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Quantitative analysis of human gait is critical for the early discovery, progressive tracking, and rehabilitation of neurological and musculoskeletal disorders, such as Parkinson’s disease, stroke, and cerebral palsy. Gait analysis typically involves estimating gait characteristics, such as spatiotemporal gait parameters and gait health indicators (e.g., step time, length, symmetry, and balance). Traditional methods of gait analysis involve the use of cameras, wearables, and force plates but are limited in operational requirements when applied in daily life, such as direct line-of-sight, carrying devices, and dense deployment. This paper introduces a novel approach for gait analysis by passively sensing floor vibrations generated by human footsteps using vibration sensors mounted on the floor surface. Our approach is low-cost, non-intrusive, and perceived as privacy-friendly, making it suitable for continuous gait health monitoring in daily life. Our algorithm estimates various gait parameters that are used as standard metrics in medical practices, including temporal parameters (step time, stride time, stance time, swing time, double-support time, and single-support time), spatial parameters (step length, width, angle, and stride length), and extracts gait health indicators (cadence/walking speed, left–right symmetry, gait balance, and initial contact types). The main challenge we addressed in this paper is the effect of different floor types on the resultant vibrations. We develop floor-adaptive algorithms to extract features that are generalizable to various practical settings, including homes, hospitals, and eldercare facilities. We evaluate our approach through real-world walking experiments with 20 adults with 12,231 labeled gait cycles across concrete and wooden floors. Our results show 90.5% (RMSE 0.08s), 71.3% (RMSE 0.38m), and 92.3% (RMSPE 7.7%) accuracy in estimating temporal, spatial parameters, and gait health indicators, respectively.

HighlightsAnalyzed the function of internal polarization field in Ni2P/FeP2 via hydroxyl spillover effect.From theoretical design to experimental verification, to optimize adsorption energy of oxygen intermediates on Ni active site, and further boost the xygen evolution reaction process.A hydroxyl spillover effect driven by internal polarization field in Ni2P/FeP2 can be amplified in low concentration alkaline electrolyte environment, and facilitate the application in anion exchange membrane water electrolyzer systems.The formation of multiple oxygen intermediates supporting efficient oxygen evolution reaction (OER) are affinitive with hydroxyl adsorption. However, ability of the catalyst to capture hydroxyl and maintain the continuous supply at active sits remains a tremendous challenge. Herein, an affordable Ni2P/FeP2 heterostructure is presented to form the internal polarization field (IPF), arising hydroxyl spillover (HOSo) during OER. Facilitated by IPF, the oriented HOSo from FeP2 to Ni2P can activate the Ni site with a new hydroxyl transmission channel and build the optimized reaction path of oxygen intermediates for lower adsorption energy, boosting the OER activity (242 mV vs. RHE at 100 mA cm–2) for least 100 h. More interestingly, for the anion exchange membrane water electrolyzer (AEMWE) with low concentration electrolyte, the advantage of HOSo effect is significantly amplified, delivering 1 A cm–2 at a low cell voltage of 1.88 V with excellent stability for over 50 h.

Abstract Background Students in higher education often experience mild depression due to academic, social, and future-oriented pressures. Enhancing emotion regulation ability is a key protective factor for mental health and can help alleviate depressive symptoms. Nursing intervention, with its systematic and person-centered approach, offers a viable method for supporting student well-being. However, existing interventions rarely incorporate an integrated nursing perspective. This study therefore examines whether a structured nursing-led emotion regulation program can improve emotional outcomes in students with mild depression, offering practical insights for campus mental health practice. Methods The study recruited 90 students from a university who were assessed as having mild depression (scores 53–62) using the Self-Rating Depression Scale (SDS). They were randomly divided into Group A (n = 30) and Group B (n = 30). Group A received an 8-week systematic nursing intervention, implemented as follows: twice weekly structured group sessions, each lasting 90 minutes, totaling 16 sessions. The content included emotion recognition education (20 minutes per session), group supportive counseling (40 minutes per session), mindful breathing training (20 minutes per session), and lifestyle rhythm adjustment guidance (10 minutes per session). Group B received conventional campus mental health support during the same period, consisting of one monthly mental health lecture (60 minutes each) and distribution of self-help mental health materials. The Emotion Regulation Questionnaire (ERQ) and SDS were administered before and after the intervention, and repeated measures ANOVA was used to compare changes in emotion regulation strategies and depressive symptoms between the two groups. Results After the intervention, Group A showed significantly greater improvement in emotion regulation ability and depressive symptoms compared to Group B. The test results are presented in Table 1. Repeated measures ANOVA revealed significant group (between Group A and Group B) × time (between pre- and post-assessment) interactions for ERQ Cognitive Reappraisal (F(1,58) = 24.36, p<.001), ERQ Expressive Suppression (F(1,58) = 12.88, p<.01), and SDS Total Score (F(1,58) = 32.15, p<.001). Post-hoc tests indicated that Group A showed significant improvement compared to Group B on all indicators (p<.01), with effect sizes ranging from moderate to strong (η2 = 0.31–0.55). Discussion The nursing-based intervention significantly improved emotion regulation and reduced depressive symptoms in students with mild depression. This integrated approach aligns with the needs of young adults in academic settings. Future work should explore tailored, technology-enhanced versions of the program for broader implementation. Funding No. 2023SJSZ0699.

Identification of neuromuscular impairments in cerebral palsy (CP) is essential to providing effective treatment. However, clinical recognition of neuromuscular impairments in CP and their contribution to gait abnormalities is limited, resulting in suboptimal treatment outcomes. While CP is the most common childhood movement disorder, clinical evaluations often do not accurately identify and delineate the primary neuromuscular and secondary musculoskeletal impairments or their specific impact on mobility. Here we discuss the primary neuromuscular impairments of CP that arise from early brain injury and the progressive secondary musculoskeletal impairments, with a focus on spastic CP, the most common form of CP. Spastic CP is characterized by four primary interrelated neuromuscular impairments: 1. muscle weakness, 2. short muscle-tendon units due to slow muscle growth relative to skeletal growth, 3. muscle spasticity characterized by increased sensitivity to stretch, and 4. impaired selective motor control including flexor and extensor muscle synergies. Specific gait events are affected by the four primary neuromuscular impairments of spastic CP and their delineation can improve evaluation to guide targeted treatment, prevent deformities and improve mobility. Emerging information on neural correlates of neuromuscular impairments in CP provides the clinician with a more complete context with which to evaluate and develop effective treatment plans. Specifically, addressing the primary neuromuscular impairments and reducing secondary musculoskeletal impairments are important treatment goals. This perspective on neuromuscular mechanisms underlying gait abnormalities in spastic CP aims to inform clinical evaluation of CP, focus treatment more strategically, and guide research priorities to provide targeted treatments for CP.

Muscular dystrophies (MD) are a group of genetic neuromuscular disorders that cause progressive weakness and loss of muscles over time, influencing 1 in 3500–5000 children worldwide. New and exciting treatment options have led to a critical need for a clinical post-marketing surveillance tool to confirm the efficacy and safety of these treatments after individuals receive them in a commercial setting. For MDs, functional gait assessment is a common approach to evaluate the efficacy of the treatments because muscle weakness is reflected in individuals’ walking patterns. However, there is little incentive for the family to continue to travel for such assessments due to the lack of access to specialty centers. While various existing sensing devices, such as cameras, force plates, and wearables can assess gait at home, they are limited by privacy concerns, area of coverage, and discomfort in carrying devices, which is not practical for long-term, continuous monitoring in daily settings. In this study, we introduce a novel functional gait assessment system using ambient floor vibrations, which is non-invasive and scalable, requiring only low-cost and sparsely deployed geophone sensors attached to the floor surface, suitable for in-home usage. Our system captures floor vibrations generated by footsteps from patients while they walk around and analyzes such vibrations to extract essential gait health information. To enhance interpretability and reliability under various sensing scenarios, we translate the signal patterns of floor vibration to pathological gait patterns related to MD, and develop a hierarchical learning algorithm that aggregates insights from individual footsteps to estimate a person’s overall gait performance. When evaluated through real-world experiments with 36 subjects (including 15 patients with MD), our floor vibration sensing system achieves a 94.8% accuracy in predicting functional gait stages for patients with MD. Our approach enables accurate, accessible, and scalable functional gait assessment, bringing MD progressive tracking into real life.

Gait abnormalities in spastic cerebral palsy (CP) result from four primary neuromuscular impairments: muscle weakness, short muscle-relative-to-skeletal-length, muscle spasticity, and impaired selective motor control. The Clinical Gait Evaluation with Neuromuscular Impairments (Clinical GENI) was developed to help clinicians identify gait abnormalities and contributing neuromuscular impairments in spastic CP for use in any clinical setting. This study evaluated use and validity of the observational-based Clinical GENI to identify gait abnormalities and contributing neuromuscular impairments in children with spastic CP. Patients with spastic CP seen in 2023 for instrumented gait analysis and physical exam of neuromuscular impairments were evaluated using the Clinical GENI. Validity was assessed by agreement between identification of gait abnormalities on the Clinical GENI compared to 3D gait kinematics. Severity of neuromuscular impairments associated with gait abnormalities listed on the Clinical GENI was compared, and severity of neuromuscular impairment was correlated with severity of gait abnormalities. Participants included 12 children with spastic CP (4 GMFCS I, 8 GMFCS II; mean age 11.25 years). The most common gait abnormalities were forefoot/flatfoot initial contact (IC) (16/24), flexed-knee IC (19/24), hip-flexion in single limb stance (12/24), and reduced pre-swing ankle plantarflexion (19/24). Strong agreement (83-100%) occurred between gait abnormalities on the Clinical GENI and kinematic values. Severity of neuromuscular impairment was higher ( < 0.05) for those with gait abnormalities vs. without and correlated to severity of gait abnormalities in a majority of comparisons. Results support clinical utility and validity of the Clinical GENI for evaluating gait abnormalities and contributing neuromuscular impairments in spastic CP.

Based on the hourly ERA5 reanalysis dataset of the European Centre for Medium-Range Weather Forecasts (ECMWF) from 1 January 1979 to 31 December 2019, the climatology of the planetary boundary layer height (PBLH) in Jiangsu, China, is studied. The PBLH based on ERA5 is verified by using radiosonde data, and the results show that the PBLH based on ERA5 fits very well with the PBLH diagnosed by the radiosonde data. Overall, the daytime average PBLH is between 700 and 1200 m, which is higher in the north and lower in the south. It is between 100 and 400 m at night, and it is lower in the north and higher in the south. The PBLH exhibits complex spatiotemporal variation. In the daytime, the PBLH in inland areas is highest in spring, followed by fall and summer, and lowest in winter. At night, the seasonal variation in the PBLH is less obvious. The seasonal variation in the PBLH in coastal areas is higher in fall and winter and lower in spring and summer. The PBLH shows an obvious diurnal cycle, usually reaching its peak at 14:00 (LST) or 15:00 (LST). The diurnal cycle of the PBLH is significantly positively correlated with the near-surface temperature and wind speed and significantly negatively correlated with the relative humidity and lower tropospheric stability. Over these 41 years, the daytime PBLH has increased significantly in most areas. The increase in the PBLH can be attributed to the increase in near-surface temperature and the decrease in near-surface relative humidity and lower tropospheric stability.

An antibacterial and pro-osteogenic coaxially electrospun nanofiber guided bone regeneration (GBR) membrane was fabricated to satisfy the complicated and phased requirements of GBR process. In this study, we synthesize dual-functional coaxially electrospun nanofiber GBR membranes by encapsulating parathyroid hormone (PTH) in the core layer and magnesium oxide nanoparticles (MgONPs) in the shell layer (MgONPs-PCL/PTH-PCL). Herein, the physicochemical characterization of MgONPs-PCL/PTH-PCL, the release rates of MgONPs and PTH, and antibacterial efficiency of the new membrane were evaluated. Furthermore, the pro-osteogenicity of the membranes was assessed both in-vitro and in-vivo. We successfully fabricated a coaxially electrospun nanofiber MgONPs-PCL/PTH-PCL membrane with the majority of nanofibers (>65%) ranged from 0.40~0.60μm in diameter. MgONPs-PCL/PTH-PCL showed outstanding antibacterial potential against (E. coli) and (S. aureus) through the release of MgONPs. We also discovered that the incorporation of MgONPs significantly prolonged the release of PTH. Furthermore, both the in-vivo and in-vitro studies demonstrated that high dosage of PTH promoted pro-osteogenicity of the membrane to improve bone regeneration efficacy with the presence of MgONPs. The new composite membrane is a promising approach to enhance bone regeneration in periodontitis or peri-implantitis patients with large-volume bone defects.

Diabetic eye disease (DED) is a leading cause of blindness in the world. Annual DED testing is recommended for adults with diabetes, but adherence to this guideline has historically been low. In 2020, Johns Hopkins Medicine (JHM) began deploying autonomous AI for DED testing. In this study, we aimed to determine whether autonomous AI implementation was associated with increased adherence to annual DED testing, and how this differed across patient populations. JHM primary care sites were categorized as “non-AI” (no autonomous AI deployment) or “AI-switched” (autonomous AI deployment by 2021). We conducted a propensity score weighting analysis to compare change in adherence rates from 2019 to 2021 between non-AI and AI-switched sites. Our study included all adult patients with diabetes (>17,000) managed within JHM and has three major findings. First, AI-switched sites experienced a 7.6 percentage point greater increase in DED testing than non-AI sites from 2019 to 2021 ( p  < 0.001). Second, the adherence rate for Black/African Americans increased by 12.2 percentage points within AI-switched sites but decreased by 0.6% points within non-AI sites ( p  < 0.001), suggesting that autonomous AI deployment improved access to retinal evaluation for historically disadvantaged populations. Third, autonomous AI is associated with improved health equity, e.g. the adherence rate gap between Asian Americans and Black/African Americans shrank from 15.6% in 2019 to 3.5% in 2021. In summary, our results from real-world deployment in a large integrated healthcare system suggest that autonomous AI is associated with improvement in overall DED testing adherence, patient access, and health equity.

Purpose This study aimed to investigate the association between cytochrome P450 (CYP) 3A4*22 and cytochrome P450 oxidoreductase (POR)*28 variations and the pharmacokinetics of tacrolimus. Methods Cochrane Central Register of Controlled Trials (CENTRAL), Web of Science (SCI), MEDLINE, and Embase were systematically searched from inception to August 2022. The outcomes were weight-adjusted daily dose and dose-adjusted trough concentration (C 0 /Dose). Results The study included 2931 renal transplant recipients from 18 publications. Weight-adjusted daily dose of CYP3A4*1/*1 carriers was 0.04 (WMD = 0.04, 95% CI: 0.02 to 0.06), 0.03 (WMD = 0.03, 95% CI: 0.02 to 0.05), 0.02 (WMD = 0.02, 95% CI: 0.01 to 0.03), or 0.02 mg/kg/day (WMD = 0.02, 95% CI: 0.00 to 0.04) higher than CYP3A4*22 carriers in Caucasians at 1 month, 3 months, 6 months, or 12 months post-transplantation. Conversely, C0/Dose was lower for CYP3A4*1/*1 carriers at 3 days (SMD = -0.35, 95% CI: -0.65 to -0.06), 1 month (SMD = -0.67, 95% CI: -1.16 to -0.18), 3 months (SMD = -0.60, 95% CI: -0.89 to -0.31), 6 months (SMD = -0.76, 95% CI: -1.49 to -0.04), or 12 months post-transplantation (SMD = -0.69, 95% CI: -1.37 to 0.00). Furthermore, C 0 /Dose of POR*1/*1 carriers was 22.64 (WMD = 22.64, 95% CI: 2.54 to 42.74) or 19.41 (ng/ml)/(mg/kg/day) (WMD = 19.41, 95% CI: 9.58 to 29.24) higher than POR*28 carriers in CYP3A5 expressers at 3 days or 7 days post-transplantation, and higher in Asians at 6 months post-transplantation (SMD = 0.96, 95% CI: 0.50 to 1.43). Conclusions CYP3A4*22 variant in Caucasians restrains the metabolism of tacrolimus, while POR*28 variant in CYP3A5 expressers enhances the metabolism of tacrolimus for renal transplant recipients. However, further well-designed prospective studies are necessary to substantiate these conclusions given some limitations.