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White matter hyperintensities (WMH) are widely observed in older adults and are closely associated with cognitive impairment. However, the underlying neuroimaging mechanisms of WMH-related cognitive dysfunction remain unknown. This study recruited 61 WMH individuals with mild cognitive impairment (WMH-MCI, n = 61), 48 WMH individuals with normal cognition (WMH-NC, n = 48) and 57 healthy control (HC, n = 57) in the final analyses. We constructed morphological networks by applying the Kullback-Leibler divergence to estimate interregional similarity in the distributions of regional gray matter volume. Based on morphological networks, graph theory was applied to explore topological properties, and their relationship to WMH-related cognitive impairment was assessed. There were no differences in small-worldness, global efficiency and local efficiency. The nodal local efficiency, degree centrality and betweenness centrality were altered mainly in the limbic network (LN) and default mode network (DMN). The rich-club analysis revealed that WMH-MCI subjects showed lower average strength of the feeder and local connections than HC (feeder connections: P = 0.034; local connections: P = 0.042). Altered morphological connectivity mediated the relationship between WMH and cognition, including language (total indirect effect: −0.010; 95 % CI: −0.024, −0.002) and executive (total indirect effect: −0.010; 95 % CI: −0.028, −0.002) function. The altered topological organization of morphological networks was mainly located in the DMN and LN and was associated with WMH-related cognitive impairment. The rich-club connection was relatively preserved, while the feeder and local connections declined. The results suggest that single-subject morphological networks may capture neurological dysfunction due to WMH and could be applied to the early imaging diagnostic protocol for WMH-related cognitive impairment.

In petroleum engineering, accurately predicting particle settling velocity during various stages of a well’s life cycle is vital. This study focuses on settling velocities of both spherical and non-spherical particles in Newtonian and non-Newtonian fluids. Utilizing a dataset of 931 experimental observations, an artificial neural network (ANN) model with a 7-42-1 architecture is developed (one input layer, one hidden layer with 42 neurons, and one output layer). This model effectively incorporates particle settling orientation and the inclusion of the settling area ratio, enhancing its predictive accuracy. Achieving an average absolute relative error (AARE) of 8.51%, the ANN model surpasses traditional empirical correlations for settling velocities in both Newtonian and power-law fluids. Key influencing factors, such as the consistency index and particle equivalent diameter, were identified. This approach in ANN model construction and data analysis represents a significant advancement in understanding particle dynamics.

BackgroundThe mechanism of gait and balance dysfunction (GBD) in cerebral small vessel disease (CSVD) remains unclear. Evidence supports cognition engages in GBD of CSVD. The cerebellum is important in motor and cognition, while little is known about the influence of the cerebellum on GBD in CSVD.MethodsThis study is a retrospective cohort study. All participants of this study were enrolled from the CSVD individuals in Nanjing Drum Tower Hospital from 2017 to 2021. The GBD of CSVD patients was defined as Tinetti Test score ≤ 23. Cerebral cortical thickness, cerebellar gray matter volume, the amplitude of low-frequency fluctuation, functional connectivity, and modular interaction were calculated to determine the cortical atrophy and activity patterns of CSVD patients with GBD. The effect of cognitive domains during GBD in CSVD patients was explored by correlation analyses.ResultsA total of 25 CSVD patients were recruited in CSVD patients with GBD group (Tinetti Test score ≤ 23, mean age ± standard deviation: 70.000 ± 6.976 years), and 34 CSVD patients were recruited in CSVD patients without GBD group (Tinetti Test score > 23, mean age ± standard deviation: 64.029 ± 9.453 years). CSVD patients with GBD displayed worse cognitive performance and cortical atrophy in the right cerebellum VIIIa and bilateral superior temporal gyrus than those without GBD. The right postcentral gyrus, left inferior temporal gyrus, right angular gyrus, right supramarginal gyrus and right middle frontal gyrus were functionally overactivated and showed decreased modular interaction with the right cerebellum. Tinetti Test scores were negatively related to the volume of the right cerebellum VIIIa in CSVD patients with GBD. Notably, memory, especially visuospatial memory, was greatly associated with GBD in CSVD.ConclusionThe cortical atrophy and altered functional activity in sensorimotor area and ventral attention network in the cerebellum and cerebrum may underlying the GBD in CSVD. Memory might be critically cognitively responsible for GBD in CSVD.

ObjectiveTo develop a prognostic prediction model of endovascular treatment (EVT) for acute ischemic stroke (AIS) induced by large-vessel occlusion (LVO), this study applied machine learning classification model light gradient boosting machine (LightGBM) to construct a unique prediction model.MethodsA total of 973 patients were enrolled, primary outcome was assessed with modified Rankin scale (mRS) at 90 days, and favorable outcome was defined using mRS 0–2 scores. Besides, LightGBM algorithm and logistic regression (LR) were used to construct a prediction model. Then, a prediction scale was further established and verified by both internal data and other external data.ResultsA total of 20 presurgical variables were analyzed using LR and LightGBM. The results of LightGBM algorithm indicated that the accuracy and precision of the prediction model were 73.77 and 73.16%, respectively. The area under the curve (AUC) was 0.824. Furthermore, the top 5 variables suggesting unfavorable outcomes were namely admitting blood glucose levels, age, onset to EVT time, onset to hospital time, and National Institutes of Health Stroke Scale (NIHSS) scores (importance = 130.9, 102.6, 96.5, 89.5 and 84.4, respectively). According to AUC, we established the key cutoff points and constructed prediction scale based on their respective weightings. Then, the established prediction scale was verified in raw and external data and the sensitivity was 80.4 and 83.5%, respectively. Finally, scores >3 demonstrated better accuracy in predicting unfavorable outcomes.ConclusionPresurgical prediction scale is feasible and accurate in identifying unfavorable outcomes of AIS after EVT.

Using mechanical drilling to obtain energy resources stored in deep and hard rock layers is becoming increasingly challenging. Therefore, laser irradiation has emerged as a new and promising drilling method. In this study, the effects of immersion conditions on rock-breaking by laser irradiation on the temperature, hole size, rock-breaking efficiency, and macro-fracture after laser irradiation were investigated. Furthermore, the mineral changes and thermogravimetric analysis of rocks were studied. As indicated by the results, the temperature area over 100 °C increases with the increase of irradiation time, and the temperature range of between 2.27 cm2 and 13.20 cm2 varies with the change of laser power at between 90 W and 135 W. The hole-diameter value of the soaked sample was smaller than that of the dried sample. In addition, the hole depth of the soaked sample reduced by 15% at a power of 90 W and 45% at a power of 135 W, compared with that of the dried sample. The value of the modified specific energy of the soaked sample increased, which was particularly noticeable at low power. The soaked sample had a larger effect on the rate of perforation at high power and a smaller effect at low power. The cracks on the surface of the rock samples became larger after being placed for one month. Fracture length increased from 0.61 to 5.09 mm for dried samples and from 2.24 to 8.7 mm for soaked samples at a laser power of 90 W. Fracture length increased from 6.30 to 9.85 mm for dried samples and from 9.04 to 11.38 mm for soaked samples at a laser power of 135 W. The soaked sample began to show differences when heated at 100 °C, which was caused by the evaporation of some free water molecules in the rock. The main weight loss temperatures of the samples occurred in the range of 640 °C to 900 °C.

Glymphatic dysfunction has been linked to cognitive decline in several neurodegenerative diseases. In cerebral small vessel disease (CSVD), the mechanism of white matter hyperintensities (WMH)-related cognitive impairment (CI) is still under investigation. The diffusion tensor image (DTI) analysis along the perivascular space (ALPS) method has been considered to be a reliable parameter to evaluate glymphatic function. Therefore, we applied the ALPS-index to determine the influence of glymphatic function on CI in CSVD. In total, 137 CSVD patients (normal cognitive group, mild CI group, and dementia group) and 52 normal controls were included in this study. The ALPS-index was calculated based on the DTI. Correlation analyses and mediation analysis were conducted to examine the relationship between glymphatic function and cognition. Remarkable differences in the ALPS-index were observed between subjects with and without CI. The ALPS-index was negatively correlated with age, WMH volume, and general cognitive function in all CSVD patients. In the mild CI group, the ALPS-index was independently positively related to episodic memory, and mediated the relationship between WMH volume and episodic memory. In conclusion, the ALPS-index is a potential marker for early recognition of CI in CSVD. Glymphatic dysfunction mediates the relationship between WMH and CI in CSVD.