Operative or Nonoperative Treatment is Predicted Accurately for Patients Who Have Hip Complaints Consulting an Orthopedic Surgeon Using Machine Learning Algorithms Trained With Prehospital Acquired History-Taking Data.

BACKGROUND: Increasing numbers of patients suffering from hip osteoartritis will lead to increased orthopaedic health care consumption. Artificial intelligence might alleviate this problem, using Machine learning (ML) to optimize orthopaedic consultation workflow by predicting treatment strategy (non-operative or operative) prior to consultation. The purpose of this study was to assess ML accuracy in clinical practice, by comparing ML predictions to the outcome of clinical consultations. METHODS: In this prospective clinical cohort study, adult patients referred for hip complaints between January 20th to February 20th 2023 were included. Patients completed a computer-assisted history taking (CAHT) form and using these CAHT answers, a ML-algorithm predicted non-operative or operative treatment outcome prior to in-hospital consultation. During consultation, orthopaedic surgeons and physician assistants were blinded to the prediction in 90 and unblinded in 29 cases. Consultation outcome (non-operative or operative) was compared to ML treatment prediction for all cases, and for blinded and unblinded conditions separately. Analysis was done on 119 consultations. RESULTS: Overall treatment strategy prediction was correct in 101 cases (accuracy 85%, P < .0001). Non-operative treatment prediction (n = 71) was 97% correct versus 67% for operative treatment prediction (n = 48). Results from unblinded consultations (86.2% correct predictions,) were not statistically different from blinded consultations (84.4% correct, P > .05). CONCLUSIONS: Machine Learning algorithms can predict non-operative or operative treatment for patients with hip complaints with high accuracy. This could facilitate scheduling of non-operative patients with physician assistants, and operative patients with orthopaedic surgeons including direct access to pre-operative screening, thereby optimizing usage of health care resources.

Machine learning algorithms trained with pre-hospital acquired history-taking data can accurately differentiate diagnoses in patients with hip complaints.

Background and purpose - Machine learning (ML) techniques are a form of artificial intelligence able to analyze big data. Analyzing the outcome of (digital) questionnaires, ML might recognize different patterns in answers that might relate to different types of pathology. With this study, we investigated the proof-of-principle of ML-based diagnosis in patients with hip complaints using a digital questionnaire and the Kellgren and Lawrence (KL) osteoarthritis score.Patients and methods - 548 patients (> 55 years old) scheduled for consultation of hip complaints were asked to participate in this study and fill in an online questionnaire. Our questionnaire consists of 27 questions related to general history-taking and validated patient-related outcome measures (Oxford Hip Score and a Numeric Rating Scale for pain). 336 fully completed questionnaires were related to their classified diagnosis (either hip osteoarthritis, bursitis or tendinitis, or other pathology). Different AI techniques were used to relate questionnaire outcome and hip diagnoses. Resulting area under the curve (AUC) and classification accuracy (CA) are reported to identify the best scoring AI model. The accuracy of different ML models was compared using questionnaire outcome with and without radiologic KL scores for degree of osteoarthritis.Results - The most accurate ML model for diagnosis of patients with hip complaints was the Random Forest model (AUC 82%, 95% CI 0.78-0.86; CA 69%, CI 0.64-0.74) and most accurate analysis with addition of KL scores was with a Support Vector Machine model (AUC 89%, CI 0.86-0.92; CA 83%, CI 0.79-0.87).Interpretation - Analysis of self-reported online questionnaires related to hip complaints can differentiate between basic hip pathologies. The addition of radiological scores for osteoarthritis further improves these outcomes.

Rapid method for proline determination in grape juice and wine.

Proline is typically the most abundant amino acid present in grape juice and wine. The amount present is influenced by viticultural and winemaking factors and can be of diagnostic importance. A method for rapid routine quantitation of proline would therefore be of benefit for wine researchers and the industry in general. Colorimetric determination utilizing isatin as a derivatizing agent has previously been applied to plant extracts, biological fluids, and protein hydrolysates. In the current study, this method has been successfully adapted to grape juice and wine and proved to be sensitive to milligram per liter amounts of proline. At sugar concentrations above 60 g/L, interference from the isatin-proline reaction was observed, such that proline concentrations were considerably underestimated in grape juice and dessert wine. However, the method was robust for the analysis of fermentation samples and table wines. Results were within ±10% agreement with data generated from typical HPLC-based analyses. The isatin method is therefore considered suitable for the routine analysis required to support research into the utilization or release of proline by yeast during fermentation.

Proline transport and stress tolerance of ammonia-insensitive mutants of the PUT4-encoded proline-specific permease in yeast.

The imino amino acid, proline, has roles in both cellular nutrition and response to stress. Proline uptake in Saccharomyces cerevisiae is largely mediated by a high affinity, specific permease, Put4p, and a low affinity general amino acid permease, Gap1p. Both are subject to nitrogen catabolite repression (NCR) and nitrogen catabolite inactivation (NCI). In order for proline to be fully exploited, its transport must be derepressed, as occurs upon depletion of preferred nitrogen sources, and molecular oxygen must be present to allow the first step of catabolism via proline oxidase. This study focuses on the isolation of variants of Put4p, which are insensitive to repression by a preferred nitrogen source (ammonia) and their subsequent effect on proline transport and stress tolerance. Specific amino acid residues in the carboxy-terminal region of Put4p were targeted by site-directed mutagenesis. Substitution at Serine(605), a potential phosphorylation target, led to the amelioration of ammonia-induced down-regulation of Put4p. When combined with a promoter mutation (-160), the S(605)A mutation resulted in increased proline uptake and accumulation. This increase in proline accumulation was associated with increased cell viability in conditions of high temperature and osmotic stress raising possible benefits in industrial fermentation applications.