TB-DROP: deep learning-based drug resistance prediction of Mycobacterium tuberculosis utilizing whole genome mutations.

The most widely practiced strategy for constructing the deep learning (DL) prediction model for drug resistance of Mycobacterium tuberculosis (MTB) involves the adoption of ready-made and state-of-the-art architectures usually proposed for non-biological problems. However, the ultimate goal is to construct a customized model for predicting the drug resistance of MTB and eventually for the biological phenotypes based on genotypes. Here, we constructed a DL training framework to standardize and modularize each step during the training process using the latest tensorflow 2 API. A systematic and comprehensive evaluation of each module in the three currently representative models, including Convolutional Neural Network, Denoising Autoencoder, and Wide & Deep, which were adopted by CNNGWP, DeepAMR, and WDNN, respectively, was performed in this framework regarding module contributions in order to assemble a novel model with proper dedicated modules. Based on the whole-genome level mutations, a de novo learning method was developed to overcome the intrinsic limitations of previous models that rely on known drug resistance-associated loci. A customized DL model with the multilayer perceptron architecture was constructed and achieved a competitive performance (the mean sensitivity and specificity were 0.90 and 0.87, respectively) compared to previous ones. The new model developed was applied in an end-to-end user-friendly graphical tool named TB-DROP (TuBerculosis Drug Resistance Optimal Prediction: https://github.com/nottwy/TB-DROP ), in which users only provide sequencing data and TB-DROP will complete analysis within several minutes for one sample. Our study contributes to both a new strategy of model construction and clinical application of deep learning-based drug-resistance prediction methods.

The impact and future of artificial intelligence in medical genetics and molecular medicine: an ongoing revolution.

Artificial intelligence (AI) platforms have emerged as pivotal tools in genetics and molecular medicine, as in many other fields. The growth in patient data, identification of new diseases and phenotypes, discovery of new intracellular pathways, availability of greater sets of omics data, and the need to continuously analyse them have led to the development of new AI platforms. AI continues to weave its way into the fabric of genetics with the potential to unlock new discoveries and enhance patient care. This technology is setting the stage for breakthroughs across various domains, including dysmorphology, rare hereditary diseases, cancers, clinical microbiomics, the investigation of zoonotic diseases, omics studies in all medical disciplines. AI's role in facilitating a deeper understanding of these areas heralds a new era of personalised medicine, where treatments and diagnoses are tailored to the individual's molecular features, offering a more precise approach to combating genetic or acquired disorders. The significance of these AI platforms is growing as they assist healthcare professionals in the diagnostic and treatment processes, marking a pivotal shift towards more informed, efficient, and effective medical practice. In this review, we will explore the range of AI tools available and show how they have become vital in various sectors of genomic research supporting clinical decisions.

Association mapping of tan spot and septoria nodorum blotch resistance in cultivated emmer wheat.

KEY MESSAGE: A total of 65 SNPs associated with resistance to tan spot and septoria nodorum blotch were identified in a panel of 180 cultivated emmer accessions through association mapping Tan spot and septoria nodorum blotch (SNB) are foliar diseases caused by the respective fungal pathogens Pyrenophora tritici-repentis and Parastagonospora nodorum that affect global wheat production. To find new sources of resistance, we evaluated a panel of 180 cultivated emmer wheat (Triticum turgidum ssp. dicoccum) accessions for reactions to four P. tritici-repentis isolates Pti2, 86-124, 331-9 and DW5, two P. nodorum isolate, Sn4 and Sn2000, and four necrotrophic effectors (NEs) produced by the pathogens. About 8-36% of the accessions exhibited resistance to the four P. tritici-repentis isolates, with five accessions demonstrating resistance to all isolates. For SNB, 64% accessions showed resistance to Sn4, 43% to Sn2000 and 36% to both isolates, with Spain (11% accessions) as the most common origin of resistance. To understand the genetic basis of resistance, association mapping was performed using SNP (single nucleotide polymorphism) markers generated by genotype-by-sequencing and the 9 K SNP Infinium array. A total of 46 SNPs were significantly associated with tan spot and 19 SNPs with SNB resistance or susceptibility. Six trait loci on chromosome arms 1BL, 3BL, 4AL (2), 6BL and 7AL conferred resistance to two or more isolates. Known NE sensitivity genes for disease development were undetected except Snn5 for Sn2000, suggesting novel genetic factors are controlling host-pathogen interaction in cultivated emmer. The emmer accessions with the highest levels of resistance to the six pathogen isolates (e.g., CItr 14133-1, PI 94634-1 and PI 377672) could serve as donors for tan spot and SNB resistance in wheat breeding programs.

Placenta-specific CYP11A1 overexpression lead to autism-like symptom in offspring with altered steroid hormone biosynthesis in the placenta-brain axis and rescued by vitamin D intervention.

Alterations in steroid hormone regulation have been implicated in the etiology and progression of autism spectrum disorders (ASD), with the enzyme cytochrome P450 family 11 subfamily A member 1 (CYP11A1)-a key catalyst in cholesterol side-chain cleavage, prominently expressed in the adrenal glands, ovaries, testes, and placenta-standing at the forefront of these investigations. The potential link between aberrations in placental Cyp11a1 expression and the resultant neurodevelopmental disorders, along with the mechanisms underpinning such associations, remains inadequately delineated. In this study, we employed a placental trophoblast-specific Cyp11a1 Hipp11 (H11) knock-in murine model to dissect the phenotypic manifestations within the placenta and progeny, thereby elucidating the underlying mechanistic pathways. Behavioral analyses revealed a diminution in social interaction capabilities alongside an augmented anxiety phenotype, as evidenced by open field and elevated plus maze assessments; both phenotypes were ameliorated after vitamin D3 supplementation. Electrophysiological assays underscored the augmented inhibition of paired-pulse facilitation, indicating impaired neuroplasticity in Cyp11a1 H11-modified mice. An elevation in progesterone concentrations was noted, alongside a significant upregulation of Th1-related cytokines (IL-6 and TNFα) across the plasma, placental, and frontal cortex-a pathological state mitigable through vitamin D3 intervention. Western blotting revealed a vitamin D-mediated rectification of vitamin D receptor and PGC-1α expression dysregulations. Immunofluorescence assays revealed microglial activation in the knock-in model, which was reversible upon vitamin D3 treatment. In conclusion, Cyp11a1 overexpression in the placenta recapitulated an autism-like phenotype in murine models, and vitamin D3 administration effectively ameliorated the resultant neurobehavioral and neuroinflammatory derangements. This study substantiates the application of Cyp11a1 as a biomarker in prenatal diagnostics and posits that prenatal vitamin D3 supplementation is a viable prophylactic measure against perturbations in steroid hormone metabolism associated with ASD pathogenesis.

Neck circumference as an additional predictor of cardiovascular disease mortality: A multi-center prospective population-based study in northeastern China.

BACKGROUND AND AIMS: This study aimed to assess the potential of neck circumference (NC) and neck-to-height ratio (NHR) as predictors of future cardiovascular disease (CVD) mortality in a general population from Northeastern China. METHODS: A multi-center prospective study was conducted in Northeastern China, involving 18, 796 participants. The associations between NC or NHR and the incidence of overall CVD mortality, stroke mortality, and coronary heart disease (CHD) mortality were examined using multivariate Cox regression models. Hazard ratios (HRs) and the corresponding 95% confidence intervals (CIs) were calculated. Reclassification analyses were conducted to determine the incremental predictive value of NC or NHR. RESULTS: NC was significantly associated with the risk of CVD mortality, independent of other anthropometric measurements for obesity. Individuals in the highest quartile of NC had a 1.83-fold (95% CI 1.29 to 2.61) and a 2.40-fold (95% CI 1.45 to 4.00) higher risk of overall CVD mortality and CHD mortality, respectively. Larger NC was significantly related to a heightened risk of ischemic stroke mortality, although no such association was observed with hemorrhagic stroke mortality. Furthermore, the risk of overall CVD mortality, stroke mortality, and CHD mortality increased by approximately 1.21 to 1.25 times per 1-SD change in NC. Similar findings were observed for NHR. The percentages of correct classification of overall CVD mortality improved by 12.1% and 16.3% after the addition of NC or NHR into established models, respectively. CONCLUSIONS: NC and NHR might be promising predictors of CVD mortality, with higher values indicating greater risk.

Prevalence of prediabetes and risk of CVD mortality in individuals with prediabetes alone or plus hypertension in Northeast China: insight from a population based cohort study.

BACKGROUND: To evaluate the current prevalence of prediabetes in northeast China, and further determine the association between prediabetes alone or coexistent with hypertension and cardiovascular disease (CVD) mortality. METHODS: In the prospective study, 15,557 participants without diabetes among aged ≥40 years in northeast China, were followed for a median of 5.5 years. Following the American Diabetes Association, prediabetes was defined as fasting plasma glucose (FPG) range of 5.6-6.9 mmol/L or glycated hemoglobin (HbA1c) range of 5.7-6.4% in people without diabetes. RESULTS: The prevalence of prediabetes was 44.3% among population aged ≥40 years in northeast China. Prediabetes alone did not promote risk of CVD mortality. However, when the subgroups were stratified by hypertension, the CVD mortality risk in prediabetes plus hypertension subjects increased significantly compared with population without prediabetes and hypertension. Multivariate-adjusted hazard ratios for CVD mortality in prediabetes subgroups plus hypertension were 2.28 (95% CI: 1.50, 3.47) for those diagnosed by FPG < 5.6 mmol/L & HbA1c 5.7-6.4%, 2.18 (95% CI: 1.53, 3.10) for those diagnosed by FPG 5.6-6.0 mmol/L & HbA1c < 6.5% and 2.35 (95% CI: 1.65, 3.35) for those diagnosed by FPG 6.1-6.9 & HbA1c < 6.5% compared with the reference group. Moreover, the percentage of hypertension in prediabetes subjects was high (60.4%), but the awareness, treatment and control rates were far from satisfactory (45.3, 35.1 and 4.8%, respectively). CONCLUSIONS: The prevalence of prediabetes remains high in northeast China, and the CVD mortality was elevated significantly in prediabetes coexistent with hypertension. Considering the high percentage and low control rate of hypertension in prediabetes, strategies focused on HbA1c screening, FPG lowering and blood pressure management should be emphasized in northeast China.

Common carotid artery diameter and the risk of cardiovascular disease mortality: a prospective cohort study in northeast China.

BACKGROUND: The association between the common carotid artery (CCA) diameter and cardiovascular disease (CVD) is recognized, but the precise nature of this link remains elusive. This study aimed to investigate the potential relationship between CCA diameter and the risk of CVD mortality in a large population in northeast China. METHODS: The current study included 5668 participants (mean age 58.9 ± 10.1 years) from a population-based study conducted in rural areas of northeast China between September 2017 and May 2018. Information on death was collected from baseline until July 31, 2022. The CCA inter-adventitial diameter was measured using ultrasound. Cox proportional-hazard models were employed to explore the relationship between the common carotid artery diameter and cardiovascular mortality. RESULTS: At baseline, the mean CCA diameter (mm) of subjects was 7.30 ± 0.99 and increased significantly with age, ranging from 6.65 ± 0.71 among people 40-49 years to 7.99 ± 1.04 among people ≥ 80 years. CCA diameter was significantly larger in males compared to females (7.51 ± 1.03 versus vs. 7.16 ± 0.94; P < 0.001). A total of 185 participants died of CVD during a median follow-up of 4.48 years. CCA diameters were divided into quartiles, and the highest quartile of carotid diameter (≥ 8.06 mm) had a 2.29 (95% confidence interval [CI]: 1.24, 4.22) times higher risk of CVD mortality than the lowest quartile (≤ 6.65 mm) (P < 0.01) in the fully adjusted model. Each increase in the diameter of the common carotid artery (per SD) raised the risk of cardiovascular death by 36% (hazard ratio [HR]: 1.36; 95% CI: 1.18, 1.57). The subgroup analysis results demonstrated that a per SD increase was associated with a 42% increased risk of CVD mortality in participants aged ≥ 64 years in the fully adjusted model (HR: 1.42; 95%CI: 1.21, 1.66). CONCLUSIONS: Our study indicates the possible incremental value of CCA diameter in optimizing the risk stratification of cardiovascular disease and provides essential insights into reducing the burden of cardiovascular disease.

[Medical image segmentation data augmentation method based on channel weight and data-efficient features].

In computer-aided medical diagnosis, obtaining labeled medical image data is expensive, while there is a high demand for model interpretability. However, most deep learning models currently require a large amount of data and lack interpretability. To address these challenges, this paper proposes a novel data augmentation method for medical image segmentation. The uniqueness and advantages of this method lie in the utilization of gradient-weighted class activation mapping to extract data efficient features, which are then fused with the original image. Subsequently, a new channel weight feature extractor is constructed to learn the weights between different channels. This approach achieves non-destructive data augmentation effects, enhancing the model's performance, data efficiency, and interpretability. Applying the method of this paper to the Hyper-Kvasir dataset, the intersection over union (IoU) and Dice of the U-net were improved, respectively; and on the ISIC-Archive dataset, the IoU and Dice of the DeepLabV3+ were also improved respectively. Furthermore, even when the training data is reduced to 70 %, the proposed method can still achieve performance that is 95 % of that achieved with the entire dataset, indicating its good data efficiency. Moreover, the data-efficient features used in the method have interpretable information built-in, which enhances the interpretability of the model. The method has excellent universality, is plug-and-play, applicable to various segmentation methods, and does not require modification of the network structure, thus it is easy to integrate into existing medical image segmentation method, enhancing the convenience of future research and applications.

A PLC-based pomegranate sprout removal device design.

Aiming at the current low degree of mechanization of pomegranate sprouting tiller pruning in China, all relying on manual pruning, this paper designs a PLC-based pomegranate sprouting tiller removal machine. This machine adopts the identification method of wireless map transmission, the sprouting tiller removal method of multi-cylinder cooperative operation, and the MCGS configuration to realize the interaction between the user and the system, which realizes the displacement and angle compensation of the end-effector under complex conditions to realize the all-around accurate removal of the pomegranate sprouting tiller. The performance test and finite element analysis showed that the device could remove up to 74.62% of sprouting tillers, and the damage rate was as low as 18%. This meets the requirements of pomegranate plantations for the removal of emergent tillers.

Exploring the key deteriorative microorganisms on ancient ivories unearthed from the Sanxingdui Ruins site during temporary cold storage.

Introduction: The ancient ivories unearthed from the Sanxingdui Ruins site are valuable cultural relics, however, the microbial biodeterioration on ivories during temporary cold storage poses a great threat to their later long-term preservation. Methods: Here, the combination of high-throughput sequencing and biochemical assays was applied for the in-depth investigation of the key deteriorative microorganisms colonizing on the ivories and the tracing of their origin, as well as the assessment of the ethanol disinfection impact on the microbial communities on ivories. Results: It was observed that the surfaces of ivories were scattered by the fungal patches of white, dark grey, and hedge green colors during cold storage. The high-throughput sequencing results showed that the genera Mortierella (38.51%), Ilyonectria (14.43%), Penicillium (1.15%), and Aspergillus (1.09%) were the dominant fungi, while Pseudomonas (22.63%), Sphingopyxis (3.06%), and Perlucidibaca (2.92%) were the dominant bacteria on ivories. The isolated Aspergillus A-2 resulted in the highest amount of calcium releasing from the degradation of hydroxyapatite (HAP), the main component of ivory, by the organic acids produced, including oxalic acid and citric acid. The fast expectation-maximization for microbial source tracking (FEAST) analysis revealed that the majority of the fungi (57.45%) and bacteria (71.84%) colonizing on the ivories were derived from the soils surrounding ivories in the sacrifice pits, indicating soils as the primary source for the spoilage microbes growing on ivories. The dominant strains could degrade cellulose, the key components of wet cotton towels commonly applied on ivories for moisture maintenance, aiding the spoilage microbes colonizing on ivories. Notably, the ivory disinfection with 75% ethanol during the cleansing significantly decreased the relative abundance of the dominant genera of Ilyonectria, Aspergillus, and Pseudomonas, with Mortierella becoming the dominant one on ivories. Discussion: Together, the fungi, particularly Aspergillus and Penicillium, played a significant role in the microbial biodeterioration of unearthed ancient ivories by producing the organic acids. These results may improve the control of the microbial biodeterioration and develop more efficient strategies for the long-time conservation of unearthed ancient ivories and other cultural relics.

Non-covalent binding of chlorogenic acid to myofibrillar protein improved its bio-functionality properties and metabolic fate.

As natural antioxidants added to meat products, polyphenols can interact with proteins, and the acid-base environment influenced the extent of non-covalent and covalent interactions between them. This study compared the bio-functional characteristics and metabolic outcomes of the myofibrillar protein-chlorogenic acid (MP-CGA) complexes binding in different environments (pH 6.0 and 8.5). The results showed that CGA bound with MP significantly enhanced its antioxidant activity and inhibitory effect on metabolism enzymes. CGA bound deeply into the MP structure hydrophobic cavity at pH 6.0, which reduced its degradation by digestive enzymes, thus increasing its bio-accessibility from 59.5% to 71.6%. The digestion products of the two complexes exhibited significant differences, with the non-covalent MP-CGA complexes formed at pH 6.0 showing significantly higher concentrations of rhetsinine and piplartine, two well-known compounds to modulate diabetes. This study demonstrated that non-covalent binding between protein and polyphenol in the acidic environment held greater promising prospects for improving health.