Clinical Use Research Articles

Proton ARC based LATTICE radiation therapy: feasibility study, energy layer optimization and LET optimization.

This work will develop novel proton LATTICE method via proton ARC (with many beam angles) to overcome these challenges in target coverage and OAR sparing, with optimized delivery efficiency via energy layer optimization and optimized biological dose distribution via LET optimization, to enable the clinical use of proton LATTICE.
Approach: ARC based proton LATTICE is formulated and solved with energy layer optimization, during which plan quality and delivery efficiency are jointly optimized. In particular, the number of energy jumps (NEJ) is explicitly modelled and minimized during plan optimization for improving delivery efficiency, while target dose conformality and OAR dose objectives are optimized. The plan deliverability is ensured by considering the minimum-monitor-unit (MMU) constraint, and the plan robustness is accounted for using robust optimization. The biological dose is optimized via LET optimization. The optimization solution algorithm utilizes iterative convex relaxation method to handle the dose-volume constraint and the MMU constraint, with spot-weight optimization subproblems solved by proximal descent method.
Main results: ARC based proton LATTCE substantially improved plan quality from IMPT based proton LATTICE. Energy layer optimization improved plan delivery efficiency for ARC based proton LATTICE.
Significance: The feasibility of high-plan-quality proton LATTICE is demonstrated via proton ARC with substantially improved target dose coverage and OAR sparing compared to IMPT, while the plan delivery efficiency for ARC based proton LATTICE can be optimized using energy layer optimization.

The clinical utility of plasma circulating tumor DNA in the diagnosis and disease surveillance in non-diffuse large B-cell non-Hodgkin lymphomas.

Aim: Advances in circulating tumor DNA (ctDNA) analysis for diffuse large B-cell lymphoma (DLBCL) have prompted the evaluation of its utility in other non-Hodgkin lymphomas (NHLs), leading to significant insights into its potential applications.Methods: We retrospectively studied paired plasma and tissue/bone marrow biopsies of 203 non-DLBCL NHLs [87 follicular lymphomas (FL), 64 mantle cell lymphomas (MCL), 30 chronic lymphocytic leukemia/small lymphocytic lymphomas (CLL/SLL) and 22 marginal zone lymphomas (MZL)]. Genomic profiling was performed using a targeted next generation sequencing panel (Hemasalus™). Longitudinal analyses were performed to explore plasma ctDNA utility in disease monitoring.Results: High plasma ctDNA detection rates were observed across NHL subtypes (FL: 88.5%, MCL: 90.6%, CLL/SLL: 100%, MZL: 68.2%), with high concordance of actionable mutations (FL: 87.4%, MCL: 93.8%, CLL/SLL: 93.3%, MZL: 81.8%) and multiple genetic aberrations exclusively identified in plasma. Particularly, IGH-BCL2 and IGH-CCND1 fusions were concordant between plasma and tumor biopsies in FLs (91.1%) and MCLs (91.3%), respectively. Longitudinal data demonstrated that ctDNA clearance correlated with complete response but ctDNA increases preceded radiological relapses.Conclusion: ctDNA exhibited high concordance with tumor biopsy in detecting genetic aberrations and demonstrated potential as a promising noninvasive approach to disease surveillance in non-DLBCL NHLs.

Construction of vulnerable plaque prediction model based on multimodal vascular ultrasound parameters and clinical risk factors

The rupture of vulnerable plaque (VP) are significant pathogenic factors leading to cardiovascular and cerebrovascular diseases. This study aims to construct a vulnerable plaque prediction model (VPPM) by combining multimodal vascular ultrasound parameters and clinical risk factors, and to validate it. A total of 196 atherosclerotic patients who underwent carotid endarterectomy (CEA) from January 2017 to December 2023 were collected and divided into a modeling group (n = 137) and a validation group (n = 59). Clinical information including: hypertension, diabetes, smoking history, and body mass index (BMI) was included in the analysis. All patients underwent carotid ultrasound and contrast-enhanced ultrasound (CEUS) examination after admission, with main ultrasound parameters including thickness, echogenicity types, stenosis degree, and CEUS neovascularization grading of plaques. Independent risk factors for VP in CEA patients were screened through binary Logistic regression analysis, and a prediction model was established along with a nomogram. The calibration curve, receiver-operating characteristic curve (ROC), and decision curve analysis (DCA) were employed to assess the calibration, diagnostic efficacy, and clinical utility of the VPPM model. There were no significant statistical differences in multimodal vascular ultrasound parameters and clinical risk factors between the modeling and validation groups (P > 0.05). Binary Logistic regression analysis identified plaque thickness, echo type, CEUS neovascularization grading, BMI, and smoking history as 5 variables entering the prediction model. The VPPM model showed good diagnostic efficacy, with an area under the ROC curve of 0.959 (95% CI 0.915–0.999). Using the nomogram with a VPPM risk assessment score of 135.42 as the diagnostic cutoff value in the modeling group, the sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and Youden index were 88.1%, 94.1%, 14.98, 0.126, and 82.2%, respectively. In the DCA curve, the VPPM model curve was significantly better than two extreme lines, indicating good clinical utility. The VPPM model constructed by integrating multimodal ultrasound parameters and clinical key risk factors has high diagnostic efficacy and is expected to be an auxiliary tool for clinical diagnosis of vulnerable plaques.

Personality Disorders in Criminal Offenders - A Systematic Literature Review.

We summarized studies investigating measures related to the Alternative Model for Personality Disorders (AMPD) of the DSM-5 and the personality model in ICD-11 in offenders in forensic-psychiatric treatment or prison to evaluate its forensic utility. The reformation of the DSM and ICD with regards to the introduction of dimensional assessments of personality disorders holds many advantages over categorical models concerning clinical utility. With regards to DSM-5 AMPD Criterion A, a limited number of studies (k = 4) report impairments in interpersonal functioning in offenders. Studies assessing Criterion B (k = 13) predominantly report higher personality impairment measures for offenders, especially for antagonism and disinhibition. Due to the heterogeneity of the selected studies, this review cannot draw conclusions with regard to the predictive value of dimensional models for offenders in forensic-psychiatric treatment or prison, but provides initial evidence for the validity and utility of DSM-5 AMPD and ICD-11 in these settings.

Artificial Intelligence and Advanced Technology in Glaucoma: A Review

Background: Glaucoma is a leading cause of irreversible blindness worldwide, necessitating precise management strategies tailored to individual patient characteristics. Artificial intelligence (AI) holds promise in revolutionizing the approach to glaucoma care by providing personalized interventions. Aim: This review explores the current landscape of AI applications in the personalized management of glaucoma patients, highlighting advancements, challenges, and future directions. Methods: A systematic search of electronic databases, including PubMed, Scopus, and Web of Science, was conducted to identify relevant studies published up to 2024. Studies exploring the use of AI techniques in personalized management strategies for glaucoma patients were included. Results: The review identified diverse AI applications in glaucoma management, ranging from early detection and diagnosis to treatment optimization and prognosis prediction. Machine learning algorithms, particularly deep learning models, demonstrated high accuracy in diagnosing glaucoma from various imaging modalities such as optical coherence tomography (OCT) and visual field tests. AI-driven risk stratification tools facilitated personalized treatment decisions by integrating patient-specific data with predictive analytics, enhancing therapeutic outcomes while minimizing adverse effects. Moreover, AI-based teleophthalmology platforms enabled remote monitoring and timely intervention, improving patient access to specialized care. Conclusions: Integrating AI technologies in the personalized management of glaucoma patients holds immense potential for optimizing clinical decision-making, enhancing treatment efficacy, and mitigating disease progression. However, challenges such as data heterogeneity, model interpretability, and regulatory concerns warrant further investigation. Future research should focus on refining AI algorithms, validating their clinical utility through large-scale prospective studies, and ensuring seamless integration into routine clinical practice to realize the full benefits of personalized glaucoma care.

Competing and Noncompeting Risk Models for Predicting Kidney Allograft Failure.

Prognostic models are becoming increasingly relevant in clinical trials as potential surrogate endpoints, and for patient management as clinical decision support tools. However, the impact of competing risks on model performance remains poorly investigated. We aimed to carefully assess the performance of competing risk and noncompeting risk models in the context of kidney transplantation, where allograft failure and death with a functioning graft are two competing outcomes. We included 11,046 kidney transplant recipients enrolled in 10 countries. We developed prediction models for long-term kidney graft failure prediction, without accounting (i.e., censoring) and accounting for the competing risk of death with a functioning graft, using Cox, Fine-Gray, and cause-specific Cox regression models. To this aim, we followed a detailed and transparent analytical framework for competing and noncompeting risk modelling, and carefully assessed the models' development, stability, discrimination, calibration, overall fit, clinical utility, and generalizability in external validation cohorts and subpopulations. More than 15 metrics were used to provide an exhaustive assessment of model performance. Among 11,046 recipients in the derivation and validation cohorts, 1,497 (14%) lost their graft and 1,003 (9%) died with a functioning graft after a median follow-up post-risk evaluation of 4.7 years (IQR 2.7-7.0). The cumulative incidence of graft loss was similarly estimated by Kaplan-Meier and Aalen-Johansen methods (17% versus 16% in the derivation cohort). Cox and competing risk models showed similar and stable risk estimates for predicting long-term graft failure (average mean absolute prediction error of 0.0140, 0.0138 and 0.0135 for Cox, Fine-Gray, and cause-specific Cox models, respectively). Discrimination and overall fit were comparable in the validation cohorts, with concordance index ranging from 0.76 to 0.87. Across various subpopulations and clinical scenarios, the models performed well and similarly, although in some high-risk groups (such as donors over 65 years old), the findings suggest a trend towards moderately improved calibration when using a competing risk approach. Competing and noncompeting risk models performed similarly in predicting long-term kidney graft failure.

Vagus nerve stimulation for epilepsy: A narrative review of factors predictive of response.

Vagus nerve stimulation (VNS) is an established therapy for drug-resistant epilepsy. However, there is a lack of reliable predictors of VNS response in clinical use. The identification of factors predictive of VNS response is important for patient selection and stratification as well as tailored stimulation programming. We conducted a narrative review of the existing literature on prognostic markers for VNS response using clinical, demographic, biochemical, and modality-specific information such as from electroencephalography (EEG), magnetoencephalography, and magnetic resonance imaging (MRI). No individual marker demonstrated sufficient predictive power for individual patients, although several have been suggested, with some promising initial findings. Combining markers from underresearched modalities such as T1-weighted MRI morphometrics and EEG may provide better strategies for treatment optimization.

Inertial measurement units to evaluate the efficacity of Equino Varus Foot surgery in post stroke hemiparetic patients: a feasibility study

IntroductionThis study evaluates the gait analysis obtained by Inetial Measurement Units (IMU) before and after surgical management of Spastic Equino Varus Foot (SEVF) in hemiplegic post-stroke patients and to compare it with the functional results obtained in a monocentric prospective cohort.MethodsPatients with post-stroke SEVF, who underwent surgery in a single hospital between November 2019 and December 2021 were included. The follow-up duration was 6 months and included a functional analysis using Goal Attainment Scaling (GAS) and a Gait analysis using an innovative Multidimensional Gait Evaluation using IMU: the semiogram.Results20 patients had a gait analysis preoperatively and at 6 months postoperatively. 90% (18/20) patients had a functional improvement (GAS T score ≥ 50) and 50% (10/20) had an improvement in walking technique as evidenced by the cessation of the use of a walking aid (WA). In patients with functional improvement and modification of WA the change in the semiogram area was + 9.5%, sd = 27.5%, and it was + 15.4%, sd = 28%. In the group with functional improvement without change of WA. For the 3 experiences (two patients) with unfavorable results, the area under the curve changed by + 2.3%, -10.2% and − 9.5%. The measurement of the semiogram area weighted by average speed demonstrated very good reproducibility (ICC(1, 3) = 0.80).DiscussionIMUs appear to be a promising solution for the assessment of post-stroke hemiplegic patients who have undergone SEVF surgery. They can provide a quantified, objective, reliable in individual longitudinal follow up automated gait analysis solution for routine clinical use. Combined with a functional scale such as the GAS, they can provide a global analysis of the effect of surgery.

Potential Use of Organoids in Regenerative Medicine.

In vitro cell culture is crucial for studying human diseases and development. Compared to traditional monolayer cultures, 3D culturing with organoids offers significant advantages by more accurately replicating natural tissues' structural and functional features. This advancement enhances disease modeling, drug testing, and regenerative medicine applications. Organoids, derived from stem cells, mimic tissue physiology in a more relevant manner. Despite their promise, the clinical use of regenerative medicine currently needs to be improved by reproducibility, scalability, and maturation issues. This article overviews recent organoid research, focusing on their types, sources, 3D culturing methods, and applications in regenerative medicine. A literature review of "organoid" and "regenerative medicine" in PubMed/MEDLINE highlighted relevant studies published over the past decade, emphasizing human-sourced organoids and their regenerative benefits, as well as the availability of free full-text articles. The review uses descriptive data, including tables and text, to illustrate the challenges and potential of organoids in regenerative medicine. The transition from 2D to 3D models, particularly organoids, has significantly advanced in vitro research. This review covers a decade of progress in various organoid types-such as liver, cholangiocyte, intestinal, pancreatic, cardiac, brain, thymus, and mammary organoids-and their 3D culture methods and applications. It addresses critical issues of maturity, scalability, and reproducibility and underscores the need for standardization and improved production techniques to facilitate broader clinical applications in regenerative medicine. Successful therapy requires increased scalability and standardization. Organoids have enormous potential in biological research, notwithstanding obstacles.

Pharmacoepidemiology of combination pulmonary vasodilator therapy in critically ill infants.

New drugs to target different pathways in pulmonary hypertension has resulted in increased combination therapy, but details of this use in infants are not well described. In this large multicenter database study, we describe the pharmacoepidemiology of combination pulmonary vasodilator therapy in critically ill infants. We identified inborn infants discharged home from a Pediatrix neonatal ICU from 1997 to 2020 exposed to inhaled nitric oxide, sildenafil, epoprostenol, or bosentan for greater than two consecutive days. We compared clinical variables and drug utilisation between infants receiving simultaneous combination and monotherapy. We reported each combination's frequency, timing, and duration and graphically represented drug use over time. Of the 7681 infants that met inclusion criteria, 664 (9%) received combination therapy. These infants had a lower median gestational age and birth weight, were more likely to have cardiac and pulmonary anomalies, receive cardiorespiratory support, and had higher in-hospital mortality than those receiving monotherapy. Inhaled nitric oxide and sildenafil were most frequently used, and utilisation of combination and monotherapy for all drugs increased over time. Inhaled nitric oxide and epoprostenol were used in infants with a higher gestational age, earlier postnatal age, and shorter duration than sildenafil and bosentan. Dual therapy with inhaled nitric oxide and sildenafil was the most common combination therapy. Our study revealed an increased use of combination pulmonary vasodilator therapy, favouring inhaled nitric oxide and sildenafil, yet with considerable practice variation. Further research is needed to determine the optimal combination, sequence, dosing, and disease-specific indications for combination therapy.

An Early Exploration into Individuals in Treatment Who Co-Use Alcohol and Hallucinogens

There is renewed interest in the therapeutic properties of hallucinogens in the treatment of psychiatric disorders including alcohol use disorder (AUD). Interestingly, however, there is little data regarding the comorbid use of hallucinogens and alcohol among persons with AUD. The paucity of such data constrains both scientific interpretation and treatment development. This analysis, which utilizes data collected from individuals seeking treatment in facilities across North Central Florida, aims to address this gap by examining the use of hallucinogens among individuals seeking treatment for problems with alcohol. Further, by focusing on use patterns among those who report recent and regular use of both alcohol and hallucinogens, this analysis describes correlates with potential clinical utility. Analyses revealed that recent and regular users of hallucinogens and alcohol were significantly younger than those in treatment who did not use hallucinogens (p <0.001). Additionally, treatment seekers who reported regular use experienced more negative life consequences than those who were not regular users (p <0.01). Results suggest that trends in age and drug consequences may contain valuable information to inform treatment modalities, risk prevention strategies, and general scientific knowledge with the help of future investigations.

The Role of Propofol in Alcohol Withdrawal Syndrome: A Systematic Review.

The objective of this review was to evaluate the efficacy and safety of propofol in the treatment of critically ill patients diagnosed with alcohol withdrawal syndrome (AWS). A review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) criteria, and Embase, MEDLINE (PubMed), Cochrane CENTRAL, and Web of Science were queried for results through June 2024. Studies providing efficacy or safety data associated with propofol with a reported diagnosis of AWS in critically ill patients were included. Studies evaluating pediatric patients, those without quantitative and qualitative outcome data, and those not readily translatable to English were excluded. Five retrospective cohort analyses of 218 patients were included in this systematic review. Patients were found to have both significant and non-significant increases in time to resolution of AWS symptoms when treated with propofol versus the AWS standard of care. Adjunct treatment with propofol was generally associated with reductions in total benzodiazepine use and increases in both ICU length of stay and duration of mechanical ventilation. The results of this systematic review provide the evidence necessary to support the use of propofol as an efficacious and safe medication in the management of severe and refractory AWS. Further investigation is required to determine optimal dosing strategies and durations of therapy. The results of this systematic review demonstrate the clinical utility of propofol as part of the management strategy for severe and refractory AWS.

Prediction of lymphovascular invasion in invasive breast cancer based on clinical-MRI radiomics features

ObjectiveWe aim to develop a predictive model for lymphovascular invasion (LVI) in patients with invasive breast cancer (IBC), using magnetic resonance imaging (MRI)-based radiomics features.MethodsA total of 204 patients with IBC admitted to our hospital were included in this retrospective study. The data was split into training and validation sets at a 7:3 ratio. Feature normalization was conducted, followed by feature selection using ANOVA, correlation analysis, and LASSO in the training set. The final step involved building a logistic regression model. The LVI prediction models were established by single sequence image and combined different sequence images as follows: A: prediction model based on the optimal sequence in the 7-phase enhanced MRI scans; B: prediction model based on the optimal sequences in the sequences T1WI, T2WI, and DWI; and C: the combined model based on the optimal sequences selected from A and B. Subjects' work characteristic curves (ROC) and decision curves (DCA) were plotted to determine the extent to which they predicted LVI performance in the training and validation sets. Simultaneously, nomogram models were constructed by integrating radiomics features and independent risk factors. In addition, an additional 16 patients from the center between January and August 2024 were collected as the Nomogram external validation set. The ROC and DCA were used to evaluate the performance of the model.ResultsIn the enhanced images, Model A built based on the enhanced 2-phase achieved the best average AUC, with a validation set of 0.764. Model B built based on the T2WI had better results, with a validation set of 0.693. Model C built by combining enhanced 2-phase and T2WI sequences had a mean AUC of 0.705 in the validation set. In addition, the tumor size, whether the tumor boundary was clear or not, and whether there was a coelom in the tumor tissue had a statistically significant effect on the LVI of IBC, and a clinical-radiomics nomogram was established. DCAs as well as Nomogram also indicate that Model A has good clinical utility. The AUC of the nomogram in the training set, internal validation set, and external validation set were 0.703, 0.615, and 0.609, respectively. The DCA also showed that the radiomics nomogram combined with clinical factors had good predictive ability for LVI.ConclusionIn IBC, MRI radiomics can serve as a noninvasive predictor of LVI. The clinical-MRI radiomics model, as an efficient visual prognostic tool, shows promise in forecasting LVI. This highlights the significant potential of pre-radiomics prediction in enhancing treatment strategies.

Construction and evaluation of a combined diagnostic model for chronic periprosthetic joint infection based on serological tests

BackgroundEarly diagnosis of chronic periprosthetic joint infection (CPJI) is crucial for ensuring effective treatment and improving patient outcomes. However, many auxiliary diagnostic tests are challenging to implement on a large scale due to economic and technical constraints, making CPJI diagnosis difficult. This study aims to design and validate a combined diagnostic model based on commonly used serological tests to evaluate its diagnostic value for CPJI and develop a diagnostic nomogram.MethodsA retrospective study from January 2019 to February 2024 involving 170 patients undergoing knee and hip arthroplasty revision for CPJI and aseptic loosening (AL) was conducted across two medical centers. These patients were divided into the training set and validation set. Patients were categorized into CPJI and AL groups based on infection status. Serological tests conducted upon admission were collected, and single-factor and multi-factor logistic regression analyses were used to identify independent diagnostic factors for early infection. These factors were integrated to construct a nomogram model. The model's performance was evaluated using the receiver operating characteristic area under the curve (AUC), Hosmer–Lemeshow test, decision curve analysis (DCA), and calibration curve, with external validation conducted on the validation set.ResultsMultivariate logistic regression analysis showed that C-reactive protein (CRP), procalcitonin (PCT), and Platelet count/mean platelet volume ratio (PVR) were independent diagnostic factors for CPJI (p < 0.05). The AUCs for diagnosing CPJI using these individual factors were 0.806, 0.616, and 0.700 (p < 0.05), respectively, while their combined detection achieved an AUC of 0.861 (p < 0.05). The DCA clinical impact curve shows the combined model has good clinical utility when the threshold probability of infection presence is between 0.16 and 0.95. Similar results were obtained in the external validation cohort, with the combined detection having an AUC of 0.893.ConclusionThe combined diagnostic model of CRP, PCT, and PVR significantly improves the The combined diagnostic model of CRP, PCT, and PVR significantly improves the diagnostic performance for CPJI compared to individual serum biomarkers. It exhibits good sensitivity, specificity, and clinical applicability, providing valuable references for CPJI diagnosis.

CDC7 Inhibition Potentiates Antitumor Efficacy of PARP Inhibitor in Advanced Ovarian Cancer.

Poly (ADP-ribose) Polymerase inhibitors (PARPi) have demonstrated remarkable clinical efficacy in treating ovarian cancer (OV) with BRCA1/2 mutations. However, drug resistance inevitably limits their clinical applications and there is an urgent need for improved therapeutic strategies to enhance the clinical utility of PARPi, such as Olaparib. Here, compelling evidence indicates that sensitivity of PARPi is associated with cell cycle dysfunction. Through high-throughput drug screening with a cell cycle kinase inhibitor library, XL413, a potent cell division cycle 7 (CDC7) inhibitor, is identified which can synergistically enhance the anti-tumor efficacy of Olaparib. Mechanistically, the combined administration of XL413 and Olaparib demonstrates considerable DNA damage and DNA replication stress, leading to increased sensitivity to Olaparib. Additionally, a robust type-I interferon response is triggered through the induction of thecGAS/STING signaling pathway. Using murine syngeneic tumor models, the combination treatment further demonstrates enhanced antitumor immunity, resulting in tumor regression. Collectively, this study presents an effective treatment strategy for patients with advanced OV by combining CDC7 inhibitors (CDC7i) and PARPi, offering a promising therapeutic approach for patients with limited response to PARPi.

Targeted axillary dissection using carbon marking for patients with node-positive breast cancer following neoadjuvant therapy (TADCOM): study protocol for a prospective, multicenter, randomized controlled trial

BackgroundNeoadjuvant chemotherapy (NAC) for breast cancer enables pathological complete response (pCR) in patients initially diagnosed with axillary lymph node metastases, potentially obviating the need for axillary lymph node dissection (ALND). Current targeted axillary dissection (TAD) techniques, guided by traditional tissue markers placed prior to NAC, face challenges such as marker loss and high costs. Carbon nanoparticle suspension injection (CNSI) offers a stable and reliable alternative for marking, which could enhance the TAD procedure. This study aims to evaluate the feasibility and accuracy of different TAD strategies using CNSIs and to explore their clinical utility in locally advanced breast cancer.MethodsThis prospective, multicenter, randomized controlled trial will enroll 126 biopsy-proven breast cancer patients with suspicious axillary lymph node metastases (cN1-2a) who achieve ycN0 status following NAC. Participants will be randomized in a 1:1:1 ratio to undergo TAD guided by: [1] conventional tissue clips (CG-TAD); [2] CNSI lymph node marking (CN-LNM); or [3] peritumoral CNSI mapping (PCN-MAP). Primary endpoints include retrieval rate of marked lymph nodes, number of sentinel and marked lymph nodes, concordance rates, and complication rates. Secondary endpoints encompass regional and distant recurrence rates, survival outcomes, surgical duration, postoperative complications, quality of life scores, and margin status in breast-conserving surgery. Statistical analyses will adhere strictly to the CONSORT guidelines.DiscussionThis study aims to evaluate the feasibility and accuracy of CNSI for targeted axillary dissection in breast cancer patients following neoadjuvant chemotherapy and to explore its clinical significance in reducing surgical complications and costs, as well as improving surgical precision.Trial registrationClinicaltrials.gov, NCT04744506, Registered 27 December 2020, Updated 24 September 2024. Protocol Version Ver 1.2, 17/9/2024.

Considerations for the application of polygenic scores to clinical care of individuals with substance use disorders.

Substance use disorders (SUDs) are highly prevalent and associated with excess morbidity, mortality, and economic costs. Thus, there is considerable interest in the early identification of individuals who may be more susceptible to developing SUDs and in improving personalized treatment decisions for those who have SUDs. SUDs are known to be influenced by both genetic and environmental factors. Polygenic scores (PGSs) provide a single measure of genetic liability that could be used as a biomarker in predicting disease development, progression, and treatment response. Although PGSs are rapidly being integrated into clinical practice, there is little information to guide clinicians in their responsible use and interpretation. In this Review, we discuss the potential benefits and pitfalls of the use of PGSs in the clinical care of SUDs, highlighting current research. We also provide suggestions for important considerations prior to implementing the clinical use of PGSs and recommend future directions for research.

Modulating Microbial Resistance: Transcription Factor Regulation of Antibiotic Resistance in Escherichia coli

Abstract Introduction/Objective Antibiotic resistance is a global health emergency, with resistance detected to all antibiotics currently in clinical use and only a few novel drugs in the development pipeline. Antibiotics target key biological processes in bacteria, such as protein synthesis, cell wall synthesis, DNA replication and protein translation. Cells tightly control their internal states in different environments by regulating their transcriptional program through the activity of transcription factors (TFs) that control the expression of multiple downstream effector genes. We hypothesized that efficacy of specific antibiotics can be adjusted by altering the transcriptional states of bacteria prior to treatment, using TF knockout (KO) and over- expression (O-E) strains. Methods/Case Report Aim: Calculate the IC50 of 9 TF KO strains and 9 TF O-E strains across across a panel of representative antibiotics. A previous barcode screen study had identified TF KOs which were associated with antibiotic resistance/susceptibility. A serial antibiotic dilution was prepared in M9 media. E. coli TF KO and O-E strains were cultured for 24 hours in M9 media+Antibiotic (37°C, shaking). Each strain/antibiotic combination contained a technical replicate. Growth was measured by OD, allowing for calculation of growth curves, drug response curves, IC50, and EC50. Strain-specific IC50 and EC50 were compared to wild-type (WT) IC50 and EC50 as a measure of drug resistance/susceptibility. Results (if a Case Study enter NA) Experiments demonstrated consistent, TF-specific patterns of drug susceptibility. Compared to WT, TF KO strains demonstrated altered antibiotic IC50s (drug susceptibility). Compared to WT, TF O-E strains also demonstrated altered antibiotic IC50s (drug susceptibility). Conclusion Hypothesis Weakly Supported – TF states affect IC50s (Abx resistance). The relationships outlined in initial screen were consistently represented in experimental trials. Strain resistance/susceptibility is directly related to TF system and drug mechanism of action. For example, drugs that inhibited ribosomal function demonstrated greater effectiveness against KO/O-E strains that relied more heavily on protein synthesis pathways. Drug resistance is not binary but is shaped by a number of internal cellular factors, including gene regulation by TFs.

Decoding mutational hotspots in human disease through the gene modules governing thymic regulatory T cells

Computational strategies to extract meaningful biological information from multiomics data are in great demand for effective clinical use, particularly in complex immune-mediated disorders. Regulatory T cells (Tregs) are essential for immune homeostasis and self-tolerance, controlling inflammatory and autoimmune processes in many diseases with a multigenic basis. Here, we quantify the Transcription Factor (TF) differential occupancy landscape to uncover the Gene Regulatory Modules governing lineage-committed Tregs in the human thymus, and show that it can be used as a tool to prioritise variants in complex diseases. We combined RNA-seq and ATAC-seq and generated a matrix of differential TF binding to genes differentially expressed in Tregs, in contrast to their counterpart conventional CD4 single-positive thymocytes. The gene loci of both established and novel genetic interactions uncovered by the Gene Regulatory Modules were significantly enriched in rare variants carried by patients with common variable immunodeficiency, here used as a model of polygenic-based disease with severe inflammatory and autoimmune manifestations. The Gene Regulatory Modules controlling the Treg signature can, therefore, be a valuable resource for variant classification, and to uncover new therapeutic targets. Overall, our strategy can also be applied in other biological processes of interest to decipher mutational hotspots in individual genomes.

An audit to assess the clinical use of blood and blood components at a tertiary care hospital

An audit to assess the clinical use of blood and blood components at a tertiary care hospital