Prediction Of Therapy Outcome Research Articles

Imaging-based prediction of early recurrence and neoadjuvant therapy outcomes for resectable beyond Milan HCC.

To develop and validate an MRI-based model for predicting postoperative early (≤2 years) recurrence-free survival (RFS) in patients receiving upfront surgical resection (SR) for beyond Milan hepatocellular carcinoma (HCC) and to assess the model's performance in separate patients receiving neoadjuvant therapy for similar-stage tumors. This single-center retrospective study included consecutive patients with resectable BCLC A/B beyond Milan HCC undergoing upfront SR or neoadjuvant therapy. All images were independently evaluated by three blinded radiologists. In patients receiving upfront SR, an MRI-based Early Recurrence Outside Milan (EROM) score was developed and validated for predicting early RFS via Cox regression analyses and compared with the BCLC staging system. In separate patients undergoing neoadjuvant therapy, interval tumor progression rate and postoperative early RFS were compared between EROM-predicted high- and low-risk groups. 279 patients (median, 56years; 236 men) were included, 220 (78.9%) undergoing upfront SR and 59 (21.1%) received transarterial chemoembolization-based neoadjuvant therapy. Alpha-fetoprotein>20ng/mL (HR, 2.03; P=0.007), size of the largest tumor (HR, 1.10; P=0.016), infiltrative appearance (HR, 2.20; P=0.032), and<50% arterial phase hyperenhancement (HR, 1.74; P=0.023) formed the EROM score, with superior testing dataset C-index than the BCLC system (0.69 vs. 0.52, P<0.001). The EROM-predicted high-risk (>15.3 points) patients had higher tumor progression (25.0% vs. 0.0%, P=0.033) and lower postoperative 2-year RFS (16.0% vs. 39.3%, P=0.025) rates after neoadjuvant therapy. In patients with resectable beyond Milan HCC, EROM allowed noninvasive prediction of postoperative early RFS and informed interval tumor progression risks after neoadjuvant therapy.

Heart Rate Variability Predicts Therapy Outcome in Anxiety Disorders: The Role of Inhibitory Learning.

Exposure therapy has been shown to be useful for the treatment of anxiety disorders. However, there are individual differences in the extent to which this intervention is effective in reducing symptoms, and a substantial number of patients may experience a return of fear (ROF). The factors associated with successful therapy outcomes are an important topic of investigation as these factors might influence the nature of the interventions as well as enhance our understanding of the process associated with the disorder and its treatment. Here, we investigated the effect of resting heart rate variability (HRV) on ROF following exposure therapy in social phobics. In particular, using path modeling, we assessed the hypothesis that resting HRV prospectively predicts inhibitory learning, which, in turn, prospectively predicts ROF at follow-up. Forty adult participants (60% female) diagnosed with Social Anxiety Disorder were assigned to a single massed exposure therapy session. Self-reported behavioral and physiological responses were recorded pre-treatment, immediately following treatment, and at one-month follow-up. The Personalized Implicit Association Task (PIAT) was used as an implicit measure of inhibitory learning, and HF-HRV was taken as a measure of vagal activity. Results revealed that those with high pre-treatment HRV reported less negative implicit attitude towards public speaking after exposure (b = -0.044, p =.047) and showed reduced residual symptoms one month after treatment. (b = 2.247, p =.013). Taken together these results support exposure therapy models that emphasize the importance of inhibitory learning in extinction and are consistent with research linking HRV to inhibition.

Artificial intelligence in the care of children and adolescents with chronic diseases: a systematic review

The integration of artificial intelligence (AI) and machine learning (ML) has shown potential for various applications in the medical field, particularly for diagnosing and managing chronic diseases among children and adolescents. This systematic review aims to comprehensively analyze and synthesize research on the use of AI for monitoring, guiding, and assisting pediatric patients with chronic diseases. Five major electronic databases were searched (Medline, Scopus, PsycINFO, ACM, Web of Science), along with manual searches of gray literature, personal archives, and reference lists of relevant papers. All original studies as well as conference abstracts and proceedings, focusing on AI applications for pediatric chronic disease care were included. Thirty-one studies met the inclusion criteria. We extracted AI method used, study design, population, intervention, and main results. Two researchers independently extracted data and resolved discrepancies through discussion. AI applications are diverse, encompassing, e.g., disease classification, outcome prediction, or decision support. AI generally performed well, though most models were tested on retrospective data. AI-based tools have shown promise in mental health analysis, e.g., by using speech sampling or social media data to predict therapy outcomes for various chronic conditions. Conclusions: While AI holds potential in pediatric chronic disease care, most reviewed studies are small-scale research projects. Prospective clinical implementations are needed to validate its effectiveness in real-world scenarios. Ethical considerations, cultural influences, and stakeholder attitudes should be integrated into future research.What is known:• Artificial Intelligence (AI) will play a more dominant role in medicine and healthcare in the future and many applications are already being developed.What is new:• Our review provides an overview on how AI-driven systems might be able to support children and adolescents with chronic illnesses. • While many applications are being researched, few have been tested on real-world, prospective, clinical data.

Focal therapy of prostate cancer: Use of artificial intelligence to define tumour volume and predict treatment outcomes.

The aim of this study is to evaluate new software (Unfold AI) in the estimation of prostate tumour volume (TV) and prediction of focal therapy outcomes. Subjects were 204 men with prostate cancer (PCa) of grade groups 2-4 (GG ≥2), who were enrolled in a trial of partial gland cryoablation (PGA) at UCLA from 2017 to 2022. Magnetic resonance imaging (MRI)-guided biopsy (MRGB) was performed at diagnosis and at 6 and 18 months following PGA. Utilising Unfold AI (FDA-cleared 2022), which generates a 3D map of GG ≥2 PCa margins, we retrospectively estimated TV for each patient. TV was compared against conventional baseline variables as a correlate of a successful primary outcome-defined here as the absence of GG ≥2 on follow-up MRGB at 6 months. Secondary outcomes were MRGB at 18 months and failure-free survival, that is, lack of metastasis or salvage whole gland therapy. Receiver operating curves and multivariate analysis were used to determine significance. A successful primary outcome was observed in 77.7% of patients. Significant correlates of a successful ablation were percent pattern 4 and TV; areas under the curve (AUCs) were 0.60 and 0.73, respectively. GG was not a correlate of success (AUC =0.51). A TV of 1.5 cc provided the optimal combination of sensitivity (55.8%) and specificity (85.7%) at 6months. TV was also significantly associated with secondary outcomes. In multivariate analysis, TV was the variable most associated with 6- and 18-month biopsy success (adjusted odds ratios [aORs] were 6.1 and 4.2). Utilising TV ≤1.5cc as a PGA criterion would have prevented 72% of failures at the cost of 42% of successes. The AI-based software Unfold AI estimates TV, which is significantly associated with biopsy outcomes after focal cryoablation. The rate of treatment success is inversely related to TV.

Prediction of electroconvulsive therapy outcome: A network analysis approach.

While electroconvulsive therapy (ECT) for the treatment of major depressive disorder is effective, individual response is variable and difficult to predict. These difficulties may in part result from heterogeneity at the symptom level. We aim to predict remission using baseline depression symptoms, taking the associations among symptoms into account, by using a network analysis approach. We combined individual patient data from two randomized controlled trials (total N = 161) and estimated a Mixed Graphical Model to estimate which baseline depression symptoms (corresponding to HRSD-17 items) uniquely predicted remission (defined as either HRSD≤7 or MADRS<10). We included study as moderator to evaluate study heterogeneity. For symptoms directly predictive of remission we computed odds ratios. Three baseline symptoms were uniquely predictive of remission: suicidality negatively predicted remission (OR = 0.75; bootstrapped confidence interval (bCI) = 0.44-1.00) whereas retardation (OR = 1.21; bCI = 1.00-2.02) and hypochondriasis (OR = 1.31; bCI = 1.00-2.25) positively predicted remission. The estimated effects did not differ across trials as no moderation effects were found. By using a network analysis approach this study identified that the presence of suicidal ideation predicts an overall worse treatment outcome. Psychomotor retardation and hypochondriasis, on the other hand, seem to be associated with a better outcome.

Evolving Artificial Intelligence (AI) at the Crossroads: Potentiating Productive vs. Declining Disruptive Cancer Research.

Artificial intelligence (AI), encompassing several tools and platforms such as artificial "general" intelligence (AGI) and generative artificial intelligence (GenAI), has facilitated cancer research, enhancing productivity in terms of research publications and translational value for cancer patients. AGI tools, such as ChatGPT, assist preclinical and clinical scientists in identifying tumor heterogeneity, predicting therapy outcomes, and streamlining research publications. However, this perspective review also explores the potential of AI's influence on cancer research with regard to its impact on disruptive sciences and discoveries by preclinical and clinical scientists. The increasing reliance on AI tools may compromise biological intelligence, disrupting abstraction, creativity, and critical thinking. This could contribute to the declining trend of disruptive sciences, hindering landmark discoveries and innovations. This perspective review narrates the role of different forms of AI in the potentiation of productive cancer research and the potential disruption of disruptive sciences due to AI's influence.

Effect of methimazole treatment on Th1, Th17, and Th22 lymphocytes in pediatric Graves' disease patients.

Graves' disease is the leading cause of autoimmune hyperthyroidism. Thyroid hormones are an essential element of the endocrine system, playing a pivotal role in the body's development, especially important in children with intensified growth. Disturbance within thyroid tissue certainly affected the whole body. Nowadays, numerous research studies indicate different factors contributing to the onset of the disease; however, the exact pathomechanism of Graves' disease is still not fully understood, especially in the context of immune-related processes. Th1, Th17, and Th22 effector lymphocytes were found to be crucial participants in the disease outcome, as well as in autoimmune diseases. Here, our study aimed at assessing selected effector T lymphocytes, Th1, Th17, and Th22, in newly diagnosed pediatric Graves' disease patients, together with their association with thyroid-related parameters and the potential outcome of disease management. We indicated significant increases in the frequencies and absolute numbers of selected effector lymphocytes in Graves' disease patients. In addition, their mutual ratios, as well as Th1/Th17, Th/Th22, and Th17/Th22, seem to be significant in those diseases. Notably, low Th17/Th22 ratio values were distinguished as potential prognostic factors for normalizing TSH levels in response to methimazole treatment. To sum up, our research determines the crucial contribution of Th1, Th17, and Th22 cells in the pathogenesis of Graves' disease. Moreover, the mentioned subset of T cells is highly likely to play a substantial role in the potential prediction of therapy outcomes.

Predicting the outcome of psychotherapy for chronic depression by person-specific symptom networks.

Psychotherapies are efficacious in the treatment of depression, albeit only with a moderate effect size. It is hoped that personalization of treatment can lead to better outcomes. The network theory of psychopathology offers a novel approach suggesting that symptom interactions as displayed in person-specific symptom networks could guide treatment planning for an individual patient. In a sample of 254 patients with chronic depression treated with either disorder-specific or non-specific psychotherapy for 48 weeks, we investigated if person-specific symptom networks predicted observer-rated depression severity at the end of treatment and one and two years after treatment termination. Person-specific symptom networks were constructed based on a time-varying multilevel vector autoregressive model of patient-rated symptom data. We used statistical parameters that describe the structure of these person-specific networks to predict therapy outcome. First, we used symptom centrality measures as predictors. Second, we used a machine learning approach to select parameters that describe the strength of pairwise symptom associations. We found that information on person-specific symptom networks strongly improved the accuracy of the prediction of observer-rated depression severity at treatment termination compared to common covariates recorded at baseline. This was also shown for predicting observer-rated depression severity at one- and two-year follow-up. Pairwise symptom associations were better predictors than symptom centrality parameters for depression severity at the end of therapy and one year later. Replication and external validation of our findings, methodological developments, and work on possible ways of implementation are needed before person-specific networks can be reliably used in clinical practice. Nevertheless, our results indicate that the structure of person-specific symptom networks can provide valuable information for the personalization of treatment for chronic depression.

Comparison of Modified ROX Index Score and ROX Index Score for Early Prediction of High Flow Nasal Oxygen Therapy Outcome in Patients with Acute Respiratory Failure: A Prospective Observational Cohort Study.

We compared the modified ROX index and ROX index scores in earlier predictions of high-flow nasal oxygen (HFNO) therapy outcomes in patients with acute respiratory failure. We conducted a prospective observational study on 151 acute respiratory failure patients initiated on HFNO therapy. The primary objective of this research was to compare the modified ROX index and ROX index to investigate which score predicted HFNO treatment outcome earlier. The modified ROX index score had better predictive power than the ROX score at different time points, especially one hour following the start of HFNO therapy (AUC 0.790; 95% CI: 0.717-0.863; p < 0.001). For the ROX Index at 1 hour, the ideal cut-off value for HFNO outcome was 4.36 (sensitivity: 72.6%, specificity: 53.9%), and for the modified ROX index at 1 hour, it was 4.63 (sensitivity: 74.2%, specificity: 69.7%). The presence of various comorbidities didn't show any change in ROX-HR cut-off values. The modified ROX index is a better predictor of the success of HFNO therapy than the ROX index. Furthermore, the presence of any comorbidities did not affect modified ROX index cut-off values or the outcome of HFNO therapy. Sarkar AG, Sharma A, Kothari N, Goyal S, Meshram T, Kumari K, et al. Comparison of Modified ROX Index Score and ROX Index Score for Early Prediction of High Flow Nasal Oxygen Therapy Outcome in Patients with Acute Respiratory Failure: A Prospective Observational Cohort Study. Indian J Crit Care Med 2024;28(9):842-846.

Hair glucocorticoid levels decrease after multimodal inpatient treatment and predict therapy outcome in burnout-related depressive disorders

Objectives Hair cortisol concentration (HCC) indicates chronic stress exposure, which is a risk factor in the pathogenesis of burnout and depression. However, findings on HCC are inconsistent. Similarly, intervention studies show mixed effects on HCC. The present study aimed to shed light on these inconsistencies, by additionally considering also hair cortisone. Methods Twenty-five patients with a burnout-related depressive disorder receiving a multimodal inpatient treatment for clinical burnout and 17 matched healthy controls participated in this study. All participants provided 1 cm long hair samples at the beginning and end of the treatment. HCC and hair cortisone levels (HCNC) were determined. Meteorological data and duration of sick leave were considered as potential covariates. Burnout and depression were assessed with self-ratings, the latter also with examiner ratings. Results There were no significant group differences in glucocorticoid levels. Treatment led to a decrease in both depression severity and hair glucocorticoid concentration in inpatients, while lower HCNC in particular predicted a greater reduction in depression severity. Moreover, meteorological data and the duration of sick leave were also found to have an effect on hair glucocorticoid concentrations. Conclusions These results suggest that multimodal inpatient treatment of clinical burnout considerably reduced stress on both a psychological and biological level. In parallel, hair glucocorticoids appear to be sensitive biomarkers for the evaluation of treatment success and prediction. Examining both HCC and HCNC in intervention studies may provide clearer results than the usual examination of HCC alone.

Plasma mutational burden in PIK3CA and TP53 independently predicts early progression in patients with HR+/HER2- metastatic breast cancer (MBC) enrolled in the GEICAM/2014-12 FLIPPER trial.

1029 Background: There is an unmet need for molecular diagnostics to predict therapy outcomes using blood-based biomarker monitoring to guide treatments in HR+/HER2- MBC patients. Here, we report initial results of serial ctDNA analyses in a trial of Fulvestrant (F) + CDK4/6 inhibitor (CDK4/6i) Palbociclib (Pa) vs. F + Placebo (Pl). Endocrine therapy + CDK4/6i are currently the cornerstones of treatment for HR+/HER2- MBC. However, between 10–30% of patients suffer primary resistance. Thus, early identification of HR+/HER2- MBC patients that progress during CDK4/6i treatment is required. Methods: The GEICAM FLIPPER trial (NCT02690480) is a multicenter, double-blind, randomized, phase II study comparing F+Pa vs F+Pl in postmenopausal women with HR+/HER2- MBC. Sequential plasma was collected from 187 patients at baseline, 12, 24, 36, and 48 weeks on treatment and at progression. In total, 903 samples were analyzed using the Plasma-SeqSensei BC IVD kit (Sysmex Inostics) that identifies and quantifies ctDNA mutations in six BC-related genes. The mutant allele frequency (MAF) of PIK3CA and TP53 variants was assessed in baseline samples (median value as cutoff). These variants, identified as the most prevalent, were used as surrogate markers for mutational burden. The Kaplan-Meier method was used to estimate median (m) PFS and overall survival (OS). Hazard ratio (HR) and odds ratio (OR) were calculated with 95% confidence interval (CI) using Cox proportional hazards regression model and logistic regression respectively, unadjusted or adjusted by age, site of disease, disease presentation at study entry and clinical subtype. Results: Previously we showed that F+Pa improved 1-year PFS (83.5% vs. 71.9%, p=0.064) and mPFS (31.8 vs. 22.0 months, p=0.001) vs F+Pl. Here, ctDNA analyses revealed that having ≥1 mutation in PIK3CA + TP53 was prognostic of early (≤12 months) progression (OR 2.37; 95% CI 1.1-5.12, p=0.0274) independent of treatment arm. Furthermore, having ≥2 mutations was associated with treatment resistance (not achieving objective response; OR 3.55; 95% CI 1.28-9.87, p=0.0151). Additionally, worse mPFS and mOS were seen in patients with ≥2 mutations in PIK3CA + TP53 (PFS: HR 3.6; 95% CI 2.25-5.76, p&lt;0.0001; OS: HR 2.92; 95% CI 1.66-5.12, p=0.002) and in patients with high MAF (PFS: HR 2.43; 95% CI 1.6-3.68, p&lt;0.0001; OS: HR 2.83; 95% CI 1.75-4.58, p&lt;0.0001). Finally, having ≥2 mutations in PIK3CA + TP53 was predictive of early progression (OR 22.32; 95% CI 4.12-120.9, p&lt;0.0001) and worse mOS (61.8 vs. 34.79 months) in the F+Pl arm (HR 5.91; 95% CI 2.65-13.16, p&lt;0.0001) but not in the F+Pa arm. Conclusions: Baseline plasma mutational burden in both PIK3CA and TP53 genes was significantly associated with Fulvestrant +/- Palbociclib treatment outcome in HR+/HER2- MBC and appeared to be predictive of Palbociclib benefit. Clinical trial information: NCT02690480 .

The intelligent Impella: Future perspectives of artificial intelligence in the setting of Impella support.

Artificial intelligence (AI) has emerged as a potential useful tool to support clinical treatment of heart failure, including the setting of mechanical circulatory support (MCS). Modern Impella pumps are equipped with advanced technology (SmartAssist), enabling real-time acquisition and display of data related to both pump performance and the patient's haemodynamic status. These data emerge as an 'ideal' source for data-driven AI applications to predict the clinical course of an ongoing therapeutic protocol. Yet, no evidence of effective application of AI tools in the setting of Impella support is available. On this background, we aimed at identifying possible future applications of AI-based tools in the setting of temporary MCS with an Impella device. We explored the state of research and development at the intersection of AI and Impella support and derived future potential applications of AI in routine Impella clinical management. We identified different areas where the future implementation of AI tools may contribute to addressing important clinical challenges in the setting of Impella support, including (i) early identification of the best suited pathway of care according to patients' conditions at presentation and intention to treat, (ii) prediction of therapy outcomes according to different possible therapeutic actions, (iii) optimization of device implantation procedures and evaluation of proper pump position over the whole course of support and (iv) prevention and/or rationale management of haemocompatibility-related adverse events. For each of those areas, we discuss the potential advantages, challenges and implications of harnessing AI-driven insights in the setting of MCS with an Impella device. Temporary MCS with an Impella device has great potential to benefit from the integration of AI-based tools. Such tools may indeed translate into groundbreaking innovation supporting clinical decision-making and therapy regulation, in particular in complex scenarios such as the multidevice MCS strategy.

#2388 Relevance of anti-PLA2R levels in therapy decision and prediction of therapy outcome in patients with membranous nephropathy

Abstract Background and Aims Detection of anti-phospholipase A2 receptor (PLA2R) antibodies in patients with primary membranous nephropathy (MN) supports diagnosis as well as disease monitoring. An individualised therapy approach was introduced for anti-PLA2R positive MN patients at the Radboud University Medical Center. Immunosuppressive treatment (cyclophosphamide combined with steroids) was stopped when anti-PLA2R results by indirect immunofluorescence testing (IIFT) became negative. Here, we evaluated the relevance of anti-PLA2R levels for therapeutic decisions and the outcome of MN, comparing qualitative and quantitative detection methods. Method Stored serum samples were retrieved for beginning of treatment (baseline), decision point, and follow-up. Anti-PLA2R levels were determined qualitatively by IIFT as well as quantitatively by enzyme-linked immunosorbent assay (ELISA) and chemiluminescence immunoassay (ChLIA) at baseline as well as after treatment and correlated to immunological remission and persistence. Results For an evaluation of the relevance of anti-PLA2R levels at the start of therapy, serum samples of 60 patients were available. Patients sampled at the beginning of the therapy were grouped according to tertiles of anti-PLA2R levels determined by ChLIA (n[lowest tertile] = 20, n[middle tertile] = 20, n[highest tertile] = 20). Lower anti-PLA2R levels were seen in patients with relapsing disease. Higher anti-PLA2R levels were associated with more severe proteinuria. Patients in the lowest tertile of anti-PLA2R were more likely to develop immunological remission after 8 weeks of therapy. In total, 90% vs 75% vs 45% of patients in the tertiles turned anti-PLA2R negative after being treated for 8 weeks. Moreover, in this subgroup of patients who showed immunological remission after 8 weeks of therapy, only 11% of the patients in the lowest tertile needed renewed immunosuppressive therapy, while this percentage increased in the middle and highest tertiles (11% vs 53% vs 33%, p=0.033). For the assessment of anti-PLA2R levels before and after therapy, samples at baseline and week 8 of treatment were examined. At baseline, 50/50 (100%) tested positive in IIFT and ChLIA as well as 48/50 (94%) in ELISA. After 8 weeks on treatment, immunological remission based on IIFT was found in 37/51 (73%) patients. At this point, the overall agreement compared to IIFT was 92% for ChLIA and 82% for ELISA. Yet the availability of quantitative levels provided further insight into this group of patients in whom treatment was discontinued. With ChLIA, anti-PLA2R titers &amp;gt;5 RU/mL after 8 weeks of therapy were found in 4% of IIFT negative patients with persistent remission, vs 55% of IIFT negative patients with early relapse (p=0.006). Conclusion Individualised treatment of MN patients with cyclophosphamide and steroids has been recently introduced. In this respect, the quantitative determination of anti-PLA2R levels adds further value. Of the examined quantitative methods, ChLIA demonstrated the highest agreement with IIFT. Additional studies are needed to evaluate the impact on clinical decision making and outcome.

Client preferences, therapy activities and preference-activity match as predictors of therapy outcome

ABSTRACT Objective: This study investigated whether distinct types of psychotherapy activities, the client’s preference towards these activities prior to therapy, and the degree of match between client preferences and therapy activities, served as predictors of treatment outcomes. Methods: A total of 621 clients (M age = 42 years, 71.7% female) received individual psychotherapy by 54 psychologists. Associations between activity preferences, therapy activities, and preference-activity match as predictors, and symptom change and treatment dropout as outcomes were analyzed using multilevel longitudinal and logistic modelling and polynomial regression models with response surface analysis. Results: No type of therapy activity or activity preference significantly predicted symptom change in therapy, while higher levels of inward orientation therapy activities predicted an increased risk of dropout. Moreover, matching and higher levels of inward orientation and affect expression activities predicted an increased risk of dropout, and matching and higher levels of outward orientation activities predicted a decreased risk of dropout. Finally, a preference-activity mismatch in affect suppression predicted an increased risk of dropout from therapy, both at higher and lower levels of affect suppression. Conclusion: Distinct types of therapy activity preferences may, especially when (mis)matched with similar levels of the same therapy activities, differentially predict particular dropout from therapy. Trial registration: ClinicalTrials.gov identifier: NCT05630560.

Identification of SMC2 and SMC4 as prognostic markers in breast cancer through bioinformatics analysis.

Breast cancer (BRCA) is one of the most common malignant tumors. The structural maintenance of chromosome (SMC) gene family has been shown to play an important role in human cancers. However, the role of SMC families in BRCA is unclear. This study aimed to explore the role and potential clinical value of whole SMCs in BRCA. TIMER and UALCAN database were used to analysis the expression level. Genetic variations were analyzed by cBioPortal. Promoter methylation and protein level were analyzed by UCLCAN. GO and KEGG were analyzed by Metascape database. Prognostic value of SMCs was analyzed by Kaplan-Meier and multivariate cox regression analyses. Immune infiltration analysis was conducted by CIBERSORT. Immunotherapy outcome prediction was conducted by Cancer Immunome Atlas. Targeted drug therapy outcome prediction was taken by GDSC and R language. The cell viability was tested by CCK8 and migration was tested by wound healing assay. Xenograft model was used to investigate the in vivo role of SMC2. Expression levels of SMC1A, SMC2, SMC4, SMC5 and SMC6 mRNA were increased in BRCA tissues, and negatively correlated with promoter methylation. Overexpression of SMC2 and SMC4 was negatively correlated with survival. Function of SMCs family regulatory genes was mainly related to ATPase activity. Expression of most SMCs was negatively correlated with immunotherapy and drug therapy outcomes. Interfere SMC2 and SMC4 decreased IC50 values of 5-fluorouracil and oxaliplatin and inhibited the migration of MCF7 cells. Tumor growth and weights were significantly decreased in si-SMC2 groups. Combined bioinformatics and clinical specimen analysis verified SMC2 and SMC4 as independent prognostic factors in BRCA, suggesting their significance for the diagnosis and treatment of BRCA.

Using host-mimicking conditions and a murine cutaneous abscess model to identify synergistic antibiotic combinations effective against Pseudomonas aeruginosa.

Antibiotic drug combination therapy is critical for the successful treatment of infections caused by multidrug resistant pathogens. We investigated the efficacy of β-lactam and β-lactam/β-lactamase inhibitor combinations with other antibiotics, against the hypervirulent, ceftazidime/avibactam resistant Pseudomonas aeruginosa Liverpool epidemic strain (LES) B58. Although minimum inhibitory concentrations in vitro differed by up to eighty-fold between standard and host-mimicking media, combinatorial effects only marginally changed between conditions for some combinations. Effective combinations in vitro were further tested in a chronic, high-density murine infection model. Colistin and azithromycin demonstrated combinatorial effects with ceftazidime and ceftazidime/avibactam both in vitro and in vivo. Conversely, while tobramycin and tigecycline exhibited strong synergy in vitro, this effect was not observed in vivo. Our approach of using host-mimicking conditions and a sophisticated animal model to evaluate drug synergy against bacterial pathogens represents a promising approach. This methodology may offer insights into the prediction of combination therapy outcomes and the identification of potential treatment failures.

Correlation analysis between the quantitative parameters of iodine-131 single-photon emission computed tomography-computed tomography thyroid bed uptake and the efficacy of radioactive iodine adjuvant therapy for papillary thyroid cancer.

Single-photon emission computed tomography-computed tomography (SPECT/CT) quantification has emerged as a valuable tool for assessing disease prognosis by accurately identifying and characterizing abnormal lesions with accumulated radionuclides. Papillary thyroid carcinoma (PTC) is the most prevalent type of thyroid cancer, and radioactive iodine (RAI) therapy is a standard treatment following total thyroidectomy. This study aimed to explore the potential utility the quantitative parameters of the thyroid bed under iodine-131 (I-131) SPECT/CT in the efficacy of RAI adjuvant therapy for patients with PTC. The retrospective cohort study enrolled 107 patients with PTC who underwent RAI adjuvant therapy from June 2020 to January 2023. Three days after the RAI adjuvant therapy, all patients underwent I-131 whole-body scans and SPECT/CT imaging. The quantitative parameters, including maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), and percent injected dose (%ID), were measured using image analysis software based on I-131 SPECT/CT thyroid bed uptake. Successful therapy was defined as inhibitory thyroglobulin (Tg) <0.2 ng/mL with negative thyroglobulin antibody (TgAb) and negative imaging examination 6 months after RAI adjuvant therapy. The relationship between the quantitative parameters and the treatment efficacy, in addition to the potential influencing factors, were analyzed. The quantitative parameters from the successful group [SUVmax: median 6.15 g/mL, interquartile range (IQR) 2.34-13.80 g/mL; SUVmean: median 2.02 g/mL, IQR 0.89-4.93 g/mL; %ID: median 2.00%, IQR 1.00-4.00%] were significantly lower than those from the unsuccessful group (SUVmax: median 19.03 g/mL, IQR 5.31-45.10 g/mL, SUVmean 4.64 g/mL, IQR 2.07-19.05 g/mL; %ID: median 8.00%, IQR 3.00-18.00%) (SUVmax: Z=-3.755; SUVmean; Z=-3.671; %ID: Z=-4.070; all P values <0.001). SUVmax, SUVmean and %ID were positively correlated with the stimulated thyroglobulin (sTg) and inhibitory Tg at 6 months after RAI adjuvant therapy, respectively (all P values <0.001). SUVmax [odds ratio (OR) =1.045], SUVmean (OR =1.130), and %ID (OR =1.092) were predictive factors for the failure of RAI adjuvant therapy (all P values <0.001). Our study suggested that quantitative parameters (SUVmax, SUVmean, and %ID) derived from I-131 SPECT/CT imaging of the thyroid bed can serve as useful tools for predicting therapy outcomes following RAI adjuvant therapy.

Bone marrow infiltration in diffuse large B-cell lymphoma: impact of 18FDG-PET/CT in detection and prediction of therapy outcome

Bone marrow infiltration in diffuse large B-cell lymphoma: impact of 18FDG-PET/CT in detection and prediction of therapy outcome

Determining differences between therapists using an extended version of the facilitative interpersonal skills performance test.

The therapist-facilitative interpersonal skills (FIS) has shown to predict therapy outcomes, demonstrating that high FIS therapists are more effective than low FIS therapists. There is a need for more insight into the variability in strengths and weaknesses in therapist skills. This study investigates whether a revised and extended FIS-scoring leads to more differentiation in measuring therapists'interpersonal skills. Furthermore, we explorative examine whether subgroups of therapists can be distinguished in terms of differences in their interpersonal responses. Using secondary data analysis, 93 therapists were exposed to seven FIS-clips. Responses of therapists using the original and the extended FIS scoring were rated. Three factors were found on the extended FIS scoring distinguishing supportive, expressive, and persuasive interpersonal responses of therapists. A latent profile analysis enlightened the presence of six subgroups of therapists. Using the revised and extended FIS-scoring contributes to our understanding of the role of interpersonal skills in the therapeutic setting by unraveling the question what works for whom.

Preliminary results of anti-inflammatory cytokine concentrations predicting therapy outcome in panic disorder

BackgroundPatients with panic disorder (PD) show alterations of the immune reactivity to acute stress, which could serve as a marker for effective treatment. Nevertheless, the effect of immune reactivity under acute stress before treatment on therapy outcome remains unclear. MethodsA total of N = 16 PD patients performed the Trier Social Test. Blood sample collection of anti-inflammatory cytokine IL-10 accompanied the TSST. The Mobility Inventory was handed out for the assessment of avoidance behavior before and after treatment. Area under the curve with respect to the ground (AUCG) and increase (AUCI) were calculated for assessed cytokine levels and were used as predictors for therapy outcome in regression analyses. ResultsAUCG significantly predicts avoidance behavior in company after treatment (β = −0.007, p = .033) but not avoidance behavior alone (β = −0.003, p = .264). AUCI does not significantly predict therapy outcome. ConclusionHigher concentrations of anti-inflammatory cytokine IL-10 under acute stress before treatment predicts less avoidance behavior in company after therapy. Immune markers seem to play a crucial role in the maintenance of mental disorders such as PD. Underlying mechanisms and IL-10 as a marker for individualized treatments should be investigated in future studies.