Assessing the prognostic utility of clinical and radiomic features for COVID-19 patients admitted to ICU: challenges and lessons learned.

Severe cases of COVID-19 often necessitate escalation to the Intensive Care Unit (ICU), where patients may face grave outcomes, including mortality. Chest X-rays play a crucial role in the diagnostic process for evaluating COVID-19 patients. Our collaborative efforts with Michigan Medicine in monitoring patient outcomes within the ICU have motivated us to investigate the potential advantages of incorporating clinical information and chest X-ray images for predicting patient outcomes.

Modified Banxiaxiexin decoction benefitted chemotherapy in treating gastric cancer by regulating multiple targets and pathways.

Ethnopharmacological Relevance: Chemotherapy tolerance weakened efficacy of chemotherapy drugs in the treating gastric cancer (GC). Banxiaxiexin decoction (BXXXD) was widely used in digestive diseases for thousands of years in Traditional Chinese medicine (TCM). In order to better treat GC, three other herbs were added to BXXXD to create a new prescription named Modified Banxiaxiexin decoction (MBXXXD).

Ferroptosis: a new hunter of hepatocellular carcinoma.

Ferroptosis is an iron ion-dependent, regulatory cell death modality driven by intracellular lipid peroxidation that plays a key role in the development of HCC. Studies have shown that various clinical agents (e.g.

Interventions to improve medication adherence in adults with mental-physical multimorbidity in primary care: a systematic review.

Background: Medication non-adherence is a notable contributor to healthcare inefficiency, resulting in poor medication management, impaired patient outcomes, and ineffective symptom control.

Aim: To summarise interventions targeting medication adherence for adults with mental-physical multimorbidity in primary healthcare settings.

Design And Setting: A systematic review of the literature - published in any language and with any country of origin - was conducted.

Simultaneous selection and inference for varying coefficients with zero regions: a soft-thresholding approach.

Varying coefficient models have been used to explore dynamic effects in many scientific areas, such as in medicine, finance, and epidemiology. As most existing models ignore the existence of zero regions, we propose a new soft-thresholded varying coefficient model, where the coefficient functions are piecewise smooth with zero regions. Our new modeling approach enables us to perform variable selection, detect the zero regions of selected variables, obtain point estimates of the varying coefficients with zero regions, and construct a new type of sparse confidence intervals that accommodate zero regions.

Use of machine learning to assess the prognostic utility of radiomic features for in-hospital COVID-19 mortality.

As portable chest X-rays are an efficient means of triaging emergent cases, their use has raised the question as to whether imaging carries additional prognostic utility for survival among patients with COVID-19. This study assessed the importance of known risk factors on in-hospital mortality and investigated the predictive utility of radiomic texture features using various machine learning approaches. We detected incremental improvements in survival prognostication utilizing texture features derived from emergent chest X-rays, particularly among older patients or those with a higher comorbidity burden.

Effects of "Taking the Waist as the Axis" Therapy on trunk postural control disorder after stroke: A randomized controlled trial.

Background: Sufficient attention to trunk rehabilitation after stroke is still lacking. Loss of trunk selective activity is considered to be the leading cause of trunk postural control disorder after stroke. "Taking the Waist as the Axis" Therapy (WAT) was developed as a combination of the concept of "Taking the Waist as the Axis" from Tai Chi and the rehabilitation of trunk dysfunction after stroke.

Data Integration in Causal Inference.

Integrating data from multiple heterogeneous sources has become increasingly popular to achieve a large sample size and diverse study population. This paper reviews development in causal inference methods that combines multiple datasets collected by potentially different designs from potentially heterogeneous populations. We summarize recent advances on combining randomized clinical trial with external information from observational studies or historical controls, combining samples when no single sample has all relevant variables with application to two-sample Mendelian randomization, distributed data setting under privacy concerns for comparative effectiveness and safety research using real-world data, Bayesian causal inference, and causal discovery methods.

Comprehensive evaluation of COVID-19 patient short- and long-term outcomes: Disparities in healthcare utilization and post-hospitalization outcomes.

Background: Understanding risk factors for short- and long-term COVID-19 outcomes have implications for current guidelines and practice. We study whether early identified risk factors for COVID-19 persist one year later and through varying disease progression trajectories.

Methods: This was a retrospective study of 6,731 COVID-19 patients presenting to Michigan Medicine between March 10, 2020 and March 10, 2021.