Treatment Response Research Articles

Inflammatory Neuropathy Consortium base (INCbase): a protocol of a global prospective observational cohort study for the development of a prediction model for treatment response in chronic inflammatory demyelinating polyneuropathy

BackgroundINCbase is an international, multicenter prospective observational study using a customizable web-based modular registry to study the clinical, biological and electrophysiological variation and boundaries of chronic inflammatory demyelinating polyneuropathy (CIDP). The primary objective of INCbase is to develop and validate a clinical prediction model for treatment response.MethodsAll patients meeting clinical criteria for CIDP can be included in INCbase. Collected data include demographics, clinical history, diagnostics and various domains of clinical outcomes. Data is collected at a minimum of every 6 months for two years, and more frequently at the discretion of the investigational site to allow for assessment of unexpected changes in treatment response or clinical status. Participants can be enrolled in various sub-studies designed to capture data relevant to specific groups of interest. Data is entered directly into the web-based data entry system by local investigators and/or participants. Collection and local storage of biomaterial is optional. To develop a clinical prediction model for treatment response, newly diagnosed patients with active disease warranting start of first-line treatment will be included. The study population will be split into a development and validation cohort. Univariate and multivariate logistic regression analysis will be used to identify and combine predictors at start of treatment for treatment response at six months. Model performance will be assessed through discrimination and calibration in an external validation cohort. The externally validated prediction model will be made available to researchers and clinicians on the INCbase website.DiscussionWith this study, we aim to create a clinically relevant and implementable prediction model for treatment response to first line treatments in CIDP. INCbase enrollment started in April 2021, with 29 centers across 8 countries and 303 patients participating to date. This collaborative effort between academia, patient advocacy organizations and pharmaceutical industry will deepen our understanding of how to diagnose and treat CIDP.

Predicting Prognosis of Early-Stage Mycosis Fungoides with Utilization of Machine Learning

Mycosis fungoides (MF) is the most prevalent type of cutaneous T cell lymphomas. Studies on the prognosis of MF are limited, and no research exists on the potential of artificial intelligence to predict MF prognosis. This study aimed to compare the predictive capabilities of various machine learning (ML) algorithms in predicting progression, treatment response, and relapse and to assess their predictive power against that of the Cox proportional hazards (CPH) model in patients with early-stage MF. The data of patients aged 18 years and over who were diagnosed with early-stage MF at Ankara University Faculty of Medicine Hospital from 2006 to 2024 were retrospectively reviewed. ML algorithms were utilized to predict complete response, relapse, and disease progression using patient data. Of the 185 patients, 94 (50.8%) were female, and 91 (49.2%) were male. Complete response was observed in 114 patients (61.6%), while relapse and progression occurred in 69 (37.3%) and 54 (29.2%) patients, respectively. For predicting progression, the Support Vector Machine (SVM) algorithm demonstrated the highest success rate, with an accuracy of 75%, outperforming the CPH model (C-index: 0.652 for SVM vs. 0.501 for CPH). The most successful model for predicting complete response was the Ensemble model, with an accuracy of 68.89%, surpassing the CPH model (C-index: 0.662 for the Ensemble model vs. 0.543 for CPH). For predicting relapse, the decision tree classifier showed the highest performance, with an accuracy of 78.17%, outperforming the CPH model (C-index: 0.782 for the decision tree classifier vs. 0.505 for CPH). The results suggest that ML algorithms may be useful in predicting prognosis in early-stage MF patients.

Comparative analysis of testicular and non-testicular choriocarcinoma: a population-based study

Background: Germ cell tumors (GCTs) are common solid tumors in young men, originating in the testicles or outside the gonads. Choriocarcinoma, a rare and aggressive subtype, primarily affects females but can also occur in males. Treatment options depend on the stage and location of the tumor, with early recognition being crucial for better outcomes. Comparative studies between testicular and non-testicular choriocarcinoma are crucial for understanding distinct features and prognoses. Methods: The study utilized SEER*Stat software to extract data and applied statistical methods such as chi-square analysis and Kaplan-Meier method. Inclusion criteria focused on patients diagnosed with choriocarcinoma between 2000 and 2018, while exclusion criteria eliminated cases without histological confirmation or with other tumors. Results: Among 363 patients, 270 (74.4%) had testicular CC, and 93 (25.6%) had non-testicular CC. Notably, testicular CC was more common in white patients, which could indicate demographic or environmental factors at play. Patients with testicular CC were more likely to undergo surgery, suggesting a significant treatment trend. It’s worth exploring whether patient preferences or observed post-surgery improvements contribute to this pattern. Testicular CC had a higher 5-year OS rate of 54% versus 29%, and a higher 5-year CSS rate of 56.3% versus 31.9%, respectively. Conclusion: This study reveals distinct characteristics and treatment responses in testicular and non-testicular choriocarcinoma, emphasizing the need for personalized management based on subtype. Our findings highlight racial disparities in incidence and the efficacy of surgical intervention for both types, while chemotherapy benefits extragonadal cases and radiotherapy’s role requires further evaluation.

Effects of electroconvulsive therapy on inflammatory markers and depressive symptoms in adolescents with major depressive disorder

ObjectiveLimited research exists on the use of electroconvulsive therapy (ECT) for adolescents with major depressive disorder (MDD). This study investigates the effects of ECT on inflammatory markers in adolescents aged 13-18 suffering from severe MDD, evaluating its efficacy in modulating cellular inflammatory markers and ameliorating depressive symptoms.MethodsA cohort of 38 adolescents with severe MDD received standard antidepressant therapy along with 6-8 ECT sessions spanning two weeks. A control group of 29 age-matched, healthy individuals was also assessed for comparative purposes. The investigation measured variations in depressive symptomatology and inflammatory marker levels (IL-1β, IL-6, IL-10) pre- and post-intervention.ResultsPost-ECT, a substantial decrease in pro-inflammatory cytokines (IL-1β and IL-6) and an increase in the anti-inflammatory cytokine (IL-10) were noted. Participants who responded to the treatment showed a significant decline in HAMD-17 scores, which accentuates ECT’s therapeutic potential. Comparative analysis indicated a significant correlation between post-treatment inflammatory marker alterations and clinical improvement, implying that shifts in inflammatory state might serve as predictors of treatment response. Moreover, the mitigation of depressive symptoms exhibited a moderate correlation with post-treatment decrements in IL-1β and IL-6 levels, underscoring MDD’s intricacy and ECT’s comprehensive impact.ConclusionWhile initial inflammatory marker levels did not predict the response to ECT, the post-treatment measures appeared to be linked to clinical improvement. These findings suggest ECT’s potential effectiveness in treating severe MDD in adolescents and point to the possible predictive value of inflammatory markers in therapeutic outcomes. The study contributes to our understanding of the biopsychosocial framework of MDD and indicates that ECT may be a viable treatment option for this population.

Distinct trajectories of symptomatic response in ulcerative colitis during filgotinib therapy: A post hoc analysis from the SELECTION study.

Filgotinib is an oral, once-daily, Janus kinase 1 preferential inhibitor approved for treatment of ulcerative colitis (UC) following the phase 2b/3 SELECTION trial. Identification of patient populations and factors associated with long-term treatment response trajectories may improve UC management. We aimed to identify and describe distinct patient subgroups of response to filgotinib based on partial Mayo Clinic Score (pMCS) trajectories over time. In these post hoc analyses of SELECTION, group-based trajectory modeling (GBTM) was applied to pMCS to describe groups of distinct, symptom-based patient trajectories using data from patients who responded to filgotinib 200 or 100mg and continued receiving filgotinib up to week 58. Patient demographics, disease characteristics, and week 10 response were compared between the groups. Achievement of a patient-level multi-component endpoint of comprehensive disease control (CDC) was assessed in each group. GBTM identified five distinct patient populations with different response trajectories; 67.5% of patients had beneficial trajectories. The beneficial trajectory groups generally had higher proportions of patients who were recently diagnosed (<1year), were receiving filgotinib 200mg and were biologic-naive versus the relapsing trajectory groups (4%-9% vs. 4%-5%; 43%-65% vs. 36%-46%; 54%-70% vs. 35%-58%, respectively). Furthermore, 55.4% of patients had sustained beneficial trajectories, with low baseline endoscopic subscores (≥43% of patients had a subscore of 2) and strong week 10 FCP responses (≥61% of patients with >50% decrease in FCP from baseline). Sustained beneficial trajectory groups had a higher probability of achieving CDC at week 58 than other groups (31%-32% vs. 0%-7%). Beneficial long-term response trajectories and achievement of CDC with filgotinib were associated with being biologic-naive and having less severe disease at baseline. Early estimation of sustained and CDC may facilitate patient identification and development of personalized management strategies in UC. NCT02914522.

Effectiveness of JAK Inhibitors Compared With Biologic Disease-Modifying Antirheumatic Drugs on Pain Reduction in Rheumatoid Arthritis: Results From a Nationwide Swedish Cohort Study.

To compare the effectiveness of JAK inhibitors (JAKis) and biologic disease-modifying antirheumatic drugs (bDMARDs) on pain in patients with rheumatoid arthritis. In this retrospective study, we investigated patients with a diagnosis of rheumatoid arthritis, starting treatment with a JAKi (n = 1,827), a tumor necrosis factor inhibitor (TNFi; n = 6,422), an interleukin-6 inhibitor (n = 887), abatacept (n = 1,102), or rituximab (n = 1,149) in 2017 to 2019, using data from several linked Swedish national registers. Differences in change in pain, assessed with a visual analogue scale (0-100 mm), from baseline to 3 months, as well as proportions of patients remaining on initial treatment with low pain (visual analogue scale pain <20) at 12 months, were compared between treatments. Comparisons of treatment responses between JAKis and bDMARDs were evaluated using multivariable linear regression, adjusted for patient characteristics, comorbidities, current comedication, and previous treatment. JAKi treatment was associated with a greater decrease in pain at 3 months compared with TNFi treatment (adjusted mean additional decrease 4.0 mm; 95% confidence interval 1.6-6.3), with similar trends in comparisons with non-TNFi bDMARDs. More patients achieved low pain at 12 months on JAKis compared with TNFis, in particular among those previously treated with at least two bDMARDs (adjusted change contrast 5.3 percentage points; 95% confidence interval 1.0-9.6). JAKis had a slightly better effect on pain outcomes at 3 and 12 months compared with TNFis, with significantly greater differences in patients previously treated with at least two bDMARDs. The effect of JAKis on pain reduction was at least similar to that of non-TNFi bDMARDs.

Colorectal cancer research priorities in Uganda: perspectives from local key experts and stakeholders.

The incidence of colorectal cancer (CRC) is increasing in Uganda but there is limited local research to guide policy and programming for CRC prevention and control. A stakeholder engagement workshop took place in Kampala on 19March 2024 to identify challenges and opportunities for CRC prevention and control in Uganda. A total of 30 stakeholders with expertise in CRC primary and secondary prevention, diagnosis, treatment, palliative care as well as cancer survivors participated in the workshop. Key challenges for primary prevention included low knowledge/awareness of CRC among the general population and health workers, and rising prevalence of CRC related risk factors. Limited CRC screening, diagnostic facilities and specialists were identified as barriers to diagnosis. Treatment related challenges included limited accessibility to surgical services and drugs, late-stage presentation leading to poor treatment response, treatment abandonment and drug related toxicity. Lack of universal health coverage policies, limited community-based cancer awareness programs, and lack of national cancer registries were cited as policy and economics challenges. Opportunities to address these challenges were discussed. Our findings highlight areas for further research and prioritization to address Uganda's growing CRC burden and may be applicable to other low-resource settings.

Quantification of autoantibodies using a luminescent profiling method in autoimmune interstitial lung disease

Autoantibodies are important for the diagnosis of autoimmune interstitial lung disease (ILD). Standard immunoassays have limitations, including their qualitative nature and/or a narrow dynamic range of detection, hindering the usefulness of autoantibodies as biomarkers of disease activity. Here, the luciferase immunoprecipitation system (LIPS) was evaluated for measuring myositis-specific and other lung-related autoantibodies in 25 subjects with idiopathic inflammatory myopathies (IIM), 26 with Sjögren’s disease (SjD), and 10 healthy volunteers. LIPS detected a broad dynamic range of autoantibodies, to MDA5, Jo-1, PL12, KS, U1-70K, and Ro52, and matched seropositivity status with established immunoassays. Robust anti-MDA5 autoantibodies in four IIM-ILD patients had a median value of 1,134,000 LU (IQR 473,000-2,317,000), which was 500 times higher than in 21 seronegative IIM patients. Markedly elevated anti-Jo-1 autoantibodies in five IIM-ILD patients demonstrated a median value of 1,177,000 LU (IQR: 604,000-2,520,000), which was 1000-fold higher than in seronegative patients. Robust anti-Ro52 and other anti-tRNA-synthetase autoantibodies were detected in a subset of IIM-ILD subjects. In SjD, only anti-U1-70K and KS autoantibodies were identified in ILD patients with a prevalence of 30% and 20%, respectively. In longitudinal samples of five IIM-ILD patients, anti-Jo-1 autoantibody levels paralleled clinical improvement of lung function. LIPS can accurately quantify autoantibody levels as biomarkers for treatment response in patients with autoimmune ILD.

Mean global field power is reduced in infantile epileptic spasms syndrome after response to vigabatrin

PurposeInfantile epileptic spasms syndrome (IESS) is associated with abnormal neuronal networks during a critical period of synaptogenesis and brain plasticity. Hypsarrhythmia is a visual EEG biomarker used to diagnose IESS, assess response to treatment, and monitor relapse. Computational EEG biomarkers hold promise in providing unbiased, reliable, and objective criteria for clinical management. We hypothesized that computational and visual EEG biomarkers of IESS would correlate after treatment with vigabatrin and that these responses might differ between responders and non-responders.MethodsA retrospective analysis was conducted at a single center, involving children with IESS at initial diagnosis and following first-line treatment with vigabatrin. Visual EEG biomarkers of hypsarrhythmia were compared with computational EEG biomarkers, including spike and spike fast-oscillation source coherence, spectral power, and mean global field power, using retrospective analysis of EEG recorded at initial diagnosis and after vigabatrin treatment. Responders and non-responders were compared based on the characteristics of their follow-up EEGs.ResultsIn this pilot study, we observed a reduction in the EEG biomarker of hypsarrhythmia/modified hypsarrhythmia from 20/20 (100%) cases at the initial diagnosis to 9/20 (45%) cases after treatment with vigabatrin, indicating a 55% (11/20) responder rate. No significant difference in spike frequency was observed after treatment (p = 0.104). We observed no significant differences after treatment with vigabatrin in the computational EEG biomarkers that we assessed, including spike source coherence at 90% (p = 0.983), spike source coherence lag range (p &amp;gt; 0.999), spike gamma source coherence at 90% (p = 0.177), spike gamma source coherence lag range (p &amp;gt; 0.999), spectral power (0.642), or mean global field power (0.932). However, when follow-up EEGs were compared, there was a significant difference in mean global field power (p = 0.038) between vigabatrin responders and non-responders. In contrast, no such difference was observed for spike source coherence at 90% (p = 0.285), spike course coherence lag range (p = 0.819), spike gamma source coherence at 90% (p = 0.205), spike gamma source coherence lag range (p &amp;gt; 0.999), or spectral power (p = 0.445). Finally, our treated group did not differ significantly from healthy controls at initial diagnosis or follow-up in terms of spectral power (p = 0.420) or mean global field power (0.127).ConclusionIn this pilot study, we show that mean global field power is a computational EEG biomarker that is significantly reduced in IESS after treatment with vigabatrin. Although computational EEG biomarkers of network connectivity using spike source coherence appear to be a promising tool, future studies should further explore their potential for assessing treatment responses in IESS.

Machine Learning Models for Predicting Significant Liver Fibrosis in Patients with Severe Obesity and Nonalcoholic Fatty Liver Disease.

Although noninvasive tests can be used to predict liver fibrosis, their accuracy is limited for patients with severe obesity and nonalcoholic fatty liver disease (NAFLD). We developed machine learning (ML) models to predict significant liver fibrosis in patients with severe obesity through noninvasive tests. This prospective study included 194 patients with severe obesity who underwent wedge liver biopsy and metabolic bariatric surgery at Taipei Medical University Hospital between September 2016 and December 2020. Significant liver fibrosis was defined as a fibrosis score ≥ 2. Patients were randomly divided into a training group (70%) and a validation group (30%). ML models, including support vector machine, random forest, k-nearest neighbor, XGBoost, and logistic regression, were trained to predict significant liver fibrosis, using DM status, AST, ALT, ultrasonographic fibrosis scores, and liver stiffness measurements (LSM). An ensemble model including these ML models was also used for prediction. Among the ML models, the XGBoost model exhibited the highest AUROC of 0.77, with a sensitivity, specificity, and accuracy of 61.5%, 75.8%, and 69.5%, in validation set, while LSM, AST, ALT showed strongest effects on the model. The ensemble model outperformed all ML models in terms of sensitivity, specificity, and accuracy of 73.1%, 90.9%, and 83.1%. For patients with severe obesity and NAFLD, the XGBoost model and the ensemble model exhibit high predictive performance for significant liver fibrosis. These models may be used to screen for significant liver fibrosis in this patient group and monitor treatment response after metabolic bariatric surgery.

Intraarterial Administration of Peptide Receptor Radionuclide Therapy in Patients with Advanced Meningioma: Initial Safety and Efficacy.

Peptide receptor radionuclide therapy (PRRT) is a treatment option for patients with advanced meningioma. Recently, intraarterial application of the radiolabeled somatostatin receptor agonists has been introduced as an alternative to standard intravenous administration. In this study, we assessed the safety and efficacy of intraarterial PRRT in patients with advanced, progressive meningioma. Methods: Patients with advanced, progressive meningioma underwent intraarterial PRRT with [177Lu]Lu-HA-DOTATATE. The safety of PRRT was evaluated according to the Common Terminology Criteria for Adverse Events version 5.0. Treatment response was assessed according to the proposed Response Assessment in Neuro-Oncology criteria for meningiomas and somatostatin receptor-directed PET/CT. Results: Thirteen patients (8 women, 5 men; mean age, 65 ± 13 y) with advanced meningioma underwent 1-4 cycles (median, 4 cycles) of intraarterial PRRT with [177Lu]Lu-HA-DOTATATE (mean activity per cycle, 7,428 ± 237 MBq; range, 6,000-7,700 MBq). Treatment was well tolerated with mainly grade 1-2 hematologic toxicity. Ten of 13 patients showed radiologic disease control at follow-up after therapy (1/10 complete remission, 1/10 partial remission, 8/10 stable disease), and 9 of 13 patients showed good control of clinical symptoms. Conclusion: Intraarterial PRRT in patients with advanced meningioma is feasible and safe. It may result in improved radiologic and clinical disease control compared with intravenous PRRT. Further research to validate these initial findings and to investigate long-term outcomes is highly warranted.

Unmet Needs in Spondyloarthritis: Imaging in Axial Spondyloarthritis.

Imaging biomarkers in axial spondyloarthritis (axSpA) are currently the most specific biomarkers for the diagnosis of this condition. Despite advances in imaging, from plain radiographs-which detect only damage-to magnetic resonance imaging (MRI)-which identifies disease activity and structural change-there are still many challenges that remain. Imaging in sacroiliitis is characterized by active and structural changes. Current classification criteria stress the importance of bone marrow edema (BME); however, BME can occur in various diseases, mechanical conditions, and healthy individuals. Thus, the identification of structural lesions such as erosion, subchondral fat, backfill, and ankylosis is important to distinguish from mimics on differential diagnosis. Various imaging modalities are available to examine structural lesions, but computed tomography (CT) is considered the current reference standard. Nonetheless, recent advances in MRI allow for direct bone imaging and the reconstruction of CT-like images that can provide similar information. Therefore, the ability of MRI to detect and measure structural lesions is strengthened. Here, we present an overview of the spectrum of current and cutting-edge techniques for SpA imaging in clinical practice; namely, we discuss the advantages, disadvantages, and usefulness of imaging in SpA through radiography, low-dose and dual-energy CT, and MRI. Cutting-edge MRI sequences including volumetric interpolated breath-hold examination, ultrashort echo time, zero echo time, and deep learning-based synthetic CT that creates CT-like images without ionizing radiation, are discussed. Imaging techniques allow for quantification of inflammatory and structural lesions, which is important in the assessment of treatment response and disease progression. Radiographic damage is poorly sensitive to change. Artificial intelligence has already revolutionized radiology practice, including protocolization, image quality, and image interpretation.

Heterogeneity in the Effect of Early Goal-Directed Therapy for Septic Shock: A Secondary Analysis of Two Multicenter International Trials.

The optimal approach for resuscitation in septic shock remains unclear despite multiple randomized controlled trials (RCTs). Our objective was to investigate whether previously uncharacterized variation across individuals in their response to resuscitation strategies may contribute to conflicting average treatment effects in prior RCTs. We randomly split study sites from the Australian Resuscitation of Sepsis Evaluation (ARISE) and Protocolized Care for Early Septic Shock (ProCESS) trials into derivation and validation cohorts. We trained machine learning models to predict individual absolute risk differences (iARDs) in 90-day mortality in derivation cohorts and tested for heterogeneity of treatment effect (HTE) in validation cohorts and swapped these cohorts in sensitivity analyses. We fit the best-performing model in a combined dataset to explore roles of patient characteristics and individual components of early goal-directed therapy (EGDT) to determine treatment responses. Eighty-one sites in Australia, New Zealand, Hong Kong, Finland, Republic of Ireland, and the United States. Adult patients presenting to the emergency department with severe sepsis or septic shock. EGDT vs. usual care. A local-linear random forest model performed best in predicting iARDs. In the validation cohort, HTE was confirmed, evidenced by an interaction between iARD prediction and treatment (p < 0.001). When patients were grouped based on predicted iARDs, treatment response increased from the lowest to the highest quintiles (absolute risk difference [95% CI], -8% [-19% to 4%] and relative risk reduction, 1.34 [0.89-2.01] in quintile 1 suggesting harm from EGDT, and 12% [1-23%] and 0.64 [0.42-0.96] in quintile 5 suggesting benefit). Sensitivity analyses showed similar findings. Pre-intervention albumin contributed the most to HTE. Analyses of individual EGDT components were inconclusive. Treatment response to EGDT varied across patients in two multicenter RCTs with large benefits for some patients while others were harmed. Patient characteristics, including albumin, were most important in identifying HTE.

Neosporosis in 21 adult dogs, 2010-2023.

Limited information is available regarding the clinical features, treatment, and prognosis of neosporosis in adult dogs. Describe the clinical signs, laboratory findings, magnetic resonance imaging (MRI) findings, treatment and outcome in adult dogs (>6 months) diagnosed with neosporosis based on consistent clinical signs and positive serology (titer ≥1 : 800) at a referral hospital in Sydney, Australia. Twenty-one client-owned dogs. Retrospective case series of affected dogs between 2010 and 2023. Survival times were determined from onset of clinical signs to date of death or censoring. Clinical signs varied, and were indicative of generalized myopathy (6 dogs), multifocal intracranial disease (7 dogs), myelopathy (4 dogs), polyneuropathy (2 dogs) and single cases of focal myopathy and cerebellar disease. Serum creatine kinase activity was markedly increased (median, 3369 U/L) in most dogs. The most common MRI abnormalities were multifocal intracranial abnormalities (7/13 dogs) and muscle changes (5/13 dogs) whereas T2-weighted cerebellar abnormalities (2/13 dogs) and cerebellar atrophy (1/13) were less common. Treatment response was complete (resolution to normal) in 8 dogs, incomplete (persistent neurological deficits) in 6 dogs, but there was minimal response in 7 dogs. Thirteen dogs (62%) were alive after 6 months and 12 dogs (57%) alive after 1 year. Relapse was common, with 4 dogs experiencing at least 1 relapse event during the follow-up period. Adult-onset neosporosis is uncommon and has variable clinical presentations. Treatment response also is variable, and relapse can occur, even among patients that respond completely to initial treatment.

Efficacy of a Standalone Smartphone Application to Treat Postnatal Depression: A Randomized Controlled Trial

Introduction: Smartphone app interventions based on cognitive-behavioral therapy (CBT) are promising scalable alternatives for treating mental disorders, but the evidence of their efficacy for postpartum depression is limited. We assessed the efficacy of Motherly, a standalone CBT-based smartphone app, in reducing symptoms of postpartum depression. Methods: Women aged 18–40 with symptoms of postpartum depression were randomized either to intervention (Motherly app) or active control (COMVC app). The primary outcome was symptoms of depression measured by the Edinburgh Postnatal Depression Scale (EPDS) at post-treatment. Secondary outcomes were anxiety symptoms, parental stress, quality of sleep, behavioral activation, availability of response-contingent positive reinforcement, and clinical improvement at post-treatment and 1-month follow-up. Exploratory analyses were performed to investigate if app engagement was associated with treatment response. Results: From November 2021 to August 2022, 1,751 women volunteered, of which 264 were randomized, and 215 provided primary outcome data. No statistically significant differences were found between groups at post-treatment: intervention: mean (SD): 12.75 (5.52); active control: 13.28 (5.32); p = 0.604. There was a statistically significant effect of the intervention on some of the secondary outcomes. Exploratory analyses suggest a dose-response relationship between Motherly app engagement and outcomes. Conclusion: Our standalone app intervention did not significantly reduce postnatal depression symptoms when compared to active control. Exploratory findings suggest that negative findings might be associated with insufficient app engagement. Consistent with current literature, our findings suggest that standalone app interventions for postpartum depression are not ready to be implemented in clinical practice.

Digital Phenotyping of Mental and Physical Conditions: Remote Monitoring of Patients Through RADAR-Base Platform.

The use of digital biomarkers through remote patient monitoring offers valuable and timely insights into a patient's condition, including aspects such as disease progression and treatment response. This serves as a complementary resource to traditional health care settings leveraging mobile technology to improve scale and lower latency, cost, and burden. Smartphones with embedded and connected sensors have immense potential for improving health care through various apps and mobile health (mHealth) platforms. This capability could enable the development of reliable digital biomarkers from long-term longitudinal data collected remotely from patients. We built an open-source platform, RADAR-base, to support large-scale data collection in remote monitoring studies. RADAR-base is a modern remote data collection platform built around Confluent's Apache Kafka to support scalability, extensibility, security, privacy, and quality of data. It provides support for study design and setup and active (eg, patient-reported outcome measures) and passive (eg, phone sensors, wearable devices, and Internet of Things) remote data collection capabilities with feature generation (eg, behavioral, environmental, and physiological markers). The back end enables secure data transmission and scalable solutions for data storage, management, and data access. The platform has been used to successfully collect longitudinal data for various cohorts in a number of disease areas including multiple sclerosis, depression, epilepsy, attention-deficit/hyperactivity disorder, Alzheimer disease, autism, and lung diseases. Digital biomarkers developed through collected data are providing useful insights into different diseases. RADAR-base offers a contemporary, open-source solution driven by the community for remotely monitoring, collecting data, and digitally characterizing both physical and mental health conditions. Clinicians have the ability to enhance their insight through the use of digital biomarkers, enabling improved prevention, personalization, and early intervention in the context of disease management.

Correlation of T Regulatory Cells, Cytotoxic T-lymphocyte-associated Antigen 4, and Transforming Growth Factor-β1 with Treatment Response in Patients with Chronic Myeloid Leukemia Receiving Dasatinib Therapy.

Tyrosine kinase inhibitors improve chronic myeloid leukemia (CML) outcomes. Dasatinib inhibits breakpoint cluster region-Abelson 1 proto-oncogene tyrosine kinase better than imatinib in CML. T-regulatory cells prevent autoimmune diseases and aberrant immune responses by reducing oncoprotein antigen reactivity. They also reduce self-antigen-induced immune responses to maintain peripheral tolerance. In this study, T-regulatory cells in peripheral blood of chronic myeloid leukemia-chronic phase patients were measured, together with serum levels of cytotoxic T-lymphocyte-associated antigen-4 (CTLA-4) and transforming growth factor (TGF)-β1 at diagnosis and 3 months postdasatinib therapy. The Pathology and Clinical Haematology Departments at King George's Medical University, Lucknow, India, conducted this prospective analytical study. Forty CML-chronic patients and 10 healthy controls were analyzed. Flow cytometry was used to determine T-regulatory cell percentage in peripheral blood mononuclear cells of newly diagnosed CML patients before and after 3 months of dasatinib treatment; ELISA was used to measure serum levels of CTLA-4 and TGF-β1. T-regulatory cells, CTLA-4, and TGF-β1 significantly decreased in CML-chronic phase patients after 3 months of dasatinib therapy compared to the initial diagnosis. No significant change in T-regulatory cell, CTLA-4, or TGF-β1 percentages were seen between responders and poor responders. However, responders had a lower percentage of T-regulatory cells than suboptimal responders. The study concluded that dasatinib treatment improved response in CML patients with decreased Treg cells. Dasatinib reduces Treg-mediated immunological suppression, reducing CTLA-4 and TGF-β1 levels.

AKAP12 positive fibroblast determines immunosuppressive contexture and immunotherapy response in patients with TNBC by promoting macrophage M2 polarization

BackgroundTriple-negative breast cancer (TNBC) is a molecular subtype of breast cancer with high aggressiveness and poor prognosis. Cancer-associated fibroblasts (CAFs) are major components of the TNBC microenvironment and play an...

Comprehensive single-cell and bulk transcriptomic analyses to develop an NK cell-derived gene signature for prognostic assessment and precision medicine in breast cancer

BackgroundNatural killer (NK) cells play crucial roles in mediating anti-cancer activity in breast cancer (BRCA). However, the potential of NK cell-related molecules in predicting BRCA outcomes and guiding personalized therapy remains largely unexplored. This study focused on developing a prognostic and therapeutic prediction model for BRCA by incorporating NK cell-related genes.MethodsThe data analyzed primarily originated from the TCGA and GEO databases. The prognostic role of NK cells was evaluated, and marker genes of NK cells were identified via single-cell analysis. Module genes closely associated with immunotherapy resistance were identified by bulk transcriptome-based weighted correlation network analysis (WGCNA). Following taking intersection and LASSO regression, NK-related genes (NKRGs) relevant to BRCA prognosis were screened, and the NK-related prognostic signature was subsequently constructed. Analyses were further expanded to clinicopathological relevance, GSEA, tumor microenvironment (TME) analysis, immune function, immunotherapy responsiveness, and chemotherapeutics. Key NKRGs were screened by machine learning and validated by spatial transcriptomics (ST) and immunohistochemistry (IHC).ResultsTumor-infiltrating NK cells are a favorable prognostic factor in BRCA. By combining scRNA-seq and bulk transcriptomic analyses, we identified 7 NK-related prognostic NKRGs (CCL5, EFHD2, KLRB1, C1S, SOCS3, IRF1, and CCND2) and developed an NK-related risk scoring (NKRS) system. The prognostic reliability of NKRS was verified through survival and clinical relevance analyses across multiple cohorts. NKRS also demonstrated robust predictive power in various aspects, including TME landscape, immune functions, immunotherapy responses, and chemotherapeutic sensitivity. Additionally, KLRB1 and CCND2 emerged as key prognostic NKRGs identified through machine learning and external validation, with their expression correlation with NK cells confirmed in BRCA specimens by ST and IHC.ConclusionsWe developed a novel NK-related gene signature that has proven valuable for evaluating prognosis and treatment response in BRCA, expecting to advance precision medicine of BRCA.

Lessons learned: strategies for implementing and the ongoing use of LI-RADS in your practice.

The establishment of the Liver Imaging Reporting and Data System (LI-RADS) in 2011 provided a comprehensive approach to standardized imaging, interpretation, and reporting of liver observations in patients diagnosed with or at risk for hepatocellular carcinoma (HCC). Each set of algorithms provides criteria pertinent to the various components of HCC management including surveillance, diagnosis, staging, and treatment response supported by a detailed lexicon of terms applicable to a wide range of liver imaging scenarios. Before its widespread adoption, the variability in the terminology of diagnostic criteria and definitions of imaging features led to significant challenges in patient management and made it difficult to replicate findings or apply them consistently. The integration of LI-RADS into the clinical setting has enhanced the efficiency and clarity of communication between radiologists, referring providers, and patients by employing a uniform language that averts miscommunications. LI-RADS has been strengthened with its integration into the American Association for Study of Liver Diseases practice guidelines. We will provide the background on the initial development of LI-RADS and reasons for development to serve as a starting point for conveying the system's benefits and evolution over the years. We will also suggest strategies for the implementation and maintenance of a LI-RADS program will be discussed.