Prediction Of Therapeutic Outcome Research Articles

The role of miRNAs as biomarkers in breast cancer.

Breast cancer (BC) is the second most common cause of deaths reported in women worldwide, and therefore there is a need to identify BC patients at an early stage as timely diagnosis would help in effective management and appropriate monitoring of patients. This will allow for proper patient monitoring and effective care. However, the absence of a particular biomarker for BC early diagnosis and surveillance makes it difficult to accomplish these objectives. miRNAs have been identified as master regulators of the molecular pathways that are emphasized in various tumors and that lead to the advancement of malignancies. Small, non-coding RNA molecules known as miRNAs target particular mRNAs to control the expression of genes. miRNAs dysregulation has been linked to the start and development of a number of human malignancies, including BC, since there is compelling evidence that miRNAs can function as tumor suppressor genes or oncogenes. The current level of knowledge on the role of miRNAs in BC diagnosis, prognosis, and treatment is presented in this review. miRNAs can regulate the tumorigenesis of BC through targeting PI3K pathway and can be used as prognostic or diagnostic biomarkers for BC therapy. Some miRNAs, like miR-9, miR-10b, and miR-17-5p, are becoming known as biomarkers of BC for diagnosis, prognosis, and therapeutic outcome prediction. Other miRNAs, like miR-30c, miR-187, and miR-339-5p, play significant roles in the regulation of hallmark functions of BC, including invasion, metastasis, proliferation, resting death, apoptosis, and genomic instability. Other miRNAs, such as miR-155 and miR-210, are circulating in bodily fluids and are therefore of interest as novel, conveniently accessible, reasonably priced, non-invasive methods for the customized care of patients with BC.

Deep Learning Can Predict Bevacizumab Therapeutic Effect and Microsatellite Instability Directly from Histology in Epithelial Ovarian Cancer

Epithelial ovarian cancer (EOC) remains a significant cause of mortality among gynecologic cancers, with the majority of cases being diagnosed at an advanced stage. Before targeted therapies were available, EOC treatment relied largely on debulking surgery and platinum-based chemotherapy. Vascular endothelial growth factors have been identified as inducing tumor angiogenesis. According to several clinical trials, anti–vascular endothelial growth factor-targeted therapy with bevacizumab was effective in all phases of EOC treatment. However, there are currently no biomarkers accessible for regular therapeutic use despite the importance of patient selection. Microsatellite instability (MSI), caused by a deficiency of the DNA mismatch repair system, is a molecular abnormality observed in EOC associated with Lynch syndrome. Recent evidence suggests that angiogenesis and MSI are interconnected. Developing predictive biomarkers, which enable the selection of patients who might benefit from bevacizumab-targeted therapy or immunotherapy, is critical for realizing personalized precision medicine. In this study, we developed 2 improved deep learning methods that eliminate the need for laborious detailed image-wise annotations by pathologists and compared them with 3 state-of-the-art methods to not only predict the efficacy of bevacizumab in patients with EOC using mismatch repair protein immunostained tissue microarrays but also predict MSI status directly from histopathologic images. In prediction of therapeutic outcomes, the 2 proposed methods achieved excellent performance by obtaining the highest mean sensitivity and specificity score using MSH2 or MSH6 markers and outperformed 3 state-of-the-art deep learning methods. Moreover, both statistical analysis results, using Cox proportional hazards model analysis and Kaplan-Meier progression-free survival analysis, confirm that the 2 proposed methods successfully differentiate patients with positive therapeutic effects and lower cancer recurrence rates from patients experiencing disease progression after treatment (P < .01). In prediction of MSI status directly from histopathology images, our proposed method also achieved a decent performance in terms of mean sensitivity and specificity score even for imbalanced data sets for both internal validation using tissue microarrays from the local hospital and external validation using whole section slides from The Cancer Genome Atlas archive.

Patient-Derived Ex Vivo Cultures and Endpoint Assays with Surrogate Biomarkers in Functional Testing for Prediction of Therapeutic Response.

Prediction of therapeutic outcomes is important for cancer patients in order to reduce side effects and improve the efficacy of anti-cancer drugs. Currently, the most widely accepted method for predicting the efficacy of anti-cancer drugs is gene panel testing based on next-generation sequencing. However, gene panel testing has several limitations. For example, only 10% of cancer patients are estimated to have druggable mutations, even if whole-exome sequencing is applied. Additionally, even if optimal drugs are selected, a significant proportion of patients derive no benefit from the indicated drug treatment. Furthermore, most of the anti-cancer drugs selected by gene panel testing are molecularly targeted drugs, and the efficacies of cytotoxic drugs remain difficult to predict. Apart from gene panel testing, attempts to predict chemotherapeutic efficacy using ex vivo cultures from cancer patients have been increasing. Several groups have retrospectively demonstrated correlations between ex vivo drug sensitivity and clinical outcome. For ex vivo culture, surgically resected tumor tissue is the most abundant source. However, patients with recurrent or metastatic tumors do not usually undergo surgery, and chemotherapy may be the only option for those with inoperable tumors. Therefore, predictive methods using small amounts of cancer tissue from diagnostic materials such as endoscopic, fine-needle aspirates, needle cores and liquid biopsies are needed. To achieve this, various types of ex vivo culture and endpoint assays using effective surrogate biomarkers of drug sensitivity have recently been developed. Here, we review the variety of ex vivo cultures and endpoint assays currently available.

The Use of Intraoperative Esophageal Functional Luminal Imaging Probe for Prediction of Therapeutic Outcomes in Achalasia Patients

The Use of Intraoperative Esophageal Functional Luminal Imaging Probe for Prediction of Therapeutic Outcomes in Achalasia Patients

(025) Inhibition and Excitation in the Sexual Brain: An Useful Tool for the Prediction of Therapeutic Outcomes in Women with Sexual Dysfunction

Abstract Introduction The “Dual control model” of sexual response postulates that sexual desire and associated behaviors depend on the balance between excitation and inhibition. These aspects have not been evaluated in women with sexual dysfunction (FSD). Objectives The aim of our study was to explore the psychosexual correlates of excitation and inhibition in women with FSD, according to their menopausal state. We also aimed at performing a pre-post analysis in a sub-sample of patients followed for 3-6 months, investigating the possible relationships between excitation and inhibition and the changes in questionnaires related to sexual function. Methods In an observational, retrospective study, we recruited a consecutive series of 66 women consulting for FSD. Patients underwent a clinical evaluation, a structured interview and completed several self-administered questionnaires, including Sexual Inhibition/Sexual Excitation Scale (SIS/SES), Female Sexual Function Index (FSFI), Female Sexual Distress Scale-Revised (FSDS-R), Body Uneasiness Test (BUT) and Middlesex Hospital Questionnaire (MHQ). A subgroup of patients (n=28) was evaluated at 3-6 months follow-up, using the FSFI and FSDS-R questionnaires. Results Considering the whole sample, the SES scale was positively correlated with several psychosexual and relational parameters, including masturbation (p=0.005), number of lifetime partners (p=0.004) and histrionic personality traits (p=0.013), and negatively with the presence of a stable relationship (p=0.038) and its length (p=0.026). Sexual inhibition due to the fear of performance failure (SIS1 scale) positively correlated with conflicts within the couple (p=0.012), lack of privacy (p&amp;lt;0.0001), symptoms of general psychopathology (p=0.001; in particular, anxiety) and body image concerns (p=0.015). Regarding sexual parameters, the SES scale was associated with the total FSFI score (p=0.016), and the desire (p=0.011), arousal (p=0.030), lubrication (p=0.046) and orgasm domains (p=0.025), whereas the SIS1 scale was associated with the FSDS-R score (p=0.028). In premenopausal women, the SIS1 scale was associated with the diagnosis of Hypoactive Sexual Desire Disorder (HSDD), even after adjusting for confounders (p=0.047), whereas in postmenopausal women, we found an association between HSDD and the SES scale (p=0.039). Given these correlations, we developed two cut-off values for the SES and SIS1 scales, according to which a score &amp;lt; 49 on the SES (in postmenopausal women) and a score &amp;gt; 34 on the SIS1 (in premenopausal women) accurately identified patients diagnosed with HSDD. In postmenopausal women, the ROC curve for the SES scale showed an accuracy (AUC) of 0.734 +− 0.107, p=0.037, in identifying HSDD; considering a cut-off value of 49, the sensitivity and specificity for HSDD detection were 85.7% and 66.7%, respectively. In premenopausal women, the ROC curve for the SIS1 scale showed an AUC of 0.853 +− 0.081, p=0.007, in identifying HSDD; considering a cut-off value of 34, the sensitivity and specificity for HSDD detection were 78.6% and 75.0%, respectively. The mean change in FSFI total score was not correlated with the SES or SIS2 scales (p=0.214 and p=0.402, respectively), but only with the SIS1 scale (p=0.026). When comparing patients with low excitation (SES scale &amp;lt;49) and high inhibition (SIS1 scale &amp;gt;34) with the rest of the sample, they showed a significantly lower change in FSFI total score (p=0.018). Conclusions In women consulting for FSD, sexual excitation and inhibition, which may be easily assessed by the SIS/SES questionnaire, emerge as key aspects in both the diagnostic evaluation and planning of therapeutic outcomes, in the perspective of a personalized sexual medicine approach. Disclosure No.

Enhanced interhemispheric resting-state functional connectivity of the visual network is an early treatment response of paroxetine in patients with panic disorder.

This study aimed to detect alterations in interhemispheric interactions in patients with panic disorder (PD), determine whether such alterations could serve as biomarkers for the diagnosis and prediction of therapeutic outcomes, and map dynamic changes in interhemispheric interactions in patients with PD after treatment. Fifty-four patients with PD and 54 healthy controls (HCs) were enrolled in this study. All participants underwent clinical assessment and a resting-state functional magnetic resonance imaging scan at (i) baseline and (ii) after paroxetine treatment for 4weeks. A voxel-mirrored homotopic connectivity (VMHC) indicator, support vector machine (SVM), and support vector regression (SVR) were used in this study. Patients with PD showed reduced VMHC in the fusiform, middle temporal/occipital, and postcentral/precentral gyri, relative to those of HCs. After treatment, the patients exhibited enhanced VMHC in the lingual gyrus, relative to the baseline data. The VMHC of the fusiform and postcentral/precentral gyri contributed most to the classification (accuracy = 87.04%). The predicted changes were accessed from the SVR using the aberrant VMHC as features. Positive correlations (p < 0.001) were indicated between the actual and predicted changes in the severity of anxiety. These findings suggest that impaired interhemispheric coordination in the cognitive-sensory network characterized PD and that VMHC can serve as biomarkers and predictors of the efficiency of PD treatment. Enhanced VMHC in the lingual gyrus of patients with PD after treatment implied that pharmacotherapy recruited the visual network in the early stages.

586 Increase in CD8+ effector memory cells re-expressing CD45RA (TEMRA) reflects disease activity and enables early prediction of therapeutic outcome in rapidly progressive alopecia areata

586 Increase in CD8+ effector memory cells re-expressing CD45RA (TEMRA) reflects disease activity and enables early prediction of therapeutic outcome in rapidly progressive alopecia areata

Rs9679162 genotype predicts prognosis of real-world advanced hepatocellular carcinoma treated by sorafenib.

Sorafenib and lenvatinib are tyrosine kinase inhibitors widely used in the targeted therapy to treat advanced hepatocellular carcinoma (aHCC). The GALNT14-rs9679162 genotype is a predictor of therapeutic outcome in multiple gastrointestinal cancers. To investigate the predictive role of the GALNT14-rs9679162 genotype in aHCC treated with sorafenib or lenvatinib. Totally 350 real-world patients with aHCC received sorafenib or lenvatinib were enrolled for GALNT14-rs9679162 genotyping and outcome analysis. Kaplan-Meier and Cox regression analysis were conducted to evaluate therapeutic outcomes. Cell-based assays were performed to determine the underlying mechanism. Kaplan-Meier and Cox regression analysis showed that the "GG" genotype was not associated with overall survival (OS) when all patients were included. However, it was associated with shorter OS in specific clinical subgroups, including anti-hepatitis C virus antibody-positive (n= 108; P= 0.005) and hepatitis B surface antigen-negative (n= 117; P= 0.002) patients. Intriguingly, hepatitis B virus X protein trans-suppressed the GALNT14 promoter, thereby reducing the elevated expression of GALNT14 in hepatoma cells, which partially contributed to the inability of the GALNT14-rs9679162 genotypes to predict the outcome of hepatitis B-related HCC. Finally, by analyzing the outcomes of 52 patients with aHCC treated with lenvatinib, patients with the "GG" genotype were associated with a favorable/shorter time-to-response (P= 0.013). The GALNT14-rs9679162 "GG" genotype predicted shorter OS in patients with HBsAg-negative aHCC treated with sorafenib, but predicted a favorable response in all patients with aHCC treated with lenvatinib.

P297 The Toronto IBD Global Endoscopic Reporting (TIGER) Score and its Ability to Predict Outcomes Regarding Steroid and Biologic Use in Ulcerative Colitis and Crohn’s Disease Patients – A Construct Validation Study

Abstract Background The recently developed TIGER endoscopy score was established to reliably describe disease severity as it can be utilized for both Ulcerative Colitis (UC) and Crohn’s disease (CD) patients.1The aim of this prospective study was to assess the TIGER score’s ability to predict UC and CD patient outcomes including biologic and steroid use. Methods A cohort of 78 patients with UC (n=40) and CD (n=38) were included in a 52-week multiple visit prospective study. Each visit included patient interviews using the IBD disk questionnaire, blood draws for C-reactive protein (CRP), and fecal calprotectin (FC). Endoscopy assessment was performed at baseline. Administration of steroids, immunomodulators (IMN), biologics, and dose escalations were recorded. Baseline total TIGER scores were dichotomized as &amp;lt;100 points (remission-mild endoscopic activity) or ≥100 points (moderate-severe endoscopic activity) as a predictor of therapeutic outcomes. Results At baseline, compared to patients with TIGER scores &amp;lt;100, UC patients with TIGER scores ≥100 had significantly higher CRP (p&amp;lt;0.0005), FC (p&amp;lt;0.0001), and IBD disk (p&amp;lt;0.0001). In CD patients, at baseline, compared to patients with TIGER scores &amp;lt;100, patients with TIGER scores ≥100 had significantly higher CRP (p&amp;lt;0.0006), FC ( p&amp;lt;0.006), and IBD disk ( p&amp;lt;0.0001). At 52-weeks, compared to patients with baseline TIGER scores &amp;lt;100, UC and CD patients with baseline TIGER scores ≥100 had a significantly increased likelihood of requiring steroids as therapy (p&amp;lt;0.0001). Specifically, 7.1% and 0% of UC and CD patients with baseline TIGER scores &amp;lt;100 required steroids compared to 73.1% and 48.3% of UC and CD patients with baseline TIGER scores ≥100, respectively. Moreover, at 52-weeks, compared to patients with baseline TIGER scores &amp;lt;100, UC and CD patients with baseline TIGER scores &amp;gt;100 both had a significantly increased likelihood of being prescribed or having to subsequently escalate biologic therapy (p&amp;lt;0.0001). More specifically, 7.1% and 11.1% of UC and CD patients with baseline TIGER scores &amp;lt;100 required biologic therapy compared to 92.3% and 100% of UC and CD patients with baseline TIGER scores ≥100, respectively. Conclusion The TIGER endoscopy score demonstrates significant association with CRP, FC and IBD disk in UC and CD patients. Moreover, the TIGER endoscopy score can be utilized as a measure to predict the likelihood of UC and CD patients with moderate-to-severe disease burden requiring certain therapeutic interventions. These data suggest that the TIGER score reflects disease burden and the subsequent need for pharmacologic intervention in both UC and CD patients. 1. Zittan E, et al. J Crohns Colitis 2022;16:544-553.

Research progress and application of artificial intelligence in thyroid associated ophthalmopathy.

Thyroid-associated ophthalmopathy (TAO) is a complicated orbitopathy related to dysthyroid, which severely destroys the facial appearance and life quality without medical interference. The diagnosis and management of thyroid-associated ophthalmopathy are extremely intricate, as the number of professional ophthalmologists is limited and inadequate compared with the number of patients. Nowadays, medical applications based on artificial intelligence (AI) algorithms have been developed, which have proved effective in screening many chronic eye diseases. The advanced characteristics of automated artificial intelligence devices, such as rapidity, portability, and multi-platform compatibility, have led to significant progress in the early diagnosis and elaborate evaluation of these diseases in clinic. This study aimed to provide an overview of recent artificial intelligence applications in clinical diagnosis, activity and severity grading, and prediction of therapeutic outcomes in thyroid-associated ophthalmopathy. It also discussed the current challenges and future prospects of the development of artificial intelligence applications in treating thyroid-associated ophthalmopathy.

Microstructured Optical Fiber-Enhanced Light-Matter Interaction Enables Highly Sensitive Exosome-Based Liquid Biopsy of Breast Cancer.

Exosome-based liquid biopsies highlight potential utility in diagnosis and determining the prognosis of patients with cancer and other diseases. However, the existing techniques are severely limited for practical applications due to the complications of high cost, low sensitivity, tedious procedures, and large sample consumption. Herein, we report a microstructured optical fiber sensor for fast, sensitive, and accurate quantification of exosomes in blood samples of breast cancer patients. Numerical simulations are applied to demonstrate that hollow-core microstructured antiresonant fibers (HARFs) can stringently confine light in the fiber core, ensuring strong light-matter interaction and thus maximumly amplifying the signal. Taking this advantage, a AuNPs-dsDNA assembly containing gold nanoparticles, a recognizing DNA aptamer, and a fluorescent reporter DNA sequence is fabricated followed by immobilization on the fiber wall to form a AuNPs-dsDNA-HARF sensor. Cancer-derived exosomes can be recognized and captured in the fiber channel and generate dose-dependent fluorescent signals for quantification. The microfiber sensor demonstrates enhanced sensitivity and specificity, enabling the detection of single digits of exosome particles at the nanoliter sample level. In addition, by tracking exosome phenotypic changes, the proposed fiber sensor can facilitate precise drug treatment monitoring. This work provides a robust platform for exosome-based biopsy for cancer diagnosis and prediction of therapeutic outcomes.

Inhibition and excitation in the sexual brain: an useful tool for the prediction of therapeutic outcomes in women with sexual dysfunction

Inhibition and excitation in the sexual brain: an useful tool for the prediction of therapeutic outcomes in women with sexual dysfunction

Inhibition and excitation in the sexual brain: an useful tool for the prediction of therapeutic outcomes in women with sexual dysfunction

Inhibition and excitation in the sexual brain: an useful tool for the prediction of therapeutic outcomes in women with sexual dysfunction

Machine learning, pharmacogenomics, and clinical psychiatry: predicting antidepressant response in patients with major depressive disorder

ABSTRACT Introduction The efficacy of antidepressants for patients with major depressive disorder (MDD) varies from individual to individual, making the prediction of therapeutic outcomes difficult. Better methods for predicting antidepressant outcomes are needed. However, complex interactions between biological, psychological, and environmental factors affect outcomes, presenting immense computational challenges for prediction. Using machine learning (ML) techniques with pharmacogenomics data provides one pathway toward individualized prediction of therapeutic outcomes of antidepressants. Areas covered This report systematically reviews the methods, results, and limitations of individual studies of ML and pharmacogenomics for predicting response and/or remission with antidepressants in patients with MDD. Future directions for research and pragmatic considerations for the clinical implementation of ML-based pharmacogenomic algorithms are also discussed. Expert opinion ML methods utilizing pharmacogenomic and clinical data demonstrate promising results for predicting short-term antidepressant response. However, predictions of antidepressant treatment outcomes depend on contextual factors that ML algorithms may not be able to capture. As such, ML-driven prediction is best viewed as a companion to clinical judgment, not its replacement. Successful implementation and adoption of methods predicting antidepressant response warrants provider education about ML and close collaborations between computing scientists, pharmacogenomic experts, health system engineers, laboratory medicine experts, and clinicians.

Allele frequency and proportion defined by circulating tumor DNA profiling predict tyrosine kinase inhibitors' therapeutic outcomes for non-small cell lung cancer.

Circulating tumor DNA is more and more accessible for patients who cannot undergo biopsy. No consistent conclusion has been reached on whether frequency and proportion of mutations defined by ctDNA profiling can predict therapeutic outcomes. One hundred patients with non-small cell lung cancer harboring activating EGFR mutations (exon 19 deletion, L858R and T790M mutation) were collected in West China hospital from December 18, 2017 to December 31, 2019. We retrospectively analyzed the frequency and proportion distribution of ctDNA mutations and its relationship with tyrosine kinase inhibitors therapeutic outcomes. Patients with lower frequency of sensitizing EGFR mutations (< 3%) had a longer progression-free survival (PFS) time than those with higher frequency (15months vs. 10months, p = 0.028). Moreover, patients with the lower ratio of T790M mutation frequency and the maximum-somatic-allele-frequency (T790M/MSAF < 30%) had a less prolonged PFS than those with higher T790M/MSAF (7months vs. 15months, p = 0.013). The frequency and proportion of ctDNA mutations are worth clinical attention in the prediction of therapeutic outcomes.

Inhibition and excitation in the sexual brain: An useful tool for the prediction of therapeutic outcomes in women with sexual dysfunction

ABSTRACT Introduction The “Dual control model” of sexual response postulates that sexual desire and associated behaviors depend on the balance between excitation and inhibition. These aspects have not been evaluated in women with sexual dysfunction (FSD). Objective To explore the psychosexual correlates of excitation and inhibition in women with FSD, according to their menopausal state. We also aimed at performing a pre-post analysis in a sub-sample of patients followed for 3-6 months, investigating the possible relationships between excitation and inhibition and the changes in questionnaires related to sexual function. Methods In an observational, retrospective study, we recruited a consecutive series of 66 women consulting for FSD. Patients underwent a clinical evaluation, a structured interview and completed several self-administered questionnaires, including Sexual Inhibition/Sexual Excitation Scale (SIS/SES), Female Sexual Function Index (FSFI), Female Sexual Distress Scale-Revised (FSDS-R), Body Uneasiness Test (BUT) and Middlesex Hospital Questionnaire (MHQ). A subgroup of patients (n=28) was evaluated at 3-6 months follow-up, using the FSFI and FSDS-R questionnaires. Results In pre-menopausal women, the SIS1 scale (Sexual inhibition due to the fear of performance failure) was associated with the diagnosis of Hypoactive Sexual Desire Disorder (HSDD), even after adjusting for confounders (p=0.047), whereas in post-menopausal women, we found an association between HSDD and the SES scale (total sexual excitation) (p=0.039; see also Figure 1). Given these correlations, we developed two cut-off values for the SES and SIS1 scales, according to which a score &amp;lt; 49 on the SES (in postmenopausal women) and a score &amp;gt; 34 on the SIS1 (in premenopausal women) accurately identified patients diagnosed with HSDD. In post-menopausal women, the ROC curve for the SES scale showed an accuracy (AUC) of 0.734 +- 0.107, p=0.037, in identifying HSDD; considering a cut-off value of 49, the sensitivity and specificity for HSDD detection were 85.7% and 66.7%, respectively (Fig. 2). In premenopausal women, the ROC curve for the SIS1 scale showed an AUC of 0.853 +- 0.081, p=0.007, in identifying HSDD; considering a cut-off value of 34, the sensitivity and specificity for HSDD detection were 78.6% and 75.0%, respectively (Fig. 3). At follow-up, the mean change in FSFI total score was not correlated with the SES or SIS2 scales (p=0.214 and p=0.402, respectively), but only with the SIS1 scale (p=0.026). When comparing patients with low excitation (SES scale &amp;lt;49) and high inhibition (SIS1 scale &amp;gt;34) with the rest of the sample, they showed a significantly lower change in FSFI total score (p=0.018). Conclusions In women consulting for FSD, sexual excitation and inhibition, which may be easily assessed by the SIS/SES questionnaire, emerge as key aspects in both the diagnostic evaluation and planning of therapeutic outcomes, in the perspective of a personalized sexual medicine approach. Disclosure Work supported by industry: no.

Music therapeutic emotional processing (MEP): Expression, awareness, and pain predict therapeutic outcome

Successful emotional processing is pivotal for the therapeutic change, and music can support emotional processing. However, we know little on how music-based emotional processing actually predicts clinical outcomes. This study investigated music therapeutic emotional processing (MEP) as a predictor of therapeutic outcome in treatment for depression. Data consisted of self-reports of 64 clients (age range 19–57, 74% female) from a clinical trial (12 sessions) of integrative improvisational music therapy (IIMT). A 19-item MEP questionnaire was developed for assessing clients’ experiences after sessions. Emergent MEP factors were correlated with clients’ perceptions of the therapeutic value of the sessions (Session Evaluation Questionnaire [SEQ]) and with recovery from depression (Montgomery–Åsberg Depression Rating Scale [MADRS]). MEP emerged as a constitution of three factors: expression, awareness, and pain. Expression and awareness increased across the process and correlated positively with session value and MADRS change. Pain demonstrated a more complex inverted u-shaped temporal curve and correlated with roughness of session experience. The presence of pain during the late part of the therapy predicted lower recovery. The findings support MEP factors as predictors of therapeutic outcome and provide conceptual insight into the mechanisms of change in art therapies. Knowledge on music-based emotional processing holds relevance also beyond therapy context for everyday music engagement.

Musical interaction in music therapy for depression treatment

Music therapy is efficacious for the treatment of depression. Compared to other psychotherapeutic forms, it allows for the emergence of various modes of mutual interaction, thus enabling multiple channels for emotional expression and fostering therapeutic alliance. Although musical interaction patterns between client and therapist have been regarded as predictors of therapeutic outcome in depression, this has not yet been systematically investigated. We aim to address this gap by analyzing the possible linkage between musical interaction features and changes in depression score. In a clinical trial, digital piano improvisations from 58 Finnish clients and their therapists were recorded over 12 sessions of music therapy lasting 6 weeks. Subsequently, a variety of symbolic features describing pitch, rhythm, duration, and velocity were extracted from the improvisations. We observed a number of relationships between client–therapist interaction and clinical improvement. Clients with largest improvements displayed higher overall interaction, particularly more musical interaction in the middle of the therapy process than in the beginning and end. In contrast, clients with lower depression change score exhibited overall lower interaction and yielded other temporal profiles of interaction. The association between clinical improvement and an inverted U-shaped curve of musical interaction is discussed in the light of process-outcome literature.

Electroclinical features and long-term therapeutic response in patients with typical absence seizures.

To characterize in detail the electroclinical features of typical absence seizures and elucidate whether EEG or semiology features, alone or in combination, can predict long-term therapeutic outcome. We analysed video-EEG recordings from 213 typical absence seizures from 61 patients with idiopathic generalized epilepsy. We extracted semiological features, in addition to hallmark manifestations (motor/behavioural arrest, non-responsiveness), their location, timing and frequency. We evaluated the duration and frequency of generalized spike-wave discharges and the presence of polyspikes. We used a supervised machine-learning approach (random forest) to search for classifier features for long-term therapeutic outcome (>one year). Besides the hallmark manifestations, additional semiological features were identified in 87% of patients (75% of seizures). The most common additional semiological features were automatisms and eye blinking (observed in 45% and 41.5% of seizures, respectively). Automatisms were associated with longer seizure duration, and oral automatisms occurred earlier compared to limb automatisms (4.03 vs. 6.19 seconds; p=0.005). The mean duration of the ictal spike-wave discharges was nine seconds, and the median frequency was 3Hz. Polyspikes occurred in 46 seizures (21.6%), in 19 patients (31%). Median follow-up was five years, and 73% of the patients were seizure-free at the end of the follow-up. None of the semiological features, alone or in combination, were predictors of therapeutic outcome. The only significant classifier was the presence of polyspikes, predicting a non-seizure-free outcome with an accuracy of 73% (95%CI: 70-77%), positive predictive value of 92% (95%CI: 84-98%) and negative predictive value of 60% (95%CI: 39-81%). Semiological features, in addition to behavioural arrest and non-responsiveness, are common in typical absence seizures, but they do not predict long-term therapeutic outcome. The presence of polyspikes has a high positive predictive value for unfavourable therapeutic outcome, and their presence should therefore be included when reporting EEGs in patients with typical absence seizures.

Clinical profile and predictors of therapeutic outcome with azithromycin in pediatric scrub typhus: A study from Eastern India.

In this prospective study, children from 1-month to 12-years, admitted with positive IgM ELISA testing for scrub typhus, were enrolled over 1-year and administered an empirical single dose of azithromycin (10 mg/kg). All 189 (median age 4.84-years) children had fever with median duration of 8 days at admission. Shortness of breath, altered sensorium, headache, lymphadenopathy, hepatosplenomegaly, pallor, oedema and hypotension were the most prominent symptoms/signs. About 46% of children developed complications, among them hepatic, neurological and cardiovascular were most common. Our study showed a remarkable response to azithromycin, with mean defervescence of 32 h and no mortality. The presence of shortness of breath, headache, altered sensorium, hepatosplenomegaly, meningeal signs, severe anemia, leucocytosis, hyponatremia along with/without any of above mentioned three systemic complications were independent predictors for delayed treatment response by multivariate logistic regression analysis. Hepatic, neurological and cardiovascular complications are an emerging trend. Empiric azithromycin based on clinical suspicion of scrub typhus can be lifesaving.