Citations

Abstract Increased posterior tibial slope (PTS) is a risk factor for anterior cruciate ligament (ACL) tear and ACL reconstruction failure. Anterior closing‐wedge osteotomy (ACWO) decreases the PTS and the risk of reinjury after revision ACL reconstruction. ACWO techniques include supratubercular, transtubercular, or infratubercular. However, there are limitations to the amount of slope correction an ACWO can achieve in the context of a massively abnormal slope. If the necessary slope correction is greater than 20°, a supratubercular ACWO cannot achieve the required correction without high risk of brittle fixation, a transtubercular ACWO would introduce increased morbidity of a secondary osteotomy and change the biomechanics of the patellofemoral joint resulting in significant patella alta, and an infratubercular ACWO would require a major anterior tibial metaphysodiaphyseal resection. The tibial plateau leveling osteotomy (TPLO) is an alternative curved osteotomy commonly used in canines that avoids the adverse events associated with a large ACWO. Further benefits of TPLO for massive slope correction include improved maintenance of native soft‐tissue tension, avoidance of major recurvatum, and the ability to control the correct degree. We describe an open technique for a TPLO to decrease the PTS and the risk of recurrent ACL injury. Technique Video video See video under supplementary data.

SummaryBackground and AimsMachine learning (ML) can identify the hidden patterns without hypothesis in heterogeneous diseases like acute‐on‐chronic live failure (ACLF). We employed ML to describe and predict yet unknown clusters in ACLF.MethodsClinical data of 1568 patients with ACLF from a tertiary care centre (2015–2023) were subjected to distance‐, density‐ and model‐based clustering algorithms. Final model was selected on best cluster separation, viz. Silhouette width and Dunn's index (for distance‐ or density‐based algorithms) and minimum BIC (for model‐based algorithms). Cluster assignments, patient trajectories and survival were analysed through inferential statistics. Supervised ML models were trained in 70% data that predicted clusters in remaining 30% data followed by an temporal validation.ResultsThe cohort was male‐predominant (87%), aged 44.3 years, with alcohol‐associated hepatitis (62.9%) and survival of 50.5%. Due to poor performance of distance‐ and density‐based algorithms and better explainability, the latent class model (LCM) was selected for exploration. LCM revealed four clusters with distinct trajectories, reversibility and survival (independent of MELD, CLIF‐C ACLF and AARC scores). Cluster1 had patients with none/one organ failure and highest reversibility. Cluster2 had females with viral hepatitis and two organ failures. More‐than‐one acute precipitant, severity, infections, organ failures and irreversibility escalated from clusters 1 to 4. Circulatory and renal failures critically influenced cluster assignments. Incorporating clusters to CLIF‐C ACLF, infection and ACLF definition improved the discriminative accuracy of CLIF‐C‐ACLF by 11%. Extreme gradient boost and decision trees could predict clusters with AUCs of 0.989 (0.979–0.995) and 0.875 (0.865–0.890). MELD, CLIF‐C‐OF, haemoglobin, lactate, CLIF‐C‐ACLF and ALT were critical variables for cluster prediction. Clusters with distinct survival were documented in a temporal validation cohort.ConclusionsML for the first time could identify clusters with distinct phenotypes, trajectories and outcomes in ACLF. Stratification into clusters can address heterogeneity, guide prognosis, recruitment in trials, resource allocation and liver transplant discussions in ACLF.

Coronary CT angiography (CCTA) is now endorsed by all major cardiology guidelines for the investigation of chest pain and assessment for coronary artery disease (CAD) in appropriately selected patients. CAD is a leading cause of morbidity and mortality. There is extensive literature to support CCTA diagnostic and prognostic value both for stable and acute symptoms. It enables rapid and cost-effective rule-out of CAD, and permits quantification and characterization of coronary plaque and associated significance. In this comprehensive review, we detail the road traveled as CCTA evolved to include quantitative assessment of plaque stenosis and extent, characterization of plaque characteristics including high-risk features, functional assessment including fractional flow reserve-CT (FFR-CT), and CT perfusion techniques. The state of current guideline recommendations and clinical applications are reviewed, as well as future directions in the rapidly advancing field of CT technology, including photon counting and applications of artificial intelligence (AI).

Background: Myxoid liposarcoma predominantly affects young and middle-aged individuals, and little is known regarding treatment tolerability and outcomes in older patients. This study aims to better understand this older patient population. Methods: This single institution retrospective study included patients aged 70 years and older with localized (non-metastatic) myxoid liposarcoma. Results: Sixteen patients were included. The median age was 75 years, and 9 (56%) were female. Fourteen (88%) were extremity tumors and two (12%) were trunk. The median tumor size was 10.4 cm (range, 3.6 to 28 cm). Five (31%) tumors had a round cell component. All patients had surgery. Fourteen (88%) had perioperative radiation, and three (19%) had perioperative chemotherapy. One patient had postoperative infection, and one patient had neutropenic fever from preoperative chemotherapy. The median follow up from surgery was 6.3 years. Eight (50%) patients died from MLPS. The median relapse-free survival and overall survival were 34 months and 75 months, respectively. Conclusions: Most older patients with localized MLPS received perioperative radiation therapy with surgery, and few serious toxicities were reported. Even with treatment, half of the patients relapsed.

Summary Background There are inconsistencies in the results of the studies investigating the association between inflammatory bowel disease (IBD) and lymphoma. Aims The aim of this study is to systematically appraise the risk of lymphoma development in patients with IBD. Methods We searched Embase, PubMed and Scopus from inception to 30 April 2024 to identify population‐based cohort studies that evaluated the risk of lymphoma in patients with IBD in comparison with those without IBD. We carried out random‐effects meta‐analyses and estimated pooled relative risks (RRs) with 95% confidence intervals (CIs). Results We identified 23 eligible studies reporting 2078 lymphoma events in 656,731 patients with IBD. Patients with IBD had 30% higher odds of lymphoma (RR = 1.30 [95% CI: 1.21–1.40]). The risk of developing both Hodgkin's lymphoma (nine studies, RR = 1.29 [95% CI: 1.06–1.53]) and non‐Hodgkin's lymphoma (16 studies, RR = 1.31 [95% CI: 1.20–1.42]) was increased in patients with IBD (p for interaction = 0.881). The increased risk of lymphoma was observed in both Crohn's disease (17 studies, RR = 1.54 [95% CI: 1.27–1.80]) and ulcerative colitis (20 studies, RR = 1.22 [95% CI: 1.09–1.35]) (p for interaction = 0.026). Meta‐regression demonstrated that mean age of patients, study year, mean study follow‐up duration, and percentages of immunomodulators and biologics use did not influence study outcome. Conclusions The risk of lymphoma is only modestly increased in patients with IBD, with Crohn's disease having a slightly higher risk than ulcerative colitis. In IBD, there appears to be no difference between the risks of Hodgkin's and non‐Hodgkin's lymphoma.

Abstract Current studies regarding the secondary use of electronic health records (EHR) predominantly rely on domain expertise and existing medical knowledge. Though significant efforts have been devoted to investigating the application of machine learning algorithms in the EHR, efficient and powerful representation of patients is needed to unleash the potential of discovering new medical patterns underlying the EHR. Here, we present an unsupervised method for embedding high-dimensional EHR data at the patient level, aimed at characterizing patient heterogeneity in complex diseases and identifying new disease patterns associated with clinical outcome disparities. Inspired by the architecture of modern language models—specifically transformers with attention mechanisms, we use patient diagnosis and procedure codes as vocabularies and treat each patient as a sentence to perform the patient embedding. We applied this approach to 34,851 unique medical codes across 1,046,649 longitudinal patient events, including 102,739 patients from the electronic Medical Records and GEnomics (eMERGE) Network. The resulting patient vectors demonstrated excellent performance in predicting future disease events (median AUROC = 0.87 within one year) and bulk phenotyping (median AUROC = 0.84). We then illustrated the utility of these patient vectors in revealing heterogeneous comorbidity patterns, exemplified by disease subtypes in colorectal cancer and systemic lupus erythematosus, and capturing distinct longitudinal disease trajectories. External validation using EHR data from the University of Washington confirmed robust model performance, with median AUROCs of 0.83 and 0.84 for bulk phenotyping tasks and disease onset prediction, respectively. Importantly, the model reproduced the clustering results of disease subtypes identified in the eMERGE cohort and uncovered variations in overall mortality among these subtypes. Together, these results underscore the potential of representation learning in EHRs to enhance patient characterization and associated clinical outcomes, thereby advancing disease forecasting and facilitating personalized medicine.

Matrix metalloproteinases (MMPs) are significant drivers of many diseases, including cancer, and are established targets for drug development. Tissue inhibitors of metalloproteinases (TIMPs) are endogenous MMP inhibitors and are being pursued for the development of anti-MMP therapeutics. TIMPs possess many attractive properties for drug candidates, such as complete MMP inhibition, low toxicity, low immunogenicity, and high tissue permeability. However, a major challenge with TIMPs is their rapid clearance from the bloodstream due to their small size. This study explores a method for extending the plasma half-life of the N-terminal domain of TIMP2 (N-TIMP2) by appending it with a long, intrinsically unfolded tail containing Pro, Ala, and Thr (PATylation). We designed and produced two PATylated N-TIMP2 constructs with tail lengths of 100 and 200 amino acids (N-TIMP2-PAT100 and N-TIMP2-PAT200). Both constructs demonstrated higher apparent molecular weights and retained high inhibitory activity against MMP-9. N-TIMP2-PAT200 significantly increased plasma half-life in mice compared to the non-PATylated variant, enhancing its therapeutic potential. PATylation offers distinct advantages for half-life extension, such as fully genetic encoding, monodispersion, and biodegradability. It can be easily applied to N-TIMP2 variants engineered for high affinity and selectivity toward individual MMPs, creating promising candidates for drug development against MMP-related diseases.

Abstract Heterotopic ossification (HO), often arising in response to traumatic challenges, results from the aberrant osteochondral differentiation of mesenchymal stem cells (MSCs). Nevertheless, the impact of trauma‐induced inflammatory exposure on MSC fate determination remains ambiguous. In this study, the cellular diversity within inflammatory lesions is elucidated, comprising MSCs and several innate and adaptive immune cells. It is observed that quiescent MSCs transition into cycling MSCs, subsequently giving rise to chondrogenic (cMSC) and/or osteogenic (oMSC) lineages within the inflammatory microenvironment following muscle or tendon injuries, as revealed through single‐cell RNA sequencing (scRNA‐seq), spatial transcriptome and lineage tracing analysis. Moreover, these investigations demonstrate that neutrophils and natural killer (NK) cells enhance transition of quiescent MSCs into cycling MSCs, which is also controlled by M1 macrophages, a subpopulation of macrophages can also stimulate cMSC and oMSC production from cycling MSCs. Additionally, M2 macrophages, CD4 + and CD8 + T lymphocytes are found to promote chondrogenesis. Further analysis demonstrates that immune cells promotes the activation of signaling transducers and activators of transcription (STAT) pathway and phosphoinositide 3 (PI3K)/protein kinase B (AKT) pathway in MSC proliferation and osteochondral progenitors’ production, respectively. These findings highlight the dynamics of MSC fate within the inflammatory lesion and unveil the molecular landscape of osteoimmunological interactions, which holds promise for advancing HO treatment.