Clinical Use Research Articles

EVA NEXUS-Phaco performance study

AIM: To investigate a novel phacoemulsification system “EVA NEXUS” (D.O.R.C., Dutch Opthalmic Research Center) in comparison to the existing system “EVA” in clinical use. And to compare both phacoemulsification systems in terms of efficiency, safety and postoperative inflammatory activity. METHODS: In this study standardized cataract surgery was performed on both eyes of the study participant, using the “EVA system” (control group, n=20) on one eye and the “EVA NEXUS system” (intervention group, n=20) on the other eye. Only patients with cataract LOCS Grading 1-3 and no accompanying eye diseases were included in this study. A total of 20 patients were included in this study, with each treatment arm including 20 eyes. During surgery a 0.1 mL aqueous humor sample was collected 1min after phacoemulsification to measure the total prostaglanin E2 concentrations using an enzyme-linked immunosorbent assay. The endothelial cell count, visual and refractive outcomes, and anterior chamber flare were evaluated preoperatively, and 1d, 1wk, and 3mo postoperatively. RESULTS: There were no statistically significant differences between both groups regarding intraoperative safety parameters including effective phacoemulsification time (P=0.904), balanced saline solution flow (P=0.701) and total surgery time (P=0.565). Postoperative prostaglandin E2 levels, anterior chamber flare as well as endothelial cell loss tended to be lower in the NEXUS-Group, however not being statistically significant (P=0.718; 0.164; 0.486). Both systems provided similar clinical outcomes, regarding best corrected visual acuity and refractive parameters, showing no statistically significant differences between both groups. CONCLUSION: Both systems show a high level of safety and efficency with similar results in terms of safety parameters including postoperative inflammatory activity and endothelial cell loss as well as visual and refractive outcomes. Although statistically not significant, the EVA NEXUS system tends to cause less postoperative inflammation with lower prostaglandin E2 levels and lower anterior chamber flare values.

MRI-Derived Modeling of Disease Progression Patterns in Patients With Temporal Lobe Epilepsy.

Temporal lobe epilepsy (TLE) is assumed to follow a steady course that is similar across patients. To date, phenotypic and temporal diversities of TLE evolution remain unknown. In this study, we aimed at simultaneously characterizing these sources of variability based on cross-sectional data. We studied consecutive patients with TLE referred for evaluation by neurologists to the Montreal Neurological Institute epilepsy clinic, who underwent in-patient video EEG monitoring and multimodal imaging at 3 Tesla, comprising 3D T1 and fluid-attenuated inversion recovery and 2D diffusion-weighted MRI. The cohort included patients with drug-resistant epilepsy and patients with drug-responsive epilepsy. The neuropsychological evaluation included Wechsler Adult Intelligence Scale-III and Leonard tapping task. The control group consisted of participants without TLE recruited through advertisement and who underwent the same MRI acquisition as patients. Based on surface-based analysis of key MRI markers of pathology (gray matter morphology and white matter microstructure), the Subtype and Stage Inference algorithm estimated subtypes and stages of brain pathology to which individual patients were assigned. The number of subtypes was determined by running the algorithm 100 times and estimating mean and SD of disease trajectories and the consistency of patients' assignments based on 1,000 bootstrap samples. Effect of normal aging was subtracted from patients. We examined associations with clinical and cognitive parameters and utility for individualized predictions. We studied 82 patients with TLE (52 female, mean age 35 ± 10 years; 11 drug-responsive) and 41 control participants (23 male, mean age 32 ± 8 years). Among 57 operated, 43/37/20 had Engel-I outcome/hippocampal sclerosis/hippocampal isolated gliosis, respectively. We identified 3 trajectory subtypes: S1 (n = 35), led by ipsilateral hippocampal atrophy and gliosis, followed by white-matter damage; S2 (n = 27), characterized by bilateral neocortical atrophy, followed by ipsilateral hippocampal atrophy and gliosis; and S3 (n = 20), typified by bilateral limbic white-matter damage, followed by bilateral hippocampal gliosis. Patients showed high assignability to their subtypes and stages (>90% bootstrap agreement). S1 had the highest proportions of patients with early disease onset (effect size d = 0.27 vs S2, d = 0.73 vs S3), febrile convulsions (χ2 = 3.70), drug resistance (χ2 = 2.94), a positive MRI (χ2 = 8.42), hippocampal sclerosis (χ2 = 7.57), and Engel-I outcome (χ2 = 1.51), pFDR < 0.05 across all comparisons. S2 and S3 exhibited the intermediate and lowest proportions, respectively. Verbal IQ and digit span were lower in S1 (d = 0.65 and d = 0.50, pFDR < 0.05) and S2 (d = 0.76 and d = 1.09, pFDR < 0.05), compared with S3. We observed progressive decline in sequential motor tapping in S1 and S3 (T = -3.38 and T = -4.94, pFDR = 0.027), compared with S2 (T = 2.14, pFDR = 0.035). S3 showed progressive decline in digit span (T = -5.83, p = 0.021). Supervised classifiers trained on subtype and stage outperformed subtype-only and stage-only models predicting drug response in 73% ± 1.0% (vs 70% ± 1.4% and 63% ± 1.3%) and 76% ± 1.6% for Engel-I outcome (vs 71% ± 0.8% and 72% ± 1.1%), pFDR < 0.05 across all comparisons. Cross-sectional MRI-derived models provide reliable prognostic markers of TLE disease evolution, which follows distinct trajectories, each associated with divergent patterns of hippocampal and whole-brain structural alterations, as well as cognitive and clinical profiles.

Age-Dependent Differences in Blood Levels of Glial Fibrillary Acidic Protein but Not Ubiquitin Carboxy-Terminal Hydrolase L1 in Children.

Despite the growing evidence of the clinical utility of blood-brain biomarkers in adults with traumatic brain injury (TBI), less is known about the performance of these biomarkers in children. We characterize age-dependent differences in levels of ubiquitin carboxy-terminal hydrolase L1 (UCH-L1) and glial fibrillary acidic protein (GFAP) in children without TBI. Plasma biobank specimens from children and adolescents aged 0-<19 years without TBI were obtained, and UCH-L1 and GFAP levels were quantified. The relationship between age and biomarker expression was determined using previously defined aged epochs (<3.5 years, 3.5 years to <11 years, 11 years and older), then biomarker levels were compared with established thresholds for ruling out the need for a head CT in adults with a mild TBI (mTBI) (UCH-L1 400 pg/mL, GFAP 35 pg/mL). The age range of the 366 control patients was 3 months-18 years. There was a significant negative association between age and GFAP but not UCH-L1. Only 1.4% of samples exceeded the UCH-L1 cutoff; however, 20% of samples exceeded the GFAP cutoff and 100% children younger than 3.5 years had values that exceeded the cutoff. Age seems to modify physiologic plasma GFAP levels. Diagnostic cutoffs for TBI based on GFAP but not UCH-L1 and may need to be adjusted in children younger than 11 years.

Water-powered, electronics-free dressings that electrically stimulate wounds for rapid wound closure.

Chronic wounds affect ~2% of the U.S. population and increase risks of amputation and mortality. Unfortunately, treatments for such wounds are often expensive, complex, and only moderately effective. Electrotherapy represents a cost-effective treatment; however, its reliance on bulky equipment limits its clinical use. Here, we introduce water-powered, electronics-free dressings (WPEDs) that offer a unique solution to this issue. The WPED performs even under harsh conditions-situations wherein many present treatments fail. It uses a flexible, biocompatible magnesium-silver/silver chloride battery and a pair of stimulation electrodes; upon the addition of water, the battery creates a radial electric field. Experiments in diabetic mice confirm the WPED's ability to accelerate wound closure and promote healing by increasing epidermal thickness, modulating inflammation, and promoting angiogenesis. Across preclinical wound models, the WPED-treated group heals faster than the control with wound closure rates comparable to treatments requiring expensive biologics and/or complex electronics. The results demonstrate the WPED's potential as an effective and more practical wound treatment dressing.

DCE-Qnet: deep network quantification of dynamic contrast enhanced (DCE) MRI.

Quantification of dynamic contrast-enhanced (DCE)-MRI has the potential to provide valuable clinical information, but robust pharmacokinetic modeling remains a challenge for clinical adoption. A 7-layer neural network called DCE-Qnet was trained on simulated DCE-MRI signals derived from the Extended Tofts model with the Parker arterial input function. Network training incorporated B1 inhomogeneities to estimate perfusion (Ktrans, vp, ve), tissue T1 relaxation, proton density and bolus arrival time (BAT). The accuracy was tested in a digital phantom in comparison to a conventional nonlinear least-squares fitting (NLSQ). In vivo testing was conducted in ten healthy subjects. Regions of interest in the cervix and uterine myometrium were used to calculate the inter-subject variability. The clinical utility was demonstrated on a cervical cancer patient. Test-retest experiments were used to assess reproducibility of the parameter maps in the tumor. The DCE-Qnet reconstruction outperformed NLSQ in the phantom. The coefficient of variation (CV) in the healthy cervix varied between 5 and 51% depending on the parameter. Parameter values in the tumor agreed with previous studies despite differences in methodology. The CV in the tumor varied between 1 and 47%. The proposed approach provides comprehensive DCE-MRI quantification from a single acquisition. DCE-Qnet eliminates the need for separate T1 scan or BAT processing, leading to a reduction of 10 min per scan and more accurate quantification.

Deep learning MR reconstruction in knees and ankles in children and young adults. Is it ready for clinical use?

To evaluate the diagnostic performance and image quality of accelerated Turbo Spin Echo sequences using deep-learning (DL) reconstructions compared to conventional sequences in knee and ankle MRIs of children and young adults. IRB-approved prospective study consisting of 49 MRIs from 48 subjects (10 males, mean age 16.4years, range 7-29years), with each MRI consisting of both conventional and DL sequences. Sequences were evaluated blindly to determine predictive values, sensitivity, and specificity of DL sequences using conventional sequences and knee arthroscopy (if available) as references.Physeal patency and appearance were evaluated. Qualitative parameters were compared. Presence of undesired image alterations was assessed. The prevalence of abnormal findings in the knees and ankles were 11.7% (75/640), and 11.5% (19/165), respectively. Using conventional sequences as reference, sensitivity and specificity of DL sequences in knees were 90.7% and 99.3%, and in ankles were 100.0% and 100.0%. Using arthroscopy as reference, sensitivity and specificity of DL sequences were 80.0% and 95.8%, and of conventional sequences were 80.0% and 97.9%. Agreement of physeal status was 100.0%. DL sequences were qualitatively "same-or-better" compared to conventional (p < 0.032), except for pixelation artifact for the PDFS sequence (p = 0.233). No discrete image alteration was identified in the knee DL sequences. In the ankle, we identified one DL artifact involving a tendon (0.8%, 1/125). DL sequences were faster than conventional sequences by a factor of 2 (p < 0.001). In knee and ankle MRIs, DL sequences provided similar diagnostic performance and "same-or-better" image quality than conventional sequences at half the acquisition time.

Evaluating Linear Heuristics for Ventricular Volume in Healthy Adults Using a Fully Automated Algorithm: Implications for Defining the Normal.

Linear metrics for ventricular volume play a large role in the rapid, approximate evaluation of ventricular volume. In this article, we automatically extract linear measures of ventricular volume to explore their correlation with lateral ventricular volume (LVV) in the healthy adult population and comprehensively define normal values. We automatically extract Evans' ratio (ER), Frontal-Occipital Horn Ratio (FOHR), and anteroposterior lateral ventricle index (ALVI) from an open MRI data set of healthy adults (https://brain-development.org/ixi-dataset/). Indices have been correlated with corresponding LVVs and lateral ventricular volumes divided by supratentorial brain volumes. Spearman rank correlation was used to compare strength of correlation. ER shows correlation with lateral ventricle volume based on sex (r = 0.58; men, r = 0.65; women P < .001), including when controlling for supratentorial volume (r = 0.57; men, r = 0.63). ER did not profoundly correlate with age (r = 0.29, men; r = 0.35, women; P < .001) and seemed normally distributed around 0.25. ALVI showed strong correlation with LVV with only slight gender differences (r = 0.83, men; r = 0.84, women) and LVV to supratentorial cortical volume ratio (r = 0.9, men; r = 0.86, women). FOHR was also normally distributed around a value of 0.37 and showed moderate correlation with LVV (r = 0.68, men; r = 0.73, women) and LVV to supratentorial cortical volume ratio (r = 0.69, men; r = 0.74, women). ALVI is a newer index with strong correlation with LVV and has strong potential for clinical use. Both FOHR and ER show moderate correlation with LVV. Reference values for linear estimates of ventricular volume may help clinicians better identify patients with pathological ventriculomegaly.

Validation of HAND OA US inflammatory and structural damage score (HOUSE) for thumb base joints: Systematic review on truth & discrimination.

Recently, the HAND osteoarthritis (OA) ULTRASOUND (US) Examination (HOUSE) inflammatory and structural damage scores were developed by the OMERACT US working group. However, the thumb base was not or only partly included. This systematic review examines US scoring methods and scanning techniques assessing thumb base OA, alongside existing evidence on validity, reliability, and responsiveness. A comprehensive search strategy in three different databases identified 30 eligible studies. In general, studies predominantly focused on US assessment of the carpometacarpal (CMC) 1 joint, with fewer investigating the scaphotrapeziotrapezoid (STT) joint. Most studies utilized a semiquantitative scale for scoring structural and inflammatory features, aligning with the HOUSE scoring system. Validity was supported by a limited number of studies, with one demonstrating a positive association between US structural damage and radiographic damage, and another showing a similar association with function. Associations between US inflammatory features and pain were observed, albeit with some variability. Reliability was from moderate to good for the CMC1 joint but limited for STT joint. Responsiveness varied across studies. The methodological quality of included studies varied, indicating areas for future research improvement. While promising, additional research is necessary to validate the HOUSE scoring system and improve its clinical utility for thumb base OA assessment. Future research should concentrate on optimal scanning positions and on the reliability and responsiveness of the HOUSE scoring system.

Development and Validation of a Prognostic Model to Predict Late Seizures After Traumatic Brain Injury: A Retrospective Analysis.

Posttraumatic epilepsy (PTE) is considered to be one of the most severe and enduring outcomes that can arise from traumatic brain injury (TBI). The authors' study aims to create and authenticate a prognostic model for forecasting the PTE occurrence after TBI. The clinical prognostic model was developed in 475 people who had a TBI history in Nanning using a multivariate logistic regression model. The score in the authors' prognostic model participants was subjected to external validation from other cities in Guangxi and assessed its performance with the area under the receiver operating characteristic curve (area under the curve), calibration plots, and decision curve analysis. Six variables were selected to establish the nomogram for PTE, including time, Glasgow Coma Scale, location, cranial imaging (midline shift), intracranial infection, and titanium mesh cranioplasty. The area under the curve was found to be 0.860 in the training cohort and 0.735 in the validation cohort, revealing that the nomogram exhibited a satisfactory level of discriminative ability. The calibration plots exhibited a substantial degree of concordance between the prognostic predictions generated by the nomogram and the observed outcomes in both the training and validation groups. In addition, the decision curve analysis demonstrated the clinical utility of the nomogram. The cutoff value for the training cohort was determined to be 0.381, whereas for the validation cohort, it was 0.380. This suggests that patients with a probability >0.381 should be given special consideration. A prognostic nomogram was formulated and verified to aid health care clinicians in assessing the prognosis of patients with PTE.

The Role of Preoperative Abdominal Ultrasound in the Preparation of Patients Undergoing Primary Metabolic and Bariatric Surgery: A Machine Learning Algorithm on 4418 Patients' Records.

The utility of preoperative abdominal ultrasonography (US) in evaluating patients with obesity before metabolic bariatric surgery (MBS) remains ambiguously defined. Retrospective analysis whereby patients were classified into four groups based on ultrasound results. Group 1 had normal findings. Group 2 had non-significant findings that did not affect the planned procedure. Group 3 required additional or follow-up surgeries without changing the surgical plan. Group 4, impacting the procedure, needed further investigations and was subdivided into 4A, delaying surgery for more assessments, and 4B, altering or canceling the procedure due to critical findings. Machine learning techniques were utilized to identify variables. Four thousand four hundred eighteen patients' records were analyzed. Group 1 was 45.7%. Group 2, 35.7%; Group 3, 17.0%; Group 4, 1.5%, Group 4A, 0.8%; and Group 4B, 0.7%, where surgeries were either canceled (0.3%) or postponed (0.4%). The hyperparameter tuning process identified a Decision Tree classifier with a maximum tree depth of 7 as the most effective model. The model demonstrated high effectiveness in identifying patients who would benefit from preoperative ultrasound before MBS, with training and testing accuracies of 0.983 and 0.985. It also showed high precision (0.954), recall (0.962), F1 score (0.958), and an AUC of 0.976. Our study found that preoperative ultrasound demonstrated clinical utility for a subset of patients undergoing metabolic bariatric surgery. Specifically, 15.9% of the cohort benefited from the identification of chronic calculous cholecystitis, leading to concomitant cholecystectomy. Additionally, surgery was postponed in 1.4% of the cases due to other findings. While these findings indicate a potential benefit in certain cases, further research, including a cost-benefit analysis, is necessary to fully evaluate routine preoperative ultrasound's overall utility and economic impact in this patient population.

Applications of artificial intelligence to myeloproliferative neoplasms: a narrative review.

Artificial intelligence (AI) is a rapidly growing field of computational research with the potential to extract nuanced biomarkers for the prediction of outcomes of interest. AI implementations for the prediction for clinical outcomes for myeloproliferative neoplasms (MPNs) are currently under investigation. In this narrative review, we discuss the AI investigations for the improvement of MPN clinical care utilizing either clinically available data or experimental laboratory findings. Abstracts and manuscripts were identified upon querying PubMed and the American Society of Hematology conference between 2000 and 2023. Overall, multidisciplinary researchers have developed AI methods in MPNs attempting to improve diagnostic accuracy, risk prediction, therapy selection, or pre-clinical investigations to identify candidate molecules as novel therapeutic agents. It is our expert opinion that AI methods in MPN care and hematology will continue to grow with increasing clinical utility. We believe that AI models will assist healthcare workers as clinical decision support tools if appropriately developed with AI-specific regulatory guidelines. Though the reported findings in this review are early investigations for AI in MPNs, the collective work developed by the research community provides a promising framework for improving decision making in the future of MPN clinical care.

Public value judgments about the criteria for reimbursement of medicines in South Korea

ABSTRACT Objectives This study quantified the public value (PV) of the criteria and sub-criteria in the current drug reimbursement systems in South Korea and examined sociodemographic factors that associated with PV. Methods The Analytic Hierarchy Process (AHP) was used to quantify the PVs of criteria and sub-criteria. We developed a questionnaire to generate pairwise comparison matrices among criteria and sub-criteria. From 27 March to 1 April 2023, we recruited 1,000 study participants using a quota sampling method stratified by age, sex, and region based on Korean census data. Results The PVs for the criteria were highest for clinical usefulness (28.5%), followed by cost-effectiveness (27.1%), budget impact (24.3%), and reimbursement in other countries (20.1%). The sociodemographic characteristics of the participants had a significant impact on the PVs of the criteria. Willingness to pay additional premiums for national health insurance was negatively associated with PV for clinical usefulness and cost-effectiveness and positively associated with PV for reimbursement in other countries. Conclusions The public prioritized clinical usefulness and cost-effectiveness as the main criteria. However, the PVs of the criteria were divergent and associated with sociodemographic factors. Divergent public interests require an evidence-informed deliberative process for reimbursement decisions.

Carbapenem-resistant Enterobacterales sepsis following endoscopic retrograde cholangiopancreatography: risk factors for 30-day all-cause mortality and the development of a nomogram based on a retrospective cohort

BackgroundEndoscopic retrograde cholangiopancreatography (ERCP) has become a routine endoscopic procedure that is essential for diagnosing and managing various conditions, including gallstone extraction and the treatment of bile duct and pancreatic tumors. Despite its efficacy, post-ERCP infections – particularly those caused by carbapenem-resistant Enterobacterales (CRE) – present significant risks. These risks highlight the need for accurate predictive models to enhance postprocedural care, reduce the mortality risk associated with post-ERCP CRE sepsis, and improve patient outcomes in the context of increasing antibiotic resistance.ObjectiveThis study aimed to examine the risk factors for 30-day mortality in patients with CRE sepsis following ERCP and to develop a nomogram for accurately predicting 30-day mortality risk.MethodsData from 195 patients who experienced post-ERCP CRE sepsis between January 2010 and December 2022 were analyzed. Variable selection was optimized via the least absolute shrinkage and selection operator (LASSO) regression model. Multivariate logistic regression analysis was then employed to develop a predictive model, which was evaluated in terms of discrimination, calibration, and clinical utility. Internal validation was achieved through bootstrapping.ResultsThe nomogram included the following predictors: age > 80 years (hazard ratio [HR] 2.61), intensive care unit (ICU) admission within 90 days prior to ERCP (HR 2.64), hypoproteinemia (HR 4.55), quick Pitt bacteremia score ≥ 2 (HR 2.61), post-ERCP pancreatitis (HR 2.52), inappropriate empirical therapy (HR 3.48), delayed definitive therapy (HR 2.64), and short treatment duration (< 10 days) (HR 5.03). The model demonstrated strong discrimination and calibration.ConclusionsThis study identified significant risk factors associated with 30-day mortality in patients with post-ERCP CRE sepsis and developed a nomogram to accurately predict this risk. This tool enables healthcare practitioners to provide personalized risk assessments and promptly administer appropriate therapies against CRE, thereby reducing mortality rates.

Feasibility of caregiver diabetes distress screening in routine clinical care of youth with type 1 diabetes.

Caregiver diabetes distress (DD) consists of feeling overwhelmed, sad, and/or concerned; one-third of parents of youth with type 1 diabetes (T1D) report severe distress up to 4 years after T1D diagnosis. PAID-PR (Problem Areas in Diabetes Survey-Parent Revised) assesses DD primarily in research settings; however, less is known about its clinical utility. We aimed to identify the feasibility of implementing PAID-PR screening at a diverse, academic US paediatric diabetes center during routine clinic follow-up visits through quality improvement methodologies. The PAID-PR was intended to be offered in English to caregivers at all paediatric T1D appointments, by the front desk during appointment check-in, or through REDCap prior to telehealth appointments or at local sites to all eligible caregivers. Adult psychosocial resources were provided to all, regardless of score. Forms were scored after appointments; scores ≥80 were referred to Diabetes Psychology providers for follow-up. A total of 391 caregivers completed the PAID-PR, though only half of eligible caregivers received it in person. Response rates were highest in person (90%), compared to REDCap (25%). In total, 27% (n = 107) scored ≥56 (DD). Of those with DD, 21% (n = 23) scored ≥80 and were referred to psychology. Demographics are reported in Table1. PAID-PR score was positively correlated to A1c (p = 0.038) and inversely to child age (p = 0.014). Clinic caregiver DD screening was implemented with higher response rates in person; however, expanding in-person screening to all eligible caregivers is necessary. Furthermore, since the PAID-PR was in English, some caregivers with DD were likely missed. Future directions include screening in additional languages.

Image analysis for bright-field HER2 in situ hybridization: validation for clinical use.

The aim of the present study was to develop and validate a quantitative image analysis (IA) algorithm to aid pathologists in assessing bright-field HER2 in situ hybridization (ISH) tests in solid cancers. A cohort of 80 sequential cases (40 HER2-negative and 40 HER2-positive) were evaluated for HER2 gene amplification with bright-field ISH. We developed an IA algorithm using the ISH Module from HALO software to automatically quantify HER2 and CEP17 copy numbers per cell as well as the HER2/CEP17 ratio. We observed a high correlation of HER2/CEP17 ratio, an average of HER2 and CEP17 copy number per cell between visual and IA quantification (Pearson's correlation coefficient of 0.842, 0.916, and 0.765, respectively). IA was able to count from 124 cells to 47,044 cells (median of 5565 cells). The margin of error for the visual quantification of the HER2/CEP17 ratio and of the average of HER2 copy number per cell decreased from a median of 0.23 to 0.02 and from a median of 0.49 to 0.04, respectively, in IA. Curve estimation regression models showed that a minimum of 469 or 953 invasive cancer cells per case is needed to reach an average margin of error below 0.1 for the HER2/CEP17 ratio or for the average of HER2 copy number per cell, respectively. Lastly, on average, a case took 212.1s to execute the IA, which means that it evaluates about 130 cells/s and requires 6.7s/mm2. The concordance of the IA software with the visual scoring was 95%, with a sensitivity of 90% and a specificity of 100%. All four discordant cases were able to achieve concordant results after the region of interest adjustment. In conclusion, this validation study underscores the usefulness of IA in HER2 ISH testing, displaying excellent concordance with visual scoring and significantly reducing margins of error.

Implementing pharmacogenomic testing in Australian general practice: an exploratory qualitative study.

Aim: To explore general practitioners' (GPs) views on implementing pharmacogenomic testing in Australian general practice. Methods: Semi-structured interviews were conducted with nine GPs in Australia, recruited from primary care networks. Interviews were analyzed using thematic analysis. Themes were mapped onto the Consolidated Framework for Implementation Research domains. Results: Barriers to implementation included lack of knowledge, education, standardized pharmacogenomic reports and national clinical guidelines and financial inaccessibility. Facilitators included positive exposure to pharmacogenomics, peer influences, interdisciplinary collaboration and proven clinical utility. Current uptake was minimal; however, GPs shared positive perceptions of clinical use. Conclusion: Recommendations for successful implementation include building and disseminating clinical evidence, developing national guidelines and standardized reports, incorporation into formal education and increasing financial accessibility.

MXene-Polydopamine-antiCEACAM1 Antibody Complex as a Strategy for Targeted Ablation of Melanoma.

Photothermal therapy (PTT) is a method for eradicating tumor tissues through the use of photothermal materials and photosensitizing agents that absorb light energy from laser sources and convert it into heat, which selectively targets and destroys cancer cells while sparing healthy tissue. MXenes have been intensively investigated as photosensitizing agents for PTT. However, achieving the selectivity of MXenes to the tumor cells remains a challenge. Specific antibodies (Ab) against tumor antigens can achieve homing of the photosensitizing agents toward tumor cells, but their immobilization on MXene received little attention. Here, we offer a strategy for the selective ablation of melanoma cells using MXene-polydopamine-antiCEACAM1 Ab complexes. We coated Ti3C2Tx MXene with polydopamine (PDA), a natural compound that attaches Ab to the MXene surface, followed by conjugation with an anti-CEACAM1 Ab. Our experiments confirm the biocompatibility of the Ti3C2Tx-PDA and Ti3C2Tx-PDA-antiCEACAM1 Ab complexes across various cell types. We also established a protocol for the selective ablation of CEACAM1-positive melanoma cells using near-infrared irradiation. The obtained complexes exhibit high selectivity and efficiency in targeting and eliminating CEACAM1-positive melanoma cells while sparing CEACAM1-negative cells. These results demonstrate the potential of MXene-PDA-Ab complexes for cancer therapy. They underline the critical role of targeted therapies in oncology, offering a promising avenue for the precise and safe treatment of melanoma and possibly other cancers characterized by specific biomarkers. Future research will aim to refine these complexes for clinical use, paving the way for new strategies for cancer treatment.

Artificial intelligence-assisted metastasis and prognosis model for patients with nodular melanoma.

The objective of this study was to identify the risk factors that influence metastasis and prognosis in patients with nodular melanoma (NM), as well as to develop and validate a prognostic model using artificial intelligence (AI) algorithms. The Surveillance, Epidemiology, and End Results (SEER) database was queried for 4,727 patients with NM based on the inclusion/exclusion criteria. Their clinicopathological characteristics were retrospectively reviewed, and logistic regression analysis was utilized to identify risk factors for metastasis. This was followed by employing Multilayer Perceptron (MLP), Adaptive Boosting (AB), Bagging (BAG), logistic regression (LR), Gradient Boosting Machine (GBM), and eXtreme Gradient Boosting (XGB) algorithms to develop metastasis models. The performance of the six models was evaluated and compared, leading to the selection and visualization of the optimal model. Through integrating the prognostic factors of Cox regression analysis with the optimal models, the prognostic prediction model was constructed, validated, and assessed. Logistic regression analyses identified that marital status, gender, primary site, surgery, radiation, chemotherapy, system management, and N stage were all independent risk factors for NM metastasis. MLP emerged as the optimal model among the six models (AUC = 0.932, F1 = 0.855, Accuracy = 0.856, Sensitivity = 0.878), and the corresponding network calculator (https://shimunana-nm-distant-m-nm-m-distant-8z8k54.streamlit.app/) was developed. The following were examined as independent prognostic factors: MLP, age, marital status, sequence number, laterality, surgery, radiation, chemotherapy, system management, T stage, and N stage. System management and surgery emerged as protective factors (HR < 1). To predict 1-, 3-, and 5-year overall survival (OS), a nomogram was created. The validation results demonstrated that the model exhibited good discrimination and consistency, as well as high clinical usefulness. The developed prediction model more effectively reflects the prognosis of patients with NM and differentiates between the risk level of patients, serving as a useful supplement to the classical American Joint Committee on Cancer (AJCC) staging system and offering a reference for clinically stratified individualized treatment and prognosis prediction. Furthermore, the model enables clinicians to quantify the risk of metastasis in NM patients, assess patient survival, and administer precise treatments.

Comprehensive analysis of L-PRF exudate components and their impact on whole blood platelets.

This study assessed the cellular composition and effects of leukocyte-platelet-rich fibrin (L-PRF) exudate on whole blood platelets from healthy volunteers. Key objectives included evaluating leukocyte subpopulations, platelet activation markers, platelet-leukocyte interactions and quantifying inflammatory cytokines within the L-PRF exudate. L-PRF was obtained from 20 healthy donors. Flow cytometry methodologies were used to assess intracellular calcium kinetics and activated GPIIbIIIa, and P-selectin expression. Leukocyte subpopulations and platelet-leukocyte interactions were characterized using monoclonal antibodies. Inflammatory cytokines (IL-8, IL-1β, IL-6, IL-10, TNF, IL-12p70) within L-PRF exudate were quantified using a cytometric bead array. The expression of activated GPIIbIIIa, and P-selectin exhibited a significant increase (p < 0.001) when L-PRF exudate was added to platelets of whole blood. Regarding intracellular Ca2+ mobilization, the L-PRF exudate elicited significant responses (p < 0.001). L-PRF exudate contained different leukocytes populations, being TCD4 + the most representative of T cells. It was possible to stablish a profile of cytokines produced by the L-PRF exudate, with human IL-8 cytokine exhibiting the highest average (16.90 pg/mL). Despite the study limitations, the research yielded important insights: 1- L-PRF exudate can stimulate platelet activation, essential in healing, tissue inflammation and remodeling. 2-The presence of leukocyte subpopulations within L-PRF exudate reflexes its complexity and potential to enhance immune responses. 3-The analysis of inflammatory cytokines within L-PRF exudate revealed its immunomodulatory potential. These findings are valuable evidences for understanding the potential role of L-PRF exudate in regenerative dentistry and medicine, offering innovative therapeutic strategies. This research highlights crucial aspects that could significantly influence the clinical use of L-PRF exudate in the oral cavity. The findings support the application of L-PRF exudate in both surgical and regenerative dentistry, facilitating the development of innovative therapeutic strategies to enhance patient outcomes.

Automated remote sleep monitoring needs uncertainty quantification.

Wearable electroencephalography devices emerge as a cost-effective and ergonomic alternative to gold-standard polysomnography, paving the way for better health monitoring and sleep disorder screening. Machine learning allows to automate sleep stage classification, but trust and reliability issues have hampered its adoption in clinical applications. Estimating uncertainty is a crucial factor in enhancing reliability by identifying regions of heightened and diminished confidence. In this study, we used an uncertainty-centred machine learning pipeline, U-PASS, to automate sleep staging in a challenging real-world dataset of single-channel electroencephalography and accelerometry collected with a wearable device from an elderly population. We were able to effectively limit the uncertainty of our machine learning model and to reliably inform clinical experts of which predictions were uncertain to improve the machine learning model's reliability. This increased the five-stage sleep-scoring accuracy of a state-of-the-art machine learning model from 63.9% to 71.2% on our dataset. Remarkably, the machine learning approach outperformed the human expert in interpreting these wearable data. Manual review by sleep specialists, without specific training for sleep staging on wearable electroencephalography, proved ineffective. The clinical utility of this automated remote monitoring system was also demonstrated, establishing a strong correlation between the predicted sleep parameters and the reference polysomnography parameters, and reproducing known correlations with the apnea-hypopnea index. In essence, this work presents a promising avenue to revolutionize remote patient care through the power of machine learning by the use of an automated data-processing pipeline enhanced with uncertainty estimation.