Outcomes in children with delayed surgical treatment of medial humeral condyle fractures.

PURPOSE: Medial humeral condyle (MHC) fractures are easily overlooked in young patients. This can lead to delayed or incorrect diagnosis, resulting in delayed treatment, which is often associated with complications such as nonunion, osteonecrosis, fishtail deformity, and cubitus varus. The purpose of this study is to evaluate the clinical and radiographic outcomes in a cohort of paediatric patients who underwent delayed surgery for an untreated MHC fracture. METHODS: From January 2017 to December 2022, we conducted a retrospective study of paediatric patients who underwent delayed treatment for a MHC fracture. In all cases, the initial diagnosis was incorrect and surgery was performed at least one week after injury. Patients were divided into two groups based on the time between trauma and surgery: Group 1 consisted of individuals who underwent early delayed treatment within seven to 30 days of injury, while Group 2 consisted of those who underwent late delayed treatment more than one month after injury. Elbow function was assessed using the Mayo Elbow Performance Score (MEPS) and range of motion (ROM). The related literature was also reviewed (1970-2023). RESULTS: We enrolled 12 patients (7 boys, 5 girls); the average age at the time of surgery was 7.7 years (range, 2-14 years). Six patients underwent early delayed treatment (Group 1) while another six underwent late delayed treatment (Group 2). The mean time from injury to surgery was 17.7 days (range, 7-30 days) and 33.3 months (range, 70 days-9 years) in Groups 1 and 2, respectively. Open reduction and internal fixation were performed via a medial approach in 11 patients, while one patient underwent closing wedge osteotomy and internal fixation to correct cubitus varus deformity. The mean duration of follow-up was 39.4 months (range, 8-60 months). The average MEPS score was 98.3 in Group 1 (range, 95-100) and 94.2 in Group 2 (range, 85-100; P = 0.21). The following postoperative complications were recorded: heterotopic ossification (n = 2), fishtail deformity (n = 1), MHC necrosis (n = 1), and reduction of elbow ROM (n = 1); one complication occurred in Group 1 and five occurred in Group 2 (P = 0.18). We reviewed nine related studies (n = 14 patients). CONCLUSIONS: Diagnosis of MHC fractures can be challenging in paediatric patients, especially in younger individuals with incompletely ossified trochlea. Patients requiring surgery for delayed MHC fractures with an unossified trochlea should undergo ORIF to prevent progressive varus deformity. On the other hand, in patients with cubitus varus and an already ossified trochlea, distal humeral osteotomy should be considered instead of ORIF. This will minimize the potential negative impact on joint mobility.

Deep learning for predicting 16S rRNA gene copy number.

Culture-independent 16S rRNA gene metabarcoding is a commonly used method for microbiome profiling. To achieve more quantitative cell fraction estimates, it is important to account for the 16S rRNA gene copy number (hereafter 16S GCN) of different community members. Currently, there are several bioinformatic tools available to estimate the 16S GCN values, either based on taxonomy assignment or phylogeny. Here we present a novel approach ANNA16, Artificial Neural Network Approximator for 16S rRNA gene copy number, a deep learning-based method that estimates the 16S GCN values directly from the 16S gene sequence strings. Based on 27,579 16S rRNA gene sequences and gene copy number data from the rrnDB database, we show that ANNA16 outperforms the commonly used 16S GCN prediction algorithms. Interestingly, Shapley Additive exPlanations (SHAP) shows that ANNA16 can identify unexpected informative positions in 16S rRNA gene sequences without any prior phylogenetic knowledge, which suggests potential applications beyond 16S GCN prediction.

Programmable Protein Stabilization with Language Model-Derived Peptide Guides.

Dysregulated protein degradation via the ubiquitin-proteasomal pathway can induce numerous disease phenotypes, including cancer, neurodegeneration, and diabetes. Stabilizing improperly ubiquitinated proteins via target-specific deubiquitination is thus a critical therapeutic goal. Building off the major advances in targeted protein degradation (TPD) using bifunctional small-molecule degraders, targeted protein stabilization (TPS) modalities have been described recently. However, these rely on a limited set of chemical linkers and warheads, which are difficult to generate de novo for new targets and do not exist for classically "undruggable" targets. To address the limited reach of small molecule-based degraders, we previously engineered ubiquibodies (uAbs) by fusing computationally-designed "guide" peptides to E3 ubiquitin ligase domains for modular, CRISPR-analogous TPD. Here, we expand the TPS target space by engineering "deubiquibodies" (duAbs) via fusion of computationally-designed guides to the catalytic domain of the potent OTUB1 deubiquitinase. In human cells, duAbs effectively stabilize exogenous and endogenous proteins in a DUB-dependent manner. To demonstrate duAb modularity, we swap in new target-binding peptides designed via our generative language models to stabilize diverse target proteins, including key tumor suppressor proteins such as p53 and WEE1, as well as heavily-disordered fusion oncoproteins, such as PAX3::FOXO1. In total, our duAb system represents a simple, programmable, CRISPR-analogous strategy for TPS.

De Novo Design of Peptide Binders to Conformationally Diverse Targets with Contrastive Language Modeling.

Designing binders to target undruggable proteins presents a formidable challenge in drug discovery, requiring innovative approaches to overcome the lack of putative binding sites. Recently, generative models have been trained to design binding proteins via three-dimensional structures of target proteins, but as a result, struggle to design binders to disordered or conformationally unstable targets. In this work, we provide a generalizable algorithmic framework to design short, target-binding linear peptides, requiring only the amino acid sequence of the target protein. To do this, we propose a process to generate naturalistic peptide candidates through Gaussian perturbation of the peptidic latent space of the ESM-2 protein language model, and subsequently screen these novel linear sequences for target-selective interaction activity via a CLIP-based contrastive learning architecture. By integrating these generative and discriminative steps, we create a Peptide Prioritization via CLIP (PepPrCLIP) pipeline and validate highly-ranked, target-specific peptides experimentally, both as inhibitory peptides and as fusions to E3 ubiquitin ligase domains, demonstrating functionally potent binding and degradation of conformationally diverse protein targets in vitro. Overall, our design strategy provides a modular toolkit for designing short binding linear peptides to any target protein without the reliance on stable and ordered tertiary structure, enabling generation of programmable modulators to undruggable and disordered proteins such as transcription factors and fusion oncoproteins.

Algorithm selection for protein-ligand docking: strategies and analysis on ACE.

The present study investigates the use of algorithm selection for automatically choosing an algorithm for any given protein-ligand docking task. In drug discovery and design process, conceptualizing protein-ligand binding is a major problem. Targeting this problem through computational methods is beneficial in order to substantially reduce the resource and time requirements for the overall drug development process. One way of addressing protein-ligand docking is to model it as a search and optimization problem. There have been a variety of algorithmic solutions in this respect. However, there is no ultimate algorithm that can efficiently tackle this problem, both in terms of protein-ligand docking quality and speed. This argument motivates devising new algorithms, tailored to the particular protein-ligand docking scenarios. To this end, this paper reports a machine learning-based approach for improved and robust docking performance. The proposed set-up is fully automated, operating without any expert opinion or involvement both on the problem and algorithm aspects. As a case study, an empirical analysis was performed on a well-known protein, Human Angiotensin-Converting Enzyme (ACE), with 1428 ligands. For general applicability, AutoDock 4.2 was used as the docking platform. The candidate algorithms are also taken from AutoDock 4.2. Twenty-eight distinctly configured Lamarckian-Genetic Algorithm (LGA) are chosen to build an algorithm set. ALORS which is a recommender system-based algorithm selection system was preferred for automating the selection from those LGA variants on a per-instance basis. For realizing this selection automation, molecular descriptors and substructure fingerprints were employed as the features characterizing each target protein-ligand docking instance. The computational results revealed that algorithm selection outperforms all those candidate algorithms. Further assessment is reported on the algorithms space, discussing the contributions of LGA's parameters. As it pertains to protein-ligand docking, the contributions of the aforementioned features are examined, which shed light on the critical features affecting the docking performance.

Diagnosis and treatment of "medial to lateral diagonal injury of the elbow" in children: Concomitant medial epicondylar and radial neck fractures.

Purpose: The simultaneous and ipsilateral occurrence of medial epicondylar and radial neck fractures is rare. This study evaluated the clinical and radiological outcomes of medial to lateral diagonal injury of the elbow (MELAINE). Methods: Six males and 6 females were diagnosed with MELAINE (left: 10, 83.3%; right: 2, 16.7%). Medial epicondylar and radial neck fractures were classified according to Papavasiliou's classification (seven type II, two type III, three type IV) and Judet's classification (three type I, four type II and five type III), respectively. All patients underwent surgery. The carrying angle, range of motion, and Kim et al. Elbow Performance Score were used to evaluate clinical and functional outcomes; related complications were recorded. Results: Mean age at injury and mean follow-up were 11.1 ± 2.5 (range, 6-14) and 40 ± 25.6 months (range, 13-90), respectively. All fractures consolidated in 6.3 ± 1.2 weeks on average (4-9). Outcomes were good (n = 1; 8.3%) to excellent (n = 11; 91.7%). The carrying angle of the injured and uninjured side was 15.5°± 2.6° and 14.7°± 2°, respectively (p = 0.218). The range of motion of elbow flexion-extension and forearm pronation-supination of the injured side was 144.2°± 10.4°, 4.6°± 5.4°, 76.7°± 9.1°, 80.4°± 9.2°, respectively, with no significant differences from the healthy side (p > 0.05). The Elbow Performance Score of the injured and uninjured side was 96.3 ± 5.3 and 98.8 ± 2.3, respectively (p = 0.139). No cases of infection, cubitus valgus, stiffness, or instability were recorded. Conclusion: Although uncommon, MELAINE should not be neglected. Surgery aims to stabilize the elbow and avoid valgus deformity. If diagnosed and treated, clinical and radiological results are excellent in most cases.

Titanium surfaces with biomimetic topography and copper incorporation to modulate behaviors of stem cells and oral bacteria.

Purpose: Insufficient osseointegration and implant-associated infection are major factors in the failure of Ti-based implants, thus spurring scientists to develop multifunctional coatings that are better suited for clinical requirements. Here, a new biomimetic micro/nanoscale topography coating combined with antibacterial copper was simultaneously designed for Ti-based implant surfaces by adopting a hybrid approach combining plasma electrolytic oxidation and hydrothermal treatment. Results: The biological interactions between this biofunctionalized material interface and stem cells promoted cellular adhesion and spreading during initial attachment and supported cellular proliferation for favorable biocompatibility. Bone marrow mesenchymal stem cells (BMMSCs) on the coating displayed enhanced cellular mineral deposition ability, higher alkaline phosphatase activity, and upregulated expression of osteogenic-related markers without the addition of osteoinductive chemical factors, which improved osseointegration. More interestingly, this new coating reduced the viability of oral pathogens (Fusobacterium nucleatum and Porphyromonas gingivalis)-the primary causes of implant-associated infections as indicated by damage of cellular structures and decreased population. This is the first study investigating the antibacterial property of dental implants modified by a hybrid approach against oral pathogens to better mimic the oral environment. Conclusion: These findings suggest that biofunctionalization of the implant coating by surface modification methods and the incorporation of antibacterial copper (Cu) offer superior osteogenesis capability and effective antibacterial activity, respectively. These strategies have great value in orthopedic and dental implant applications.

PepMLM: Target Sequence-Conditioned Generation of Peptide Binders via Masked Language Modeling.

Target proteins that lack accessible binding pockets and conformational stability have posed increasing challenges for drug development. Induced proximity strategies, such as PROTACs and molecular glues, have thus gained attention as pharmacological alternatives, but still require small molecule docking at binding pockets for targeted protein degradation (TPD). The computational design of protein-based binders presents unique opportunities to access "undruggable" targets, but have often relied on stable 3D structures or predictions for effective binder generation. Recently, we have leveraged the expressive latent spaces of protein language models (pLMs) for the prioritization of peptide binders from sequence alone, which we have then fused to E3 ubiquitin ligase domains, creating a CRISPR-analogous TPD system for target proteins. However, our methods rely on training discriminator models for ranking heuristically or unconditionally-derived "guide" peptides for their target binding capability. In this work, we introduce PepMLM, a purely target sequence-conditioned de novo generator of linear peptide binders. By employing a novel masking strategy that uniquely positions cognate peptide sequences at the terminus of target protein sequences, PepMLM tasks the state-of-the-art ESM-2 pLM to fully reconstruct the binder region, achieving low perplexities matching or improving upon previously-validated peptide-protein sequence pairs. After successful in silico benchmarking with AlphaFold-Multimer, we experimentally verify PepMLM's efficacy via fusion of model-derived peptides to E3 ubiquitin ligase domains, demonstrating endogenous degradation of target substrates in cellular models. In total, PepMLM enables the generative design of candidate binders to any target protein, without the requirement of target structure, empowering downstream programmable proteome editing applications.

Nucleus Pulposus-Targeting Nanocarriers Facilitate Mirna-Based Therapeutics for Intervertebral Disc Degeneration.

Intervertebral disc degeneration (IDD) is a common cause of low back pain. Understanding its molecular mechanisms is the basis for developing specific treatment. To demonstrate that miR-22-3p is critical in the regulation of IDD, miRNA microarray analyses are conducted in conjunction with in vivo and in vitro experiments. The miR-22-3p knockout (KO) mice show a marked decrease in the histological scores. Bioinformatic analysis reveals that miR-22-3p plays a mechanistic role in the development of IDD by targeting SIRT1, which in turn activates the JAK1/STAT3 signaling pathway. This is confirmed by a luciferase reporter assay and western blot analysis. Therapeutically, the delivery of miR-22-3p inhibitors and mimics through the synthesized nanoparticles in the IDD model alleviates and aggravates IDD, respectively. The nanocarriers enhance transportation of miR-22-3p to nucleus pulposus cells, thus enabling the in vivo inhibition of miR-22-3p for therapeutic purposes and consequently promoting the development of miRNA-specific drugs for IDD.

SaLT&PepPr is an interface-predicting language model for designing peptide-guided protein degraders.

Protein-protein interactions (PPIs) are critical for biological processes and predicting the sites of these interactions is useful for both computational and experimental applications. We present a Structure-agnostic Language Transformer and Peptide Prioritization (SaLT&PepPr) pipeline to predict interaction interfaces from a protein sequence alone for the subsequent generation of peptidic binding motifs. Our model fine-tunes the ESM-2 protein language model (pLM) with a per-position prediction task to identify PPI sites using data from the PDB, and prioritizes motifs which are most likely to be involved within inter-chain binding. By only using amino acid sequence as input, our model is competitive with structural homology-based methods, but exhibits reduced performance compared with deep learning models that input both structural and sequence features. Inspired by our previous results using co-crystals to engineer target-binding "guide" peptides, we curate PPI databases to identify partners for subsequent peptide derivation. Fusing guide peptides to an E3 ubiquitin ligase domain, we demonstrate degradation of endogenous ß-catenin, 4E-BP2, and TRIM8, and highlight the nanomolar binding affinity, low off-targeting propensity, and function-altering capability of our best-performing degraders in cancer cells. In total, our study suggests that prioritizing binders from natural interactions via pLMs can enable programmable protein targeting and modulation.

Genetically Predicted Milk Intake Increased Femoral Neck Bone Mineral Density in Women But Not in Men.

Background: Cow milk contains more calcium, magnesium, potassium, zinc, and phosphorus minerals. For a long time, people have believed that increasing milk intake is beneficial to increasing bone density. Many confounding factors can affect milk consumption, and thus the association described to date may not be causal. We explored the causal relationship between genetically predicted milk consumption and Bone Mineral Density (BMD) of the femoral neck and lumbar spine based on 53,236 individuals from 27 studies of European ancestry using the Mendelian randomization (MR) study. 32,961 individuals of European and East Asian ancestry were used for sensitivity analysis. Methods: A genetic instrument used for evaluating milk consumption is rs4988235, a locus located at 13,910 base pairs upstream of the LCT gene. A Mendelian randomization (MR) analysis was conducted to study the effect of selected single nucleotide polymorphisms (SNPs) and BMD. The summary-level data for BMD of the femoral neck and lumbar spine were obtained from two GWAS meta-analyses ['Data Release 2012' and 'Data Release 2015' in the GEnetic Factors for OSteoporosis Consortium (GEFOS)]. Results: we found that genetically predicted milk consumption was not associated with FN-BMD(OR 1.007; 95% CI 0.991-1.023; P = 0.385), LS-BMD(OR 1.003; 95% CI 0.983-1.024; P = 0.743) by performing a meta-analysis of several different cohort studies. High levels of genetically predicted milk intake were positively associated with increased FN-BMD in Women. The OR for each additional milk intake increasing allele was 1.032 (95%CI 1.005-1.059; P = 0.014). However, no causal relationship was found between milk consumption and FN-BMD in men (OR 0.996; 95% CI 0.964-1.029; P = 0.839). Genetically predicted milk consumption was not significantly associated with LS-BMD in women (OR 1.017; 95% CI 0.991-1.043; P = 0.198) and men (OR 1.011; 95% CI 0.978-1.045; P = 0.523). Conclusion: Our study found that women who consume more milk have a higher FN-BMD. When studying the effect of milk consumption on bone density in further studies, we need to pay more attention to women.

Causal Association Between Tea Consumption and Bone Health: A Mendelian Randomization Study.

Background: Much observational research reported that tea consumption decreases the risk of osteoarthritis (OA), rheumatoid arthritis (RA), and osteoporosis (OP) which are the three major bone disorders. However, the observed correlation is inconclusive. To determine the causal relationship between genetically predicted tea intake and OA, RA, and OP, we performed a two-sample Mendelian randomization (MR) study based on large samples. Methods: The European population's genome-wide association meta-analysis (GWAS) dataset identified SNPs associated with tea consumption was obtained from Neale Lab's analysis of UK Biobank data that comprised 349,376 participants of European ancestry. We extracted genetic data for knee OA (17,885 controls and 4,462 cases), hip OA (50,898 controls and 12,625 cases), and RA (43,923 controls and 14,361 cases) from the UK Biobank and OP cases (93083 controls and 1,175 cases) from FinnGen Data Freeze 2. A MR study was conducted to examine the effect of selected single nucleotide polymorphisms (SNPs) and OA, RA, and OP risk. Several sensitivity analyses were performed with weighted median and inverse-variance weighted methods for estimating the causal effects. Results: In this MR study, the genetically predicted per one cup increase of tea consumption was not associated with knee OA (OR 1.11,95% CI: 0.79-1.55) using IVW with random effect. Genetic predisposition to tea consumption was not associated with hip OA (OR: 1.20, 95% CI: 0.84-1.71), RA (OR: 1.24 95% CI: 0.81-1.91), and OP (OR: 1.11, 95% CI: 0.89, 1.39). Following the sensitivity analysis, there was no potential pleiotropy. Conclusion: According to our study, According to our study, there was no statistical power to confirm a causal relationship between tea consumption and the risk of knee OA, hip OA, RA, and OP.

Relationship Between Plasma Vitamin C and COVID-19 Susceptibility and Severity: A Two-Sample Mendelian Randomization Study.

Background: Considering the antioxidant function of Vitamin C, also called ascorbic acid, it is widely used against viral infections such as coronavirus disease (COVID-19) based on in vitro, observational, and ecological studies. Many confounding factors that can affect Vitamin C levels. Thus, the association described to date may not be causal. To determine the causal relationship between genetically predicted plasma Vitamin C and COVID-19 susceptibility and severity, we performed two-sample Mendelian randomization (MR) based on large samples. Methods: The summary-level data for Vitamin C was obtained from a GWAS meta-analysis, which included 52,018 individuals from four studies of European ancestry. Data for COVID-19 HGI results were obtained from the meta-analysis of 35 GWASs with more than 1,000,000 subjects of European ancestry, including 32,494 cases with COVID-19 susceptibility and 1,316,207 controls, 9,986 cases with COVID-19 hospitalization and 1,877,672 controls, and 5,101 cases with COVID-19 severe disease and 1,383,241 controls. Mendelian randomization (MR) analysis was conducted to examine the effect of selected single nucleotide polymorphisms and COVID-19 susceptibility, hospitalization, disease severity. Several sensitivity analyses were performed with inverse-variance weighted (random-effect model), inverse variance weighted (fixed-effect model), weighted median, and maximum likelihood methods for estimating the causal effects. Results: In this MR study, genetic predisposition to the levels of plasma Vitamin C was not associated with COVID-19 susceptibility (OR: 0.99, 95% CI: 0.84-1.17, P = 0.91), hospitalization (OR: 1.10, 95% CI: 0.71-1.71, P = 0.67) and severity (OR: 0.83, 95% CI: 0.43-1.59, P = 0.58). The association was consistent in complementary analyses. No potential heterogeneities and directional pleiotropies were observed for the analysis results. Conclusion: According to our study, no correlation was observed between plasma Vitamin C levels and COVID-19 susceptibility and severity. Further studies in different ethnics are necessary to explore the potential role and mechanisms of circulating serum Vitamin C levels on COVID-19.