different preoperative BMI. METHODS: We included patients with complete follow-up data who underwent metabolic and bariatric surgery (BMS). We termed optimal clinical response as TWL% >20% at one year following MBS. To investigate dose-response association between preoperative BMI and optimal clinical response, preoperative BMI was analyzed in three ways: (1) as quartiles; (2) per 2.5 kg/m2 units (3) using RCS, with 3 knots as recommended. RESULTS: A total of 291 patients with obesity were included in our study. The corresponding quartile odds ratios associated with optimal clinical response and adjusted for potential confounders were 1.00 (reference), 1.434 [95% confidence interval (95%CI)   =  0.589-3.495], 4.926 (95%CI   =  1.538-15.772), and 2.084 (95%CI   =  0.941-1.005), respectively. RCS analysis showed a non-linear inverted U-shaped association between preoperative BMI and optimal clinical response (Nonlinear P   =  0.009). In spline analysis, when preoperative BMI was no less than 42.9 kg/m2, the possibility of optimal clinical response raised as preoperative BMI increased. When preoperative BMI was greater than 42.9 kg/m2, the possibility of optimal clinical response had a tendency to decline as preoperative BMI increased. CONCLUSION: Our research indicated the non-linear inverted U-shaped correlation between preoperative BMI and adequate weight loss. Setting a preoperative BMI threshold of 42.9 is critical to predicting optimal clinical outcomes.

Recent Progress of Protein Tertiary Structure Prediction.

The prediction of three-dimensional (3D) protein structure from amino acid sequences has stood as a significant challenge in computational and structural bioinformatics for decades. Recently, the widespread integration of artificial intelligence (AI) algorithms has substantially expedited advancements in protein structure prediction, yielding numerous significant milestones. In particular, the end-to-end deep learning method AlphaFold2 has facilitated the rise of structure prediction performance to new heights, regularly competitive with experimental structures in the 14th Critical Assessment of Protein Structure Prediction (CASP14). To provide a comprehensive understanding and guide future research in the field of protein structure prediction for researchers, this review describes various methodologies, assessments, and databases in protein structure prediction, including traditionally used protein structure prediction methods, such as template-based modeling (TBM) and template-free modeling (FM) approaches; recently developed deep learning-based methods, such as contact/distance-guided methods, end-to-end folding methods, and protein language model (PLM)-based methods; multi-domain protein structure prediction methods; the CASP experiments and related assessments; and the recently released AlphaFold Protein Structure Database (AlphaFold DB). We discuss their advantages, disadvantages, and application scopes, aiming to provide researchers with insights through which to understand the limitations, contexts, and effective selections of protein structure prediction methods in protein-related fields.

Integrating deep learning, threading alignments, and a multi-MSA strategy for high-quality protein monomer and complex structure prediction in CASP15.

We report the results of the "UM-TBM" and "Zheng" groups in CASP15 for protein monomer and complex structure prediction. These prediction sets were obtained using the D-I-TASSER and DMFold-Multimer algorithms, respectively. For monomer structure prediction, D-I-TASSER introduced four new features during CASP15: (i) a multiple sequence alignment (MSA) generation protocol that combines multi-source MSA searching and a structural modeling-based MSA ranker; (ii) attention-network based spatial restraints; (iii) a multi-domain module containing domain partition and arrangement for domain-level templates and spatial restraints; (iv) an optimized I-TASSER-based folding simulation system for full-length model creation guided by a combination of deep learning restraints, threading alignments, and knowledge-based potentials. For 47 free modeling targets in CASP15, the final models predicted by D-I-TASSER showed average TM-score 19% higher than the standard AlphaFold2 program. We thus showed that traditional Monte Carlo-based folding simulations, when appropriately coupled with deep learning algorithms, can generate models with improved accuracy over end-to-end deep learning methods alone. For protein complex structure prediction, DMFold-Multimer generated models by integrating a new MSA generation algorithm (DeepMSA2) with the end-to-end modeling module from AlphaFold2-Multimer. For the 38 complex targets, DMFold-Multimer generated models with an average TM-score of 0.83 and Interface Contact Score of 0.60, both significantly higher than those of competing complex prediction tools. Our analyses on complexes highlighted the critical role played by MSA generating, ranking, and pairing in protein complex structure prediction. We also discuss future room for improvement in the areas of viral protein modeling and complex model ranking.

Disentangling Population History and Character Evolution among Hybridizing Lineages.

Understanding the origin and maintenance of adaptive phenotypic novelty is a central goal of evolutionary biology. However, both hybridization and incomplete lineage sorting can lead to genealogical discordance between the regions of the genome underlying adaptive traits and the remainder of the genome, decoupling inferences about character evolution from population history. Here, to disentangle these effects, we investigated the evolutionary origins and maintenance of Batesian mimicry between North American admiral butterflies (Limenitis arthemis) and their chemically defended model (Battus philenor) using a combination of de novo genome sequencing, whole-genome resequencing, and statistical introgression mapping. Our results suggest that balancing selection, arising from geographic variation in the presence or absence of the unpalatable model, has maintained two deeply divergent color patterning haplotypes that have been repeatedly sieved among distinct mimetic and nonmimetic lineages of Limenitis via introgressive hybridization.

FUpred: detecting protein domains through deep-learning-based contact map prediction.

MOTIVATION: Protein domains are subunits that can fold and function independently. Correct domain boundary assignment is thus a critical step toward accurate protein structure and function analyses. There is, however, no efficient algorithm available for accurate domain prediction from sequence. The problem is particularly challenging for proteins with discontinuous domains, which consist of domain segments that are separated along the sequence. RESULTS: We developed a new algorithm, FUpred, which predicts protein domain boundaries utilizing contact maps created by deep residual neural networks coupled with coevolutionary precision matrices. The core idea of the algorithm is to retrieve domain boundary locations by maximizing the number of intra-domain contacts, while minimizing the number of inter-domain contacts from the contact maps. FUpred was tested on a large-scale dataset consisting of 2549 proteins and generated correct single- and multi-domain classifications with a Matthew's correlation coefficient of 0.799, which was 19.1% (or 5.3%) higher than the best machine learning (or threading)-based method. For proteins with discontinuous domains, the domain boundary detection and normalized domain overlapping scores of FUpred were 0.788 and 0.521, respectively, which were 17.3% and 23.8% higher than the best control method. The results demonstrate a new avenue to accurately detect domain composition from sequence alone, especially for discontinuous, multi-domain proteins. AVAILABILITY AND IMPLEMENTATION: https://zhanglab.ccmb.med.umich.edu/FUpred. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

LOMETS2: improved meta-threading server for fold-recognition and structure-based function annotation for distant-homology proteins.

The LOMETS2 server (https://zhanglab.ccmb.med.umich.edu/LOMETS/) is an online meta-threading server system for template-based protein structure prediction. Although the server has been widely used by the community over the last decade, the previous LOMETS server no longer represents the state-of-the-art due to aging of the algorithms and unsatisfactory performance on distant-homology template identification. An extension of the server built on cutting-edge methods, especially techniques developed since the recent CASP experiments, is urgently needed. In this work, we report the recent advancements of the LOMETS2 server, which comprise a number of major new developments, including (i) new state-of-the-art threading programs, including contact-map-based threading approaches, (ii) deep sequence search-based sequence profile construction and (iii) a new web interface design that incorporates structure-based function annotations. Large-scale benchmark tests demonstrated that the integration of the deep profiles and new threading approaches into LOMETS2 significantly improve its structure modeling quality and template detection, where LOMETS2 detected 176% more templates with TM-scores >0.5 than the previous LOMETS server for Hard targets that lacked homologous templates. Meanwhile, the newly incorporated structure-based function prediction helps extend the usefulness of the online server to the broader biological community.

A large-scale comparative assessment of methods for residue-residue contact prediction.

Sequence-based prediction of residue-residue contact in proteins becomes increasingly more important for improving protein structure prediction in the big data era. In this study, we performed a large-scale comparative assessment of 15 locally installed contact predictors. To assess these methods, we collected a big data set consisting of 680 nonredundant proteins covering different structural classes and target difficulties. We investigated a wide range of factors that may influence the precision of contact prediction, including target difficulty, structural class, the alignment depth and distribution of contact pairs in a protein structure. We found that: (1) the machine learning-based methods outperform the direct-coupling-based methods for short-range contact prediction, while the latter are significantly better for long-range contact prediction. The consensus-based methods, which combine machine learning and direct-coupling methods, perform the best. (2) The target difficulty does not have clear influence on the machine learning-based methods, while it does affect the direct-coupling and consensus-based methods significantly. (3) The alignment depth has relatively weak effect on the machine learning-based methods. However, for the direct-coupling-based methods and consensus-based methods, the predicted contacts for targets with deeper alignment tend to be more accurate. (4) All methods perform relatively better on ß and α + ß proteins than on α proteins. (5) Residues buried in the core of protein structure are more prone to be in contact than residues on the surface (22 versus 6%). We believe these are useful results for guiding future development of new approach to contact prediction.

Detecting distant-homology protein structures by aligning deep neural-network based contact maps.

Accurate prediction of atomic-level protein structure is important for annotating the biological functions of protein molecules and for designing new compounds to regulate the functions. Template-based modeling (TBM), which aims to construct structural models by copying and refining the structural frameworks of other known proteins, remains the most accurate method for protein structure prediction. Due to the difficulty in recognizing distant-homology templates, however, the accuracy of TBM decreases rapidly when the evolutionary relationship between the query and template vanishes. In this study, we propose a new method, CEthreader, which first predicts residue-residue contacts by coupling evolutionary precision matrices with deep residual convolutional neural-networks. The predicted contact maps are then integrated with sequence profile alignments to recognize structural templates from the PDB. The method was tested on two independent benchmark sets consisting collectively of 1,153 non-homologous protein targets, where CEthreader detected 176% or 36% more correct templates with a TM-score >0.5 than the best state-of-the-art profile- or contact-based threading methods, respectively, for the Hard targets that lacked homologous templates. Moreover, CEthreader was able to identify 114% or 20% more correct templates with the same Fold as the query, after excluding structures from the same SCOPe Superfamily, than the best profile- or contact-based threading methods. Detailed analyses show that the major advantage of CEthreader lies in the efficient coupling of contact maps with profile alignments, which helps recognize global fold of protein structures when the homologous relationship between the query and template is weak. These results demonstrate an efficient new strategy to combine ab initio contact map prediction with profile alignments to significantly improve the accuracy of template-based structure prediction, especially for distant-homology proteins.

Comparison of various prediction models in the effect of laparoscopic sleeve gastrectomy on type 2 diabetes mellitus in the Chinese population 5 years after surgery.

BACKGROUND: The effect of bariatric surgery on type 2 diabetes mellitus (T2DM) control can be assessed based on predictive models of T2DM remission. Various models have been externally verified internationally. However, long-term validated results after laparoscopic sleeve gastrectomy (LSG) surgery are lacking. The best model for the Chinese population is also unknown. METHODS: We retrospectively analyzed Chinese population data 5 years after LSG at Beijing Shijitan Hospital in China between March 2009 and December 2016. The independent t -test, Mann-Whitney U test, and chi-squared test were used to compare characteristics between T2DM remission and non-remission groups. We evaluated the predictive efficacy of each model for long-term T2DM remission after LSG by calculating the area under the curve (AUC), sensitivity, specificity, Youden index, positive predictive value (PPV), negative predictive value (NPV), and predicted-to-observed ratio, and performed calibration using Hosmer-Lemeshow test for 11 prediction models. RESULTS: We enrolled 108 patients, including 44 (40.7%) men, with a mean age of 35.5 years. The mean body mass index was 40.3 ± 9.1 kg/m 2 , the percentage of excess weight loss (%EWL) was (75.9 ± 30.4)%, and the percentage of total weight loss (%TWL) was (29.1± 10.6)%. The mean glycated hemoglobin A1c (HbA1c) level was (7.3 ± 1.8)% preoperatively and decreased to (5.9 ± 1.0)% 5 years after LSG. The 5-year postoperative complete and partial remission rates of T2DM were 50.9% [55/108] and 27.8% [30/108], respectively. Six models, i.e., "ABCD", individualized metabolic surgery (IMS), advanced-DiaRem, DiaBetter, Dixon et al' s regression model, and Panunzi et al 's regression model, showed a good discrimination ability (all AUC >0.8). The "ABCD" (sensitivity, 74%; specificity, 80%; AUC, 0.82 [95% confidence interval [CI]: 0.74-0.89]), IMS (sensitivity, 78%; specificity, 84%; AUC, 0.82 [95% CI: 0.73-0.89]), and Panunzi et al' s regression models (sensitivity, 78%; specificity, 91%; AUC, 0.86 [95% CI: 0.78-0.92]) showed good discernibility. In the Hosmer-Lemeshow goodness-of-fit test, except for DiaRem ( P <0.01), DiaBetter ( P <0.01), Hayes et al ( P = 0.03), Park et al ( P = 0.02), and Ramos-Levi et al' s ( P <0.01) models, all models had a satifactory fit results ( P >0.05). The P values of calibration results of the "ABCD" and IMS were 0.07 and 0.14, respectively. The predicted-to-observed ratios of the "ABCD" and IMS were 0.87 and 0.89, respectively. CONCLUSION: The prediction model IMS was recommended for clinical use because of excellent predictive performance, good statistical test results, and simple and practical design features.

Odorant Receptor BdorOR49b Mediates Oviposition and Attraction Behavior of Bactrocera dorsalis to Benzothiazole.

The ability to recognize a host plant is crucial for insects to meet their nutritional needs and locate suitable sites for laying eggs. Bactrocera dorsalis is a highly destructive pest in fruit crops. Benzothiazole has been found to induce oviposition behavior in the gravid B. dorsalis. However, the ecological roles and the olfactory receptor responsible for benzothiazole are not yet fully understood. In this study, we found that adults were attracted to benzothiazole, which was an effective oviposition stimulant. In vitro experiments showed that BdorOR49b was narrowly tuned to benzothiazole. The electroantennogram results showed that knocking out BdorOR49b significantly reduced the antennal electrophysiological response to benzothiazole. Compared with wild-type flies, the attractiveness of benzothiazole to BdorOR49b knockout adult was significantly attenuated, and mutant females exhibited a severe decrease in oviposition behavior. Altogether, our work provides valuable insights into chemical communications and potential strategies for the control of this pest.

Prevalence, Risk Factors, and Metabolic Characteristics of Metabolically Healthy Obesity in Patients Seeking Bariatric Surgery: A Cohort Study.

BACKGROUND: Bariatric surgery is an effective treatment for morbid obesity. However, a subset of individuals seeking bariatric surgery may exhibit a metabolically healthy obesity (MHO) phenotype, suggesting that they may not experience metabolic complications despite being overweight. OBJECTIVE: This study aimed to determine the prevalence and metabolic features of MHO in a population undergoing bariatric surgery. METHODS: A representative sample of 665 participants aged 14 or older who underwent bariatric surgery at our center from January 1, 2010 to January 1, 2020 was included in this cohort study. MHO was defined based on specific criteria, including blood pressure, waist-to-hip ratio, and absence of diabetes. RESULTS: Among the 665 participants, 80 individuals (12.0%) met the criteria for MHO. Female gender (P = .021) and younger age (P < .001) were associated with a higher likelihood of MHO. Smaller weight and BMI were observed in individuals with MHO. However, a considerable proportion of those with MHO exhibited other metabolic abnormalities, such as fatty liver (68.6%), hyperuricemia (55.3%), elevated lipid levels (58.7%), and abnormal lipoprotein levels (88%). CONCLUSION: Approximately 1 in 8 individuals referred for bariatric surgery displayed the phenotype of MHO. Despite being metabolically healthy based on certain criteria, a significant proportion of individuals with MHO still exhibited metabolic abnormalities, such as fatty liver, hyperuricemia, elevated lipid levels, and abnormal lipoprotein levels, highlighting the importance of thorough metabolic evaluation in this population.

Analysis Using Various Models on the Effect of Metabolic Surgery on Cardiovascular Disease Risk in the Chinese Population with Obesity.

Background: Some research have indicated that Bariatric and metabolic surgery (BMS) can reduce the risk of cardiovascular disease (CVD) among individuals with obesity. However, there are few reports available that focuses on assessing effect of BMS on the risk of CVD in Chinese population using multiple models. Objective: This research aims to assess the function of BMS on the risk of CVD in Chinese patients with obesity using multiple CVD risk models. Methods: We performed a retrospective analysis of the basic data and glycolipid metabolism data preoperatively and postoperatively from patients with obesity at our hospital. Subgroup analysis was carried out according to different surgical procedures. Then, the function of BMS on the risk of CVD in the Chinese population was assessed using four models, including: China-PAR risk model, Framingham risk score (FRS), World Health Organization (WHO) risk model, and Globorisk model. Results: We enrolled 64 patients, 24 (37.5%) of whom underwent laparoscopic sleeve gastrectomy (LSG) while 40 (62.5%) underwent Roux-en-Y gastric bypass (RYGB). The 10-year CVD risk for patients calculated using the China-PAR risk model decreased from 6.3% preoperatively to 2.0% at 1 year postoperatively and was statistically significantly different. Similarly, the 10-year CVD risk of patients calculated using the FRS, WHO, Global risk model decreased significantly at 1 year postoperatively compared to preoperatively. When the FRS risk model was used to calculate the patients' 30-year postoperative CVD risk, there was a significant decrease at 1 year after surgery compared to the preoperative period. When employing various models to evaluate the 10-year CVD risk for LSG and RYGB, no statistically significant difference was found in the 1-year postoperative RRR between the procedures. Conclusion: The CVD risk after BMS was significantly reduced compared to preoperatively. In terms of improving cardiovascular risk, SG and RYGB appear to be equally effective.

Comparison of various prediction models in the effect of Roux-en-Y gastric bypass on type 2 diabetes in the Chinese population 5 years after surgery.

BACKGROUND: Various prediction models of type 2 diabetes (T2D) remission have been externally verified internationally. However, long-term validated results after Roux-en-Y gastric bypass (RYGB) surgery are lacking. The best model for the Chinese population is also unknown. OBJECTIVES: To evaluate the prediction effect of various prediction models on the long-term diabetes remission after RYGB in the Chinese population and to provide reference for clinical use. SETTING: A retrospective clinical study at a university hospital. METHODS: We retrospectively analyzed Chinese population data 5 years after RYGB and externally validated 11 predictive models to evaluate the predictive effect of each model on long-term T2D remission after RYGB. RESULTS: We enrolled 84 patients. The mean body mass index was 41 kg/m2, and the percentage of excess weight loss (%EWL) was 72.3%. The mean glycated hemoglobin level was 8.4% preoperatively and decreased to 5.9% after 5 years. The 5-year postoperative complete and partial remission rates of T2D were 31% and 70.2%, respectively. The ABCD scoring model (sensitivity, 84%; specificity, 76%; area under the curve [AUC], .866) and the Panuzi et al. [34] study (sensitivity, 84%; specificity, 81%; AUC, .842) showed excellent results. In the Hosmer-Lemeshow goodness-of-fit test, calibration values for ABCD and Panuzi et al. [34] were .14 and .21, respectively. The predicted-to-observed ratios of ABCD and Panuzi et al. [34] were .83 and .88, respectively. CONCLUSIONS: T2D was relieved to varying degrees 5 years after RYGB in patients with obesity. The prediction models in ABCD and the Panuzi et al. [34] studies showed the best prediction effects. ABCD was recommended for clinical use because of excellent predictive performance, good statistical test results, and simple and practical design features.

Analysis of Correlation between Age and Satisfied Total Weight Loss Percentage Outcome at 1 Year after Bariatric Surgery using the Restricted Cubic Spline Function: A Retrospective Study in China.

OBJECTIVE: This study aims to explore the relationship between age and whether the percentage of total weight loss (TWL%) is ≥ 25% or not at 1 year after bariatric surgery (BS). We aimed to provide evidence for the stratified treatment of spatients with obesity at different ages. METHODS: The primary outcome evaluated was whether TWL% was no less than 25% at 1 year after BS. A TWL% ≥ 25% was defined as a satisfied TWL% outcome. Logistic regression analysis and the restricted cubic spline (RCS) function were used to analyze the relationship between age and the satisfied TWL% outcome at 1 year after BS. RESULTS: Two hundred and ninety-one patients were included in our study. After adjusting for potential confounders, the odds ratios (ORs) of the corresponding quartiles of age associated with satisfied TWL% outcome were 1.00 (reference), 1.117 [95% confidence interval (95% CI) = 0.540-2.311], 1.378 (95% CI = 0.647-2.935), and 0.406 (95% CI = 0.184-0.895). RCS analysis revealed a non-linear inverted L-shaped association between age and satisfied TWL% outcome at 1 year after BS (non-linear P = 0.033). CONCLUSION: Age was an independent predictor of satisfied TWL% outcome one year following BS, and our study considered 32 years as a potential cut-off point. For Chinese patients over the age of 32 who are eligible for BS, it may be beneficial to do BS earlier as the probability of achieving a satisfied TWL% outcome may decrease with age.

MFS Transporter Bdorwp Does Not Affect Antennal Electrophysiology but Regulates Reproductive Behaviors in Bactrocera dorsalis.

Developing behavioral modifying chemicals through molecular targets is a promising way to improve semiochemical-based technology for pest management. Identifying molecular targets that affect insect behavior largely relies on functional genetic techniques such as deletions, insertions, and substitutions. Selectable markers have thus been developed to increase the efficiency of screening for successful editing events. However, the effect of selectable markers on relevant phenotypic traits needs to be considered. In this study, we cloned the wp gene ofBactrocera dorsalis. Knocking out Bdorwp causes white pupae phenotypes. Reproductive behaviors in both males and females were strongly regulated by Bdorwp. Remarkably, Bdorwp did not affect the antennal electrophysiology response to 63 chemical components with various structures. It is recommended to indirectly apply Bdorwp as a selectable marker in functional gene research on behavioral modifying chemicals. Moreover, Bdorwp could also be a potential molecular target for developing new insecticides for tephritid species control.

Establishment of a Nomogram Based on Inflammatory Response-Related Methylation Sites in Intraoperative Visceral Adipose Tissue to Predict EWL% at One Year After LSG.

Background: Laparoscopic sleeve gastrectomy (LSG) is considered as an effective bariatric and metabolic surgery for patients with severe obesity. Chronic low-grade inflammation of adipose tissue is associated with obesity and obesity-related complications. Objective: This study intends to establish a nomogram based on inflammatory response-related methylation sites in intraoperative visceral adipose tissue (VAT) to predict excess weight loss (EWL)% at one-year after LSG. Methods: Based on EWL% at one-year after LSG, patients were divided into two groups: the satisfied group (group-A, EWL%≥50%) and the unsatisfied group (group-B, EWL%<50%). Next, we defined genes corresponding to the methylation sites in the 850 K methylation microarray as methylation-related genes (MRGs). We then took the intersection of MRGs and inflammatory response-related genes. After that, inflammatory response-related methylation sites were identified based on overlapping genes. Moreover, difference analysis was carried out to obtain inflammatory response-related differentially methylated sites (IRRDMSs) between group-A and group-B. LASSO analysis was used to identify the hub methylation sites. Finally, we developed a nomogram based on the hub methylation sites. Results: There were 26 patients in the study, with 13 in group-A and 13 in group-B. After data filtering and difference analysis, 200 IRRDMSs were identified (143 hypermethylated sites and 57 hypomethylated sites). Then, we identified three hub methylation sites (cg03610073, cg03208951, and cg18746357) by LASSO analysis and built a predictive nomogram (Area under the curve=0.953). Conclusion: The predictive nomogram based on three inflammatory-related methylation sites (cg03610073, cg03208951, and cg18746357) in intraoperative visceral adipose tissue can predict one-year EWL% after LSG effectively.