Doubly Robust Proximal Causal Learning for Continuous Treatments

Proximal causal learning is a promising framework for identifying the causal effect under the existence of unmeasured confounders. Within this framework, the doubly robust (DR) estimator was derived and has shown its effectiveness in estimation, especially when the model assumption is violated. However, the current form of the DR estimator is restricted to binary treatments, while the treatment can be continuous in many real-world applications. The primary obstacle to continuous treatments resides in the delta function present in the original DR estimator, making it infeasible in causal effect estimation and introducing a heavy computational burden in nuisance function estimation. To address these challenges, we propose a kernel-based DR estimator that can well handle continuous treatments. Equipped with its smoothness, we show that its oracle form is a consistent approximation of the influence function. Further, we propose a new approach to efficiently solve the nuisance functions. We then provide a comprehensive convergence analysis in terms of the mean square error. We demonstrate the utility of our estimator on synthetic datasets and real-world applications.Comment: Preprint, under revie

The Blessings of Multiple Treatments and Outcomes in Treatment Effect Estimation

Assessing causal effects in the presence of unobserved confounding is a challenging problem. Existing studies leveraged proxy variables or multiple treatments to adjust for the confounding bias. In particular, the latter approach attributes the impact on a single outcome to multiple treatments, allowing estimating latent variables for confounding control. Nevertheless, these methods primarily focus on a single outcome, whereas in many real-world scenarios, there is greater interest in studying the effects on multiple outcomes. Besides, these outcomes are often coupled with multiple treatments. Examples include the intensive care unit (ICU), where health providers evaluate the effectiveness of therapies on multiple health indicators. To accommodate these scenarios, we consider a new setting dubbed as multiple treatments and multiple outcomes. We then show that parallel studies of multiple outcomes involved in this setting can assist each other in causal identification, in the sense that we can exploit other treatments and outcomes as proxies for each treatment effect under study. We proceed with a causal discovery method that can effectively identify such proxies for causal estimation. The utility of our method is demonstrated in synthetic data and sepsis disease.Comment: Preprint, under revie

Covid-19 and promising solutions to combat symptoms of stress, anxiety and depression

Time and research will be of the essence when deducing the long-term consequences of the Covid-19 pandemic for global mental health and emotional well-being. Prior reports from viral outbreaks and emerging evidence from the recent pandemic point towards a potential “tsunami” of stress-related disorders in the aftermath of such traumatic events [1]. In addition to neurological and psychiatric symptoms, including psychosis and neurocognitive dementia-like symptoms observed in Covid-19 patients [2], world-wide surveys at the height of the pandemic suggest increased reports of depression, anxiety and distress across a considerable proportion of medical staff as well as the general public [3]. Grief for the loss of loved ones, helplessness and excessive worry over contracting or spreading the virus to other family members are all significant stressors that may collectively contribute to an imminent rise in symptoms of depression, anxiety and suicidal ideation. Furthermore, social distancing measures for combating the viral outbreak may also have unintended consequences such as social isolation, loneliness, abrupt changes to daily habits, unemployment and financial insecurity, which have all been characterised as risk factors for major depressive and post-traumatic stress disorders with potentially long-lasting effects on brain physiology and function.NIHR Brain Injury MedTech and in vitro diagnostics Co-operative (MIC), Cambridge and the NIHR Cambridge Biomedical Research Centre (Mental Health Theme and Neurodegeneration Theme). In addition, BJS thanks the Wallitt Foundation and Eton College for funding

Development of a monoclonal antibody to ITPRIPL1 for immunohistochemical diagnosis of non-small cell lung cancers: accuracy and correlation with CD8+ T cell infiltration

Introduction: Cancer biomarkers are substances or processes highly associated with the presence and progression of cancer, which are applicable for cancer screening, progression surveillance, and prognosis prediction in clinical practice. In our previous studies, we discovered that cancer cells upregulate inositol 1,4,5-triphosphate receptor-interacting protein-like 1 (ITPRIPL1), a natural CD3 ligand, to evade immune surveillance and promote tumor growth. We also developed a monoclonal ITPRIPL1 antibody with high sensitivity and specificity. Here, we explored the application of anti-ITPRIPL1 antibody for auxiliary diagnosis of non-small cell lung cancer (NSCLC).Methods: NSCLC patient tissue samples (n = 75) were collected and stained by anti-ITPRIPL1 or anti-CD8 antibodies. After excluding the flaked samples (n = 15), we evaluated the expression by intensity (0-3) and extent (0-100%) of staining to generate an h-score for each sample. The expression status was classified into negative (h-score < 20), low-positive (20-99), and high-positive (≥ 100). We compared the h-scores between the solid cancer tissue and stroma and analyzed the correlation between the h-scores of the ITPRIPL1 and CD8 expression in situ in adjacent tissue slices.Results: The data suggested ITPRIPL1 is widely overexpressed in NSCLC and positively correlates with tumor stages. We also found that ITPRIPL1 expression is negatively correlated with CD8 staining, which demonstrates that ITPRIPL1 overexpression is indicative of poorer immune infiltration and clinical prognosis. Therefore, we set 50 as the cutoff point of ITPRIPL1 expression H scores to differentiate normal and lung cancer tissues, which is of an excellent sensitivity and specificity score (100% within our sample collection).Discussion: These results highlight the potential of ITPRIPL1 as a proteomic immunohistochemical NSCLC biomarker with possible advantages over the existing NSCLC biomarkers, and the ITPRIPL1 antibody can be applied for accurate diagnosis and prognosis prediction

Is transcranial direct current stimulation, alone or in combination with antidepressant medications or psychotherapies, effective in treating major depressive disorder? A systematic review and meta-analysis.

BACKGROUND: Transcranial direct current stimulation (tDCS) has shown mixed results for depression treatment. The efficacies of tDCS combination therapies have not been investigated deliberately. This review aims to evaluate the clinical efficacy of tDCS as a monotherapy and in combination with medication, psychotherapy, and ECT for treating adult patients with major depressive disorder (MDD) and identified the factors influencing treatment outcome measures (i.e. depression score, dropout, response, and remission rates). METHODS: The systematic review was performed in PubMed/Medline, EMBASE, PsycINFO, Web of Sciences, and OpenGrey. Two authors performed independent literature screening and data extraction. The primary outcomes were the standardized mean difference (SMD) for continuous depression scores after treatment and odds ratio (OR) dropout rate; secondary outcomes included ORs for response and remission rates. Random effects models with 95% confidence intervals were employed in all outcomes. The overall effect of tDCS was investigated by meta-analysis. Sources of heterogeneity were explored via subgroup analyses, meta-regression, sensitivity analyses, and assessment of publication bias. RESULTS: Twelve randomised, sham-controlled trials (active group: N = 251, sham group: N = 204) were included. Overall, the integrated depression score of the active group after treatment was significantly lower than that of the sham group (g = - 0.442, p = 0.017), and further analysis showed that only tDCS + medication achieved a significant lower score (g = - 0.855, p < 0.001). Moreover, this combination achieved a significantly higher response rate than sham intervention (OR = 2.7, p = 0.006), while the response rate remained unchanged for the other three therapies. Dropout and remission rates were similar in the active and sham groups for each therapy and also for the overall intervention. The meta-regression results showed that current intensity is the only predictor for the response rate. None of publication bias was identified. CONCLUSION: The effect size of tDCS treatment was obviously larger in depression score compared with sham stimulation. The tDCS combined selective serotonin re-uptake inhibitors is the optimized therapy that is effective on depression score and response rate. tDCS monotherapy and combined psychotherapy have no significant effects. The most important parameter for optimization in future trials is treatment strategy

Association of specific biotypes in patients with Parkinson disease and disease progression

Objective: To identify biotypes in patients with newly diagnosed Parkinson disease (PD) and to test whether these biotypes could explain interindividual differences in longitudinal progression. Methods: In this longitudinal analysis, we use a data-driven approach clustering PD patients from the Parkinson's Progression Markers Initiative (n = 314, age 61.0 ± 9.5, years 34.1% female, 5 years of follow-up). Voxel-level neuroanatomic features were estimated with deformation-based morphometry (DBM) of T1-weighted MRI. Voxels with deformation values that were significantly correlated (p < 0.01) with clinical scores (Movement Disorder Society–sponsored revision of the Unified Parkinson’s Disease Rating Scale Parts I–III and total score, tremor score, and postural instability and gait difficulty score) at baseline were selected. Then, these neuroanatomic features were subjected to hierarchical cluster analysis. Changes in the longitudinal progression and neuroanatomic pattern were compared between different biotypes. Results: Two neuroanatomic biotypes were identified: biotype 1 (n = 114) with subcortical brain volumes smaller than heathy controls and biotype 2 (n = 200) with subcortical brain volumes larger than heathy controls. Biotype 1 had more severe motor impairment, autonomic dysfunction, and much worse REM sleep behavior disorder than biotype 2 at baseline. Although disease durations at the initial visit and follow-up were similar between biotypes, patients with PD with smaller subcortical brain volume had poorer prognosis, with more rapid decline in several clinical domains and in dopamine functional neuroimaging over an average of 5 years. Conclusion: Robust neuroanatomic biotypes exist in PD with distinct clinical and neuroanatomic patterns. These biotypes can be detected at diagnosis and predict the course of longitudinal progression, which should benefit trial design and evaluation

Monitoring blood pressure variability via chaotic global metrics using local field potential oscillations

The intention was to associate blood pressure (BP) variability (BPV) measurements to Local field potentials (LFPs). Thus, assessing how LFPs can co-vary with BPV to permit implantable brain devices (via LFPs) to control output. Elevated BPV is a considerable cardiovascular disease risk factor. Often patients are resistant to pharmacotherapies. An alternative treatment is Deep Brain Stimulation (DBS). Mathematical techniques based on nonlinear dynamics assessed their correlation of BPV chaotic global metrics to LFPs. Chaos Forward Parameter (CFP6) was computed for LFPs, at three electrode depths in the mid-brain and sensory thalamus. Mean, root mean square of the successive differences (RMSSD) and the chaotic global metrics (CFP1 to CFP7) were computed for the BP signal. The right ventroposterolateral (RVPL) nucleus provided a substantial correlation via CFP6 for BP with R-squared up to approximately 79% by means of LFP gamma oscillations. Investigation of BPV via LFPs as a proxy marker might allow therapies to be attuned in a closed-loop system. Whilst all patients were chronic pain patients the chaotic global relationship should be unperturbed. LFPs correlation does not unconditionally predict its causation. There is no certainty DBS in these locations would be therapeutic but can be used as an assessment tool