Impact of parental depression on pharmacotherapy and psychotherapy amongst children and adolescents with depression diagnoses

OBJECTIVE: Parental depression is considered amongst the greatest risk factors for the diagnosis of depression in children and adolescents. Less is known about how and to what extent parental depression influences their depressed children’s depression treatment. Building on Bronfenbrenner’s ecological model and Andersen’s model, this study aimed to assess the impact of parental depression on depressed children and adolescents’ depression treatment. DATA/STUDY POPULATION: Children and adolescents aged 3 to 17 diagnosed with depression in MarketScan Claims and Encounters Database (2010–2014) METHOD: Psychotherapy, pharmacotherapy, and combination treatment were examined. Study covariates were described. Chi-square tests with Bonferroni correction were conducted to examine the difference in study covariates between types of depression treatment. Generalized estimating equation models were used to examine the effect of parental depression on the likelihood of receiving depression treatment, controlling for study covariates, while accounting for the clustering of children and adolescents in the same household. RESULT: Children and adolescents with depressive parents were less likely to use psychotherapy (aOR: 0.80, 95% CI: (0.74, 0.86)), and more likely to use antidepressant (aOR: 1.41, 95% CI: (1.28, 1.56)) and combination treatment (aOR: 1.55, 95%CI: (1.43, 1.68)), compared to those without. Depressed children and adolescents living with depressed siblings in the same family were significantly less likely to use any depression treatment (aOR: 0.47, 95% CI: (0.41, 0.55)). CONCLUSION: The significant relationship between parental depression and their depressed children and adolescents’ treatment indicates that consideration should be given to parents’ decision-making and engagement in the treatment

Understanding the Digital Gap Among US Adults With Disability: Cross-Sectional Analysis of the Health Information National Trends Survey 2013

BACKGROUND: Disabilities affect more than 1 in 5 US adults, and those with disabilities face multiple barriers in accessing health care. A digital gap, defined as the disparity caused by differences in the ability to use advanced technologies, is assumed to be prevalent among individuals with disabilities. OBJECTIVE: This study examined the associations between disability and use of information technology (IT) in obtaining health information and between trust factors and IT use. We hypothesized that compared to US adults without disabilities, those with disabilities are less likely to refer to the internet for health information, more likely to refer to a health care provider to obtain health information, and less likely to use IT to exchange medical information with a provider. Additionally, we hypothesized that trust factors, such as trust toward health information source and willingness to exchange health information, are associated with IT use. METHODS: The primary database was the 2013 Health Information National Trends Survey 4 Cycle 3 (N=3185). Disability status, the primary study covariate, was based on 6 questions that encompassed a wide spectrum of conditions, including impairments in mobility, cognition, independent living, vision, hearing, and self-care. Study covariates included sociodemographic factors, respondents\u27 trust toward the internet and provider as information sources, and willingness to exchange medical information via IT with providers. Study outcomes were the use of the internet as the primary health information source, use of health care providers as the primary health information source, and use of IT to exchange medical information with providers. We conducted multivariate logistic regressions to examine the association between disability and study outcomes controlling for study covariates. Multiple imputations with fully conditional specification were used to impute missing values. RESULTS: We found presence of any disability was associated with decreased odds (adjusted odds ratio [AOR] 0.65, 95% CI 0.43-0.98) of obtaining health information from the internet, in particular for those with vision disability (AOR 0.27, 95% CI 0.11-0.65) and those with mobility disability (AOR 0.51, 95% CI 0.30-0.88). Compared to those without disabilities, those with disabilities were significantly more likely to consult a health care provider for health information in both actual (OR 2.21, 95% CI 1.54-3.18) and hypothetical situations (OR 1.80, 95% CI 1.24-2.60). Trust toward health information from the internet (AOR 3.62, 95% CI 2.07-6.33), and willingness to exchange via IT medical information with a provider (AOR 1.88, 95% CI 1.57-2.24) were significant predictors for seeking and exchanging such information, respectively. CONCLUSIONS: A potential digital gap may exist among US adults with disabilities in terms of their recent use of the internet for health information. Trust toward health information sources and willingness play an important role in people\u27s engagement in use of the internet for health information. Future studies should focus on addressing trust factors associated with IT use and developing tools to improve access to care for those with disabilities

MiRNA-145 increases therapeutic sensibility to gemcitabine treatment of pancreatic adenocarcinoma cells.

Pancreatic adenocarcinoma is one of the most leading causes of cancer-related deaths worldwide. Although recent advances provide various treatment options, pancreatic adenocarcinoma has poor prognosis due to its late diagnosis and ineffective therapeutic multimodality. Gemcitabine is the effective first-line drug in pancreatic adenocarcinoma treatment. However, gemcitabine chemoresistance of pancreatic adenocarcinoma cells has been a major obstacle for limiting its treatment effect. Our study found that p70S6K1 plays an important role in gemcitabine chemoresistence. MiR-145 is a tumor suppressor which directly targets p70S6K1 for inhibiting its expression in pancreatic adenocarcinoma, providing new therapeutic scheme. Our findings revealed a new mechanism underlying gemcitabine chemoresistance in pancreatic adenocarcinoma cells

Attention-Based Dense Point Cloud Reconstruction From a Single Image

Article proposes a two-stage training dense point cloud generation network

GenSim: Generating Robotic Simulation Tasks via Large Language Models

Collecting large amounts of real-world interaction data to train general robotic policies is often prohibitively expensive, thus motivating the use of simulation data. However, existing methods for data generation have generally focused on scene-level diversity (e.g., object instances and poses) rather than task-level diversity, due to the human effort required to come up with and verify novel tasks. This has made it challenging for policies trained on simulation data to demonstrate significant task-level generalization. In this paper, we propose to automatically generate rich simulation environments and expert demonstrations by exploiting a large language models' (LLM) grounding and coding ability. Our approach, dubbed GenSim, has two modes: goal-directed generation, wherein a target task is given to the LLM and the LLM proposes a task curriculum to solve the target task, and exploratory generation, wherein the LLM bootstraps from previous tasks and iteratively proposes novel tasks that would be helpful in solving more complex tasks. We use GPT4 to expand the existing benchmark by ten times to over 100 tasks, on which we conduct supervised finetuning and evaluate several LLMs including finetuned GPTs and Code Llama on code generation for robotic simulation tasks. Furthermore, we observe that LLMs-generated simulation programs can enhance task-level generalization significantly when used for multitask policy training. We further find that with minimal sim-to-real adaptation, the multitask policies pretrained on GPT4-generated simulation tasks exhibit stronger transfer to unseen long-horizon tasks in the real world and outperform baselines by 25%. See the project website (https://liruiw.github.io/gensim) for code, demos, and videos.Comment: See our project website (https://liruiw.github.io/gensim), demo and datasets (https://huggingface.co/spaces/Gen-Sim/Gen-Sim), and code (https://github.com/liruiw/GenSim) for more detail

Methodological Challenges for Epidemiologic Studies of Deprescribing at the End of Life

Purpose of Review: To describe approaches to measuring deprescribing and associated outcomes in studies of patients approaching end of life (EOL). Recent Findings: We reviewed studies published through 2020 that evaluated deprescribing in patients with limited life expectancy and approaching EOL. Deprescribing includes reducing the number of medications, decreasing medication dose(s), and eliminating potentially inappropriate medications. Tools such as STOPPFrail, OncPal, and the Unnecessary Drug Use Measure can facilitate deprescribing. Outcome measures vary and selection of measures should align with the operationalized deprescribing definition used by study investigators. Summary: EOL deprescribing considerations include medication appropriateness in the context of patient goals for care, expected benefit from medication given life expectancy, and heightened potential for medication-related harm as death nears. Additional data are needed on how EOL deprescribing impacts patient quality of life, caregiver burden, and out-of-pocket medication-related costs to patients and caregivers. Investigators should design deprescribing studies with this information in mind

MM-Diffusion: Learning Multi-Modal Diffusion Models for Joint Audio and Video Generation

We propose the first joint audio-video generation framework that brings engaging watching and listening experiences simultaneously, towards high-quality realistic videos. To generate joint audio-video pairs, we propose a novel Multi-Modal Diffusion model (i.e., MM-Diffusion), with two-coupled denoising autoencoders. In contrast to existing single-modal diffusion models, MM-Diffusion consists of a sequential multi-modal U-Net for a joint denoising process by design. Two subnets for audio and video learn to gradually generate aligned audio-video pairs from Gaussian noises. To ensure semantic consistency across modalities, we propose a novel random-shift based attention block bridging over the two subnets, which enables efficient cross-modal alignment, and thus reinforces the audio-video fidelity for each other. Extensive experiments show superior results in unconditional audio-video generation, and zero-shot conditional tasks (e.g., video-to-audio). In particular, we achieve the best FVD and FAD on Landscape and AIST++ dancing datasets. Turing tests of 10k votes further demonstrate dominant preferences for our model. The code and pre-trained models can be downloaded at https://github.com/researchmm/MM-Diffusion.Comment: Accepted by CVPR 202

A Comprehensive Survey on Deep Graph Representation Learning

Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been widely studied in a range of fields, including machine learning and data mining. Classic graph embedding methods follow the basic idea that the embedding vectors of interconnected nodes in the graph can still maintain a relatively close distance, thereby preserving the structural information between the nodes in the graph. However, this is sub-optimal due to: (i) traditional methods have limited model capacity which limits the learning performance; (ii) existing techniques typically rely on unsupervised learning strategies and fail to couple with the latest learning paradigms; (iii) representation learning and downstream tasks are dependent on each other which should be jointly enhanced. With the remarkable success of deep learning, deep graph representation learning has shown great potential and advantages over shallow (traditional) methods, there exist a large number of deep graph representation learning techniques have been proposed in the past decade, especially graph neural networks. In this survey, we conduct a comprehensive survey on current deep graph representation learning algorithms by proposing a new taxonomy of existing state-of-the-art literature. Specifically, we systematically summarize the essential components of graph representation learning and categorize existing approaches by the ways of graph neural network architectures and the most recent advanced learning paradigms. Moreover, this survey also provides the practical and promising applications of deep graph representation learning. Last but not least, we state new perspectives and suggest challenging directions which deserve further investigations in the future

Dynamic crack propagation in elasto-plastic materials using phase-field virtual modelling method

In modern engineering, dynamic fracture failure because of unexpected load or human faults may lead to catastrophic disasters. Preventive structure design and real-time maintain suggestions based on accurate numerical simulation are critical, especially when plasticity develops. It remains a challenge to efficiently model dynamic crack propagation in elasto-plastic materials while the uncertain factors in service life may significantly increase the difficulty. In this paper, a phase-field virtual modelling method (PFVM), based on the features of the novel extended support vector regression (X-SVR) method, is proposed to tackle this non-deterministic problem. The phase field method is adopted for its outstanding performance in complex fracture problems, which provides solid reference data for the virtual model's training and verification. The PFM application to dynamic elasto-plastic fracture problems is validated in two practical engineering examples. The integrated virtual modelling technique is then proven capable of instantly providing precise crack propagation prediction under multiple complex uncertainties, making up-to-date numerical dynamic fracture simulation achievable and affordable. The proposed PFVM method can minimize the contradiction between accurate modelling and high computational cost and can be utilized in various extensions like sensitivity analysis or design optimization

A randomized, double-blind, positive-controlled, Phase-II clinical trial to evaluate efficacy and safety of Fuke Qianjin capsule in Pakistani patients with pelvic inflammatory disease

Ethnopharmacological relevance: Pelvic inflammatory disease (PID) is a frequently occurring gynecological disorder mainly caused by the inflammation of a woman’s upper genital tract. Generally, antibiotics are used for treating PID, but prolonged use poses potential risks of gut bacterial imbalance, bacterial resistance, super bacteria production, and associated adverse reactions. Traditional Chinese medicine (TCM) has shown unique advantages in various ailments and has received widespread clinical research attention. Fuke Qianjin (FUKE) capsule is an approved National Medical Products Administration (NMPA License No. Z20020024) Chinese herbal prescription that has been widely used individually or in combination with other Western medicines for the treatment of various gynecological inflammatory diseases, including chronic cervicitis, endometritis, and chronic PID.Aim: This clinical trial was designed to assess the safety and efficacy of FUKE capsule in mild-to-moderate symptomatic PID patients.Materials and methods: This phase 2, randomized, double-blind, positive controlled clinical trial was conducted in mild-to-moderate symptomatic PID patients at a single center in Pakistan from 21 September 2021 to 11 March 2022. Eligible female participants were randomly assigned to a test and a control group with a ratio of 1:1. The test group subjects received two metronidazole (METRO) tablets and one doxycycline hyclate (DOXY) simulant at a time, twice daily for 14 days, and two Fuke Qianjin (FUKE) capsules, three times a day after a meal for 28 days. Subjects in the control group received two METRO tablets and one DOXY tablet at a time, twice daily for 14 days, and two FUKE simulant capsules, three times a day after meal for 28 days. The primary efficacy outcome was an improvement in pelvic pain symptoms assessed through a visual analog scale (VAS). The secondary outcomes were the improvement in secondary efficacy symptoms like local physical signs, clinical assessment of leucorrhea and cervical secretions through laboratory examination, and improvement in the maximum area of pelvic effusion assessed through gynecological ultrasound after the treatment. The safety outcomes were assessed through vital signs, laboratory tests, electrocardiogram findings, and adverse events/serious adverse events.Results: A total of 198 subjects with active PID were randomly assigned to a test group (n = 99) and a control group (n = 99). The baseline characteristics of the subjects in the two groups were similar. In the intention-to-treat analysis, the primary efficacy was 84.9% for the test group and 71.6% for the control group, with a statistically significant difference (p = 0.0370; 95% CI −0.2568 to −0.0088). The secondary clinical efficacy was 88.4% for the test group and 82.7% for the control group, with no significant difference (p = 0.2977; 95% CI −0.1632 to 0.0501). The improvement in local physical signs was 95.8% for the test group and 76.9% for the control group, with no significant difference (p = 0.0542; 95% CI −0.3697 to −0.0085). The inter-group non-inferiority comparison showed that the upper limit of the 95% CI was less than 0.15 and thus met the non-inferiority requirements of the test group to the control group. The results of clinical signs of leucorrhea and cervical secretions showed that there was no difference in the rate of improvement between the test and control groups, indicating that FUKE was non-inferior to DOXY. A total of 14 adverse events in eight subjects were observed in the trial, with an incidence rate of 4.7%. Four subjects in each group experienced seven adverse events with 4.5% and 4.8% incidence rates of adverse reactions in the test and control groups, with no statistically significant differences (p = 0.2001). No serious adverse events occurred in the trial.Conclusion: The results of this trial indicate that the test drug (Fuke Qianjin capsule) is non-inferior to the control drug (doxycycline hyclate tablet) in treating mild-to-moderate PID patients with comparable efficacy, safety, and tolerability to the control drug.Clinical Trial Registration:www.clinicaltrials.gov, identifier NCT04723069