Deep Learning Model Coupling Wearable Bioelectric and Mechanical Sensors for Refined Muscle Strength Assessment.

Muscle strength (MS) is related to our neural and muscle systems, essential for clinical diagnosis and rehabilitation evaluation. Although emerging wearable technology seems promising for MS assessment, problems still exist, including inaccuracy, spatiotemporal differences, and analyzing methods. In this study, we propose a wearable device consisting of myoelectric and strain sensors, synchronously acquiring surface electromyography and mechanical signals at the same spot during muscle activities, and then employ a deep learning model based on temporal convolutional network (TCN) + Transformer (Tcnformer), achieving accurate grading and prediction of MS. Moreover, by combining with deep clustering, named Tcnformer deep cluster (TDC), we further obtain a 25-level classification for MS assessment, refining the conventional 5 levels. Quantification and validation showcase a patient's postoperative recovery from level 3.2 to level 3.6 in the first few days after surgery. We anticipate that this system will importantly advance precise MS assessment, potentially improving relevant clinical diagnosis and rehabilitation outcomes.

Diagnosis related group and outcomes following transcatheter aortic valve implantation.

BACKGROUND: The association between Medicare Severity-Diagnosis Related Group (DRG) and early and intermediate-term outcomes in patients undergoing transcatheter aortic valve implantation (TAVI) has not been well studied. We aimed to assess the relationship between DRG and 30-day and 1-year mortality in patients undergoing TAVI. METHODS: The study population included 289 patients with severe symptomatic AS who underwent TAVI from December 2015 to June 2018 at an academic tertiary care medical center. Patients were categorized as DRG 266 or DRG 267, specifying TAVI with or without major complication or comorbidities respectively. RESULTS: Of the 289 patients, 182 patients (63.0%) were classified under DRG 267 and 107 patients (37.0%) under DRG 266. The DRG 266 group had longer hospital lengths of stay and higher rates of discharge to a skilled nursing facility. While rates of in-hospital and 30-day mortality were similar in both DRG groups, the DRG 266 group had higher 1-year all-cause mortality (26.2% vs 8.8%, P less than .001). In multivariable analysis, serum creatinine (OR 1.42, 95%CI 1.05-1.93) was the only independent predictor of 1-year mortality in the DRG 266 group while atrial fibrillation (OR 3.04, 95%CI 1.03-8.92) was the only independent predictor of mortality in the DRG 267 group. CONCLUSIONS: In this prospective registry of patients undergoing TAVI, while rates of in-hospital and 30-day mortality were similar in both DRG 266 and 267 groups, the DRG 266 group had higher 1-year all-cause mortality. Distinct predictors of mortality in each DRG group exist.

Worsening Carbon Inequality Embodied in Trade within China.

The mismatch between trade-embodied economic benefits and CO2 emissions causes carbon inequality, which is seldom analyzed from the intracountry level, especially across a long-term period. This study applied an environmentally extended multiregional input-output model to trace this mismatch and measure the carbon inequality quantitatively within China during 2007-2017. The results show that during the past decade, China's national carbon inequality was continuously worsening with carbon Gini coefficients rising regardless of production- (0.21-0.30) or consumption-based (0.12-0.18) accounting. The regional carbon inequality was deteriorating, where less developed provinces with 20% of total value-added emitted 32.9% of total CO2 emissions in 2007, while this figure rose to 42.6% in 2017. The eastern provinces (Jiangsu and Shanghai) had entered into net economic and carbon beneficiaries keeping high trade advantages, by contrast the northwest provinces (Ningxia and Xinjiang) were trapped in a lose-lose situation with trade benefits declining by 68%. The southwest provinces (Yunnan and Guangxi) shifted from being net carbon and value-added exporters to net importers, stepping into the earlier development mode of eastern provinces. This hidden and exacerbated carbon inequality calls for regional-specific measures to avoid the dilemma of economic development and CO2 mitigation, which also gives a good reminder for the rising economies, like India.

Adverse Childhood Experiences and Patient-Reported Outcome Measures in Critically Ill Children.

Adverse childhood experiences (ACEs) are linked to adverse health outcomes for adults and children in the United States. The prevalence of critically ill children who are exposed to ACEs is not known. Our objective was to compare the frequency of ACEs of critically ill children with that of the general pediatric population of Georgia and the United States using publicly available National Survey of Children's Health (NSCH) data. The impact of ACEs on patient-reported outcome measures of emotional, social, and physical health in critically ill children is not known. We sought to determine whether a higher total number of ACEs was associated with poorer patient-reported measures of emotional, social, and physical health. We conducted a prospective cross-sectional study of children < 18 years of age who were admitted to a 36-bed free-standing, quaternary academic pediatric intensive care unit in Atlanta, Georgia from June 2020-December 2021. Parents of patients who were admitted to the pediatric intensive care unit completed a survey regarding their child's ACEs, health care use patterns, and patient-reported outcome measures (PROMIS) of emotional, social, and physical health. Prevalence estimates of ACEs were compared with national and state data from the NSCH using Rao-Scott Chi-square tests. PROMIS measures reported within the PICU cohort were compared with population normed T-scores. The association of cumulative ACEs within the PICU cohort with patient-reported outcomes of emotional, social, and physical health were evaluated with a t-test. Among the 84 participants, 54% had ≥ 1 ACE, 29% had ≥ 2 ACEs, and 10% had ≥ 3 ACEs. Children with ≥ 2 ACEs had poorer anxiety and family relationship T-scores compared to those with ≤ 1 ACE. Given the high burden of ACEs in critically ill children, screening for ACEs may identify vulnerable children that would benefit from interventions and support to mitigate the negative effects of ACEs and toxic stress on emotional, social, and physical health.

[Analysis of relapse factors and risk assessment of adult acute lymphoblastic leukemia].

OBJECTIVE: To explore the risk factors of acute lymphoblastic leukemia (ALL) recurrence in adult patients and establish a prognosis index (PI) calculation model in order to improve the prevention strategy of ALL in adults. METHODS: 104 adult ALL patients from Blood Diseases Hospital & Chinese Academy of Medical Sciences between August 2008 and November 2011 were enrolled. COX proportional hazards regression stratified by Dummy variable was used to set up the prediction model; Kaplan-Meier method and Log-rank test were used to estimate and compare the survival. After calculated individual PI value, patients' expected survival should be estimated by groups. RESULTS: The overall median survival of adult ALL patients was 22.00 months (95% CI 17.00-27.00). COX regression analysis showed that chemotherapy group patients had a higher risk of recurrence than of ASCT group while setting treatment as the dummy variable (RR=2.052, 95%CI 0.877-4.799, P=0.007). Stratified Analysis showed that the risk factors of B-ALL recurrence in adult patients included HGB <100 g/L (RR=0.186, 95% CI 0.068-0.512, P=0.001), CNSL (RR=7.767,95% CI 2.951- 20.433, P=0.001), number of consolidation chemotherapy<3 (RR=0.445, 95% CI 0.211-0.940, P=0.034) and Ph chromosome positive (RR=2.771, 95% CI 1.353-5.674, P=0.005). Grouped by the PI value, the expected survival of each individual patient could be estimated as PI=0.58 base. CONCLUSION: HGB, CNSL, number of consolidation chemotherapy and Ph chromosome were independent risk factors of B-ALL recurrence in adult patients. PI value could predict the survival of adult ALL patients and provide reference for individual therapy and prognostic evaluation.